AI Grammar English

AI Grammar English — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Inception (deep learning architecture)

  Inception is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern CNN. == Version history == === Inception v1 === In 2014, a team at Google developed the GoogLeNet architecture, an instance of which won the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The name came from the LeNet of 1998, since both LeNet and GoogLeNet are CNNs. They also called it "Inception" after a "we need to go deeper" internet meme, a phrase from Inception (2010) the film. Because later, more versions were released, the original Inception architecture was renamed again as "Inception v1". The models and the code were released under Apache 2.0 license on GitHub. The Inception v1 architecture is a deep CNN composed of 22 layers. Most of these layers were "Inception modules". The original paper stated that Inception modules are a "logical culmination" of Network in Network and (Arora et al, 2014). Since Inception v1 is deep, it suffered from the vanishing gradient problem. The team solved it by using two "auxiliary classifiers", which are linear-softmax classifiers inserted at 1/3-deep and 2/3-deep within the network, and the loss function is a weighted sum of all three: L = 0.3 L a u x , 1 + 0.3 L a u x , 2 + L r e a l {\displaystyle L=0.3L_{aux,1}+0.3L_{aux,2}+L_{real}} These were removed after training was complete. This was later solved by the ResNet architecture. The architecture consists of three parts stacked on top of one another: The stem (data ingestion): The first few convolutional layers perform data preprocessing to downscale images to a smaller size. The body (data processing): The next many Inception modules perform the bulk of data processing. The head (prediction): The final fully-connected layer and softmax produces a probability distribution for image classification. This structure is used in most modern CNN architectures. === Inception v2 === Inception v2 was released in 2015, in a paper that is more famous for proposing batch normalization. It had 13.6 million parameters. It improves on Inception v1 by adding batch normalization, and removing dropout and local response normalization which they found became unnecessary when batch normalization is used. === Inception v3 === Inception v3 was released in 2016. It improves on Inception v2 by using factorized convolutions. As an example, a single 5×5 convolution can be factored into 3×3 stacked on top of another 3×3. Both has a receptive field of size 5×5. The 5×5 convolution kernel has 25 parameters, compared to just 18 in the factorized version. Thus, the 5×5 convolution is strictly more powerful than the factorized version. However, this power is not necessarily needed. Empirically, the research team found that factorized convolutions help. It also uses a form of dimension-reduction by concatenating the output from a convolutional layer and a pooling layer. As an example, a tensor of size 35 × 35 × 320 {\displaystyle 35\times 35\times 320} can be downscaled by a convolution with stride 2 to 17 × 17 × 320 {\displaystyle 17\times 17\times 320} , and by maxpooling with pool size 2 × 2 {\displaystyle 2\times 2} to 17 × 17 × 320 {\displaystyle 17\times 17\times 320} . These are then concatenated to 17 × 17 × 640 {\displaystyle 17\times 17\times 640} . Other than this, it also removed the lowest auxiliary classifier during training. They found that the auxiliary head worked as a form of regularization. They also proposed label-smoothing regularization in classification. For an image with label c {\displaystyle c} , instead of making the model to predict the probability distribution δ c = ( 0 , 0 , … , 0 , 1 ⏟ c -th entry , 0 , … , 0 ) {\displaystyle \delta _{c}=(0,0,\dots ,0,\underbrace {1} _{c{\text{-th entry}}},0,\dots ,0)} , they made the model predict the smoothed distribution ( 1 − ϵ ) δ c + ϵ / K {\displaystyle (1-\epsilon )\delta _{c}+\epsilon /K} where K {\displaystyle K} is the total number of classes. === Inception v4 === In 2017, the team released Inception v4, Inception ResNet v1, and Inception ResNet v2. Inception v4 is an incremental update with even more factorized convolutions, and other complications that were empirically found to improve benchmarks. Inception ResNet v1 and v2 are both modifications of Inception v4, where residual connections are added to each Inception module, inspired by the ResNet architecture. === Xception === Xception ("Extreme Inception") was published in 2017. It is a linear stack of depthwise separable convolution layers with residual connections. The design was proposed on the hypothesis that in a CNN, the cross-channels correlations and spatial correlations in the feature maps can be entirely decoupled. Training each network took 3 days on 60 K80 GPUs, or approximately 0.5 petaFLOP-days.

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  Holographic algorithm

  In computer science, a holographic algorithm is an algorithm that uses a holographic reduction. A holographic reduction is a constant-time reduction that maps solution fragments many-to-many such that the sum of the solution fragments remains unchanged. These concepts were introduced by Leslie Valiant, who called them holographic because "their effect can be viewed as that of producing interference patterns among the solution fragments". The algorithms are unrelated to laser holography, except metaphorically. Their power comes from the mutual cancellation of many contributions to a sum, analogous to the interference patterns in a hologram. Holographic algorithms have been used to find polynomial-time solutions to problems without such previously known solutions for special cases of satisfiability, vertex cover, and other graph problems. They have received notable coverage due to speculation that they are relevant to the P versus NP problem and their impact on computational complexity theory. Although some of the general problems are #P-hard problems, the special cases solved are not themselves #P-hard, and thus do not prove FP = #P. Holographic algorithms have some similarities with quantum computation, but are completely classical. == Holant problems == Holographic algorithms exist in the context of Holant problems, which generalize counting constraint satisfaction problems (#CSP). A #CSP instance is a hypergraph G=(V,E) called the constraint graph. Each hyperedge represents a variable and each vertex v {\displaystyle v} is assigned a constraint f v . {\displaystyle f_{v}.} A vertex is connected to an hyperedge if the constraint on the vertex involves the variable on the hyperedge. The counting problem is to compute ∑ σ : E → { 0 , 1 } ∏ v ∈ V f v ( σ | E ( v ) ) , ( 1 ) {\displaystyle \sum _{\sigma :E\to \{0,1\}}\prod _{v\in V}f_{v}(\sigma |_{E(v)}),~~~~~~~~~~(1)} which is a sum over all variable assignments, the product of every constraint, where the inputs to the constraint f v {\displaystyle f_{v}} are the variables on the incident hyperedges of v {\displaystyle v} . A Holant problem is like a #CSP except the input must be a graph, not a hypergraph. Restricting the class of input graphs in this way is indeed a generalization. Given a #CSP instance, replace each hyperedge e of size s with a vertex v of degree s with edges incident to the vertices contained in e. The constraint on v is the equality function of arity s. This identifies all of the variables on the edges incident to v, which is the same effect as the single variable on the hyperedge e. In the context of Holant problems, the expression in (1) is called the Holant after a related exponential sum introduced by Valiant. == Holographic reduction == A standard technique in complexity theory is a many-one reduction, where an instance of one problem is reduced to an instance of another (hopefully simpler) problem. However, holographic reductions between two computational problems preserve the sum of solutions without necessarily preserving correspondences between solutions. For instance, the total number of solutions in both sets can be preserved, even though individual problems do not have matching solutions. The sum can also be weighted, rather than simply counting the number of solutions, using linear basis vectors. === General example === It is convenient to consider holographic reductions on bipartite graphs. A general graph can always be transformed it into a bipartite graph while preserving the Holant value. This is done by replacing each edge in the graph by a path of length 2, which is also known as the 2-stretch of the graph. To keep the same Holant value, each new vertex is assigned the binary equality constraint. Consider a bipartite graph G=(U,V,E) where the constraint assigned to every vertex u ∈ U {\displaystyle u\in U} is f u {\displaystyle f_{u}} and the constraint assigned to every vertex v ∈ V {\displaystyle v\in V} is f v {\displaystyle f_{v}} . Denote this counting problem by Holant ( G , f u , f v ) . {\displaystyle {\text{Holant}}(G,f_{u},f_{v}).} If the vertices in U are viewed as one large vertex of degree |E|, then the constraint of this vertex is the tensor product of f u {\displaystyle f_{u}} with itself |U| times, which is denoted by f u ⊗ | U | . {\displaystyle f_{u}^{\otimes |U|}.} Likewise, if the vertices in V are viewed as one large vertex of degree |E|, then the constraint of this vertex is f v ⊗ | V | . {\displaystyle f_{v}^{\otimes |V|}.} Let the constraint f u {\displaystyle f_{u}} be represented by its weighted truth table as a row vector and the constraint f v {\displaystyle f_{v}} be represented by its weighted truth table as a column vector. Then the Holant of this constraint graph is simply f u ⊗ | U | f v ⊗ | V | . {\displaystyle f_{u}^{\otimes |U|}f_{v}^{\otimes |V|}.} Now for any complex 2-by-2 invertible matrix T (the columns of which are the linear basis vectors mentioned above), there is a holographic reduction between Holant ( G , f u , f v ) {\displaystyle {\text{Holant}}(G,f_{u},f_{v})} and Holant ( G , f u T ⊗ ( deg ⁡ u ) , ( T − 1 ) ⊗ ( deg ⁡ v ) f v ) . {\displaystyle {\text{Holant}}(G,f_{u}T^{\otimes (\deg u)},(T^{-1})^{\otimes (\deg v)}f_{v}).} To see this, insert the identity matrix T ⊗ | E | ( T − 1 ) ⊗ | E | {\displaystyle T^{\otimes |E|}(T^{-1})^{\otimes |E|}} in between f u ⊗ | U | f v ⊗ | V | {\displaystyle f_{u}^{\otimes |U|}f_{v}^{\otimes |V|}} to get f u ⊗ | U | f v ⊗ | V | {\displaystyle f_{u}^{\otimes |U|}f_{v}^{\otimes |V|}} = f u ⊗ | U | T ⊗ | E | ( T − 1 ) ⊗ | E | f v ⊗ | V | {\displaystyle =f_{u}^{\otimes |U|}T^{\otimes |E|}(T^{-1})^{\otimes |E|}f_{v}^{\otimes |V|}} = ( f u T ⊗ ( deg ⁡ u ) ) ⊗ | U | ( f v ( T − 1 ) ⊗ ( deg ⁡ v ) ) ⊗ | V | . {\displaystyle =\left(f_{u}T^{\otimes (\deg u)}\right)^{\otimes |U|}\left(f_{v}(T^{-1})^{\otimes (\deg v)}\right)^{\otimes |V|}.} Thus, Holant ( G , f u , f v ) {\displaystyle {\text{Holant}}(G,f_{u},f_{v})} and Holant ( G , f u T ⊗ ( deg ⁡ u ) , ( T − 1 ) ⊗ ( deg ⁡ v ) f v ) {\displaystyle {\text{Holant}}(G,f_{u}T^{\otimes (\deg u)},(T^{-1})^{\otimes (\deg v)}f_{v})} have exactly the same Holant value for every constraint graph. They essentially define the same counting problem. === Specific examples === ==== Vertex covers and independent sets ==== Let G be a graph. There is a 1-to-1 correspondence between the vertex covers of G and the independent sets of G. For any set S of vertices of G, S is a vertex cover in G if and only if the complement of S is an independent set in G. Thus, the number of vertex covers in G is exactly the same as the number of independent sets in G. The equivalence of these two counting problems can also be proved using a holographic reduction. For simplicity, let G be a 3-regular graph. The 2-stretch of G gives a bipartite graph H=(U,V,E), where U corresponds to the edges in G and V corresponds to the vertices in G. The Holant problem that naturally corresponds to counting the number of vertex covers in G is Holant ( H , OR 2 , EQUAL 3 ) . {\displaystyle {\text{Holant}}(H,{\text{OR}}_{2},{\text{EQUAL}}_{3}).} The truth table of OR2 as a row vector is (0,1,1,1). The truth table of EQUAL3 as a column vector is ( 1 , 0 , 0 , 0 , 0 , 0 , 0 , 1 ) T = [ 1 0 ] ⊗ 3 + [ 0 1 ] ⊗ 3 {\displaystyle (1,0,0,0,0,0,0,1)^{T}={\begin{bmatrix}1\\0\end{bmatrix}}^{\otimes 3}+{\begin{bmatrix}0\\1\end{bmatrix}}^{\otimes 3}} . Then under a holographic transformation by [ 0 1 1 0 ] , {\displaystyle {\begin{bmatrix}0&1\\1&0\end{bmatrix}},} OR 2 ⊗ | U | EQUAL 3 ⊗ | V | {\displaystyle {\text{OR}}_{2}^{\otimes |U|}{\text{EQUAL}}_{3}^{\otimes |V|}} = ( 0 , 1 , 1 , 1 ) ⊗ | U | ( [ 1 0 ] ⊗ 3 + [ 0 1 ] ⊗ 3 ) ⊗ | V | {\displaystyle =(0,1,1,1)^{\otimes |U|}\left({\begin{bmatrix}1\\0\end{bmatrix}}^{\otimes 3}+{\begin{bmatrix}0\\1\end{bmatrix}}^{\otimes 3}\right)^{\otimes |V|}} = ( 0 , 1 , 1 , 1 ) ⊗ | U | [ 0 1 1 0 ] ⊗ | E | [ 0 1 1 0 ] ⊗ | E | ( [ 1 0 ] ⊗ 3 + [ 0 1 ] ⊗ 3 ) ⊗ | V | {\displaystyle =(0,1,1,1)^{\otimes |U|}{\begin{bmatrix}0&1\\1&0\end{bmatrix}}^{\otimes |E|}{\begin{bmatrix}0&1\\1&0\end{bmatrix}}^{\otimes |E|}\left({\begin{bmatrix}1\\0\end{bmatrix}}^{\otimes 3}+{\begin{bmatrix}0\\1\end{bmatrix}}^{\otimes 3}\right)^{\otimes |V|}} = ( ( 0 , 1 , 1 , 1 ) [ 0 1 1 0 ] ⊗ 2 ) ⊗ | U | ( ( [ 0 1 1 0 ] [ 1 0 ] ) ⊗ 3 + ( [ 0 1 1 0 ] [ 0 1 ] ) ⊗ 3 ) ⊗ | V | {\displaystyle =\left((0,1,1,1){\begin{bmatrix}0&1\\1&0\end{bmatrix}}^{\otimes 2}\right)^{\otimes |U|}\left(\left({\begin{bmatrix}0&1\\1&0\end{bmatrix}}{\begin{bmatrix}1\\0\end{bmatrix}}\right)^{\otimes 3}+\left({\begin{bmatrix}0&1\\1&0\end{bmatrix}}{\begin{bmatrix}0\\1\end{bmatrix}}\right)^{\otimes 3}\right)^{\otimes |V|}} = ( 1 , 1 , 1 , 0 ) ⊗ | U | ( [ 0 1 ] ⊗ 3 + [ 1 0 ] ⊗ 3 ) ⊗ | V | {\displaystyle =(1,1,1,0)^{\otimes |U|}\left({\begin{bmatrix}0\\1\end{bmatrix}}^{\otimes 3}+{\begin{bmatrix}1\\0\end{bmatrix}}^{\otimes 3}\right)^{\otimes |V|}} = NAND 2 ⊗ | U | EQUAL 3 ⊗ | V | , {\displaystyle ={\text{NAND}}_{2}^{\otim

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  Lancichinetti–Fortunato–Radicchi benchmark

  Lancichinetti–Fortunato–Radicchi benchmark is an algorithm that generates benchmark networks (artificial networks that resemble real-world networks). They have a priori known communities and are used to compare different community detection methods. The advantage of the benchmark over other methods is that it accounts for the heterogeneity in the distributions of node degrees and of community sizes. == The algorithm == The node degrees and the community sizes are distributed according to a power law, with different exponents. The benchmark assumes that both the degree and the community size have power law distributions with different exponents, γ {\displaystyle \gamma } and β {\displaystyle \beta } , respectively. N {\displaystyle N} is the number of nodes and the average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . There is a mixing parameter μ {\displaystyle \mu } , which is the average fraction of neighboring nodes of a node that do not belong to any community that the benchmark node belongs to. This parameter controls the fraction of edges that are between communities. Thus, it reflects the amount of noise in the network. At the extremes, when μ = 0 {\displaystyle \mu =0} all links are within community links, if μ = 1 {\displaystyle \mu =1} all links are between nodes belonging to different communities. One can generate the benchmark network using the following steps. Step 1: Generate a network with nodes following a power law distribution with exponent γ {\displaystyle \gamma } and choose extremes of the distribution k min {\displaystyle k_{\min }} and k max {\displaystyle k_{\max }} to get desired average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . Step 2: ( 1 − μ ) {\displaystyle (1-\mu )} fraction of links of every node is with nodes of the same community, while fraction μ {\displaystyle \mu } is with the other nodes. Step 3: Generate community sizes from a power law distribution with exponent β {\displaystyle \beta } . The sum of all sizes must be equal to N {\displaystyle N} . The minimal and maximal community sizes s min {\displaystyle s_{\min }} and s max {\displaystyle s_{\max }} must satisfy the definition of community so that every non-isolated node is in at least in one community: s min > k min {\displaystyle s_{\min }>k_{\min }} s max > k max {\displaystyle s_{\max }>k_{\max }} Step 4: Initially, no nodes are assigned to communities. Then, each node is randomly assigned to a community. As long as the number of neighboring nodes within the community does not exceed the community size a new node is added to the community, otherwise stays out. In the following iterations the “homeless” node is randomly assigned to some community. If that community is complete, i.e. the size is exhausted, a randomly selected node of that community must be unlinked. Stop the iteration when all the communities are complete and all the nodes belong to at least one community. Step 5: Implement rewiring of nodes keeping the same node degrees but only affecting the fraction of internal and external links such that the number of links outside the community for each node is approximately equal to the mixing parameter μ {\displaystyle \mu } . == Testing == Consider a partition into communities that do not overlap. The communities of randomly chosen nodes in each iteration follow a p ( C ) {\displaystyle p(C)} distribution that represents the probability that a randomly picked node is from the community C {\displaystyle C} . Consider a partition of the same network that was predicted by some community finding algorithm and has p ( C 2 ) {\displaystyle p(C_{2})} distribution. The benchmark partition has p ( C 1 ) {\displaystyle p(C_{1})} distribution. The joint distribution is p ( C 1 , C 2 ) {\displaystyle p(C_{1},C_{2})} . The similarity of these two partitions is captured by the normalized mutual information. I n = ∑ C 1 , C 2 p ( C 1 , C 2 ) log 2 ⁡ p ( C 1 , C 2 ) p ( C 1 ) p ( C 2 ) 1 2 H ( { p ( C 1 ) } ) + 1 2 H ( { p ( C 2 ) } ) {\displaystyle I_{n}={\frac {\sum _{C_{1},C_{2}}p(C_{1},C_{2})\log _{2}{\frac {p(C_{1},C_{2})}{p(C_{1})p(C_{2})}}}{{\frac {1}{2}}H(\{p(C_{1})\})+{\frac {1}{2}}H(\{p(C_{2})\})}}} If I n = 1 {\displaystyle I_{n}=1} the benchmark and the detected partitions are identical, and if I n = 0 {\displaystyle I_{n}=0} then they are independent of each other.

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  Operational data store

  An operational data store (ODS) is used for operational reporting and as a source of data for the enterprise data warehouse (EDW). It is a complementary element to an EDW in a decision support environment, and is used for operational reporting, controls, and decision making, as opposed to the EDW, which is used for tactical and strategic decision support. An ODS is a database designed to integrate data from multiple sources for additional operations on the data, for reporting, controls and operational decision support. Unlike a production master data store, the data is not passed back to operational systems. It may be passed for further operations and to the data warehouse for reporting. An ODS should not be confused with an enterprise data hub (EDH). An operational data store will take transactional data from one or more production systems and loosely integrate it, in some respects it is still subject oriented, integrated and time variant, but without the volatility constraints. This integration is mainly achieved through the use of EDW structures and content. An ODS is not an intrinsic part of an EDH solution, although an EDH may be used to subsume some of the processing performed by an ODS and the EDW. An EDH is a broker of data. An ODS is certainly not. Because the data originates from multiple sources, the integration often involves cleaning, resolving redundancy and checking against business rules for integrity. An ODS is usually designed to contain low-level or atomic (indivisible) data (such as transactions and prices) with limited history that is captured "real time" or "near real time" as opposed to the much greater volumes of data stored in the data warehouse generally on a less-frequent basis. == General use == The general purpose of an ODS is to integrate data from disparate source systems in a single structure, using data integration technologies like data virtualization, data federation, or extract, transform, and load (ETL). This will allow operational access to the data for operational reporting, master data or reference data management. An ODS is not a replacement or substitute for a data warehouse or for a data hub but in turn could become a source.

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  Color

  Color (or colour in Commonwealth English) is the visual perception produced by the activation of the different types of cone cells in the eye caused by light. Though color is not an inherent property of matter, color perception is related to an object's light absorption, emission, reflection and transmission. For most humans, visible wavelengths of light are the ones perceived in the visible light spectrum, with three types of cone cells (trichromacy). Other animals may have a different number of cone cell types or have eyes sensitive to different wavelengths, such as bees that can distinguish ultraviolet, and thus have a different color sensitivity range. Animal perception of color originates from different light wavelength or spectral sensitivity in cone cell types, which is then processed by the brain. Colors have perceived properties such as hue, colorfulness, and lightness. Colors can also be additively mixed (mixing light) or subtractively mixed (mixing pigments). If one color is mixed in the right proportions, because of metamerism, they may look the same as another stimulus with a different reflection or emission spectrum. For convenience, colors can be organized in a color space, which when being abstracted as a mathematical color model can assign each region of color with a corresponding set of numbers. Thus, color spaces are an essential tool for color reproduction in print, photography, computer monitors, and television. Some of the most well-known color models and color spaces are RGB, CMYK, HSL/HSV, CIE Lab, and YCbCr/YUV. Because the perception of color is an important aspect of human life, different colors have been associated with emotions, activity, and nationality. Names of color regions in different cultures can have different, sometimes overlapping areas. In visual arts, color theory is used to govern the use of colors in an aesthetically pleasing and harmonious way. The theory of color includes the color complements; color balance; and classification of primary colors, secondary colors, and tertiary colors. The study of colors in general is called color science. == Physical properties == Electromagnetic radiation is characterized by its wavelength (or frequency) and its intensity. When the wavelength is within the visible spectrum (the range of wavelengths humans can perceive, approximately from 390 nm to 700 nm), it is known as "visible light". Most light sources emit light at many different wavelengths; a source's spectrum is a distribution giving its intensity at each wavelength. Although the spectrum of light arriving at the eye from a given direction determines the color sensation in that direction, there are many more possible spectral combinations than color sensations. In fact, one may formally define a color as a class of spectra that give rise to the same color sensation, although such classes would vary widely among different animal species, and to a lesser extent among individuals within the same species. In each such class, the members are called metamers of the color in question. This effect can be visualized by comparing the light sources' spectral power distributions and the resulting colors. === Spectral colors === The familiar colors of the rainbow in the spectrum—named using the Latin word for appearance or apparition by Isaac Newton in 1671—include all those colors that can be produced by visible light of a single wavelength only, the pure spectral or monochromatic colors. The spectrum above shows approximate wavelengths (in nm) for spectral colors in the visible range. Spectral colors have 100% purity, and are fully saturated. A complex mixture of spectral colors can be used to describe any color, which is the definition of a light power spectrum. The spectral colors form a continuous spectrum, and how it is divided into distinct colors linguistically is a matter of culture and historical contingency. Despite the ubiquitous ROYGBIV mnemonic used to remember the spectral colors in English, the inclusion or exclusion of colors is contentious, with disagreement often focused on indigo and cyan. Even if the subset of color terms is agreed, their wavelength ranges and borders between them may not be. The intensity of a spectral color, relative to the context in which it is viewed, may alter its perception considerably. For example, a low-intensity orange-yellow is brown, and a low-intensity yellow-green is olive green. Additionally, hue shifts towards yellow or blue happen if the intensity of a spectral light is increased; this is called Bezold–Brücke shift. In color models capable of representing spectral colors, such as CIELUV, a spectral color has the maximal saturation. In Helmholtz coordinates, this is described as 100% purity. === Color of objects === The physical color of an object depends on how it absorbs and scatters light. Most objects scatter light to some degree and do not reflect or transmit light specularly like glasses or mirrors. A transparent object allows almost all light to transmit or pass through, thus transparent objects are perceived as colorless. Conversely, an opaque object does not allow light to transmit through and instead absorbs or reflects the light it receives. Like transparent objects, translucent objects allow light to transmit through, but translucent objects are seen colored because they scatter or absorb certain wavelengths of light via internal scattering. The absorbed light is often dissipated as heat. == Color vision == === Development of theories of color vision === Although Aristotle and other ancient scientists had already written on the nature of light and color vision, it was not until Isaac Newton that light was identified as the source of the color sensation. In 1810, Johann Wolfgang von Goethe published his comprehensive Theory of Colors in which he provided a rational description of color experience, which "tells us how it originates, not what it is". In 1801, Thomas Young proposed his trichromatic theory, to explain how a wide spectrum of different wavelengths could be detected by the human eye. It would be unreasonable to suppose that the human eye contained hundreds of different receptors each responding to the presence of a specific wavelength. Instead, he suggested that the human experience of color derives from a complex interaction and mixing from the output three receptors. This theory was later confirmed by James Clerk Maxwell and refined by Hermann von Helmholtz. Maxwell experimentally demonstrated that any color could be matched with a combination of three lights. As Helmholtz puts it, "the principles of Newton's law of mixture were experimentally confirmed by Maxwell in 1856. Young's theory of color sensations, like so much else that this marvelous investigator achieved in advance of his time, remained unnoticed until Maxwell directed attention to it." At the same time as Helmholtz, Ewald Hering developed the opponent process theory of color, noting that color blindness and afterimages typically come in opponent pairs (red-green, blue-orange, yellow-violet, and black-white). Ultimately these two theories were synthesized in 1957 by Hurvich and Jameson, who showed that retinal processing corresponds to the trichromatic theory, while processing at the level of the lateral geniculate nucleus corresponds to the opponent theory. In 1931, the International Commission on Illumination (CIE), an international group of experts, developed a mathematical color model which mapped out the space of observable colors, allowing every individual color able to be specified with a set of three numbers. === Color in the eye === The ability of the human eye to distinguish colors is based upon the varying sensitivity of different cells in the retina to light of different wavelengths. Humans are trichromatic—the retina contains three types of color receptor cells, or cones. One type, relatively distinct from the other two, is most responsive to light that is perceived as blue or blue-violet, with wavelengths around 450 nm; cones of this type are sometimes called short-wavelength cones or S cones (or misleadingly, blue cones). The other two types are closely related genetically and chemically: middle-wavelength cones, M cones, or green cones are most sensitive to light perceived as green, with wavelengths around 540 nm, while the long-wavelength cones, L cones, or red cones, are most sensitive to light that is perceived as greenish yellow, with wavelengths around 570 nm. Light, no matter how complex its composition of wavelengths, is reduced to three color components by the eye. Each cone type adheres to the principle of univariance, which is that each cone's output is determined by the amount of light that falls on it over all wavelengths. For each location in the visual field, the three types of cones yield three signals based on the extent to which each is stimulated. These amounts of stimulation are sometimes called tristimulus values. The response cu

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  Reservoir sampling

  Reservoir sampling is a family of randomized algorithms for choosing a simple random sample, without replacement, of k items from a population of unknown size n in a single pass over the items. The size of the population n is not known to the algorithm and is typically too large for all n items to fit into main memory. The population is revealed to the algorithm over time, and the algorithm cannot look back at previous items. At any point, the current state of the algorithm must permit extraction of a simple random sample without replacement of size k over the part of the population seen so far. == Motivation == Suppose we see a sequence of items, one at a time. We want to keep 10 items in memory, and we want them to be selected at random from the sequence. If we know the total number of items n and can access the items arbitrarily, then the solution is easy: select 10 distinct indices i between 1 and n with equal probability, and keep the i-th elements. The problem is that we do not always know the exact n in advance. == Simple: Algorithm R == A simple and popular but slow algorithm, Algorithm R, was created by Jeffrey Vitter. Initialize an array R {\displaystyle R} indexed from 1 {\displaystyle 1} to k {\displaystyle k} , containing the first k items of the input x 1 , . . . , x k {\displaystyle x_{1},...,x_{k}} . This is the reservoir. For each new input x i {\displaystyle x_{i}} , generate a random number j uniformly in { 1 , . . . , i } {\displaystyle \{1,...,i\}} . If j ∈ { 1 , . . . , k } {\displaystyle j\in \{1,...,k\}} , then set R [ j ] := x i . {\displaystyle R[j]:=x_{i}.} Otherwise, discard x i {\displaystyle x_{i}} . Return R {\displaystyle R} after all inputs are processed. This algorithm works by induction on i ≥ k {\displaystyle i\geq k} . While conceptually simple and easy to understand, this algorithm needs to generate a random number for each item of the input, including the items that are discarded. The algorithm's asymptotic running time is thus O ( n ) {\displaystyle O(n)} . Generating this amount of randomness and the linear run time causes the algorithm to be unnecessarily slow if the input population is large. This is Algorithm R, implemented as follows: == Optimal: Algorithm L == If we generate n {\displaystyle n} random numbers u 1 , . . . , u n ∼ U [ 0 , 1 ] {\displaystyle u_{1},...,u_{n}\sim U[0,1]} independently, then the indices of the smallest k {\displaystyle k} of them is a uniform sample of the k {\displaystyle k} -subsets of { 1 , . . . , n } {\displaystyle \{1,...,n\}} . The process can be done without knowing n {\displaystyle n} : Keep the smallest k {\displaystyle k} of u 1 , . . . , u i {\displaystyle u_{1},...,u_{i}} that has been seen so far, as well as w i {\displaystyle w_{i}} , the index of the largest among them. For each new u i + 1 {\displaystyle u_{i+1}} , compare it with u w i {\displaystyle u_{w_{i}}} . If u i + 1 < u w i {\displaystyle u_{i+1} Read more →

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  Apple Intelligence

  Apple Intelligence is a generative artificial intelligence system developed by Apple Inc. Relying on a combination of on-device and server processing, it was announced on June 10, 2024, at the 2024 Worldwide Developers Conference, as a built-in feature of Apple's iOS 18, iPadOS 18, and macOS Sequoia, which were announced alongside Apple Intelligence. Apple Intelligence is free for all users with supported devices. On macOS, Apple Intelligence is available only on Apple silicon Mac computers; Intel-based Mac computers are not supported. Features include writing tools that assist users with grammar and proofreading, image generation, summaries of system notifications, AI-assisted image retouching in the Photos app, and integration with ChatGPT, the popular chatbot by OpenAI. As of March 2026, Apple Intelligence is not available yet on devices purchased in mainland China or on any device using an Apple Account set to mainland China, even if the device was bought elsewhere. == History == === Background === Apple first implemented artificial intelligence features in its products with the release of Siri in the iPhone 4S in 2011. In the years after its release, Apple engaged in efforts to ensure its artificial intelligence operations remained covert; according to University of California, Berkeley professor Trevor Darrell, the company's secrecy deterred graduate students. The company started expanding its artificial intelligence team in 2015, opening up its operations by publishing more scientific papers and joining AI industry research groups. Apple reportedly acquired more AI companies from 2016 to 2020. In 2017, Apple released the iPhone 8 and the iPhone X with the A11 Bionic processor, which featured its first dedicated Neural Engine for accelerating common machine learning tasks. Despite its investments in artificial intelligence, Siri was criticized both by reviewers and internally at Apple for lagging behind other AI assistants. The rapid development of generative artificial intelligence and the release of ChatGPT in late 2022 reportedly blindsided Apple executives and forced the company to refocus its efforts on AI. In an interview with Good Morning America, Apple CEO Tim Cook stated that generative AI had "great promise" but had some potential dangers, and that it was "looking closely" at ChatGPT. It was first reported in July 2023 that Apple was creating its own internal large language model, codenamed "Ajax". In October 2023, Apple was reportedly on track to release new generative AI features into its operating systems by 2024, including a significantly redeveloped Siri. In an earnings call in February 2024, Cook stated that the company was spending a "tremendous amount of time and effort" into AI features that would be shared "later that year". === Google deal === In January 2026, Apple and Google announced a multi-year partnership under which Apple’s next-generation foundation models are expected to incorporate Google’s Gemini models and cloud infrastructure. According to the companies, the collaboration is intended to support future Apple Intelligence features, including enhancements to Siri, while Apple Intelligence will continue to operate on Apple devices and through Apple’s Private Cloud Compute system, which Apple states is designed to preserve user privacy. On an earnings call, Apple reported to investors that they were integrating an on-device model of the Google Gemini AI to Siri, as the development of their model was beset with setbacks. Apple has previously tested and used other third-party AI models like ChatGPT, but according to a Bloomberg article by Mark Gurman, Apple pushed forward the proposed Google deal; by using Google's Gemini model possessing 1.2 trillion parameters, Apple would integrate a much larger and more complex model than those it previously developed and used. Of note, comparable AI models from other major companies (including OpenAI and Meta) have also been reported to operate at a similar “trillion-parameter” scale and to compete against Gemini-class systems on benchmarks. == Models == Apple Intelligence consists of an on-device model as well as a cloud model running on servers primarily using Apple silicon. Both models consist of a generic foundation model, as well as multiple adapter models that are more specialized to particular tasks like text summarization and tone adjustment. It was launched for developers and testers on July 29, 2024, in U.S. English, with the developer betas of iOS 18.1, macOS 15.1, and iPadOS 18.1, released partially on October 28, 2024, and will fully launch by 2026. According to a human evaluation done by Apple's machine learning division, the on-device foundation model beat or tied equivalent small models by Mistral AI, Microsoft, and Google, while the server foundation models beat the performance of OpenAI's GPT-3, while roughly matching the performance of GPT-4. Apple's cloud models are built on a Private Cloud Compute platform which is allegedly designed with user privacy and end-to-end encryption in mind. Unlike other generative AI services like ChatGPT which use servers from third parties, Apple Intelligence's cloud models are run entirely on Apple servers with custom Apple silicon hardware built for end-to-end encryption. It was also designed to make sure that the software running on said servers matches the independently verifiable software accessible to researchers. In case of a software mismatch, Apple devices will refuse to connect to the servers. On June 10, 2025, Apple announced that Apple's on-device foundation models will be available to third-party applications as part of the Foundation Models API, with support for structured data response and tool calling. == Features == === Writing tools === Apple Intelligence features writing tools that are powered by LLMs. Selected text can be proofread, rewritten, made more friendly, concise or professional, similar to the AI writing features of the popular online English-language writing assistant tool Grammarly. It can also be used to generate summaries, key points, tables, and lists from an article or piece of writing. In iOS 18.2 and macOS 15.2, a ChatGPT integration was added to Writing Tools through "Compose" and "Describe your change" features. === Real-time Translation === Apple Intelligence enables the real-time translation of messages, photos and videos, and phone calls, through Apple's hardware. For communicating with foreigners, using the Translate app on iPhone to show subtitles in their language or to play back the translated audio naturally in their language, and also by wearing AirPods with Live Translation can now help to understand what someone is saying in users' preferred language in conversation. If both have headphones, simultaneous interpretation can be achieved. === Image Playground === Apple Intelligence can be used to generate images on-device with the Image Playground app. Similarly to OpenAI's DALL-E, it can be used to generate images using AI, using phrases and descriptions to output an image with customizable styles such as Animation and Sketch. In Notes, users can access Image Playground on iPad through the Image Wand tool in the Apple Pencil palette without having to open the Image Playground app. Rough sketches made with Apple Pencil can be transformed into images. As part of iOS, iPadOS, and macOS 26, Image Playground now integrates with the image generation models built into ChatGPT. === Genmoji === Using Apple Intelligence text-to-image models, users can generate unique "Genmoji" images by typing descriptions (prompting). Users can pick people in photos to have Genmoji generate images that resemble them. Similarly to emoji, Genmoji can be added inline to text messages, tapbacks, stickers and can be shared in Messages as well in third-party applications as inline messages or as stickers. === Siri overhaul === Siri, which used to be Apple's virtual assistant, has been updated to be an LLM chatbot, with enhanced capabilities made possible by Apple Intelligence. The latest iteration features an updated user interface, improved natural language processing, and the option to interact via text by double tapping the home bar without enabling the feature in the Accessibility menu, or double-clicking the command key on macOS. In a later update, Apple Intelligence will add the ability for Siri to use personal context from device activities to answer queries. === Mail === Apple Intelligence adds a feature called Priority Messages to the Mail app, which shows urgent emails such as same-day invitations or boarding passes, with AI generated summaries of the email. The Mail app also gains the ability to categorize incoming mail into Primary, Transactions, Updates, and Promotions based on what the email contains, which Apple claims is done all on-device. === Photos === Apple's Photos app includes a feature to create custom memory movies and enhanced search capabilities. Users can describe

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  Agentic commerce

  Agentic commerce (also referred to as agent-based commerce) describes an emerging form of e-commerce in which autonomous artificial intelligence (AI) agents independently execute purchasing and payment processes on behalf of users or organizations. Unlike conventional digital commerce systems, which require direct human interaction at key decision points, agentic commerce systems are designed to search for products or services, evaluate options, make purchasing decisions, and complete payments without real-time human involvement. An emerging development within the broader fields of e-commerce, fintech, and artificial intelligence; agentic commerce combines advances in generative AI, autonomous agents, application programming interfaces (APIs), and digital payment infrastructures to direct transactions with no direct human interaction. == Characteristics == A defining feature of agentic commerce is the delegation of end-to-end commercial activities to software agents. These agents typically operate according to predefined user preferences, rules, or constraints, such as price limits, quality criteria, delivery times, or preferred payment methods. Based on these parameters, an agent can autonomously perform tasks including product discovery, price comparison, contract selection, order placement, and payment execution. In contrast to decision-support systems, which provide recommendations to human users, agentic commerce systems are designed to act independently. Human involvement may be limited to initial configuration, periodic supervision, or exception handling. == Comparison with traditional and AI-assisted commerce == Traditional e-commerce requires users to manually browse products, select offers, and authorize payments. Generative AI systems used in commerce commonly assist users by answering questions or suggesting options, and do not complete transactions autonomously. Agentic commerce differs in that decision-making authority is partially or fully transferred to AI agents. As a result, the conventional customer journey, characterized by conscious decision points, may be replaced by continuous, automated micro-decisions performed by software. == Applications and business use cases == Potential applications of agentic commerce include recurring purchases, subscription management, business-to-business procurement, inventory replenishment, and price monitoring. In such contexts, transactions are often predictable and standardized, making them suitable for automation. From a business perspective, agentic commerce systems may be used to optimize supply chains, manage inventory levels, negotiate prices algorithmically, or execute transactions across multiple platforms. Enterprises adopting the new technology include retailers Walmart, Home Depot, Wayfair and Urban Outfitters, and ad tech DSPs, including Google Ads, Amazon, and Yahoo. Chinese tech firms are using apps to provide full-service shopping and payment tools. These includes Alibaba, Tencent, and ByteDance who are currently developing AI powered shopping apps. The Qwen AI chatbot allows users to complete transactions directly within its interface. US firms are still leading in developing AI models but integration is slower due to privacy restrictions. == Payments and technical infrastructure == Agentic commerce relies on digital payment systems capable of supporting automated, machine-initiated transactions, including API-based payment processing, tokenization, real-time authorization, and continuous risk monitoring. Typical user interfaces, such as shopping carts, may be replaced by backend integrations between AI agents, merchants, and payment service providers. For example, Iike 2025, Alibaba launched Alipay AI Pay, which grew and began operating as an application for different retailers. In December 2025, Alipay teamed up with Rokid to enable developers to integrate AI payments into AI agents on Rokid's Lingzhu platform. In January 2025, Alipay unveiled the Agentic Commerce Trust Protocol in partnership with Alibaba's consumer AI applications, such as the Qwen App and Taobao Instant Commerce. Qwen adopted the platform first, connecting it to Taobao Instant Commerce and Alipay AI Pay. Users could use Qwen's agentic feature to place food and drink orders within the application instead of having to click outside to an external browser. For merchants, participation in agentic commerce may require products and services to be presented in structured, machine-readable formats to ensure discoverability and interoperability with autonomous agents. == Universal Commerce Protocol (UCP) == In January 2026, Google announced the Universal Commerce Protocol (UCP), an open-source web standard intended to enable interoperability between AI agents and retail systems across the shopping journey, from discovery and checkout to post-purchase support. UCP makes use of REST, JSON-RPC transports, and support for Agent Payments Protocol (AP2), Agent2Agent (A2A), and Model Context Protocol (MCP). == Legal, regulatory, and security considerations == The use of autonomous agents in commerce raises legal and regulatory questions, particularly regarding authorization, liability, consumer protection, and fraud prevention. Existing payment and contract frameworks are generally based on human decision-makers, and their applicability to autonomous agents remains an area of active discussion. Open issues include responsibility for unauthorized or erroneous transactions, mechanisms for dispute resolution, standards for agent authentication, and compliance with data protection and financial regulations. Continuous, automated transaction patterns may also require new approaches to security and risk assessment. Traditional fraud models centered on identity verification may be insufficient for agentic commerce, and that merchants may need intent-based detection methods using machine learning and behavioral analysis to distinguish legitimate AI agents from malicious automation. === Governance frameworks === The deployment of autonomous AI agents in commercial environments has prompted the development of dedicated governance frameworks. These aim to define operational boundaries, decision authority, oversight mechanisms, and accountability structures for agentic systems. The Agentic Commerce Framework (ACF), created in 2025 by Vincent Dorange, is a governance standard that structures the deployment of autonomous AI agents around four founding principles (Decision Sovereignty, Governance by Design, Ultimate Human Control, Traceable Accountability), four operational layers, and 18 governance KPIs. In January 2026, Singapore's Infocomm Media Development Authority (IMDA) published the Model AI Governance Framework for Agentic AI, extending its existing AI governance guidelines to address agent-specific risks including delegation chains and multi-agent coordination. The Cloud Security Alliance (CSA) has also proposed an Agentic Trust Framework applying zero-trust principles to AI agent governance. == Ecosystem and implementation == The adoption of agentic commerce typically requires changes in commerce architecture, data modeling, identity and permissions, and API-based orchestration of checkout and post-purchase workflows. Management consultancies have identified agentic commerce as a structural evolution of digital commerce, emphasizing the role of AI-driven agents in automating discovery, decision-making, and transaction processes across commerce systems. McKinsey & Company has described agentic commerce as a significant shift in how consumers interact with brands and how enterprises design their commerce operating models. In Europe, this ecosystem also includes digital commerce consultancies specializing in the adoption of agentic commerce. Consulting firms such as Horrea support brands in understanding and implementing the technological and organizational shifts associated with agentic commerce. == Market development and outlook == Agentic commerce is generally regarded as an early-stage development. Industry analysts have projected that AI-driven agents could account for a small but growing share of digital payment transactions within the coming years. Due to the scale of global digital commerce, even limited adoption could represent substantial transaction volumes. Analysts expect that by 2029, AI agents could handle between 1% and 4% of all digital payment transactions. With a projected total transaction volume of over $36 trillion a year, even a small share translates into a market worth up to $1.47 trillion. According to a McKinsey study from October 2025, agentic commerce projects that by 2030, the U.S. business-to-consumer retail market alone could see up to $1 trillion in revenue orchestrated through agentic commerce. On a global scale, the opportunity could range from $3 trillion to $5 trillion. Early experiments and pilot projects have demonstrated both the potential and current limitations of the

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  INDIAai

  INDIAai is a web portal launched by the Government of India on 07 March 2024 for artificial intelligence-related developments in India. It is known as the National AI Portal of India, which was jointly started by the Ministry of Electronics and Information Technology (MeitY), the National e-Governance Division (NeGD) and the National Association of Software and Service Companies (NASSCOM) with support from the Department of School Education and Literacy (DoSE&L) and Ministry of Human Resource Development. == History == The portal was launched on 30 May 2020, by Ravi Shankar Prasad, the Union Minister for Electronics and IT, Law and Justice and Communications, on the first anniversary of the second tenure of Prime Minister Narendra Modi-led government. A national program for the youth, 'Responsible AI for Youth', was also launched on the same day. As of 2022, the website was visited by more than 4.5 lakh users with 1.2 million page views. It has 1151 articles on artificial intelligence, 701 news stories, 98 reports, 95 case studies and 213 videos on its portal. It maintains a database on AI ecosystem of India featuring 121 government initiatives and 281 startups. In May 2022, INDIAai released a book titled 'AI for Everyone' that covers the basics of AI. Cabinet chaired by the Prime Minister Narendra Modi has approved the comprehensive national-level IndiaAI mission with a budget outlay of Rs.10,371.92 crore. The Mission will be implemented by ‘IndiaAI’ Independent Business Division (IBD) under Digital India Corporation (DIC). == Objective and features == It aims to function as a one-stop portal for all AI-related development in India. The platform publishes resources such as articles, news, interviews, and investment funding news and events for AI startups, AI companies, and educational firms related to artificial intelligence in India. It also distributes documents, case studies, and research reports. Additionally, the platform provides education and employment opportunities related to AI. It offers AI courses, both free and paid.

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  DPVweb

  DPVweb is a database for virologists working on plant viruses combining taxonomic, bioinformatic and symptom data. == Description == DPVweb is a central web-based source of information about viruses, viroids and satellites of plants, fungi and protozoa. It provides comprehensive taxonomic information, including brief descriptions of each family and genus, and classified lists of virus sequences. It makes use of a large database that also holds detailed, curated, information for all sequences of viruses, viroids and satellites of plants, fungi and protozoa that are complete or that contain at least one complete gene. There are currently about 10,000 such sequences. For comparative purposes, DPVweb also contains a representative sequence of all other fully sequenced virus species with an RNA or single-stranded DNA genome. For each curated sequence the database contains the start and end positions of each feature (gene, non-translated region, etc.), and these have been checked for accuracy. As far as possible, the nomenclature for genes and proteins are standardized within genera and families. Sequences of features (either as DNA or amino acid sequences) can be directly downloaded from the website in FASTA format. The sequence information can also be accessed via client software for personal computers. == History == The Descriptions of Plant Viruses (DPVs) were first published by the Association of Applied Biologists in 1970 as a series of leaflets, each one written by an expert describing a particular plant virus. In 1998 all of the 354 DPVs published in paper were scanned, and converted into an electronic format in a database and distributed on CDROM. In 2001 the descriptions were made available on the new DPVweb site, providing open access to the now 400+ DPVs (currently 415) as well as taxonomic and sequence data on all plant viruses. == Uses == DPVweb is an aid to researchers in the field of plant virology as well as an educational resource for students of virology and molecular biology. The site provides a single point of access for all known plant virus genome sequences making it easy to collect these sequences together for further analysis and comparison. Sequence data from the DPVweb database have proved valuable for a number of projects: survey of codon usage bias amongst all plant viruses, two-way comparisons between comprehensive sets of sequences from the families Flexiviridae and Potyviridae that have helped inform taxonomy and clarify genus and species discrimination criteria, a survey and verification of the polyprotein cleavage sites within the family Potyviridae.

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  Chinese speech synthesis

  Chinese speech synthesis is the application of speech synthesis to the Chinese language (usually Standard Chinese). It poses additional difficulties due to Chinese characters frequently having different pronunciations in different contexts and the complex prosody, which is essential to convey the meaning of words, and sometimes the difficulty in obtaining agreement among native speakers concerning what the correct pronunciation is of certain phonemes. == Concatenation (Ekho and KeyTip) == Recordings can be concatenated in any desired combination, but the joins sound forced (as is usual for simple concatenation-based speech synthesis) and this can severely affect prosody; these synthesizers are also inflexible in terms of speed and expression. However, because these synthesizers do not rely on a corpus, there is no noticeable degradation in performance when they are given more unusual or awkward phrases. Ekho is an open source TTS which simply concatenates sampled syllables. It currently supports Cantonese, Mandarin, and experimentally Korean. Some of the Mandarin syllables have been pitched-normalised in Praat. A modified version of these is used in Gradint's "synthesis from partials". cjkware.com used to ship a product called KeyTip Putonghua Reader which worked similarly; it contained 120 Megabytes of sound recordings (GSM-compressed to 40 Megabytes in the evaluation version), comprising 10,000 multi-syllable dictionary words plus single-syllable recordings in 6 different prosodies (4 tones, neutral tone, and an extra third-tone recording for use at the end of a phrase). == Lightweight synthesizers (eSpeak and Yuet) == The lightweight open-source speech project eSpeak, which has its own approach to synthesis, has experimented with Mandarin and Cantonese. eSpeak was used by Google Translate from May 2010 until December 2010. The commercial product "Yuet" is also lightweight (it is intended to be suitable for resource-constrained environments like embedded systems); it was written from scratch in ANSI C starting from 2013. Yuet claims a built-in NLP model that does not require a separate dictionary; the speech synthesised by the engine claims clear word boundaries and emphasis on appropriate words. Communication with its author is required to obtain a copy. Both eSpeak and Yuet can synthesis speech for Cantonese and Mandarin from the same input text, and can output the corresponding romanisation (for Cantonese, Yuet uses Yale and eSpeak uses Jyutping; both use Pinyin for Mandarin). eSpeak does not concern itself with word boundaries when these don't change the question of which syllable should be spoken. == Corpus-based == A "corpus-based" approach can sound very natural in most cases but can err in dealing with unusual phrases if they can't be matched with the corpus. The synthesiser engine is typically very large (hundreds or even thousands of megabytes) due to the size of the corpus. === iFlyTek === Anhui USTC iFlyTek Co., Ltd (iFlyTek) published a W3C paper in which they adapted Speech Synthesis Markup Language to produce a mark-up language called Chinese Speech Synthesis Markup Language (CSSML) which can include additional markup to clarify the pronunciation of characters and to add some prosody information. The amount of data involved is not disclosed by iFlyTek but can be seen from the commercial products that iFlyTek have licensed their technology to; for example, Bider's SpeechPlus is a 1.3 Gigabyte download, 1.2 Gigabytes of which is used for the highly compressed data for a single Chinese voice. iFlyTek's synthesiser can also synthesise mixed Chinese and English text with the same voice (e.g. Chinese sentences containing some English words); they claim their English synthesis to be "average". The iFlyTek corpus appears to be heavily dependent on Chinese characters, and it is not possible to synthesize from pinyin alone. It is sometimes possible by means of CSSML to add pinyin to the characters to disambiguate between multiple possible pronunciations, but this does not always work. === NeoSpeech === There is an online interactive demonstration for NeoSpeech speech synthesis, which accepts Chinese characters and also pinyin if it's enclosed in their proprietary "VTML" markup. === Mac OS === Mac OS had Chinese speech synthesizers available up to version 9. This was removed in 10.0 and reinstated in 10.7 (Lion). === Historical corpus-based synthesizers (no longer available) === A corpus-based approach was taken by Tsinghua University in SinoSonic, with the Harbin dialect voice data taking 800 Megabytes. This was planned to be offered as a download but the link was never activated. Nowadays, only references to it can be found on Internet Archive. Bell Labs' approach, which was demonstrated online in 1997 but subsequently removed, was described in a monograph "Multilingual Text-to-Speech Synthesis: The Bell Labs Approach" (Springer, October 31, 1997, ISBN 978-0-7923-8027-6), and the former employee who was responsible for the project, Chilin Shih (who subsequently worked at the University of Illinois) put some notes about her methods on her website.

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  Record linkage

  Record linkage (also known as data matching, data linkage, entity resolution, and many other terms) is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and databases). Record linkage is necessary when joining different data sets based on entities that may or may not share a common identifier (e.g., database key, URI, National identification number), which may be due to differences in record shape, storage location, or curator style or preference. A data set that has undergone RL-oriented reconciliation may be referred to as being cross-linked. == Naming conventions == "Record linkage" is the term used by statisticians, epidemiologists, and historians, among others, to describe the process of joining records from one data source with another that describe the same entity. However, many other terms are used for this process. Unfortunately, this profusion of terminology has led to few cross-references between these research communities. Computer scientists often refer to it as "data matching" or as the "object identity problem". Commercial mail and database applications refer to it as "merge/purge processing" or "list washing". Other names used to describe the same concept include: "coreference/entity/identity/name/record resolution", "entity disambiguation/linking", "fuzzy matching", "duplicate detection", "deduplication", "record matching", "(reference) reconciliation", "object identification", "data/information integration" and "conflation". While they share similar names, record linkage and linked data are two separate approaches to processing and structuring data. Although both involve identifying matching entities across different data sets, record linkage standardly equates "entities" with human individuals; by contrast, Linked Data is based on the possibility of interlinking any web resource across data sets, using a correspondingly broader concept of identifier, namely a URI. == History == The initial idea of record linkage goes back to Halbert L. Dunn in his 1946 article titled "Record Linkage" published in the American Journal of Public Health. Howard Borden Newcombe then laid the probabilistic foundations of modern record linkage theory in a 1959 article in Science. These were formalized in 1969 by Ivan Fellegi and Alan Sunter, in their pioneering work "A Theory For Record Linkage", where they proved that the probabilistic decision rule they described was optimal when the comparison attributes were conditionally independent. In their work they recognized the growing interest in applying advances in computing and automation to large collections of administrative data, and the Fellegi-Sunter theory remains the mathematical foundation for many record linkage applications. Since the late 1990s, various machine learning techniques have been developed that can, under favorable conditions, be used to estimate the conditional probabilities required by the Fellegi-Sunter theory. Several researchers have reported that the conditional independence assumption of the Fellegi-Sunter algorithm is often violated in practice; however, published efforts to explicitly model the conditional dependencies among the comparison attributes have not resulted in an improvement in record linkage quality. On the other hand, machine learning or neural network algorithms that do not rely on these assumptions often provide far higher accuracy, when sufficient labeled training data is available. Record linkage can be done entirely without the aid of a computer, but the primary reasons computers are often used to complete record linkages are to reduce or eliminate manual review and to make results more easily reproducible. Computer matching has the advantages of allowing central supervision of processing, better quality control, speed, consistency, and better reproducibility of results. == Methods == === Data preprocessing === Record linkage is highly sensitive to the quality of the data being linked, so all data sets under consideration (particularly their key identifier fields) should ideally undergo a data quality assessment before record linkage. Many key identifiers for the same entity can be presented quite differently between (and even within) data sets, which can greatly complicate record linkage unless understood ahead of time. For example, key identifiers for a man named William J. Smith might appear in three different data sets as follows: In this example, the different formatting styles lead to records that look different but in fact all refer to the same entity with the same logical identifier values. Most, if not all, record linkage strategies would result in more accurate linkage if these values were first normalized or standardized into a consistent format (e.g., all names are "Surname, Given name", and all dates are "YYYY/MM/DD"). Standardization can be accomplished through simple rule-based data transformations or more complex procedures such as lexicon-based tokenization and probabilistic hidden Markov models. Several of the packages listed in the Software Implementations section provide some of these features to simplify the process of data standardization. === Entity resolution === Entity resolution is an operational intelligence process, typically powered by an entity resolution engine or middleware, whereby organizations can connect disparate data sources with a view to understand possible entity matches and non-obvious relationships across multiple data silos. It analyzes all of the information relating to individuals and/or entities from multiple sources of data, and then applies likelihood and probability scoring to determine which identities are a match and what, if any, non-obvious relationships exist between those identities. Entity resolution engines are typically used to uncover risk, fraud, and conflicts of interest, but are also useful tools for use within customer data integration (CDI) and master data management (MDM) requirements. Typical uses for entity resolution engines include terrorist screening, insurance fraud detection, USA Patriot Act compliance, organized retail crime ring detection and applicant screening. For example, across different data silos – employee records, vendor data, watch lists, etc. – an organization may have several variations of an entity named ABC, which may or may not be the same individual. These entries may, in fact, appear as ABC1, ABC2, or ABC3 within those data sources. By comparing similarities between underlying attributes such as address, date of birth, or social security number, the user can eliminate some possible matches and confirm others as very likely matches. Entity resolution engines then apply rules, based on common sense logic, to identify hidden relationships across the data. In the example above, perhaps ABC1 and ABC2 are not the same individual, but rather two distinct people who share common attributes such as address or phone number. ==== Data matching ==== While entity resolution solutions include data matching technology, many data matching offerings do not fit the definition of entity resolution. Here are four factors that distinguish entity resolution from data matching, according to John Talburt, director of the UALR Center for Advanced Research in Entity Resolution and Information Quality: Works with both structured and unstructured records, and it entails the process of extracting references when the sources are unstructured or semi-structured Uses elaborate business rules and concept models to deal with missing, conflicting, and corrupted information Utilizes non-matching, asserted linking (associate) information in addition to direct matching Uncovers non-obvious relationships and association networks (i.e. who's associated with whom) In contrast to data quality products, more powerful identity resolution engines also include a rules engine and workflow process, which apply business intelligence to the resolved identities and their relationships. These advanced technologies make automated decisions and impact business processes in real time, limiting the need for human intervention. === Deterministic record linkage === The simplest kind of record linkage, called deterministic or rules-based record linkage, generates links based on the number of individual identifiers that match among the available data sets. Two records are said to match via a deterministic record linkage procedure if all or some identifiers (above a certain threshold) are identical. Deterministic record linkage is a good option when the entities in the data sets are identified by a common identifier, or when there are several representative identifiers (e.g., name, date of birth, and sex when identifying a person) whose quality of data is relatively high. As an example, consider two standardized data sets, Set A and Set B, that contain different bits of information about patients in a hospital system. T

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  Pixorial

  Pixorial was a cloud-based consumer photo sharing, video sharing and video editing platform. The company was formed in 2007 in Centennial, Colorado as a media conversion service. In 2013, Pixorial was chosen as one of two video storage companies to partner with the launch of Google Drive. Pixorial allowed users to edit and share videos on social channels by connecting through their Pixorial account. The company closed on July 18, 2014, and its assets were acquired by LifeLogger Technologies Corp in November 2015. == History == The company was founded in 2007 and launched in 2009 by former Netscape employee Andres Espineira. Changing its focus to video editing software in 2009, Pixorial began developing an app that would be launched for iOS and Android devices in 2011. Later developments in the app in 2012 would also included real time filters, which were later removed. With the launch of Google Drive in 2012, Pixorial was chosen as an integrated video partner. This integration with Google Drive allowed users to access videos stored in Google Drive within the web app of Pixorial. After the Google Drive launch, Pixorial developed a crowdsourced, location-based video sharing app, Krowds. The app was cited in July 2012 by PC Magazine as one of "The 8 Best Apps for Making and Sharing Videos on Your iPhone". In late July, Pixorial replaced its original mobile app with the MyPlayer HD app that optimized HD video viewing for large screen viewing including tablets and smart televisions. Pixorial's services terminated on July 18, 2014. == Products == === Krowds App === Pixorial's app was launched in April 2013 for iOS, and in May for Android, as a tool to aggregate event videos through location based collections. The app was launched to generally positive reviews. === Movie Creator === Launched July 12, 2012 Pixorial's Movie Creator allowed users to edit movies in a simple story-telling platform Movie Creator's features include transitions, text boxes, access to free music tracks, credits, and social media sharing capabilities. The Pixorial platform allowed users to view, share, and edit videos without modifying the original. Movie Creator integrated pictures and video to create user movies. == Awards == 2012 Apex Award from the Colorado Technology Association, for Best Technology Project of the Year 2010 Computerworld Laureate for Media, Arts and Entertainment

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  Reservoir sampling

  Reservoir sampling is a family of randomized algorithms for choosing a simple random sample, without replacement, of k items from a population of unknown size n in a single pass over the items. The size of the population n is not known to the algorithm and is typically too large for all n items to fit into main memory. The population is revealed to the algorithm over time, and the algorithm cannot look back at previous items. At any point, the current state of the algorithm must permit extraction of a simple random sample without replacement of size k over the part of the population seen so far. == Motivation == Suppose we see a sequence of items, one at a time. We want to keep 10 items in memory, and we want them to be selected at random from the sequence. If we know the total number of items n and can access the items arbitrarily, then the solution is easy: select 10 distinct indices i between 1 and n with equal probability, and keep the i-th elements. The problem is that we do not always know the exact n in advance. == Simple: Algorithm R == A simple and popular but slow algorithm, Algorithm R, was created by Jeffrey Vitter. Initialize an array R {\displaystyle R} indexed from 1 {\displaystyle 1} to k {\displaystyle k} , containing the first k items of the input x 1 , . . . , x k {\displaystyle x_{1},...,x_{k}} . This is the reservoir. For each new input x i {\displaystyle x_{i}} , generate a random number j uniformly in { 1 , . . . , i } {\displaystyle \{1,...,i\}} . If j ∈ { 1 , . . . , k } {\displaystyle j\in \{1,...,k\}} , then set R [ j ] := x i . {\displaystyle R[j]:=x_{i}.} Otherwise, discard x i {\displaystyle x_{i}} . Return R {\displaystyle R} after all inputs are processed. This algorithm works by induction on i ≥ k {\displaystyle i\geq k} . While conceptually simple and easy to understand, this algorithm needs to generate a random number for each item of the input, including the items that are discarded. The algorithm's asymptotic running time is thus O ( n ) {\displaystyle O(n)} . Generating this amount of randomness and the linear run time causes the algorithm to be unnecessarily slow if the input population is large. This is Algorithm R, implemented as follows: == Optimal: Algorithm L == If we generate n {\displaystyle n} random numbers u 1 , . . . , u n ∼ U [ 0 , 1 ] {\displaystyle u_{1},...,u_{n}\sim U[0,1]} independently, then the indices of the smallest k {\displaystyle k} of them is a uniform sample of the k {\displaystyle k} -subsets of { 1 , . . . , n } {\displaystyle \{1,...,n\}} . The process can be done without knowing n {\displaystyle n} : Keep the smallest k {\displaystyle k} of u 1 , . . . , u i {\displaystyle u_{1},...,u_{i}} that has been seen so far, as well as w i {\displaystyle w_{i}} , the index of the largest among them. For each new u i + 1 {\displaystyle u_{i+1}} , compare it with u w i {\displaystyle u_{w_{i}}} . If u i + 1 < u w i {\displaystyle u_{i+1} Read more →

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  E-Science librarianship

  E-Science librarianship refers to a role for librarians in e-Science. == Early scholars == Early references to e-Science and librarianship involve information studies scholars researching cyberinfrastructure and emerging networked information and knowledge communities. Notably Christine Borgman, Professor and Presidential Chair in Information Studies at the University of California, Los Angeles (UCLA) was a key player in bringing e-Science, and the idea of networked knowledge communities, to the attention of the library profession. In 2004, as a visiting fellow at the Oxford Internet Institute, she conducted research and lectured publicly on e-Science, Digital Libraries, and Knowledge Communities. In 2007 Anna K. Gold, formerly of MIT and Cal Poly, San Luis Obispo, authored a series of articles in D-Lib Magazine that opened the door for academic libraries to begin exploring roles, skills, and strategies for engaging in e-Science: Cyberinfrastructure, Data, and Libraries, Part 1: A Cyberinfrastructure Primer for Librarians and Cyberinfrastructure, Data, and Libraries, Part 2: Libraries and the Data Challenge: Roles and Actions for Libraries. == Academic research and health sciences libraries == In 2007, the Association of Research Libraries (ARL) e-Science task force issued its report on e-Science and librarianship. The ARL's report encouraged its member libraries to position themselves to engage with researchers involved in e-Science (eScience) by cultivating new research support strategies and developing their digital scholarship infrastructure. E-Science has multiple attributes; Tony and Jessie Hey framed e-Science for the library community by characterizing it as a research methodology: "e-Science is not a new scientific discipline in its own right: e-Science is shorthand for the set of tools and technologies required to support collaborative, networked science". In addition to academic libraries' interests in providing support for their researchers engaging in e-Science, the health sciences library community also emerged as a major proponent for creating librarian positions for supporting the information needs of large-scale, networked, research collaborations on their campuses. Neil Rambo, current director of NYU's Health Sciences Library and former director of University of Washington Health Sciences Library, was the first to use the term in the Journal of the Medical Library Association, in his 2009 editorial e-Science and the Biomedical Library. Rambo's definition of e-Science highlighted the potential e-Science held for creating data as a research product: "E-science is a new research methodology, fueled by networked capabilities and the practical possibility of gathering and storing vast amounts of data." In response to this article the University of Massachusetts Medical School Lamar Soutter Library and National Network of Libraries of Medicine, New England Region encouraged health sciences libraries to cooperate to identify skills and develop a program for training e-Science Librarians. Then, in 2013, Shannon Bohle, an archivist who was employed in the library at Cold Spring Harbor Laboratory, an NCI-designated basic cancer research facility, used experience gained there and previous papers and presentations about preserving scientific archival materials to expand the traditional definition of e-Science by including the terms, principles, and practices used in archival science. These included in the definition the "long-term storage and accessibility of all materials generated through the scientific process," as well as examples of material types traditionally preserved in archives, like "electronic/digitized laboratory notebooks, raw and fitted data sets, manuscript production and draft versions, pre-prints," as well as library materials ("print and/or electronic publications"). == Roles == Many areas of science are about to be transformed by the availability of vast amounts of new scientific data that can potentially provide insights at a level of detail never before envisaged. However, this new data dominant era brings new challenges for the scientists and they will need the skills and technologies both of computer scientists and of the library community to manage, search and curate these new data resources. Libraries will not be immune from change in this new world of research. Karen Williams identifies roles in the following areas for librarians in the developing world of e-Science. Campus Engagement Content/Collection Development and Management Teaching and Learning Scholarly Communication E-Scholarship and Digital Tools Reference/Help Services Outreach Fund Raising Exhibit and Event Planning Leadership == Challenges for research libraries == E-science tends toward inter- and multidisciplinary approaches that depend on computation and computer science. Research libraries have traditionally been discipline focused and, although increasingly technologically sophisticated, do not have systems of the scale or complexity of the e-science environment. E-science is data intensive, but research libraries have not typically been responsible for scientific data. E-science is frequently conducted in a team context, often distributed across multiple institutions and on a global scale. The primary constituency of libraries generally comprises those affiliated with the local institution. Licenses for electronic content are typically restricted to a particular institutional community, and the infrastructure to move institutional licenses into a multi-institutional environment is not well developed. E-science challenges all these traditional paradigms of research library organization and services. == Skills == Garritano & Carlson were among the first to outline a skill set for librarians seeking to support the data needs of e-Science; they identified five skill categories librarians new to this area should expect to adapt or develop when participating on such projects: Library and information science expertise Subject expertise Partnerships and outreach (both internal and external) Participating in sponsored research Balancing workload An example of librarians reconfiguring traditional librarian skills to meet the needs of researchers engaging in e-Science is Witt & Carlson's adaptation of the traditional reference interview into a "data interview" in order to provide effective data management and e-Science services. This interview consists of ten practical queries necessary for understanding the provenance and expectations for the preservation of datasets typical of e-Science that also help illustrate some of the educational tools and skills needed by a librarian new to e-Science. "What is the story of the data? What form and format are the data in? What is the expected lifespan of the dataset? How could the data be used, reused, and repurposed? How large is the dataset, and what is its rate of growth? Who are the potential audiences for the data? Who owns the data? Does the dataset include any sensitive information? What publications or discoveries have resulted from the data? How should the data be made accessible?" == Resources == In 2009 the Lamar Soutter Library at the University of Massachusetts Medical School (UMMS) and the National Network of Libraries of Medicine, New England Region (NN/LM NER) funded an e-Science program for building the skills highlighted above for librarians. Elaine Russo Martin, Director of Library Services at the Lamar Soutter Library and Director of the NN/LM NER developed this comprehensive e-Science program to build librarians' subject expertise in the sciences, developing their data management skills, and their familiarity with cyberinfrastructure and e-Science. Three major products of this program are the e-Science web portal for librarians, the E-Science Symposium, and the New England Collaborative Data Management Curriculum (NECDMC). This portal includes educational resources for specific tools and subject/discipline tutorials and modules to assist librarians new to e-Science. UMMS and NN/LM NER also publish an open access journal called the Journal of eScience Librarianship.

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