Shader lamps is a computer graphic technique used to change the appearance of physical objects. The still or moving objects are illuminated, using one or more video projectors, by static or animated texture or video stream. The method was invented at University of North Carolina at Chapel Hill by Ramesh Raskar, Greg Welch, Kok-lim Low and Deepak Bandyopadhyay in 1999 [1] as a follow on to Spatial Augmented Reality [2] also invented at University of North Carolina at Chapel Hill in 1998 by Ramesh Raskar, Greg Welch and Henry Fuchs. A 3D graphic rendering software is typically used to compute the deformation caused by the non perpendicular, non-planar or even complex projection surface. Complex objects (or aggregation of multiple simple objects) create self shadows that must be compensated by using several projectors. The objects are typically replaced by neutral color ones, the projection giving all its visual properties, thus the name shader lamps. The technique can be used to create a sense of invisibility, by rendering transparency. The object is illuminated not by a replacement of its own visual properties, but by the corresponding visual surface placed behind the object as seen from an arbitrary viewing point.
Affinity (software)
Affinity is a graphics editor developed by Serif, a subsidiary of Canva. It is simultaneously a vector graphics editor, a raster graphics editor and a desktop publishing application. It was first released in 2025 as a successor to Serif's Affinity Designer, Affinity Photo and Affinity Publisher, uniting the three editors into one application. While the previous versions competed individually against Adobe's Illustrator, Photoshop, and InDesign, Affinity 3.0 integrates their functionality into a single application. It uses a freemium model monetized by AI features exclusive to Canva Pro subscribers. == Functionality == Affinity is divided into a number of workspaces ("studios"), which are equivalent to the previous suite of Affinity applications: "vector" for vector graphics (Designer), "pixel" for raster editing (Photo), and "layout" for desktop publishing (Publisher). Additionally, it introduces the ability to create custom workspaces. The application supports real-time previews and non-destructive editing, which are based on GPU acceleration. Supported file formats include Adobe Photoshop, InDesign and Illustrator files, PDF, SVG, and TIFF, as well as a custom .af file format. === Vector editing === === Raster editing === Affinity includes photo editing tools including adjustments, masks, blend modes, batch processing, and retouching facilities. Additionally, the application can develop RAW files, similar to Adobe Lightroom. === Desktop publishing === Publishing features include master pages, text styles, and advanced typography. === AI features === The application supports Canva's existing AI features, such as background removal and generative fill. This requires a Canva subscription. == Development == === Background and acquisition (2014–2024) === Serif launched the original Affinity suite starting with Affinity Designer in 2014, followed by Photo (2015) and Publisher (2019). The software gained popularity for its one-time purchase model, contrasting with Adobe's subscription-based Creative Cloud. In November 2022, Serif released Version 2 of the suite, introducing a "Universal License" that covered all three apps across all platforms. In March 2024, Canva acquired Serif for approximately A$580 million (£300 million). Following user backlash regarding a potential shift to subscriptions, Canva and Serif issued a joint "Pledge" committing to four key principles: fair pricing, no mandatory subscriptions, perpetual licenses for existing products, and continued development of Affinity as a standalone suite. === Unified release (2025) === In September 2025, Serif pulled all existing versions of Affinity Designer, Affinity Photo and Affinity Publisher from sale ahead an upcoming announcement on 30 October; also ahead of the announcement, the iPadOS versions of the Affinity suite became free on App Store. During a "Creative Freedom" keynote on 30 October 2025, Canva released a new version now simply branded as "Affinity" (also known as "Affinity by Canva"), and referred to internally as version 3.0. Version 3 drops the separate applications and integrates their functionality into a singular application, and adds the ability to export directly to the Canva platform. It also adds a Canva AI studio, including background removal, "Expand & Edit", and generative fill. As of version 3, Affinity has switched to a freemium model; it is now available at no charge to users, although access to Canva AI features are locked behind the existing Canva Pro subscription service. Serif stated that the perpetually-licensed version 2 will remain available to existing owners, although it will no longer be actively maintained. The new version is currently available for macOS and Windows only, with an iPadOS version to be released soon. == Reception == The change in business model by Canva in 2025 was met with mixed reception, including concerns about its incorporation of AI features. Some users were concerned that their projects would be used for machine learning purposes, or that future versions would suffer from a lack of maintenance or become adware. Additionally, some felt it turned Affinity into fundamentally subscription-based software, given the prevalence of these features in professional contexts. Affinity publicly stated on social media that it would remain "free forever", users' projects would not be used to train AI models, and that "Canva has built a sustainable business model that allows this kind of generosity. And when more professionals use Affinity, Canva can sell more seats into businesses."
Australian Geoscience Data Cube
The Australian Geoscience Data Cube (AGDC) is an approach to storing, processing and analyzing large collections of Earth observation data. The technology is designed to meet challenges of national interest by being agile and flexible with vast amounts of layered grid data. The AGDC reduces processing time of traditional image analysis by calibrating, pre-computing known extents, pixel alignment and storing metadata in a cell lattice structure. The temporal-pixel aligned data can often be analysed faster across space and time dimensions than previous scene based techniques. This allows the AGDC to be flexible in tackling future challenges and improve analysis times on every-increasing data repositories of earth observation. The AGDC has also been used internationally to allow countries to maintain ecologically sustainable programs and reduce the difficulty curve of utilizing Remote Sensing data. == Background == The AGDC was originally conceived by Geoscience Australia but is now maintained in a partnership between Geoscience Australia, Commonwealth Scientific and Industrial Research Organisation (CSIRO) and National Computational Infrastructure National Facility (Australia) (NCI). This is made possible by the funding from the partnership and a number of organisations such as National Collaborative Research Infrastructure Strategy (NCRIS). == Analysis ready data, ingestion and indexing == The data processed in the cube is made analysis ready before being ingested and indexed into the AGDC. Analysis ready data is pre-processed data that has applied corrections for instrument calibration (gains and offsets), geolocation (spatial alignment) and radiometry (solar illumination, incidence angle, topography, atmospheric interference). The ingestion process manages the translation of datasets into the storage units while maintaining a database index. The data within the storage and index can be accessed via API calls often compiled within code such as Python (programming language). Example: s2a_l1c = dc.load(product='s2a_level1c_granule',x=(147.36, 147.41), y=(-35.1, -35.15), measurements=['04','03','02'], output_crs='EPSG:4326', resolution=(-0.00025,0.00025)) === Datasets currently stored === Geoscience Australia Landsat Surface Reflectance (1987 to present) Landsat Pixel Quality Landsat Fractional Cover Landsat NDVI === Datasets that have been piloted === USGS Landsat Surface Reflectance SRTM DEM Himawari 8 MODIS Sentinel-2 L1C / S2A Australian Gridded Climate Data == Open source == The AGDC code base is situated in GitHub as an open repository. The core code base moved to the Open Data Cube in early 2017 as part of an international collaboration. Whilst the code base is the Open Data Cube, individual cubes exist as their own right such as the AGDC on the National Computational Infrastructure National Facility (Australia) (NCI) using the High-Performance Computing Cluster HPCC. The core code can be installed on personal computers or public computers (using git) and has many unit tests. Documentation for the code base exists on Read the Docs. == Challenges of the AGDC == The AGDC is designed to meet nationally significant challenges such as the following. Sustainability Environment Water resource management Disaster assist Policy development Community planning Forest preservation Carbon measurement == International awards == The AGDC won the 2016 Content Platform of the Year award from Geospatial World Forum.
Ontology-based data integration
Ontology-based data integration involves the use of one or more ontologies to effectively combine data or information from multiple heterogeneous sources. It is one of the multiple data integration approaches and may be classified as Global-As-View (GAV). The effectiveness of ontology‑based data integration is closely tied to the consistency and expressivity of the ontology used in the integration process. == Background == Data from multiple sources are characterized by multiple types of heterogeneity. The following hierarchy is often used: Syntactic heterogeneity: is a result of differences in representation format of data Schematic or structural heterogeneity: the native model or structure to store data differ in data sources leading to structural heterogeneity. Schematic heterogeneity that particularly appears in structured databases is also an aspect of structural heterogeneity. Semantic heterogeneity: differences in interpretation of the 'meaning' of data are source of semantic heterogeneity System heterogeneity: use of different operating system, hardware platforms lead to system heterogeneity Ontologies, as formal models of representation with explicitly defined concepts and named relationships linking them, are used to address the issue of semantic heterogeneity in data sources. In domains like bioinformatics and biomedicine, the rapid development, adoption and public availability of ontologies [1] Archived 2007-06-16 at the Wayback Machine has made it possible for the data integration community to leverage them for semantic integration of data and information. == The role of ontologies == Ontologies enable the unambiguous identification of entities in heterogeneous information systems and assertion of applicable named relationships that connect these entities together. Specifically, ontologies play the following roles: Content Explication The ontology enables accurate interpretation of data from multiple sources through the explicit definition of terms and relationships in the ontology. Query Model In some systems like SIMS, the query is formulated using the ontology as a global query schema. Verification The ontology verifies the mappings used to integrate data from multiple sources. These mappings may either be user specified or generated by a system. == Approaches using ontologies for data integration == There are three main architectures that are implemented in ontology‑based data integration applications, namely, Single ontology approach A single ontology is used as a global reference model in the system. This is the simplest approach as it can be simulated by other approaches. SIMS is a prominent example of this approach. The Structured Knowledge Source Integration component of Research Cyc is another prominent example of this approach. (Title = Harnessing Cyc to Answer Clinical Researchers' Ad Hoc Queries). The Gellish Taxonomic Dictionary-Ontology follows this approach as well. Multiple ontologies Multiple ontologies, each modeling an individual data source, are used in combination for integration. Though, this approach is more flexible than the single ontology approach, it requires creation of mappings between the multiple ontologies. Ontology mapping is a challenging issue and is focus of large number of research efforts in computer science [2]. The OBSERVER system is an example of this approach. Hybrid approaches The hybrid approach involves the use of multiple ontologies that subscribe to a common, top-level vocabulary. The top-level vocabulary defines the basic terms of the domain. Thus, the hybrid approach makes it easier to use multiple ontologies for integration in presence of the common vocabulary.
Berlekamp–Rabin algorithm
In number theory, Berlekamp's root finding algorithm, also called the Berlekamp–Rabin algorithm, is the probabilistic method of finding roots of polynomials over the field F p {\displaystyle \mathbb {F} _{p}} with p {\displaystyle p} elements. The method was discovered by Elwyn Berlekamp in 1970 as an auxiliary to the algorithm for polynomial factorization over finite fields. The algorithm was later modified by Rabin for arbitrary finite fields in 1979. The method was also independently discovered before Berlekamp by other researchers. == History == The method was proposed by Elwyn Berlekamp in his 1970 work on polynomial factorization over finite fields. His original work lacked a formal correctness proof and was later refined and modified for arbitrary finite fields by Michael Rabin. In 1986 René Peralta proposed a similar algorithm for finding square roots in F p {\displaystyle \mathbb {F} _{p}} . In 2000 Peralta's method was generalized for cubic equations. == Statement of problem == Let p {\displaystyle p} be an odd prime number. Consider the polynomial f ( x ) = a 0 + a 1 x + ⋯ + a n x n {\textstyle f(x)=a_{0}+a_{1}x+\cdots +a_{n}x^{n}} over the field F p ≃ Z / p Z {\displaystyle \mathbb {F} _{p}\simeq \mathbb {Z} /p\mathbb {Z} } of remainders modulo p {\displaystyle p} . The algorithm should find all λ {\displaystyle \lambda } in F p {\displaystyle \mathbb {F} _{p}} such that f ( λ ) = 0 {\textstyle f(\lambda )=0} in F p {\displaystyle \mathbb {F} _{p}} . == Algorithm == === Randomization === Let f ( x ) = ( x − λ 1 ) ( x − λ 2 ) ⋯ ( x − λ n ) {\textstyle f(x)=(x-\lambda _{1})(x-\lambda _{2})\cdots (x-\lambda _{n})} . Finding all roots of this polynomial is equivalent to finding its factorization into linear factors. To find such factorization it is sufficient to split the polynomial into any two non-trivial divisors and factorize them recursively. To do this, consider the polynomial f z ( x ) = f ( x − z ) = ( x − λ 1 − z ) ( x − λ 2 − z ) ⋯ ( x − λ n − z ) {\textstyle f_{z}(x)=f(x-z)=(x-\lambda _{1}-z)(x-\lambda _{2}-z)\cdots (x-\lambda _{n}-z)} where z {\displaystyle z} is some element of F p {\displaystyle \mathbb {F} _{p}} . If one can represent this polynomial as the product f z ( x ) = p 0 ( x ) p 1 ( x ) {\displaystyle f_{z}(x)=p_{0}(x)p_{1}(x)} then in terms of the initial polynomial it means that f ( x ) = p 0 ( x + z ) p 1 ( x + z ) {\displaystyle f(x)=p_{0}(x+z)p_{1}(x+z)} , which provides needed factorization of f ( x ) {\displaystyle f(x)} . === Classification of === F p {\displaystyle \mathbb {F} _{p}} elements Due to Euler's criterion, for every monomial ( x − λ ) {\displaystyle (x-\lambda )} exactly one of following properties holds: The monomial is equal to x {\displaystyle x} if λ = 0 {\displaystyle \lambda =0} , The monomial divides g 0 ( x ) = ( x ( p − 1 ) / 2 − 1 ) {\textstyle g_{0}(x)=(x^{(p-1)/2}-1)} if λ {\displaystyle \lambda } is quadratic residue modulo p {\displaystyle p} , The monomial divides g 1 ( x ) = ( x ( p − 1 ) / 2 + 1 ) {\textstyle g_{1}(x)=(x^{(p-1)/2}+1)} if λ {\displaystyle \lambda } is quadratic non-residual modulo p {\displaystyle p} . Thus if f z ( x ) {\displaystyle f_{z}(x)} is not divisible by x {\displaystyle x} , which may be checked separately, then f z ( x ) {\displaystyle f_{z}(x)} is equal to the product of greatest common divisors gcd ( f z ( x ) ; g 0 ( x ) ) {\displaystyle \gcd(f_{z}(x);g_{0}(x))} and gcd ( f z ( x ) ; g 1 ( x ) ) {\displaystyle \gcd(f_{z}(x);g_{1}(x))} . === Berlekamp's method === The property above leads to the following algorithm: Explicitly calculate coefficients of f z ( x ) = f ( x − z ) {\displaystyle f_{z}(x)=f(x-z)} , Calculate remainders of x , x 2 , x 2 2 , x 2 3 , x 2 4 , … , x 2 ⌊ log 2 p ⌋ {\textstyle x,x^{2},x^{2^{2}},x^{2^{3}},x^{2^{4}},\ldots ,x^{2^{\lfloor \log _{2}p\rfloor }}} modulo f z ( x ) {\displaystyle f_{z}(x)} by squaring the current polynomial and taking remainder modulo f z ( x ) {\displaystyle f_{z}(x)} , Using exponentiation by squaring and polynomials calculated on the previous steps calculate the remainder of x ( p − 1 ) / 2 {\textstyle x^{(p-1)/2}} modulo f z ( x ) {\textstyle f_{z}(x)} , If x ( p − 1 ) / 2 ≢ ± 1 ( mod f z ( x ) ) {\textstyle x^{(p-1)/2}\not \equiv \pm 1{\pmod {f_{z}(x)}}} then gcd {\displaystyle \gcd } mentioned below provide a non-trivial factorization of f z ( x ) {\displaystyle f_{z}(x)} , Otherwise all roots of f z ( x ) {\displaystyle f_{z}(x)} are either residues or non-residues simultaneously and one has to choose another z {\displaystyle z} . If f ( x ) {\displaystyle f(x)} is divisible by some non-linear primitive polynomial g ( x ) {\displaystyle g(x)} over F p {\displaystyle \mathbb {F} _{p}} then when calculating gcd {\displaystyle \gcd } with g 0 ( x ) {\displaystyle g_{0}(x)} and g 1 ( x ) {\displaystyle g_{1}(x)} one will obtain a non-trivial factorization of f z ( x ) / g z ( x ) {\displaystyle f_{z}(x)/g_{z}(x)} , thus algorithm allows to find all roots of arbitrary polynomials over F p {\displaystyle \mathbb {F} _{p}} . === Modular square root === Consider equation x 2 ≡ a ( mod p ) {\textstyle x^{2}\equiv a{\pmod {p}}} having elements β {\displaystyle \beta } and − β {\displaystyle -\beta } as its roots. Solution of this equation is equivalent to factorization of polynomial f ( x ) = x 2 − a = ( x − β ) ( x + β ) {\textstyle f(x)=x^{2}-a=(x-\beta )(x+\beta )} over F p {\displaystyle \mathbb {F} _{p}} . In this particular case problem it is sufficient to calculate only gcd ( f z ( x ) ; g 0 ( x ) ) {\displaystyle \gcd(f_{z}(x);g_{0}(x))} . For this polynomial exactly one of the following properties will hold: GCD is equal to 1 {\displaystyle 1} which means that z + β {\displaystyle z+\beta } and z − β {\displaystyle z-\beta } are both quadratic non-residues, GCD is equal to f z ( x ) {\displaystyle f_{z}(x)} which means that both numbers are quadratic residues, GCD is equal to ( x − t ) {\displaystyle (x-t)} which means that exactly one of these numbers is quadratic residue. In the third case GCD is equal to either ( x − z − β ) {\displaystyle (x-z-\beta )} or ( x − z + β ) {\displaystyle (x-z+\beta )} . It allows to write the solution as β = ( t − z ) ( mod p ) {\textstyle \beta =(t-z){\pmod {p}}} . === Example === Assume we need to solve the equation x 2 ≡ 5 ( mod 11 ) {\textstyle x^{2}\equiv 5{\pmod {11}}} . For this we need to factorize f ( x ) = x 2 − 5 = ( x − β ) ( x + β ) {\displaystyle f(x)=x^{2}-5=(x-\beta )(x+\beta )} . Consider some possible values of z {\displaystyle z} : Let z = 3 {\displaystyle z=3} . Then f z ( x ) = ( x − 3 ) 2 − 5 = x 2 − 6 x + 4 {\displaystyle f_{z}(x)=(x-3)^{2}-5=x^{2}-6x+4} , thus gcd ( x 2 − 6 x + 4 ; x 5 − 1 ) = 1 {\displaystyle \gcd(x^{2}-6x+4;x^{5}-1)=1} . Both numbers 3 ± β {\displaystyle 3\pm \beta } are quadratic non-residues, so we need to take some other z {\displaystyle z} . Let z = 2 {\displaystyle z=2} . Then f z ( x ) = ( x − 2 ) 2 − 5 = x 2 − 4 x − 1 {\displaystyle f_{z}(x)=(x-2)^{2}-5=x^{2}-4x-1} , thus gcd ( x 2 − 4 x − 1 ; x 5 − 1 ) ≡ x − 9 ( mod 11 ) {\textstyle \gcd(x^{2}-4x-1;x^{5}-1)\equiv x-9{\pmod {11}}} . From this follows x − 9 = x − 2 − β {\textstyle x-9=x-2-\beta } , so β ≡ 7 ( mod 11 ) {\displaystyle \beta \equiv 7{\pmod {11}}} and − β ≡ − 7 ≡ 4 ( mod 11 ) {\textstyle -\beta \equiv -7\equiv 4{\pmod {11}}} . A manual check shows that, indeed, 7 2 ≡ 49 ≡ 5 ( mod 11 ) {\textstyle 7^{2}\equiv 49\equiv 5{\pmod {11}}} and 4 2 ≡ 16 ≡ 5 ( mod 11 ) {\textstyle 4^{2}\equiv 16\equiv 5{\pmod {11}}} . == Correctness proof == The algorithm finds factorization of f z ( x ) {\displaystyle f_{z}(x)} in all cases except for ones when all numbers z + λ 1 , z + λ 2 , … , z + λ n {\displaystyle z+\lambda _{1},z+\lambda _{2},\ldots ,z+\lambda _{n}} are quadratic residues or non-residues simultaneously. According to theory of cyclotomy, the probability of such an event for the case when λ 1 , … , λ n {\displaystyle \lambda _{1},\ldots ,\lambda _{n}} are all residues or non-residues simultaneously (that is, when z = 0 {\displaystyle z=0} would fail) may be estimated as 2 − k {\displaystyle 2^{-k}} where k {\displaystyle k} is the number of distinct values in λ 1 , … , λ n {\displaystyle \lambda _{1},\ldots ,\lambda _{n}} . In this way even for the worst case of k = 1 {\displaystyle k=1} and f ( x ) = ( x − λ ) n {\displaystyle f(x)=(x-\lambda )^{n}} , the probability of error may be estimated as 1 / 2 {\displaystyle 1/2} and for modular square root case error probability is at most 1 / 4 {\displaystyle 1/4} . == Complexity == Let a polynomial have degree n {\displaystyle n} . We derive the algorithm's complexity as follows: Due to the binomial theorem ( x − z ) k = ∑ i = 0 k ( k i ) ( − z ) k − i x i {\textstyle (x-z)^{k}=\sum \limits _{i=0}^{k}{\binom {k}{i}}(-z)^{k-i}x^{i}} , we may transition from f ( x ) {\displaystyle f(x)} to f ( x − z ) {\displaystyle f(x-z)} in O ( n 2 ) {\displaystyle O(n^{2})} time. Polynomial multiplication a
Equalized odds
Equalized odds, also referred to as conditional procedure accuracy equality and disparate mistreatment, is a measure of fairness in machine learning. A classifier satisfies this definition if the subjects in the protected and unprotected groups have equal true positive rate and equal false positive rate, satisfying the formula: P ( R = + | Y = y , A = a ) = P ( R = + | Y = y , A = b ) y ∈ { + , − } ∀ a , b ∈ A {\displaystyle P(R=+|Y=y,A=a)=P(R=+|Y=y,A=b)\quad y\in \{+,-\}\quad \forall a,b\in A} For example, A {\displaystyle A} could be gender, race, or any other characteristics that we want to be free of bias, while Y {\displaystyle Y} would be whether the person is qualified for the degree, and the output R {\displaystyle R} would be the school's decision whether to offer the person to study for the degree. In this context, higher university enrollment rates of African Americans compared to whites with similar test scores might be necessary to fulfill the condition of equalized odds, if the "base rate" of Y {\displaystyle Y} differs between the groups. The concept was originally defined for binary-valued Y {\displaystyle Y} . In 2017, Woodworth et al. generalized the concept further for multiple classes.
Algorithmic management
Algorithmic management is a term used to describe certain labor management practices in the contemporary digital economy. In scholarly uses, the term was initially coined in 2015 by Min Kyung Lee, Daniel Kusbit, Evan Metsky, and Laura Dabbish to describe the managerial role played by algorithms on the Uber and Lyft platforms, but has since been taken up by other scholars to describe more generally the managerial and organisational characteristics of platform economies. However, digital direction of labor was present in manufacturing already since the 1970s and algorithmic management is becoming increasingly widespread across a wide range of industries. The concept of algorithmic management can be broadly defined as the delegation of managerial functions to algorithmic and automated systems. Algorithmic management has been enabled by "recent advances in digital technologies" which allow for the real-time and "large-scale collection of data" which is then used to "improve learning algorithms that carry out learning and control functions traditionally performed by managers". The term does not refer to a specific underlying technology, and encompasses the design choices, organisational policies, and governance that surround the managerial use of algorithms in workplaces. In the contemporary workplace, firms employ an ecology of accounting devices, such as "rankings, lists, classifications, stars and other symbols' in order to effectively manage their operations and create value without the need for traditional forms of hierarchical control." Many of these devices fall under the label of what is called algorithmic management, and were first developed by companies operating in the sharing economy or gig economy, functioning as effective labor and cost cutting measures. The Data&Society explainer of the term, for example, describes algorithmic management as 'a diverse set of technological tools and techniques that structure the conditions of work and remotely manage workforces. Data&Society also provides a list of five typical features of algorithmic management: Prolific data collection and surveillance of workers through technology; Real-time responsiveness to data that informs management decisions; Automated or semi-automated decision-making; Transfer of performance evaluations to rating systems or other metrics; and The use of "nudges" and penalties to indirectly incentivize worker behaviors. Proponents of algorithmic management claim that it "creates new employment opportunities, better and cheaper consumer services, transparency and fairness in parts of the labour market that are characterised by inefficiency, opacity and capricious human bosses." On the other hand, critics of algorithmic management claim that the practice leads to several issues, especially as it impacts the employment status of workers managed by its new array of tools and techniques. == History of the term == "Algorithmic management" was first described by Lee, Kusbit, Metsky, and Dabbish in 2015 in their study of the Uber and Lyft platforms. In their study, Lee et al. termed "software algorithms that assume managerial functions and surrounding institutional devices that support algorithms in practice" algorithmic management. Software algorithms, it was said, are increasingly used to "allocate, optimize, and evaluate work" by platforms in managing their vast workforces. In Lee et al.'s paper on Uber and Lyft this included the use of algorithms to assign work to drivers, as mechanisms to optimise pricing for services, and as systems for evaluating driver performance. In 2016, Alex Rosenblat and Luke Stark sought to extend on this understanding of algorithmic management "to elucidate on the automated implementation of company policies on the behaviours and practices of Uber drivers." Rosenblat and Stark found in their study that algorithmic management practices contributed to a system beset by power asymmetries, where drivers had little control over "critical aspects of their work", whereas Uber had far greater control over the labor of its drivers. Since this time, studies of algorithmic management have extended the use of the term to describe the management practices of various firms, where, for example, algorithms "are taking over scheduling work in fast food restaurants and grocery stores, using various forms of performance metrics ad even mood... to assign the fastest employees to work in peak times." Algorithmic management is seen to be especially prevalent in gig work on platforms, such as on Upwork and Deliveroo, and in the sharing economy, such as in the case of Airbnb. Furthermore, recent research has defined sub-constructs that fall under the umbrella term of algorithmic management, for example, "algorithmic nudging". A Harvard Business Review article published in 2021 explains: "Companies are increasingly using algorithms to manage and control individuals not by force, but rather by nudging them into desirable behavior — in other words, learning from their personalized data and altering their choices in some subtle way." While the concept builds on nudging theory popularized by University of Chicago economist Richard Thaler and Harvard Law School professor Cass Sunstein, "due to recent advances in AI and machine learning, algorithmic nudging is much more powerful than its non-algorithmic counterpart. With so much data about workers' behavioral patterns at their fingertips, companies can now develop personalized strategies for changing individuals' decisions and behaviors at large scale. These algorithms can be adjusted in real-time, making the approach even more effective." == Relationships with other labor management practices == Algorithmic management has been compared and contrasted with other forms of management, such as Scientific management approaches, as pioneered by Frederick Taylor in the early 1900s. Henri Schildt has called algorithmic management "Scientific management 2.0", where management "is no longer a human practice, but a process embedded in technology." Similarly, Kathleen Griesbach, Adam Reich, Luke Elliott-Negri, and Ruth Milkman suggest that, while "algorithmic control over labor may be relatively new, it replicates many features of older mechanisms of labor control." On the other hand, some commentators have argued that algorithmic management is not simply a new form of Scientific management or digital Taylorism, but represents a distinct approach to labor control in platform economies. David Stark and Ivana Pais, for example, state that, "In contrast to Scientific Management at the turn of the twentieth century, in the algorithmic management of the twenty-first century there are rules but these are not bureaucratic, there are rankings but not ranks, and there is monitoring but it is not disciplinary. Algorithmic management does not automate bureaucratic structures and practices to create some new form of algorithmic bureaucracy. Whereas the devices and practices of Taylorism were part of a system of hierarchical supervision, the devices and practices of algorithmic management take place within a different economy of attention and a new regime of visibility. Triangular rather than vertical, and not as a panopticon, the lines of vision in algorithmic management are not lines of supervision." Similarly, Data&Society's explainer for algorithmic management claims that the practice represents a marked departure from earlier management structures that more strongly rely on human supervisors to direct workers. In analyzing the difference and the similarities to previous management styles, David Stark and Pieter Vanden Broeck expand the applicability of algorithmic management beyond the workplace. They develop a theory of algorithmic management in terms of broader changes in the shape and structure of organization in the 21st century, attentive to the erosion of organization's boundaries whereby heterogeneous actors, assets, and activities, are coopted regardless of their place in organizational space. Stark and Vanden Broeck propose the following means of differentiating algorithmic management from other historical managerial paradigms: == Issues == Algorithmic management can provide an effective and efficient means of workforce control and value creation in the contemporary digital economy. However, commentators have highlighted several issues that algorithmic management poses, especially for the workers it manages. Criticisms of the practice often highlight several key issues pertaining to algorithmic management practices, such as the imperfection and scope of its surveillance and control measures, which also threaten to lock workers out of key decision-making processes; its lack of transparency for users and information asymmetries; its potential for bias and discrimination; its dehumanizing tendencies; and its potential to create conditions which sidestep traditional employer-employee accountability. This last point has been especi