Social media as a public utility is a theory postulating that social networking sites (such as Meta - ie:Facebook & Instagram or Alphabet - ie: YouTube & Google, but also independent sites such as Twitter, Tumblr, Snapchat etc.) are essential public services that should be regulated by the government, in a manner similar to how electric and phone utilities are typically government regulated. It is based on the notion that social media platforms have monopoly power and broad social influence. == Background == === Definitions === Social media is defined as "a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User Generated Content." Furthermore, the New Zealand Government of Internal Affairs describes it as "a set of online technologies, sites, and practices which are used to share opinions, experiences and perspectives. Fundamentally it is about the conversation. In contrast with traditional media, the nature of social media is to be highly interactive." Moreover, the term social media is described as online tools that let people interact and communicate with each other. This has become a standard word for online cultural exchange and a dominant way for individuals to engage on the internet. By using social media individuals become more closely and strongly connected than ever before. The traditional definition of the term public utility is "an infrastructural necessity for the general public where the supply conditions are such that the public may not be provided with a reasonable service at reasonable prices because of monopoly in the area." Conventional public utilities include water, natural gas, and electricity. In order to secure the interests of the public, utilities are regulated. Public utilities can also be seen as natural monopolies implying that the highest degree of efficiency is accomplished under one operator in the marketplace. Public utility regulation for social media has been largely criticized because people believe it would produce undesirable and indirect effects. However, others say that truly effective government regulation would produce valuable results. Social media as a public utility is a crucial debate because utilities get regulated, so marking social media websites as utilities would require government regulation of various social media websites and platforms such as Facebook, Google, and Twitter. Applying the term public utility to social media implies that social media websites are public necessities, and, consequently, should be regulated by the government. While social media are not as essential for survival as traditional public utilities such as electricity, water, and natural gas, many people believe it has become vital for living in an interconnected world and without it, living a successful life would be difficult. Therefore, many people believe that social media has reached utility status and should be treated as a public utility. However, others believe that this is not true because social media are constantly revolutionizing and giving such platforms "utility status" would result in government regulation, which would consequently hinder innovation. Over the past decade many have debated and questioned whether or not "Internet service providers should be considered essential facilities or natural monopolies and regulated as public utilities." === Monopoly === A monopoly is defined as "a firm that is the only seller of a product or service having no close substitutes." A natural monopoly is when the entire demand within a relevant market can be satisfied at lowest cost by one firm rather than by two or more, and if such a market contains more than one firm then the firms will "quickly shake down to one through mergers or failures, or production will continue to consume more resources than necessary." In a monopoly competition is said to be short-lived, and in a natural monopoly it is said to produce inefficient results." Public utility companies can be regulated to prevent them from gaining monopolistic control. In November 2011 AT&T's proposal for merging with T-Mobile was rejected because it would have "diminished competition," and have led to the company having monopolistic power within the telephone industry. Such regulation is permitted because the telephone industry is a public utility. Similarly, Microsoft has also been prevented from taking various business actions that could result in the company gaining monopolistic power. If social media were a public utility then regulation of Google and Facebook would similarly dictate what they could and could not do. The possibility was raised in 2018 by U.S. Representative Steve King during a House Judiciary hearing on social media filtering practices. == Arguments == Advocates of this theory believe that social media websites already act like public utilities, and therefore regulation is needed. Additionally, advocates say that in the 21st century, using such websites are as necessary for communication as using traditional public utilities such as telephone, water, electricity, and natural gas are for other everyday uses. Specifically, advocates note that Google search should be treated as a public utility and needs to be regulated because it dominates the search engine market and no website can afford to ignore it. There is the position that a social media website such as Google "is a common carrier and should be regulated as such (Newman 2011)." These are reinforced by a perception that social media companies fail to properly maintain fair platforms for discourse. === Individual level === Advocates of regulating social media as a public utility believe that having an Internet presence using social media websites is imperative for individuals to adequately take part in the 21st century. Consequently, they argue that these sites are public utilities that need to be regulated to ensure that the constitutional rights of users are protected. For example, regulation may be needed to protect freedom of speech against risks such as Internet censorship and deplatforming. Social media affects people's behavior. For instance, it plays an important role in shaping its users' decisions and actions pertaining to health. This is demonstrated in a Pew Research Center research, which showed that 72 percent of American adults turned to social media for health information in 2011. Around 70 percent of people with chronic illnesses also use the platform to find cure, diagnoses, and other health answers. This development becomes a public issue as social media are likely to provide wrong medical information. Additionally, social media sites can also facilitate deleterious health behavior such as smoking, drug use, and harmful sexual behavior. === Business level === Advocates of social media as a public utility maintain that social media services dominate the Internet and are mainly owned by three or four companies that have unparalleled power to shape user interaction, and because of this power such businesses need to be regulated as public utilities. Zeynep Tufekci, University of North Carolina Chapel Hill, claims that services on the Internet such as Google, eBay, Facebook, Amazon.com, are all natural monopolies. She has stated that these services "benefit greatly from network externalities[,] which means that the more people on the service, the more useful it is for everyone," and thus it is difficult to replace the market leader. === Government level === Advocates of social media as a public utility believe that the government should impose restrictions on social media websites, such as Google, that are designed to benefit its rivals. Due to the recent substantial growth of social media websites such as Google, advocates claim that such a website "might need search neutrality regulation modeled after net neutrality regulation and that a Federal Search Commission might be needed to enforce such a regime." danah boyd expresses a future issue which the government may have to deal with in her research: Facebook is becoming an international social media website, specifically prevalent in Canada and Europe which are "two regions that love to regulate their utilities." Furthermore, recent books by New America Foundation Senior Fellow Rebecca MacKinnon and law professor Lori Andrews advise society to start considering Facebook and Google as nation-states or the "sovereigns of cyberspace." Overall, advocates of social media as a public utility believe that due to the immense popularity and necessity of social media websites, it is imperative that the Government imposes regulations in the same manner they do for electricity, water, and natural gas. == Counterarguments == Opponents of this theory say that social media websites should not be treated as public utilities because these platforms are changing every year, and because they are not essential services for s
History of natural language processing
The history of natural language processing describes the advances of natural language processing. There is some overlap with the history of machine translation, the history of speech recognition, and the history of artificial intelligence. == Early history == The history of machine translation dates back to the seventeenth century, when philosophers such as Leibniz and Descartes put forward proposals for codes which would relate words between languages. All of these proposals remained theoretical, and none resulted in the development of an actual machine. The first patents for "translating machines" were applied for in the mid-1930s. One proposal, by Georges Artsrouni, was simply an automatic bilingual dictionary using paper tape. The other proposal, by Peter Troyanskii, a Russian, was more detailed. Troyanskii’s proposal included both the bilingual dictionary and a method for dealing with grammatical roles between languages, based on Esperanto. == Logical period == In 1950, Alan Turing published his famous article "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence. This criterion depends on the ability of a computer program to impersonate a human in a real-time written conversation with a human judge, sufficiently well that the judge is unable to distinguish reliably — on the basis of the conversational content alone — between the program and a real human. In 1957, Noam Chomsky’s Syntactic Structures revolutionized Linguistics with 'universal grammar', a rule-based system of syntactic structures. The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English. The authors claimed that within three or five years, machine translation would be a solved problem. However, real progress was much slower, and after the ALPAC report in 1966, which found that ten years long research had failed to fulfill the expectations, funding for machine translation was dramatically reduced. Little further research in machine translation was conducted until the late 1980s, when the first statistical machine translation systems were developed. Some notably successful NLP systems developed in the 1960s were SHRDLU, a natural language system working in restricted "blocks worlds" with restricted vocabularies. In 1969 Roger Schank introduced the conceptual dependency theory for natural language understanding. This model, partially influenced by the work of Sydney Lamb, was extensively used by Schank's students at Yale University, such as Robert Wilensky, Wendy Lehnert, and Janet Kolodner. In 1970, William A. Woods introduced the augmented transition network (ATN) to represent natural language input. Instead of phrase structure rules ATNs used an equivalent set of finite-state automata that were called recursively. ATNs and their more general format called "generalized ATNs" continued to be used for a number of years. During the 1970s many programmers began to write 'conceptual ontologies', which structured real-world information into computer-understandable data. Examples are MARGIE (Schank, 1975), SAM (Cullingford, 1978), PAM (Wilensky, 1978), TaleSpin (Meehan, 1976), QUALM (Lehnert, 1977), Politics (Carbonell, 1979), and Plot Units (Lehnert 1981). During this time, many chatterbots were written including PARRY, Racter, and Jabberwacky. == Statistical period == Up to the 1980s, most NLP systems were based on complex sets of hand-written rules. Starting in the late 1980s, however, there was a revolution in NLP with the introduction of machine learning algorithms for language processing. This was due both to the steady increase in computational power resulting from Moore's law and the gradual lessening of the dominance of Chomskyan theories of linguistics (e.g. transformational grammar), whose theoretical underpinnings discouraged the sort of corpus linguistics that underlies the machine-learning approach to language processing. Some of the earliest-used machine learning algorithms, such as decision trees, produced systems of hard if-then rules similar to existing hand-written rules. Increasingly, however, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to the features making up the input data. The cache language models upon which many speech recognition systems now rely are examples of such statistical models. Such models are generally more robust when given unfamiliar input, especially input that contains errors (as is very common for real-world data), and produce more reliable results when integrated into a larger system comprising multiple subtasks. === Datasets === The emergence of statistical approaches was aided by both increase in computing power and the availability of large datasets. At that time, large multilingual corpora were starting to emerge. Notably, some were produced by the Parliament of Canada and the European Union as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government. Many of the notable early successes occurred in the field of machine translation. In 1993, the IBM alignment models were used for statistical machine translation. Compared to previous machine translation systems, which were symbolic systems manually coded by computational linguists, these systems were statistical, which allowed them to automatically learn from large textual corpora. Though these systems do not work well in situations where only small corpora is available, so data-efficient methods continue to be an area of research and development. In 2001, a one-billion-word large text corpus, scraped from the Internet, referred to as "very very large" at the time, was used for word disambiguation. To take advantage of large, unlabelled datasets, algorithms were developed for unsupervised and self-supervised learning. Generally, this task is much more difficult than supervised learning, and typically produces less accurate results for a given amount of input data. However, there is an enormous amount of non-annotated data available (including, among other things, the entire content of the World Wide Web), which can often make up for the inferior results. == Neural period == Neural language models were developed in 1990s. In 1990, the Elman network, using a recurrent neural network, encoded each word in a training set as a vector, called a word embedding, and the whole vocabulary as a vector database, allowing it to perform such tasks as sequence-predictions that are beyond the power of a simple multilayer perceptron. A shortcoming of the static embeddings was that they didn't differentiate between multiple meanings of homonyms. Yoshua Bengio developed the first neural probabilistic language model in 2000. Novel algorithms, availability of larger datasets and higher processing power made possible training of larger and larger language models. Attention mechanism was introduced by Bahdanau et al. in 2014. This work laid the foundations for the famous "Attention Is All You Need" paper that introduced the Transformer architecture in 2017. The concept of large language model (LLM) emerged in late 2010s. LLM is a language model trained with self-supervised learning on vast amount of text. Earliest public LLMs had hundreds of millions of parameters, but this number quickly rose to billion and even trillions. In recent years, advancements in deep learning and large language models have significantly enhanced the capabilities of natural language processing, leading to widespread applications in areas such as healthcare, customer service, and content generation. == Software ==
Genetic representation
In computer programming, genetic representation is a way of presenting solutions/individuals in evolutionary computation methods. The term encompasses both the concrete data structures and data types used to realize the genetic material of the candidate solutions in the form of a genome, and the relationships between search space and problem space. In the simplest case, the search space corresponds to the problem space (direct representation). The choice of problem representation is tied to the choice of genetic operators, both of which have a decisive effect on the efficiency of the optimization. Genetic representation can encode appearance, behavior, physical qualities of individuals. Difference in genetic representations is one of the major criteria drawing a line between known classes of evolutionary computation. Terminology is often analogous with natural genetics. The block of computer memory that represents one candidate solution is called an individual. The data in that block is called a chromosome. Each chromosome consists of genes. The possible values of a particular gene are called alleles. A programmer may represent all the individuals of a population using binary encoding, permutational encoding, encoding by tree, or any one of several other representations. == Representations in some popular evolutionary algorithms == Genetic algorithms (GAs) are typically linear representations; these are often, but not always, binary. Holland's original description of GA used arrays of bits. Arrays of other types and structures can be used in essentially the same way. The main property that makes these genetic representations convenient is that their parts are easily aligned due to their fixed size. This facilitates simple crossover operation. Depending on the application, variable-length representations have also been successfully used and tested in evolutionary algorithms (EA) in general and genetic algorithms in particular, although the implementation of crossover is more complex in this case. Evolution strategy uses linear real-valued representations, e.g., an array of real values. It uses mostly gaussian mutation and blending/averaging crossover. Genetic programming (GP) pioneered tree-like representations and developed genetic operators suitable for such representations. Tree-like representations are used in GP to represent and evolve functional programs with desired properties. Human-based genetic algorithm (HBGA) offers a way to avoid solving hard representation problems by outsourcing all genetic operators to outside agents, in this case, humans. The algorithm has no need for knowledge of a particular fixed genetic representation as long as there are enough external agents capable of handling those representations, allowing for free-form and evolving genetic representations. === Common genetic representations === binary array integer or real-valued array binary tree natural language parse tree directed graph == Distinction between search space and problem space == Analogous to biology, EAs distinguish between problem space (corresponds to phenotype) and search space (corresponds to genotype). The problem space contains concrete solutions to the problem being addressed, while the search space contains the encoded solutions. The mapping from search space to problem space is called genotype-phenotype mapping. The genetic operators are applied to elements of the search space, and for evaluation, elements of the search space are mapped to elements of the problem space via genotype-phenotype mapping. == Relationships between search space and problem space == The importance of an appropriate choice of search space for the success of an EA application was recognized early on. The following requirements can be placed on a suitable search space and thus on a suitable genotype-phenotype mapping: === Completeness === All possible admissible solutions must be contained in the search space. === Redundancy === When more possible genotypes exist than phenotypes, the genetic representation of the EA is called redundant. In nature, this is termed a degenerate genetic code. In the case of a redundant representation, neutral mutations are possible. These are mutations that change the genotype but do not affect the phenotype. Thus, depending on the use of the genetic operators, there may be phenotypically unchanged offspring, which can lead to unnecessary fitness determinations, among other things. Since the evaluation in real-world applications usually accounts for the lion's share of the computation time, it can slow down the optimization process. In addition, this can cause the population to have higher genotypic diversity than phenotypic diversity, which can also hinder evolutionary progress. In biology, the Neutral Theory of Molecular Evolution states that this effect plays a dominant role in natural evolution. This has motivated researchers in the EA community to examine whether neutral mutations can improve EA functioning by giving populations that have converged to a local optimum a way to escape that local optimum through genetic drift. This is discussed controversially and there are no conclusive results on neutrality in EAs. On the other hand, there are other proven measures to handle premature convergence. === Locality === The locality of a genetic representation corresponds to the degree to which distances in the search space are preserved in the problem space after genotype-phenotype mapping. That is, a representation has a high locality exactly when neighbors in the search space are also neighbors in the problem space. In order for successful schemata not to be destroyed by genotype-phenotype mapping after a minor mutation, the locality of a representation must be high. === Scaling === In genotype-phenotype mapping, the elements of the genotype can be scaled (weighted) differently. The simplest case is uniform scaling: all elements of the genotype are equally weighted in the phenotype. A common scaling is exponential. If integers are binary coded, the individual digits of the resulting binary number have exponentially different weights in representing the phenotype. Example: The number 90 is written in binary (i.e., in base two) as 1011010. If now one of the front digits is changed in the binary notation, this has a significantly greater effect on the coded number than any changes at the rear digits (the selection pressure has an exponentially greater effect on the front digits). For this reason, exponential scaling has the effect of randomly fixing the "posterior" locations in the genotype before the population gets close enough to the optimum to adjust for these subtleties. == Hybridization and repair in genotype-phenotype mapping == When mapping the genotype to the phenotype being evaluated, domain-specific knowledge can be used to improve the phenotype and/or ensure that constraints are met. This is a commonly used method to improve EA performance in terms of runtime and solution quality. It is illustrated below by two of the three examples. == Examples == === Example of a direct representation === An obvious and commonly used encoding for the traveling salesman problem and related tasks is to number the cities to be visited consecutively and store them as integers in the chromosome. The genetic operators must be suitably adapted so that they only change the order of the cities (genes) and do not cause deletions or duplications. Thus, the gene order corresponds to the city order and there is a simple one-to-one mapping. === Example of a complex genotype-phenotype mapping === In a scheduling task with heterogeneous and partially alternative resources to be assigned to a set of subtasks, the genome must contain all necessary information for the individual scheduling operations or it must be possible to derive them from it. In addition to the order of the subtasks to be executed, this includes information about the resource selection. A phenotype then consists of a list of subtasks with their start times and assigned resources. In order to be able to create this, as many allocation matrices must be created as resources can be allocated to one subtask at most. In the simplest case this is one resource, e.g., one machine, which can perform the subtask. An allocation matrix is a two-dimensional matrix, with one dimension being the available time units and the other being the resources to be allocated. Empty matrix cells indicate availability, while an entry indicates the number of the assigned subtask. The creation of allocation matrices ensures firstly that there are no inadmissible multiple allocations. Secondly, the start times of the subtasks can be read from it as well as the assigned resources. A common constraint when scheduling resources to subtasks is that a resource can only be allocated once per time unit and that the reservation must be for a contiguous period of time. To achieve this in a timely manner, which is a c
Distributional Soft Actor Critic
Distributional Soft Actor Critic (DSAC) is a suite of model-free off-policy reinforcement learning algorithms, tailored for learning decision-making or control policies in complex systems with continuous action spaces. Distinct from traditional methods that focus solely on expected returns, DSAC algorithms are designed to learn a Gaussian distribution over stochastic returns, called value distribution. This focus on Gaussian value distribution learning notably diminishes value overestimations, which in turn boosts policy performance. Additionally, the value distribution learned by DSAC can also be used for risk-aware policy learning. From a technical standpoint, DSAC is essentially a distributional adaptation of the well-established soft actor-critic (SAC) method. To date, the DSAC family comprises two iterations: the original DSAC-v1 and its successor, DSAC-T (also known as DSAC-v2), with the latter demonstrating superior capabilities over the Soft Actor-Critic (SAC) in Mujoco benchmark tasks. The source code for DSAC-T can be found at the following URL: Jingliang-Duan/DSAC-T. Both iterations have been integrated into an advanced, Pytorch-powered reinforcement learning toolkit named GOPS: GOPS (General Optimal control Problem Solver).
Generalized blockmodeling
In generalized blockmodeling, the blockmodeling is done by "the translation of an equivalence type into a set of permitted block types", which differs from the conventional blockmodeling, which is using the indirect approach. It's a special instance of the direct blockmodeling approach. Generalized blockmodeling was introduced in 1994 by Patrick Doreian, Vladimir Batagelj and Anuška Ferligoj. == Definition == Generalized blockmodeling approach is a direct one, "where the optimal partition(s) is (are) identified based on minimal values of a compatible criterion function defined by the difference between empirical blocks and corresponding ideal blocks". At the same time, the much broader set of block types is introduced (while in conventional blockmodeling only certain types are used). The conventional blockmodeling is inductive due to nonspecification of neither the clusters or the location of block types, while in generalized blockmodeling the blockmodel is specified with more detail than just the permition of certain block types (e.g., prespecification). Further, it's possible to define departures from the permitted (ideal) blocktype, using criterion function. Using local optimization procedure, firstly the initial clustering (with specified number of clusters is done, based on random creation. How the clusters are neighboring to each other, is based on two transformations: 1) a vertex is moved from one to another cluster or 2) a pair of vertices is interchanged between two different clusters. This process of transformation steps is repeated many times, until only the best fitting partitions (with the minimized value of the criterion function) are kept as blockmodels for the future exploration of the network. Different types of generalized blockmodeling are: generalized binary blockmodeling, generalized valued blockmodeling and generalized homogeneity blockmodeling. == Benefits == According to Patrick Doreian, the benefits of generalized blockmodeling, are as follows: usage of explicit criterion function, compatible with a given type of equivalence, results to in-built measure of fit, which is integral to the establishment of the blockmodels (in conventional blockmodeling, there is no compelling and coherent measures of fit); partitions, based on generalized blockmodeling, regularly outperform and never perform less well than the partitions, based on conventional approach; with generalized blockmodeling it's possible to specify new types of blockmodels; this potentially unlimited set of new block types also results in permittion of inclusion of substantively driven blockmodels; in generalized blockmodeling, the specification of the block types and the location of some of them in the blockmodel is possible; researcher can speficy which (pair of) vertices must be (not) clustered together; this approach also allows the imposition of penalties, resulting into identification of empirical null blocks without inconsistencies with a corresponding ideal null block. == Problems == According to Doreian, the problems of generalized blockmodeling, are as follows: unknown sensitivity to particular data features, examination of boundary problems, computationally burdensome, which results in a constraint regarding practical network size (generalized blockmodeling is thus primarily used to analyse smaller networks (below 100 units)), identifying structure from incomplete network information, most of generalized blockmodeling is based on binary networks, but there is also development in the field of valued networks, criterion function is minimized for a specified blockmodel, with results in issues of evaluating statistically, based on the structural data alone, problems regarding three dimensional network data, problems regarding the evolution of fundamental network structure. == Book == The book with the same title, Generalized blockmodeling, written by Patrick Doreian, Vladimir Batagelj and Anuška Ferligoj, was in 2007 awarded the Harrison White Outstanding Book Award by the Mathematical Sociology Section of American Sociological Association.
Message queuing service
A message queueing service is a message-oriented middleware or MOM deployed in a compute cloud using software as a service model. Service subscribers access queues and or topics to exchange data using point-to-point or publish and subscribe patterns. It's important to differentiate between event-driven and message-driven (aka queue driven) services: Event-driven services (e.g. AWS SNS) are decoupled from their consumers. Whereas queue / message driven services (e.g. AWS SQS) are coupled with their consumers. Message queues can be a good buffer to handle spiky workloads but they have a finite capacity. According to Gregor Hohpe, message queues require proper mechanisms (aka flow controls) to avoid filling the queue beyond its manageable capacity and to keep the system stable. == Ordering Guarantees in Message Queues == Amazon SQS FIFO and Azure Service Bus sessions are queue-based messaging systems that provide ordering guarantees within a message group or session attempt but do not necessarily guarantee ordered delivery in cases of retries or failures. In SQS FIFO, messages in the same message group are processed in order, with subsequent messages held until the preceding message is successfully processed or moved to the dead-letter queue (DLQ). Once a message is placed in the DLQ, it is no longer retried, creating a gap in the sequence. However, the remaining messages continue to be delivered in order. Azure Service Bus sessions function similarly by maintaining ordering within a session, provided a single consumer processes messages sequentially. The implementation differs from SQS FIFO but follows the same fundamental ordering principle. In contrast, Apache Kafka is a distributed log-based messaging system that guarantees ordering within individual partitions rather than across the entire topic. Unlike queue-based systems, Kafka retains messages in a durable, append-only log, allowing multiple consumers to read at different offsets. Kafka uses manual offset management, giving consumers control over retries and failure handling. If a consumer fails to process a message, it can delay committing the offset, preventing further progress in that partition while other partitions remain unaffected. This partition-based design enables fault isolation and parallel processing while allowing ordering to be maintained within partitions, depending on consumer handling. == Vendors == Apache Kafka Apache Kafka is a distributed system consisting of servers that store and forward messages between producer client and consumer applications. IBM MQ IBM MQ offers a managed service that can be used on IBM Cloud and Amazon Web Services. Microsoft Azure Service Bus Service Bus offers queues, topics & subscriptions, and rules/actions in order to support publish-subscribe, temporal decoupling, and load balancing scenarios. Azure Service Bus is built on AMQP allowing any existing AMQP 1.0 client stack to interact with Service Bus directly or via existing .Net, Java, Node, and Python clients. Standard and Premium tiers allow for pay as you go or isolated resources at massive scale. Oracle Messaging Cloud Service This service provides a messaging solution for applications for asynchronous communication and is influenced by the Java Message Service (JMS) API specification. Any application platform that understands HTTP can also use Oracle Messaging Cloud Service through the REST interface. For Java applications, Oracle Messaging Cloud Service provides a Java library that implements and extends the JMS 1.1 interface. The Java library implements the JMS API by acting as a client of the REST API. Amazon Simple Queue Service Supports messages natively up to 256K, or up to 2GB by transmitting payload via S3. Highly scalable, durable and resilient. Provides loose-FIFO and 'at least once' delivery in order to provide massive scale. Supports REST API and optional Java Message Service client. Low latency. Utilizes Amazon Web Services. IronMQ Supports messages up to 64k; guarantees order; guarantees once only delivery; no delays retrieving messages. Supports REST API and beanstalkd open source protocol. Runs on multiple clouds including AWS and Rackspace. Scaling must be managed by user. RabbitMQ RabbitMQ is a reliable and mature messaging and streaming broker, which is easy to deploy on cloud environments, on-premises, and on your local machine. Supports AMQP, STOMP, MQTT StormMQ Open platform supports messages up to 50Mb. Uses AMQP to avoid vendor lock-in and provide language neutrality. Locate-It Option allows customers to audit the location of their data at all times and satisfy data protection principles. AnypointMQ An enterprise multi-tenant, cloud messaging service that performs advanced asynchronous messaging scenarios between applications. Anypoint MQ is fully integrated with Anypoint Platform, offering role based access control, client application management, and connectors.
Plate notation
In Bayesian inference, plate notation is a method of representing variables that repeat in a graphical model. Instead of drawing each repeated variable individually, a plate or rectangle is used to group variables into a subgraph that repeat together, and a number is drawn on the plate to represent the number of repetitions of the subgraph in the plate. The assumptions are that the subgraph is duplicated that many times, the variables in the subgraph are indexed by the repetition number, and any links that cross a plate boundary are replicated once for each subgraph repetition. == Example == In this example, we consider Latent Dirichlet allocation, a Bayesian network that models how documents in a corpus are topically related. There are two variables not in any plate; α is the parameter of the uniform Dirichlet prior on the per-document topic distributions, and β is the parameter of the uniform Dirichlet prior on the per-topic word distribution. The outermost plate represents all the variables related to a specific document, including θ i {\displaystyle \theta _{i}} , the topic distribution for document i. The M in the corner of the plate indicates that the variables inside are repeated M times, once for each document. The inner plate represents the variables associated with each of the N i {\displaystyle N_{i}} words in document i: z i j {\displaystyle z_{ij}} is the topic distribution for the jth word in document i, and w i j {\displaystyle w_{ij}} is the actual word used. The N in the corner represents the repetition of the variables in the inner plate N j {\displaystyle N_{j}} times, once for each word in document i. The circle representing the individual words is shaded, indicating that each w i j {\displaystyle w_{ij}} is observable, and the other circles are empty, indicating that the other variables are latent variables. The directed edges between variables indicate dependencies between the variables: for example, each w i j {\displaystyle w_{ij}} depends on z i j {\displaystyle z_{ij}} and β. == Extensions == A number of extensions have been created by various authors to express more information than simply the conditional relationships. However, few of these have become standard. Perhaps the most commonly used extension is to use rectangles in place of circles to indicate non-random variables—either parameters to be computed, hyperparameters given a fixed value (or computed through empirical Bayes), or variables whose values are computed deterministically from a random variable. The diagram on the right shows a few more non-standard conventions used in some articles in Wikipedia (e.g. variational Bayes): Variables that are actually random vectors are indicated by putting the vector size in brackets in the middle of the node. Variables that are actually random matrices are similarly indicated by putting the matrix size in brackets in the middle of the node, with commas separating row size from column size. Categorical variables are indicated by placing their size (without a bracket) in the middle of the node. Categorical variables that act as "switches", and which pick one or more other random variables to condition on from a large set of such variables (e.g. mixture components), are indicated with a special type of arrow containing a squiggly line and ending in a T junction. Boldface is consistently used for vector or matrix nodes (but not categorical nodes). == Software implementation == Plate notation has been implemented in various TeX/LaTeX drawing packages, but also as part of graphical user interfaces to Bayesian statistics programs such as BUGS and BayesiaLab and PyMC.