AI For Business Strategy Mit

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  • Gas (app)

    Gas (app)

    Gas (sometimes stylized in all caps), formerly known as Melt as well as Crush, was an American anonymous social media app. Launched in August 2022, the app is oriented towards high schoolers. The app was developed by Nikita Bier, Isaiah Turner, and former Facebook engineer Dave Schatz. Gas was largely based upon the prior tbh app developed by co-founder Nikita Bier, along with Erik Hazzard, Kyle Zaragoza, and Nicolas Ducdodon in September 2017. tbh was acquired by Facebook inc. (now Meta Platforms) on October 16, 2017, and nearly a year later in July 2018 was dissolved, owing to low usage. Gas follows a similar purpose to tbh in being a social media app oriented towards high schoolers. In the app, users participate in anonymous polls regarding pre-written complimentary statements to their peers, such as "I'd say yes if (blank) asked me out on a date," "I think (blank) is the coolest kid in school," or "would make an ugly face and still look pretty." Winners of said polls receive a "flame." The name of the app is derived from this, with "gassing someone up" being Gen Z slang for complimenting someone. Users can pay a $6.99 subscription that enables "God Mode," which shows hints regarding who voted for them in a poll. Gas overtook TikTok and BeReal as the most downloaded app on the Apple App Store in October 2022 (the app is currently not available for Android). The app has over 5.1 million downloads as of early November 2022, over a million active users and 300 thousand daily downloads as of October 2022. Currently, the app is available in Canada and the majority of the United States. On January 17, 2023, Gas was acquired by Discord, however it would remain a standalone app and its developers became Discord staff members. On October 18, 2023, Discord announced that service for Gas would be permanently ending effective November 7, 2023, due to a steep decline in users. Effective November 7, the app became completely unusable. == Controversy regarding human-trafficking == Beginning in October 2022, rumors spread largely throughout TikTok and Snapchat alleged that the app was linked to human trafficking (in particular sex trafficking). According to Bier, the rumor originated with a single user review from China on October 5, and then was disseminated through TikTok accounts with "few to no US teen followers." Although largely dismissed as a hoax by experts, who cite how the app doesn't log user locations and general anonymity, the hoax became pervasive to the extent that various police departments, school systems, and local news outlets began issuing warnings regarding the app. For instance, on October 31, 2022, the police department of Piedmont, Oklahoma issued a warning to parents, encouraging them to check their children's phones, while on November 3, the Oklahoma Oktaha Public School system stated in a Facebook post that "Children are being kidnapped in other towns and this new app is thought to be the source of predators finding their location." (both statements have since been retracted by Police Chief Scott Singer and Superintendent Jerry Needham respectively). Additionally, local medial outlets such as KOCO in Oklahoma City ran stories making similar statements. The rumor had a negative impact on the app, with downloads plateauing for a two-week period in late October and with 3% of users in a single day reportedly uninstalling the app. Revenue and ratings have also reportedly dropped and the company's social media accounts have been bombarded with comments labeling them as sex-traffickers. Additionally, the four-person development team has reportedly been bombarded with various death threats as a result.

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  • Point-in-time recovery

    Point-in-time recovery

    Point-in-time recovery (PITR) in the context of computers involves systems, often databases, whereby an administrator can restore or recover a set of data or a particular setting from a time in the past. Note for example Windows's capability to restore operating-system settings from a past date (for instance, before data corruption occurred). Time Machine for macOS provides another example of point-in-time recovery. Once PITR logging starts for a PITR-capable database, a database administrator can restore that database from backups to the state that it had at any time since.

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  • Terminology extraction

    Terminology extraction

    Terminology extraction (also known as term extraction, glossary extraction, term recognition, or terminology mining) is a subtask of information extraction. The goal of terminology extraction is to automatically extract relevant terms from a given corpus. In the semantic web era, a growing number of communities and networked enterprises started to access and interoperate through the internet. Modeling these communities and their information needs is important for several web applications, like topic-driven web crawlers, web services, recommender systems, etc. The development of terminology extraction is also essential to the language industry. One of the first steps to model a knowledge domain is to collect a vocabulary of domain-relevant terms, constituting the linguistic surface manifestation of domain concepts. Several methods to automatically extract technical terms from domain-specific document warehouses have been described in the literature. Typically, approaches to automatic term extraction make use of linguistic processors (part of speech tagging, phrase chunking) to extract terminological candidates, i.e. syntactically plausible terminological noun phrases. Noun phrases include compounds (e.g. "credit card"), adjective noun phrases (e.g. "local tourist information office"), and prepositional noun phrases (e.g. "board of directors"). In English, the first two (compounds and adjective noun phrases) are the most frequent. Terminological entries are then filtered from the candidate list using statistical and machine learning methods. Once filtered, because of their low ambiguity and high specificity, these terms are particularly useful for conceptualizing a knowledge domain or for supporting the creation of a domain ontology or a terminology base. Furthermore, terminology extraction is a very useful starting point for semantic similarity, knowledge management, human translation and machine translation, etc. == Bilingual terminology extraction == The methods for terminology extraction can be applied to parallel corpora. Combined with e.g. co-occurrence statistics, candidates for term translations can be obtained. Bilingual terminology can be extracted also from comparable corpora (corpora containing texts within the same text type, domain but not translations of documents between each other).

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  • VMDS

    VMDS

    VMDS abbreviates the relational database technology called Version Managed Data Store provided by GE Energy as part of its Smallworld technology platform and was designed from the outset to store and analyse the highly complex spatial and topological networks typically used by enterprise utilities such as power distribution and telecommunications. VMDS was originally introduced in 1990 as has been improved and updated over the years. Its current version is 6.0. VMDS has been designed as a spatial database. This gives VMDS a number of distinctive characteristics when compared to conventional attribute only relational databases. == Distributed server processing == VMDS is composed of two parts: a simple, highly scalable data block server called SWMFS (Smallworld Master File Server) and an intelligent client API written in C and Magik. Spatial and attribute data are stored in data blocks that reside in special files called data store files on the server. When the client application requests data it has sufficient intelligence to work out the optimum set of data blocks that are required. This request is then made to SWMFS which returns the data to the client via the network for processing. This approach is particularly efficient and scalable when dealing with spatial and topological data which tends to flow in larger volumes and require more processing then plain attribute data (for example during a map redraw operation). This approach makes VMDS well suited to enterprise deployment that might involve hundreds or even thousands of concurrent clients. == Support for long transactions == Relational databases support short transactions in which changes to data are relatively small and are brief in terms in duration (the maximum period between the start and the end of a transaction is typically a few seconds or less). VMDS supports long transactions in which the volume of data involved in the transaction can be substantial and the duration of the transaction can be significant (days, weeks or even months). These types of transaction are common in advanced network applications used by, for example, power distribution utilities. Due to the time span of a long transaction in this context the amount of change can be significant (not only within the scope of the transaction, but also within the context of the database as a whole). Accordingly, it is likely that the same record might be changed more than once. To cope with this scenario VMDS has inbuilt support for automatically managing such conflicts and allows applications to review changes and accept only those edits that are correct. == Spatial and topological capabilities == As well as conventional relational database features such as attribute querying, join fields, triggers and calculated fields, VMDS has numerous spatial and topological capabilities. This allows spatial data such as points, texts, polylines, polygons and raster data to be stored and analysed. Spatial functions include: find all features within a polygon, calculate the Voronoi polygons of a set of sites and perform a cluster analysis on a set of points. Vector spatial data such as points, polylines and polygons can be given topological attributes that allow complex networks to be modelled. Network analysis engines are provided to answer questions such as find the shortest path between two nodes or how to optimize a delivery route (the travelling salesman problem). A topology engine can be configured with a set of rules that define how topological entities interact with each other when new data is added or existing data edited. == Data abstraction == In VMDS all data is presented to the application as objects. This is different from many relational databases that present the data as rows from a table or query result using say JDBC. VMDS provides a data modelling tool and underlying infrastructure as part of the Smallworld technology platform that allows administrators to associate a table in the database with a Magik exemplar (or class). Magik get and set methods for the Magik exemplar can be automatically generated that expose a table's field (or column). Each VMDS row manifests itself to the application as an instance of a Magik object and is known as an RWO (or real world object). Tables are known as collections in Smallworld parlance. # all_rwos hold all the rwos in the database and is heterogeneous all_rwos << my_application.rwo_set() # valve_collection holds the valve collection valves << all_rwos.select(:collection, {:valve}) number_of_valves << valves.size Queries are built up using predicate objects: # find 'open' valves. open_valves << valves.select(predicate.eq(:operating_status, "open")) number_of_open_valves << open_valves.size _for valve _over open_valves.elements() _loop write(valve.id) _endloop Joins are implemented as methods on the parent RWO. For example, a manager might have several employees who report to him: # get the employee collection. employees << my_application.database.collection(:gis, :employees) # find a manager called 'Steve' and get the first matching element steve << employees.select(predicate.eq(:name, "Steve").and(predicate.eq(:role, "manager")).an_element() # display the names of his direct reports. name is a field (or column) # on the employee collection (or table) _for employee _over steve.direct_reports.elements() _loop write(employee.name) _endloop Performing a transaction: # each key in the hash table corresponds to the name of the field (or column) in # the collection (or table) valve_data << hash_table.new_with( :asset_id, 57648576, :material, "Iron") # get the valve collection directly valve_collection << my_application.database.collection(:gis, :valve) # create an insert transaction to insert a new valve record into the collection a # comment can be provide that describes the transaction transaction << record_transaction.new_insert(valve_collection, valve_data, "Inserted a new valve") transaction.run()

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  • Confusion matrix

    Confusion matrix

    In machine learning, a confusion matrix, also known as error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one. In unsupervised learning it is usually called a matching matrix. The term is used specifically in the problem of statistical classification. Each row of the matrix represents the instances in an actual class while each column represents the instances in a predicted class, or vice versa – both variants are found in the literature. The diagonal of the matrix therefore represents all instances that are correctly predicted. The name stems from the fact that it makes it easy to identify whether the system is confusing two classes (i.e., commonly mislabeling one class as another). The confusion matrix has its origins in human perceptual studies of auditory stimuli. It was adapted for machine learning studies and used by Frank Rosenblatt, among other early researchers, to compare human and machine classifications of visual (and later auditory) stimuli. It is a special kind of contingency table, with two dimensions ("actual" and "predicted"), and identical sets of "classes" in both dimensions (each combination of dimension and class is a variable in the contingency table). == Example == Given a sample of 12 individuals, 8 that have been diagnosed with cancer and 4 that are cancer-free, where individuals with cancer belong to class 1 (positive) and non-cancer individuals belong to class 0 (negative), we can display that data as follows: Assume that we have a classifier that distinguishes between individuals with and without cancer in some way, we can take the 12 individuals and run them through the classifier. The classifier then makes 9 accurate predictions and misses 3: 2 individuals with cancer wrongly predicted as being cancer-free (sample 1 and 2), and 1 person without cancer that is wrongly predicted to have cancer (sample 9). Notice, that if we compare the actual classification set to the predicted classification set, there are 4 different outcomes that could result in any particular column: The actual classification is positive and the predicted classification is positive (1,1). This is called a true positive result because the positive sample was correctly identified by the classifier. The actual classification is positive and the predicted classification is negative (1,0). This is called a false negative result because the positive sample is incorrectly identified by the classifier as being negative. The actual classification is negative and the predicted classification is positive (0,1). This is called a false positive result because the negative sample is incorrectly identified by the classifier as being positive. The actual classification is negative and the predicted classification is negative (0,0). This is called a true negative result because the negative sample gets correctly identified by the classifier. We can then perform the comparison between actual and predicted classifications and add this information to the table, making correct results appear in green so they are more easily identifiable. The template for any binary confusion matrix uses the four kinds of results discussed above (true positives, false negatives, false positives, and true negatives) along with the positive and negative classifications. The four outcomes can be formulated in a 2×2 confusion matrix, as follows: The color convention of the three data tables above were picked to match this confusion matrix, in order to easily differentiate the data. Now, we can simply total up each type of result, substitute into the template, and create a confusion matrix that will concisely summarize the results of testing the classifier: In this confusion matrix, of the 8 samples with cancer, the system judged that 2 were cancer-free, and of the 4 samples without cancer, it predicted that 1 did have cancer. All correct predictions are located in the diagonal of the table (highlighted in green), so it is easy to visually inspect the table for prediction errors, as values outside the diagonal will represent them. By summing up the 2 rows of the confusion matrix, one can also deduce the total number of positive (P) and negative (N) samples in the original dataset, i.e. P = T P + F N {\displaystyle P=TP+FN} and N = F P + T N {\displaystyle N=FP+TN} . == Table of confusion == In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy). Accuracy will yield misleading results if the data set is unbalanced; that is, when the numbers of observations in different classes vary greatly. For example, if there were 95 cancer samples and only 5 non-cancer samples in the data, a particular classifier might classify all the observations as having cancer. The overall accuracy would be 95%, but in more detail the classifier would have a 100% recognition rate (sensitivity) for the cancer class but a 0% recognition rate for the non-cancer class. F1 score is even more unreliable in such cases, and here would yield over 97.4%, whereas informedness removes such bias and yields 0 as the probability of an informed decision for any form of guessing (here always guessing cancer). According to Davide Chicco and Giuseppe Jurman, the most informative metric to evaluate a confusion matrix is the Matthews correlation coefficient (MCC). Other metrics can be included in a confusion matrix, each of them having their significance and use. Some researchers have argued that the confusion matrix, and the metrics derived from it, do not truly reflect a model's knowledge. In particular, the confusion matrix cannot show whether correct predictions were reached through sound reasoning or merely by chance (a problem known in philosophy as epistemic luck). It also does not capture situations where the facts used to make a prediction later change or turn out to be wrong (defeasibility). This means that while the confusion matrix is a useful tool for measuring classification performance, it may give an incomplete picture of a model’s true reliability. == Confusion matrices with more than two categories == Confusion matrix is not limited to binary classification and can be used in multi-class classifiers as well. The confusion matrices discussed above have only two conditions: positive and negative. For example, the table below summarizes communication of a whistled language between two speakers, with zero values omitted for clarity. == Confusion matrices in multi-label and soft-label classification == Confusion matrices are not limited to single-label classification (where only one class is present) or hard-label settings (where classes are either fully present, 1, or absent, 0). They can also be extended to Multi-label classification (where multiple classes can be predicted at once) and soft-label classification (where classes can be partially present). One such extension is the Transport-based Confusion Matrix (TCM), which builds on the theory of optimal transport and the principle of maximum entropy. TCM applies to single-label, multi-label, and soft-label settings. It retains the familiar structure of the standard confusion matrix: a square matrix sized by the number of classes, with diagonal entries indicating correct predictions and off-diagonal entries indicating confusion. In the single-label case, TCM is identical to the standard confusion matrix. TCM follows the same reasoning as the standard confusion matrix: if class A is overestimated (its predicted value is greater than its label value) and class B is underestimated (its predicted value is less than its label value), A is considered confused with B, and the entry (B, A) is increased. If a class is both predicted and present, it is correctly identified, and the diagonal entry (A, A) increases. Optimal transport and maximum entropy are used to determine the extent to which these entries are updated. TCM enables clearer comparison between predictions and labels in complex classification tasks, while maintaining a consistent matrix format across settings.

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  • Concordance (publishing)

    Concordance (publishing)

    A concordance is an alphabetical list of the principal words used in a book or body of work, listing every instance of each word with its immediate context. Historically, concordances have been compiled only for works of special importance, such as the Vedas, Bible, Qur'an or the works of Shakespeare, James Joyce or classical Latin and Greek authors, because of the time, difficulty, and expense involved in creating a concordance in the pre-computer era. A concordance is more than an index, with additional material such as commentary, definitions and topical cross-indexing which makes producing one a labor-intensive process even when assisted by computers. In the precomputing era, search technology was unavailable, and a concordance offered readers of long works such as the Bible something comparable to search results for every word that they would have been likely to search for. Today, the ability to combine the result of queries concerning multiple terms (such as searching for words near other words) has reduced interest in concordance publishing. In addition, mathematical techniques such as latent semantic indexing have been proposed as a means of automatically identifying linguistic information based on word context. A bilingual concordance is a concordance based on aligned parallel text. A topical concordance is a list of subjects that a book covers (usually The Bible), with the immediate context of the coverage of those subjects. Unlike a traditional concordance, the indexed word does not have to appear in the verse. The best-known topical concordance is Nave's Topical Bible. The first Bible concordance was compiled for the Vulgate Bible by Hugh of St Cher (d.1262), who employed 500 friars to assist him. In 1448, Rabbi Mordecai Nathan completed a concordance to the Hebrew Bible. It took him ten years. A concordance to the Greek New Testament was published in 1546 by Sixt Birck, and the Septuagint was done a by Conrad Kircher in 1602. The first concordance to the English Bible was published in 1550 by John Merbecke. According to Cruden, it did not employ the verse numbers devised by Robert Stephens in 1545, but "the pretty large concordance" of Mr Cotton did. Then followed Cruden's Concordance and Strong's Concordance. == Use in linguistics == Concordances are frequently used in linguistics, when studying a text. For example: comparing different usages of the same word analysing keywords analysing word frequencies finding and analysing phrases and idioms finding translations of subsentential elements, e.g. terminology, in bitexts and translation memories creating indexes and word lists (also useful for publishing) Concordancing techniques are widely used in national text corpora such as American National Corpus (ANC), British National Corpus (BNC), and Corpus of Contemporary American English (COCA) available on-line. Stand-alone applications that employ concordancing techniques are known as concordancers or more advanced corpus managers. Some of them have integrated part-of-speech taggers (POS taggers) and enable the user to create their own POS-annotated corpora to conduct various types of searches adopted in corpus linguistics. == Inversion == The reconstruction of the text of some of the Dead Sea Scrolls involved a concordance. Access to some of the scrolls was governed by a "secrecy rule" that allowed only the original International Team or their designates to view the original materials. After the death of Roland de Vaux in 1971, his successors repeatedly refused to even allow the publication of photographs to other scholars. This restriction was circumvented by Martin Abegg in 1991, who used a computer to "invert" a concordance of the missing documents made in the 1950s which had come into the hands of scholars outside of the International Team, to obtain an approximate reconstruction of the original text of 17 of the documents. This was soon followed by the release of the original text of the scrolls.

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  • Archetype (information science)

    Archetype (information science)

    In the field of informatics, an archetype is a formal re-usable model of a domain concept. Traditionally, the term archetype is used in psychology to mean an idealized model of a person, personality or behaviour (see Archetype). The usage of the term in informatics is derived from this traditional meaning, but applied to domain modelling instead. An archetype is defined by the OpenEHR Foundation (for health informatics) as follows: An archetype is a computable expression of a domain content model in the form of structured constraint statements, based on some reference model. openEHR archetypes are based on the openEHR reference model. Archetypes are all expressed in the same formalism. In general, they are defined for wide re-use, however, they can be specialized to include local particularities. They can accommodate any number of natural languages and terminologies. == Formal specifications == The modern archetype formalism is specified and maintained by the openEHR Foundation, and although originally developed for the health IT domain, is completely domain-independent, and has been used in geospatial modelling, telecommunications, and defence. The archetype formalism consists of a number of specifications including: 'ADL 1.4': original release of Archetype Definition Language (ADL) and Archetype Object Model (AOM); widely implemented in health IT domain; 'ADL 2': modern release of Archetype Definition Language (ADL), Archetype Object Model (AOM), Archetype Identification specification and Archetype Technology Overview. The Archetype Technology Overview provides a short technical overview of the archetype formalism useful for new users. The ADL/AOM 1.4 specifications were provided to ISO TC 215 in 2008 by the openEHR Foundation and became the ISO 13606-2 standard, extant until 2019. ISO TC 215 accepted the AOM 2 specification as the basis for a revision of this standard, which was issued in 2019. In late 2015, the Object Management Group (OMG) accepted an RfP entitled 'Archetype Modeling Language (AML)' as a new candidate standard. This specification is a form of ADL re-engineered as a UML profile so as to enable archetype modelling to be supported within UML tools. == Tools == A number of tools area available for working with archetypes. Most are listed on the openEHR modelling tools page. They include: ADL Designer, a modern AOM2-based web editing application Archetype Editor, an original desktop clinical modelling tool Template Designer, an original desktop clinical templating tool LinkEHR, an archetype and data integration tool ADL Workbench, reference compiler and visualiser tool == Example ==

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  • E-Science librarianship

    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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  • Oversampled binary image sensor

    Oversampled binary image sensor

    An oversampled binary image sensor is an image sensor with non-linear response capabilities reminiscent of traditional photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. The response function of the image sensor is non-linear and similar to a logarithmic function, which makes the sensor suitable for high dynamic range imaging. == Working principle == Before the advent of digital image sensors, photography, for the most part of its history, used film to record light information. At the heart of every photographic film are a large number of light-sensitive grains of silver-halide crystals. During exposure, each micron-sized grain has a binary fate: Either it is struck by some incident photons and becomes "exposed", or it is missed by the photon bombardment and remains "unexposed". In the subsequent film development process, exposed grains, due to their altered chemical properties, are converted to silver metal, contributing to opaque spots on the film; unexposed grains are washed away in a chemical bath, leaving behind the transparent regions on the film. Thus, in essence, photographic film is a binary imaging medium, using local densities of opaque silver grains to encode the original light intensity information. Thanks to the small size and large number of these grains, one hardly notices this quantized nature of film when viewing it at a distance, observing only a continuous gray tone. The oversampled binary image sensor is reminiscent of photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. At the start of the exposure period, all pixels are set to 0. A pixel is then set to 1 if the number of photons reaching it during the exposure is at least equal to a given threshold q. One way to build such binary sensors is to modify standard memory chip technology, where each memory bit cell is designed to be sensitive to visible light. With current CMOS technology, the level of integration of such systems can exceed 109~1010 (i.e., 1 giga to 10 giga) pixels per chip. In this case, the corresponding pixel sizes (around 50~nm ) are far below the diffraction limit of light, and thus the image sensor is oversampling the optical resolution of the light field. Intuitively, one can exploit this spatial redundancy to compensate for the information loss due to one-bit quantizations, as is classic in oversampling delta-sigma converters. Building a binary sensor that emulates the photographic film process was first envisioned by Fossum, who coined the name digital film sensor (now referred to as a quanta image sensor). The original motivation was mainly out of technical necessity. The miniaturization of camera systems calls for the continuous shrinking of pixel sizes. At a certain point, however, the limited full-well capacity (i.e., the maximum photon-electrons a pixel can hold) of small pixels becomes a bottleneck, yielding very low signal-to-noise ratios (SNRs) and poor dynamic ranges. In contrast, a binary sensor whose pixels need to detect only a few photon-electrons around a small threshold q has much less requirement for full-well capacities, allowing pixel sizes to shrink further. == Imaging model == === Lens === Consider a simplified camera model shown in Fig.1. The λ 0 ( x ) {\displaystyle \lambda _{0}(x)} is the incoming light intensity field. By assuming that light intensities remain constant within a short exposure period, the field can be modeled as only a function of the spatial variable x {\displaystyle x} . After passing through the optical system, the original light field λ 0 ( x ) {\displaystyle \lambda _{0}(x)} gets filtered by the lens, which acts like a linear system with a given impulse response. Due to imperfections (e.g., aberrations) in the lens, the impulse response, a.k.a. the point spread function (PSF) of the optical system, cannot be a Dirac delta, thus, imposing a limit on the resolution of the observable light field. However, a more fundamental physical limit is due to light diffraction. As a result, even if the lens is ideal, the PSF is still unavoidably a small blurry spot. In optics, such diffraction-limited spot is often called the Airy disk, whose radius R a {\displaystyle R_{a}} can be computed as R a = 1.22 w f , {\displaystyle R_{a}=1.22\,wf,} where w {\displaystyle w} is the wavelength of the light and f {\displaystyle f} is the F-number of the optical system. Due to the lowpass (smoothing) nature of the PSF, the resulting λ ( x ) {\displaystyle \lambda (x)} has a finite spatial-resolution, i.e., it has a finite number of degrees of freedom per unit space. === Sensor === Fig.2 illustrates the binary sensor model. The s m {\displaystyle s_{m}} denote the exposure values accumulated by the sensor pixels. Depending on the local values of s m {\displaystyle s_{m}} , each pixel (depicted as "buckets" in the figure) collects a different number of photons hitting on its surface. y m {\displaystyle y_{m}} is the number of photons impinging on the surface of the m {\displaystyle m} th pixel during an exposure period. The relation between s m {\displaystyle s_{m}} and the photon count y m {\displaystyle y_{m}} is stochastic. More specifically, y m {\displaystyle y_{m}} can be modeled as realizations of a Poisson random variable, whose intensity parameter is equal to s m {\displaystyle s_{m}} , As a photosensitive device, each pixel in the image sensor converts photons to electrical signals, whose amplitude is proportional to the number of photons impinging on that pixel. In a conventional sensor design, the analog electrical signals are then quantized by an A/D converter into 8 to 14 bits (usually the more bits the better). But in the binary sensor, the quantizer is 1 bit. In Fig.2, b m {\displaystyle b_{m}} is the quantized output of the m {\displaystyle m} th pixel. Since the photon counts y m {\displaystyle y_{m}} are drawn from random variables, so are the binary sensor output b m {\displaystyle b_{m}} . === Spatial and temporal oversampling === If it is allowed to have temporal oversampling, i.e., taking multiple consecutive and independent frames without changing the total exposure time τ {\displaystyle \tau } , the performance of the binary sensor is equivalent to the sensor with same number of spatial oversampling under certain condition. It means that people can make trade off between spatial oversampling and temporal oversampling. This is quite important, since technology usually gives limitation on the size of the pixels and the exposure time. == Advantages over traditional sensors == Due to the limited full-well capacity of conventional image pixel, the pixel will saturate when the light intensity is too strong. This is the reason that the dynamic range of the pixel is low. For the oversampled binary image sensor, the dynamic range is not defined for a single pixel, but a group of pixels, which makes the dynamic range high. == Reconstruction == One of the most important challenges with the use of an oversampled binary image sensor is the reconstruction of the light intensity λ ( x ) {\displaystyle \lambda (x)} from the binary measurement b m {\displaystyle b_{m}} . Maximum likelihood estimation can be used for solving this problem. Fig. 4 shows the results of reconstructing the light intensity from 4096 binary images taken by single photon avalanche diodes (SPADs) camera. A better reconstruction quality with fewer temporal measurements and faster, hardware friendly implementation, can be achieved by more sophisticated algorithms.

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  • Algorithm engineering

    Algorithm engineering

    Algorithm engineering focuses on the design, analysis, implementation, optimization, profiling and experimental evaluation of computer algorithms, bridging the gap between algorithmics theory and practical applications of algorithms in software engineering. It is a general methodology for algorithmic research. == Origins == In 1995, a report from an NSF-sponsored workshop "with the purpose of assessing the current goals and directions of the Theory of Computing (TOC) community" identified the slow speed of adoption of theoretical insights by practitioners as an important issue and suggested measures to reduce the uncertainty by practitioners whether a certain theoretical breakthrough will translate into practical gains in their field of work, and tackle the lack of ready-to-use algorithm libraries, which provide stable, bug-free and well-tested implementations for algorithmic problems and expose an easy-to-use interface for library consumers. But also, promising algorithmic approaches have been neglected due to difficulties in mathematical analysis. The term "algorithm engineering" was first used with specificity in 1997, with the first Workshop on Algorithm Engineering (WAE97), organized by Giuseppe F. Italiano. == Difference from algorithm theory == Algorithm engineering does not intend to replace or compete with algorithm theory, but tries to enrich, refine and reinforce its formal approaches with experimental algorithmics (also called empirical algorithmics). This way it can provide new insights into the efficiency and performance of algorithms in cases where the algorithm at hand is less amenable to algorithm theoretic analysis, formal analysis pessimistically suggests bounds which are unlikely to appear on inputs of practical interest, the algorithm relies on the intricacies of modern hardware architectures like data locality, branch prediction, instruction stalls, instruction latencies which the machine model used in Algorithm Theory is unable to capture in the required detail, the crossover between competing algorithms with different constant costs and asymptotic behaviors needs to be determined. == Methodology == Some researchers describe algorithm engineering's methodology as a cycle consisting of algorithm design, analysis, implementation and experimental evaluation, joined by further aspects like machine models or realistic inputs. They argue that equating algorithm engineering with experimental algorithmics is too limited, because viewing design and analysis, implementation and experimentation as separate activities ignores the crucial feedback loop between those elements of algorithm engineering. === Realistic models and real inputs === While specific applications are outside the methodology of algorithm engineering, they play an important role in shaping realistic models of the problem and the underlying machine, and supply real inputs and other design parameters for experiments. === Design === Compared to algorithm theory, which usually focuses on the asymptotic behavior of algorithms, algorithm engineers need to keep further requirements in mind: Simplicity of the algorithm, implementability in programming languages on real hardware, and allowing code reuse. Additionally, constant factors of algorithms have such a considerable impact on real-world inputs that sometimes an algorithm with worse asymptotic behavior performs better in practice due to lower constant factors. === Analysis === Some problems can be solved with heuristics and randomized algorithms in a simpler and more efficient fashion than with deterministic algorithms. Unfortunately, this makes even simple randomized algorithms difficult to analyze because there are subtle dependencies to be taken into account. === Implementation === Huge semantic gaps between theoretical insights, formulated algorithms, programming languages and hardware pose a challenge to efficient implementations of even simple algorithms, because small implementation details can have rippling effects on execution behavior. The only reliable way to compare several implementations of an algorithm is to spend an considerable amount of time on tuning and profiling, running those algorithms on multiple architectures, and looking at the generated machine code. === Experiments === See: Experimental algorithmics === Application engineering === Implementations of algorithms used for experiments differ in significant ways from code usable in applications. While the former prioritizes fast prototyping, performance and instrumentation for measurements during experiments, the latter requires thorough testing, maintainability, simplicity, and tuning for particular classes of inputs. === Algorithm libraries === Stable, well-tested algorithm libraries like LEDA play an important role in technology transfer by speeding up the adoption of new algorithms in applications. Such libraries reduce the required investment and risk for practitioners, because it removes the burden of understanding and implementing the results of academic research. == Conferences == Two main conferences on Algorithm Engineering are organized annually, namely: Symposium on Experimental Algorithms (SEA), established in 1997 (formerly known as WEA). SIAM Meeting on Algorithm Engineering and Experiments (ALENEX), established in 1999. The 1997 Workshop on Algorithm Engineering (WAE'97) was held in Venice (Italy) on September 11–13, 1997. The Third International Workshop on Algorithm Engineering (WAE'99) was held in London, UK in July 1999. The first Workshop on Algorithm Engineering and Experimentation (ALENEX99) was held in Baltimore, Maryland on January 15–16, 1999. It was sponsored by DIMACS, the Center for Discrete Mathematics and Theoretical Computer Science (at Rutgers University), with additional support from SIGACT, the ACM Special Interest Group on Algorithms and Computation Theory, and SIAM, the Society for Industrial and Applied Mathematics.

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  • Query rewriting

    Query rewriting

    Query rewriting is a typically automatic transformation that takes a set of database tables, views, and/or queries, usually indices, often gathered data and query statistics, and other metadata, and yields a set of different queries, which produce the same results but execute with better performance (for example, faster, or with lower memory use). Query rewriting can be based on relational algebra or an extension thereof (e.g. multiset relational algebra with sorting, aggregation and three-valued predicates i.e. NULLs as in the case of SQL). The equivalence rules of relational algebra are exploited, in other words, different query structures and orderings can be mathematically proven to yield the same result. For example, filtering on fields A and B, or cross joining R and S can be done in any order, but there can be a performance difference. Multiple operations may be combined, and operation orders may be altered. The result of query rewriting may not be at the same abstraction level or application programming interface (API) as the original set of queries (though often is). For example, the input queries may be in relational algebra or SQL, and the rewritten queries may be closer to the physical representation of the data, e.g. array operations. Query rewriting can also involve materialization of views and other subqueries; operations that may or may not be available to the API user. The query rewriting transformation can be aided by creating indices from which the optimizer can choose (some database systems create their own indexes if deemed useful), mandating the use of specific indices, creating materialized and/or denormalized views, or helping a database system gather statistics on the data and query use, as the optimality depends on patterns in data and typical query usage. Query rewriting may be rule based or optimizer based. Some sources discuss query rewriting as a distinct step prior to optimization, operating at the level of the user accessible algebra API (e.g. SQL). There are other, largely unrelated concepts also named similarly, for example, query rewriting by search engines.

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  • AI notetaker

    AI notetaker

    An AI notetaker is a tool using artificial intelligence to take notes during meetings. They are created by tech companies such as Microsoft and Google; by AI transcription services such Otter.ai, and by smaller firms such as Cluely and Krisp. Some business executives send AI notetakers to attend meetings not only to take notes, but also to answer questions on their behalf. The use of AI notetakers raises ethical questions, including recording meetings without the consent of all participants and the possibility that the notetaker will hallucinate and misrepresent what was said during meetings. There are also concerns when it comes to the privacy and security of meeting data and the sensitive information that lives inside meetings. Further controversies have developed from the use of AI notetakers such as Cluely to cheat in technical job interviews. == Technology == Large technology companies have integrated transcription capabilities into broader productivity and accessibility tools, including real-time captioning, dictation, and meeting documentation features embedded in operating systems and office platforms. Standalone transcription platforms, such as Transkriptor, focus specifically on automated transcription workflows and apply AI-based speech recognition to convert audio and video recordings into text. The software supports transcription in multiple languages and processes recordings uploaded via a web interface as well as through mobile and browser extensions. Tools of this type typically provide editable, time-aligned transcripts and export options for text and subtitle formats, cloud-based processing, multilingual support, and automation in transcription technology.

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  • Data exploration

    Data exploration

    Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. These characteristics can include size or amount of data, completeness of the data, correctness of the data, possible relationships amongst data elements or files/tables in the data. Data exploration is typically conducted using a combination of automated and manual activities. Automated activities can include data profiling or data visualization or tabular reports to give the analyst an initial view into the data and an understanding of key characteristics. This is often followed by manual drill-down or filtering of the data to identify anomalies or patterns identified through the automated actions. Data exploration can also require manual scripting and queries into the data (e.g. using languages such as SQL or R) or using spreadsheets or similar tools to view the raw data. All of these activities are aimed at creating a mental model and understanding of the data in the mind of the analyst, and defining basic metadata (statistics, structure, relationships) for the data set that can be used in further analysis. Once this initial understanding of the data is had, the data can be pruned or refined by removing unusable parts of the data (data cleansing), correcting poorly formatted elements and defining relevant relationships across datasets. This process is also known as determining data quality. Data exploration can also refer to the ad hoc querying or visualization of data to identify potential relationships or insights that may be hidden in the data and does not require to formulate assumptions beforehand. Traditionally, this had been a key area of focus for statisticians, with John Tukey being a key evangelist in the field. Today, data exploration is more widespread and is the focus of data analysts and data scientists; the latter being a relatively new role within enterprises and larger organizations. == Interactive Data Exploration == This area of data exploration has become an area of interest in the field of machine learning. This is a relatively new field and is still evolving. As its most basic level, a machine-learning algorithm can be fed a data set and can be used to identify whether a hypothesis is true based on the dataset. Common machine learning algorithms can focus on identifying specific patterns in the data. Many common patterns include regression and classification or clustering, but there are many possible patterns and algorithms that can be applied to data via machine learning. By employing machine learning, it is possible to find patterns or relationships in the data that would be difficult or impossible to find via manual inspection, trial and error or traditional exploration techniques. == Software == Trifacta – a data preparation and analysis platform Paxata – self-service data preparation software Alteryx – data blending and advanced data analytics software Microsoft Power BI - interactive visualization and data analysis tool OpenRefine - a standalone open source desktop application for data clean-up and data transformation Tableau software – interactive data visualization software

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  • Sieve of Eratosthenes

    Sieve of Eratosthenes

    In mathematics, the sieve of Eratosthenes is an ancient algorithm for finding all prime numbers up to any given limit. It does so by iteratively marking as composite (i.e., not prime) the multiples of each prime, starting with the first prime number, 2. The multiples of a given prime are generated as a sequence of numbers starting from that prime, with constant difference between them that is equal to that prime. This is the sieve's key distinction from using trial division to sequentially test each candidate number for divisibility by each prime. Once all the multiples of each discovered prime have been marked as composites, the remaining unmarked numbers are primes. The earliest known reference to the sieve (Ancient Greek: κόσκινον Ἐρατοσθένους, kóskinon Eratosthénous) is in Nicomachus of Gerasa's Introduction to Arithmetic, an early 2nd-century CE book which attributes it to Eratosthenes of Cyrene, a 3rd-century BCE Greek mathematician, though describing the sieving by odd numbers instead of by primes. One of a number of prime number sieves, it is one of the most efficient ways to find all of the smaller primes. It may be used to find primes in arithmetic progressions. == Overview == A prime number is a natural number that has exactly two distinct natural number divisors: the number 1 and itself. To find all the prime numbers less than or equal to a given integer n by Eratosthenes's method: Create a list of consecutive integers from 2 through n: (2, 3, 4, ..., n). Initially, let p equal 2, the smallest prime number. Enumerate the multiples of p by counting in increments of p from 2p to n, and mark them in the list (these will be 2p, 3p, 4p, ...; the p itself should not be marked). Find the smallest number in the list greater than p that is not marked. If there was no such number, stop. Otherwise, let p now equal this new number (which is the next prime), and repeat from step 3. When the algorithm terminates, the numbers remaining not marked in the list are all the primes below n. The main idea here is that every value given to p will be prime, because if it were composite it would be marked as a multiple of some other, smaller prime. Note that some of the numbers may be marked more than once (e.g., 15 will be marked both for 3 and 5). The key property of the sieve is that only additions are needed, no multiplications or divisions are used. As a refinement, it is sufficient to mark the numbers in step 3 starting from p2, as all the smaller multiples of p will have already been marked at that point. This means that the algorithm is allowed to terminate in step 4 when p2 is greater than n. Another refinement is to initially list odd numbers only, (3, 5, ..., n), and count in increments of 2p in step 3, thus marking only odd multiples of p. This actually appears in the original algorithm. This can be generalized with wheel factorization, forming the initial list only from numbers coprime with the first few primes and not just from odds (i.e., numbers coprime with 2), and counting in the correspondingly adjusted increments so that only such multiples of p are generated that are coprime with those small primes, in the first place. === Example === To find all the prime numbers less than or equal to 30, proceed as follows. First, generate a list of natural numbers from 2 to 30: 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The first number in the list is 2; cross out every 2nd number in the list after 2 by counting up from 2 in increments of 2 (these will be all the multiples of 2 in the list): 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The next number in the list after 2 is 3; cross out every 3rd number in the list after 3 by counting up from 3 in increments of 3 (these will be all the multiples of 3 in the list): 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The next number not yet crossed out in the list after 3 is 5; cross out every 5th number in the list after 5 by counting up from 5 in increments of 5 (i.e. all the multiples of 5): 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The next number not yet crossed out in the list after 5 is 7; the next step would be to cross out every 7th number in the list after 7, but they are all already crossed out at this point, as these numbers (14, 21, 28) are also multiples of smaller primes because 7 × 7 is greater than 30. The numbers not crossed out at this point in the list are all the prime numbers below 30: 2 3 5 7 11 13 17 19 23 29 == Algorithm and variants == === Pseudocode === The sieve of Eratosthenes can be expressed in pseudocode, as follows: algorithm Sieve of Eratosthenes is input: an integer n > 1. output: all prime numbers from 2 through n. let A be an array of Boolean values, indexed by integers 2 to n, initially all set to true. for i = 2, 3, 4, ..., not exceeding √n do if A[i] is true for j = i2, i2+i, i2+2i, i2+3i, ..., not exceeding n do set A[j] := false return all i such that A[i] is true. This algorithm produces all primes not greater than n. It includes a common optimization, which is to start enumerating the multiples of each prime i from i2. The time complexity of this algorithm is O(n log log n), provided the array update is an O(1) operation, as is usually the case. === Segmented sieve === As Sorenson notes, the problem with the sieve of Eratosthenes is not the number of operations it performs but rather its memory requirements. For large n, the range of primes may not fit in memory; worse, even for moderate n, its cache use is highly suboptimal. The algorithm walks through the entire array A, exhibiting almost no locality of reference. A solution to these problems is offered by segmented sieves, where only portions of the range are sieved at a time. These have been known since the 1970s, and work as follows: Divide the range 2 through n into segments of some size Δ ≥ √n. Find the primes in the first (i.e. the lowest) segment, using the regular sieve. For each of the following segments, in increasing order, with m being the segment's topmost value, find the primes in it as follows: Set up a Boolean array of size Δ. Mark as non-prime the positions in the array corresponding to the multiples of each prime p ≤ √m found so far, by enumerating its multiples in steps of p starting from the lowest multiple of p between m - Δ and m. The remaining non-marked positions in the array correspond to the primes in the segment. It is not necessary to mark any multiples of these primes, because all of these primes are larger than √m, as for k ≥ 1, one has ( k Δ + 1 ) 2 > ( k + 1 ) Δ {\displaystyle (k\Delta +1)^{2}>(k+1)\Delta } . If Δ is chosen to be √n, the space complexity of the algorithm is O(√n), while the time complexity is the same as that of the regular sieve. For ranges with upper limit n so large that the sieving primes below √n as required by the page segmented sieve of Eratosthenes cannot fit in memory, a slower but much more space-efficient sieve like the pseudosquares prime sieve, developed by Jonathan P. Sorenson, can be used instead. === Incremental sieve === An incremental formulation of the sieve generates primes indefinitely (i.e., without an upper bound) by interleaving the generation of primes with the generation of their multiples (so that primes can be found in gaps between the multiples), where the multiples of each prime p are generated directly by counting up from the square of the prime in increments of p (or 2p for odd primes). The generation must be initiated only when the prime's square is reached, to avoid adverse effects on efficiency. It can be expressed symbolically under the dataflow paradigm as primes = [2, 3, ...] \ [[p², p²+p, ...] for p in primes], using list comprehension notation with \ denoting set subtraction of arithmetic progressions of numbers. Primes can also be produced by iteratively sieving out the composites through divisibility testing by sequential primes, one prime at a time. It is not the sieve of Eratosthenes but is often confused with it, even though the sieve of Eratosthenes directly generates the composites instead of testing for them. Trial division has worse theoretical complexity than that of the sieve of Eratosthenes in generating ranges of primes. When testing each prime, the optimal trial division algorithm uses all prime numbers not exceeding its square root, whereas the sieve of Eratosthenes produces each composite from its prime factors only, and gets the primes "for free", between the composites. The widely known 1975 functional sieve code by David Turner is often presented as an example of the sieve of Eratosthenes but is actually a sub-optimal trial division sieve. == Algorithmic complexity == The sieve of Eratosthenes is a popular way to benchmark computer performance. The time complexity of calculating all primes below n in the random access machine model is O(n log log n) ope

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  • Information professional

    Information professional

    The term information professional or information specialist refers to professionals responsible for the collection, documentation, organization, storage, preservation, retrieval, and dissemination of printed and digital information. The service delivered to the client is known as an information service. The term "information professional" is a versatile one, used to describe similar and sometimes overlapping professions, such as librarians, archivists, information managers, information systems specialists, information scientists, records managers, and information consultants. However, terminology differs among sources and organisations. Information professionals are employed in a variety of private, public, and academic institutions, as well as independently. == Skills == Since the term information professional is broad, the skills required for this profession are also varied. A Gartner report in 2011 pointed out that "Professional roles focused on information management will be different to that of established IT roles. An 'information professional' will not be one type of role or skill set, but will in fact have a number of specializations". Thus, an information professional can possess a variety of different skills, depending on the sector in which the person is employed. Some essential cross-sector skills are: IT skills, such as word-processing and spreadsheets, digitisation skills, and conducting Internet searches, together with skills loan systems, databases, content management systems, and specially designed programmes and packages. Customer service. An information professional should have the ability to address the information needs of customers. Language proficiency. This is essential in order to manage the information at hand and deal with customer needs. Soft skills. These include skills such as negotiating, conflict resolution, and time management. Management training. An information professional should be familiar with notions such as strategic planning and project management. Moreover, an information professional should be skilled in planning and using relevant systems, in capturing and securing information, and in accessing it to deliver service whenever the information is required. == Associations == Most countries have a professional association who oversee the professional and academic standards of librarians and other information professionals. There are also international associations related to LIS (library and information science), the most prominent of which is the International Federation of Library Associations and Institutions (IFLA). In many countries, LIS courses are accredited by the relevant professional association, as the American Library Association (ALA) in the USA, the Chartered Institute of Library and Information Professionals (CILIP) in the UK, and the Australian Library and Information Association (ALIA) in Australia. == Qualifications == Educational institutions around the world offer academic degrees, or degrees on related subjects such as Archival Studies, Information Systems, Information Management, and Records Management. Some of the institutions offering information science education refer to themselves as an iSchool, such as the CiSAP (Consortium of iSchools Asia Pacific, founded 2006) in Asia and the iSchool Caucus in the USA. There are also online e-learning resources, some of which offer certification for information professionals. === Africa === Information development in Africa started later than in other continents, mainly due to a lack of internet access, expertise and resources to manage digital infrastructure, and "opportunities for capacity development and knowledge-sharing". Nowadays, academic degrees in information studies are available at many universities of African countries, such as the University of Pretoria (South Africa), University of Nairobi (Kenya), Makerere University (Uganda), University of Botswana (Botswana), and University of Nigeria (Nigeria). === Asia === LIS-related studies are available in more than 30 Asian countries. Some examples listed by iSchools Inc. are the University of Hong Kong, University of Tsukuba, Japan, Yonsei University, South Korea, National Taiwan University and Wuhan University, China. Centre of Library and Information Management Science (CLIMS) at Tata Institute of Social Science in Mumbai, India. In Southeast Asia, the Congress of Southeast Asian Librarians (CONSAL) connects librarians and libraries in more than 10 countries with resources, networking opportunities, and support for growing library systems. === Australasia === The Australian Library and Information Association (ALIA) as of 2021 lists six schools offering undergraduate and postgraduate accredited university courses for "Librarian and Information Specialists" on their website. In New Zealand, the Open Polytechnic of New Zealand and the Victoria University of Wellington offer undergraduate and postgraduate degree courses for information professionals. === Europe === The majority of European countries have universities, colleges, or schools which offer bachelor's degrees in LIS studies. Over 40 universities offer master's degrees in LIS-related fields, and many institutions, such as the Swedish School of Library and Information Science at the University of Borås (Sweden), the University of Barcelona (Spain), Loughborough University (UK), and Aberystwyth University (Wales, UK) also offer PhD degrees. === North America === Information studies and degrees are available at numerous academic institutions throughout the U.S. and Canada. U.S. professional associations, together with their European counterparts, have undertaken many educational initiatives and pioneered many advances in the field of Information studies, such as increased interdisciplinarity and more effective delivery of distance learning. The Association for Intelligent Information Management, based in Silver Spring, Maryland, offers a qualification called Certified Information Professional (CIP), earned upon passing an examination, with certification remaining valid for three years. === South America === There are many schools and colleges in Latin America, which offer courses in Library Science, Archival Studies, and Information Studies, however these subjects are taught completely separately.

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