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  Kindara

  Kindara is a femtech company headquartered in Colorado that develops apps that help women identify their fertile window. The products are used for women trying to get pregnant, or women who want to track their menstrual cycle for overall health. Their latest product, Priya Fertility and Ovulation Monitor, maximizes a woman's chance of getting pregnancy by identifying her most fertile days. == Overview == Kindara was founded in 2011 by husband-and-wife team Will Sacks and Kati Bicknell. The company launched its free mobile application in 2012. Kindara's mobile application allows women to track signs of fertility, such as basal body temperature, cervical fluid, and the position of the cervix to determine when ovulation is occurring. Kindara also sells a thermometer, Wink, which records basal body temperature and syncs automatically to the Kindara fertility application. In 2018, Kindara was acquired by the company Prima-Temp.

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  Uncertain database

  An uncertain database is a kind of database studied in database theory. The goal of uncertain databases is to manage information on which there is some uncertainty. Uncertain databases make it possible to explicitly represent and manage uncertainty on the data, usually in a succinct way. == Formal definition == At the basis of uncertain databases is the notion of possible world. Specifically, a possible world of an uncertain database is a (certain) database which is one of the possible realizations of the uncertain database. A given uncertain database typically has more than one, and potentially infinitely many, possible worlds. A formalism to represent uncertain databases then explains how to succinctly represent a set of possible worlds into one uncertain database. == Types of uncertain databases == Uncertain database models differ in how they represent and quantify these possible worlds: Incomplete databases are a compact representation of the set of possible worlds – the use of NULL in SQL, arguably the most commonplace instantiation of uncertain databases, is an example of incomplete database model. Probabilistic databases are a compact representation of a probability distribution over the set of possible worlds. Fuzzy databases are a compact representation of a fuzzy set of the possible worlds. Though mostly studied in the relational setting, uncertain database models can also be defined in other relational models such as graph databases or XML databases. === Incomplete database === The most common database model is the relational model. Multiple incomplete database models have been defined over the relational model, that form extensions to the relational algebra. These have been called Imieliński–Lipski algebras: Relations with NULL values, also called Codd tables c-tables v-tables === Example === The following table is a relation of an incomplete database, described in the formalism of NULL values: There are infinitely many possible worlds for this incomplete database, obtained by replacing the "NULL" values with concrete values. For instance, the following relation is a possible world:

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  Knuth–Eve algorithm

  In computer science, the Knuth–Eve algorithm is an algorithm for polynomial evaluation. It preprocesses the coefficients of the polynomial to reduce the number of multiplications required at runtime. Ideas used in the algorithm were originally proposed by Donald Knuth in 1962. His procedure opportunistically exploits structure in the polynomial being evaluated. In 1964, James Eve determined for which polynomials this structure exists, and gave a simple method of "preconditioning" polynomials (explained below) to endow them with that structure. == Algorithm == === Preliminaries === Consider an arbitrary polynomial p ∈ R [ x ] {\displaystyle p\in \mathbb {R} [x]} of degree n {\displaystyle n} . Assume that n ≥ 3 {\displaystyle n\geq 3} . Define m {\displaystyle m} such that: if n {\displaystyle n} is odd then n = 2 m + 1 {\displaystyle n=2m+1} , and if n {\displaystyle n} is even then n = 2 m + 2 {\displaystyle n=2m+2} . Unless otherwise stated, all variables in this article represent either real numbers or univariate polynomials with real coefficients. All operations in this article are done over R {\displaystyle \mathbb {R} } . Again, the goal is to create an algorithm that returns p ( x ) {\displaystyle p(x)} given any x {\displaystyle x} . The algorithm is allowed to depend on the polynomial p {\displaystyle p} itself, since its coefficients are known in advance. === Overview === ==== Key idea ==== Using polynomial long division, we can write p ( x ) = q ( x ) ⋅ ( x 2 − α ) + ( β x + γ ) , {\displaystyle p(x)=q(x)\cdot (x^{2}-\alpha )+(\beta x+\gamma ),} where x 2 − α {\displaystyle x^{2}-\alpha } is the divisor. Picking a value for α {\displaystyle \alpha } fixes both the quotient q {\displaystyle q} and the coefficients in the remainder β {\displaystyle \beta } and γ {\displaystyle \gamma } . The key idea is to cleverly choose α {\displaystyle \alpha } such that β = 0 {\displaystyle \beta =0} , so that p ( x ) = q ( x ) ⋅ ( x 2 − α ) + γ . {\displaystyle p(x)=q(x)\cdot (x^{2}-\alpha )+\gamma .} This way, no operations are needed to compute the remainder polynomial, since it's just a constant. We apply this procedure recursively to q {\displaystyle q} , expressing p ( x ) = ( ( q ( x ) ⋅ ( x 2 − α m ) + γ m ) ⋯ ) ⋅ ( x 2 − α 1 ) + γ 1 . {\displaystyle p(x)=\left(\left(q(x)\cdot (x^{2}-\alpha _{m})+\gamma _{m}\right)\cdots \right)\cdot (x^{2}-\alpha _{1})+\gamma _{1}.} After m {\displaystyle m} recursive calls, the quotient q {\displaystyle q} is either a linear or a quadratic polynomial. In this base case, the polynomial can be evaluated with (say) Horner's method. ==== "Preconditioning" ==== For arbitrary p {\displaystyle p} , it may not be possible to force β = 0 {\displaystyle \beta =0} at every step of the recursion. Consider the polynomials p e {\displaystyle p^{e}} and p o {\displaystyle p^{o}} with coefficients taken from the even and odd terms of p {\displaystyle p} respectively, so that p ( x ) = p e ( x 2 ) + x ⋅ p o ( x 2 ) . {\displaystyle p(x)=p^{e}(x^{2})+x\cdot p^{o}(x^{2}).} If every root of p o {\displaystyle p^{o}} is real, then it is possible to write p {\displaystyle p} in the form given above. Each α i {\displaystyle \alpha _{i}} is a different root of p o {\displaystyle p^{o}} , counting multiple roots as distinct. Furthermore, if at least n − 1 {\displaystyle n-1} roots of p {\displaystyle p} lie in one half of the complex plane, then every root of p o {\displaystyle p^{o}} is real. Ultimately, it may be necessary to "precondition" p {\displaystyle p} by shifting it — by setting p ( x ) ← p ( x + t ) {\displaystyle p(x)\gets p(x+t)} for some t {\displaystyle t} — to endow it with the structure that most of its roots lie in one half of the complex plane. At runtime, this shift has to be "undone" by first setting x ← x − t {\displaystyle x\gets x-t} . === Preprocessing step === The following algorithm is run once for a given polynomial p {\displaystyle p} . At this point, the values of x {\displaystyle x} that p {\displaystyle p} will be evaluated on are not known. ==== Better choice of t ==== While any t ≥ Re ( r 2 ) {\displaystyle t\geq {\text{Re}}(r_{2})} can work, it is possible to remove one addition during evaluation if t {\displaystyle t} is also chosen such that two roots of p ( x + t ) {\displaystyle p(x+t)} are symmetric about the origin. In that case, α 1 {\displaystyle \alpha _{1}} can be chosen such that the shifted polynomial has a factor of x 2 − α 1 {\displaystyle x^{2}-\alpha _{1}} , so γ 1 = 0 {\displaystyle \gamma _{1}=0} . It is always possible to find such a t {\displaystyle t} . One possible algorithm for choosing t {\displaystyle t} is: === Evaluation step === The following algorithm evaluates p {\displaystyle p} at some, now known, point x {\displaystyle x} . Assuming t {\displaystyle t} is chosen optimally, γ 1 = 0 {\displaystyle \gamma _{1}=0} . So, the final iteration of the loop can instead run y ← y ⋅ ( s − α i ) , {\displaystyle y\gets y\cdot (s-\alpha _{i}),} saving an addition. == Analysis == In total, evaluation using the Knuth–Eve algorithm for a polynomial of degree n {\displaystyle n} requires n {\displaystyle n} additions and ⌊ n / 2 ⌋ + 2 {\displaystyle \lfloor n/2\rfloor +2} multiplications, assuming t {\displaystyle t} is chosen optimally. No algorithm to evaluate a given polynomial of degree n {\displaystyle n} can use fewer than n {\displaystyle n} additions or fewer than ⌈ n / 2 ⌉ {\displaystyle \lceil n/2\rceil } multiplications during evaluation. This result assumes only addition and multiplication are allowed during both preprocessing and evaluation. The Knuth–Eve algorithm is not well-conditioned.

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  Sieve of Pritchard

  In mathematics, the sieve of Pritchard is an algorithm for finding all prime numbers up to a specified bound. Like the ancient sieve of Eratosthenes, it has a simple conceptual basis in number theory. It is especially suited to quick hand computation for small bounds. Whereas the sieve of Eratosthenes marks off each non-prime for each of its prime factors, the sieve of Pritchard avoids considering almost all non-prime numbers by building progressively larger wheels, which represent the pattern of numbers not divisible by any of the primes processed thus far. It thereby achieves a better asymptotic complexity, and was the first sieve with a running time sublinear in the specified bound. Its asymptotic running-time has not been improved on, and it deletes fewer composites than any other known sieve. It was created in 1979 by Paul Pritchard. Since Pritchard has created a number of other sieve algorithms for finding prime numbers, the sieve of Pritchard is sometimes singled out by being called the wheel sieve (by Pritchard himself) or the dynamic wheel sieve. == Overview == A prime number is a natural number that has no natural number divisors other than the number 1 and itself. To find all the prime numbers less than or equal to a given integer N, a sieve algorithm examines a set of candidates in the range 2, 3, …, N, and eliminates those that are not prime, leaving the primes at the end. The sieve of Eratosthenes examines all of the range, first removing all multiples of the first prime 2, then of the next prime 3, and so on. The sieve of Pritchard instead examines a subset of the range consisting of numbers that occur on successive wheels, which represent the pattern of numbers left after each successive prime is processed by the sieve of Eratosthenes. For i > 0, the ith wheel Wi represents this pattern. It is the set of numbers between 1 and the product Pi = p1 · p2 ⋯ pi of the first i prime numbers that are not divisible by any of these prime numbers (and is said to have an associated length Pi). This is because adding Pi to a number does not change whether it is divisible by one of the first i prime numbers, since the remainder on division by any one of these primes is unchanged. So W1 = {1} with length P1 = 2 represents the pattern of odd numbers; W2 = {1,5} with length P2 = 6 represents the pattern of numbers not divisible by 2 or 3; etc. Wheels are so-called because Wi can be usefully visualized as a circle of circumference Pi with its members marked at their corresponding distances from an origin. Then rolling the wheel along the number line marks points corresponding to successive numbers not divisible by one of the first i prime numbers. The animation shows W2 being rolled up to 30. It is useful to define Wi → n for n > 0 to be the result of rolling Wi up to n. Then the animation generates W2 → 30 = {1,5,7,11,13,17,19,23,25,29}. Note that up to 52 − 1 = 24, this consists only of 1 and the primes between 5 and 25. The sieve of Pritchard is derived from the observation that this holds generally: for all i > 0, the values in Wi → (p2i+1 − 1) are 1 and the primes between pi+1 and p2i+1. It even holds for i = 0, where the wheel has length 1 and contains just 1 (representing all the natural numbers). So the sieve of Pritchard starts with the trivial wheel W0 and builds successive wheels until the square of the wheel's first member after 1 is at least N. Wheels grow very quickly, but only their values up to N are needed and generated. It remains to find a method for generating the next wheel. Note in the animation that W3 = {1,5,7,11,13,17,19,23,25,29} − {5 · 1 , 5 · 5} can be obtained by rolling W2 up to 30 and then removing 5 times each member of W2.This also holds generally: for all i ≥ 0, Wi+1 = (Wi → Pi+1) − {pi+1 · w | w ∈ Wi}. Rolling Wi past Pi just adds values to Wi, so the current wheel is first extended by getting each successive member starting with w = 1, adding Pi to it, and inserting the result in the set. Then the multiples of pi+1 are deleted. Care must be taken to avoid a number being deleted that itself needs to be multiplied by pi+1. The sieve of Pritchard as originally presented does so by first skipping past successive members until finding the maximum one needed, and then doing the deletions in reverse order by working back through the set. This is the method used in the first animation above. A simpler approach is just to gather the multiples of pi+1 in a list, and then delete them. Another approach is given by Gries and Misra. If the main loop terminates with a wheel whose length is less than N, it is extended up to N to generate the remaining primes. The algorithm, for finding all primes up to N, is therefore as follows: Start with a set W = {1} and length = 1 representing wheel 0, and prime p = 2. As long as p2 ≤ N, do the following: if length < N, then extend W by repeatedly getting successive members w of W starting with 1 and inserting length + w into W as long as it does not exceed p · length or N; increase length to the minimum of p · length and N. repeatedly delete p times each member of W by first finding the largest ≤ length and then working backwards. note the prime p, then set p to the next member of W after 1 (or 3 if p was 2). if length < N, then extend W to N by repeatedly getting successive members w of W starting with 1 and inserting length + w into W as long as it does not exceed N; On termination, the rest of the primes up to N are the members of W after 1. === Example === To find all the prime numbers less than or equal to 150, proceed as follows. Start with wheel 0 with length 1, representing all natural numbers 1, 2, 3...: 1 The first number after 1 for wheel 0 (when rolled) is 2; note it as a prime. Now form wheel 1 with length 2 × 1 = 2 by first extending wheel 0 up to 2 and then deleting 2 times each number in wheel 0, to get: 1 2 The first number after 1 for wheel 1 (when rolled) is 3; note it as a prime. Now form wheel 2 with length 3 × 2 = 6 by first extending wheel 1 up to 6 and then deleting 3 times each number in wheel 1, to get 1 2 3 5 The first number after 1 for wheel 2 is 5; note it as a prime. Now form wheel 3 with length 5 × 6 = 30 by first extending wheel 2 up to 30 and then deleting 5 times each number in wheel 2 (in reverse order), to get 1 2 3 5 7 11 13 17 19 23 25 29 The first number after 1 for wheel 3 is 7; note it as a prime. Now wheel 4 has length 7 × 30 = 210, so we only extend wheel 3 up to our limit 150. (No further extending will be done now that the limit has been reached.) We then delete 7 times each number in wheel 3 until we exceed our limit 150, to get the elements in wheel 4 up to 150: 1 2 3 5 7 11 13 17 19 23 25 29 31 37 41 43 47 49 53 59 61 67 71 73 77 79 83 89 91 97 101 103 107 109 113 119 121 127 131 133 137 139 143 149 The first number after 1 for this partial wheel 4 is 11; note it as a prime. Since we have finished with rolling, we delete 11 times each number in the partial wheel 4 until we exceed our limit 150, to get the elements in wheel 5 up to 150: 1 2 3 5 7 11 13 17 19 23 25 29 31 37 41 43 47 49 53 59 61 67 71 73 77 79 83 89 91 97 101 103 107 109 113 119 121 127 131 133 137 139 143 149 The first number after 1 for this partial wheel 5 is 13. Since 13 squared is at least our limit 150, we stop. The remaining numbers (other than 1) are the rest of the primes up to our limit 150. Just 8 composite numbers are removed, once each. The rest of the numbers considered (other than 1) are prime. In comparison, the natural version of Eratosthenes sieve (stopping at the same point) removes composite numbers 184 times. == Pseudocode == The sieve of Pritchard can be expressed in pseudocode, as follows: algorithm Sieve of Pritchard is input: an integer N >= 2. output: the set of prime numbers in {1,2,...,N}. let W and Pr be sets of integer values, and all other variables integer values. k, W, length, p, Pr := 1, {1}, 2, 3, {2}; {invariant: p = pk+1 and W = Wk ∩ {\displaystyle \cap } {1,2,...,N} and length = minimum of Pk,N and Pr = the primes up to pk} while p2 <= N do if (length < N) then Extend W,length to minimum of plength,N; Delete multiples of p from W; Insert p into Pr; k, p := k+1, next(W, 1) if (length < N) then Extend W,length to N; return Pr ∪ {\displaystyle \cup } W - {1}; where next(W, w) is the next value in the ordered set W after w. procedure Extend W,length to n is {in: W = Wk and length = Pk and n > length} {out: W = Wk → {\displaystyle \rightarrow } n and length = n} integer w, x; w, x := 1, length+1; while x <= n do Insert x into W; w := next(W,w); x := length + w; length := n; procedure Delete multiples of p from W,length is integer w; w := p; while pw <= length do w := next(W,w); while w > 1 do w := prev(W,w); Remove pw from W; where prev(W, w) is the previous value in the ordered set W before w. The algorithm can be initialized with W0 instead of W1 at the minor complication of making next(W, 1) a special case when k = 0. This a

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  North Atlantic Population Project

  The North Atlantic Population Project (NAPP) is a collaboration of historical demographers in Britain, Canada, Denmark, Germany, Iceland, Norway, and Sweden to produce a massive census microdata collection for the North Atlantic Region in the late-nineteenth century. The database includes complete individual-level census enumerations for each country, and provides information on over 110 million people. This large scale allows detailed analysis of small geographic areas and population subgroups. The NAPP database is designed to be compatible with the Integrated Public Use Microdata Series (IPUMS), and is disseminated through the IPUMS data-access system at the Minnesota Population Center, University of Minnesota. Major collaborators on the project include Lisa Dillon, University of Montreal; Chad Gaffield, University of Ottawa; Ólöf Garðarsdóttir, Statistics Iceland; Marianne Jarnes Erikstad, University of Tromsø; Jan Oldervall University of Bergen; Evan Roberts, University of Minnesota; Steven Ruggles, University of Minnesota; Kevin Schürer, UK Data Archive; Gunnar Thorvaldsen, University of Tromsø; and Matthew Woollard, UK Data Archive. The project is also coordinated by the Minnesota Population Center at the University of Minnesota.

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  AlphaTensor

  AlphaTensor is an artificial intelligence system developed by DeepMind for discovering efficient matrix multiplication algorithms using reinforcement learning. Introduced in 2022, the system was based on AlphaZero and formulated the search for matrix multiplication algorithms as a single-player game called TensorGame. AlphaTensor was designed to search for new ways to multiply matrices with fewer scalar multiplication operations. Matrix multiplication is a fundamental operation in linear algebra, numerical analysis, scientific computing, computer graphics, and machine learning. The system discovered thousands of matrix multiplication algorithms, including algorithms that rediscovered known human-designed methods and others that improved on previously known results for particular matrix sizes and mathematical settings. == Background == Matrix multiplication is one of the basic operations in numerical computing. The standard algorithm for multiplying two square matrices has cubic time complexity, while faster algorithms such as the Strassen algorithm reduce the number of multiplication operations by using more complex algebraic decompositions. Finding optimal matrix multiplication algorithms can be difficult because it involves searching through a large space of possible tensor decompositions. AlphaTensor approached this problem by representing algorithm discovery as TensorGame, in which each move corresponds to an operation that reduces a tensor representing matrix multiplication. The goal of the game is to find a low-rank decomposition of the matrix multiplication tensor, corresponding to an efficient multiplication algorithm. == Development == AlphaTensor was developed by DeepMind and described in a paper published in Nature in October 2022. The system built on the reinforcement-learning approach used in AlphaZero, which had previously been applied to games such as Go, chess, and shogi. Unlike those games, TensorGame involved a very large search space, requiring changes to the AlphaZero-style search method and neural network architecture. DeepMind released source code and discovered algorithms associated with the publication through a public GitHub repository. == Results == AlphaTensor discovered matrix multiplication algorithms over both standard arithmetic and finite fields. One widely reported result was a method for multiplying 4 × 4 matrices over the field with two elements using 47 multiplication operations, improving on the 49 operations required by applying Strassen's algorithm recursively in that setting. The system also found algorithms optimized for particular computer hardware, including algorithms designed for graphics processing units and Tensor Processing Units. DeepMind stated that some of the hardware-specific algorithms improved practical execution time compared with commonly used algorithms on the tested hardware. == Significance == AlphaTensor was described as an example of using machine learning not only to apply existing algorithms, but to assist in discovering new ones. The work was connected to broader research in algorithm discovery, automated machine learning, program synthesis, and computational complexity theory, especially the open problem of determining the optimal complexity of matrix multiplication. AlphaTensor later became part of a broader group of Google DeepMind systems for algorithm and mathematical discovery, alongside systems such as AlphaDev and AlphaEvolve.

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  BuildingSMART Data Dictionary

  buildingSMART Data Dictionary (bSDD) is a service provided by buildingSMART which offers free data dictionaries for the international standardization of construction planning. The structure of bSDD was defined by the Nonprofit organization Buildingsmart and is used to describe objects and their attributes in a BIM process. == Aim == The aim of bSDD is to enable architects and planners to exchange and share building data across different specialists and language boundaries and thus avoid misunderstandings caused by different interpretations of terms. The bSDD standard extends the more general IFC. Software developers can access and use the dictionaries. In May 2025 over 300 dictionaries are available, including IFC, extensions to it such as Airport Domain IFC extension module or classification systems like Uniclass. == Structure == The main structural parts of bSDD are: Dictionary: A dictionary is a collection of classes: Class: A class describes the various object types, such as Bag drop or Baggage conveyor in airport planning. A class contains properties: Property: A property describes a part of a class, e.g. color or weight. Related properties are organized in a group: GroupOfProperties: A group organizes related properties, e.g. environmental properties or electrical properties. == Creating and managing a directory == Every dictionary in bSDD must be published in the name of a registered organization. As soon as the content is activated, it receives an unchangeable URI. This means that the content remains permanently in bSDD and cannot be deleted - this ensures stable use of the dictionary. It is only possible to change the status to inactive if it is no longer to be used - however, the dictionary remains permanently.

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  StoredIQ

  StoredIQ was a company founded for information lifecycle management (ILM) of unstructured data. Founded in 2001 as Deepfile in Austin, Texas by Jeff Erramouspe, Jeff Bone, Russell Turpin, Rudy Rouhana, Laura Arbilla and Brett Funderburg, the company changed its name in 2005 to StoredIQ. It continued to operate successfully for over a decade until it was acquired in 2012 by IBM. It now serves as a platform for IBM's information life cycle governance, big data governance and enterprise content management technologies. StoredIQ was awarded five patents by the USPTO. The first, originally filed in 2003, enabled unstructured data in file systems to be manipulated in a similar way to information stored in databases. Subsequent patents built upon the patented actionable file system with further enhancements specific to Enterprise Policy Management and expanding the reach of StoredIQ's management capability all the way to individual desktops. In 2008 StoredIQ was recognized as "Best in Compliance" by Network Products Guide. At the same time, StoredIQ was being recognized as a "Top 5 Provider" by the prestigious Socha-Gelbmann eDiscovery survey. There were takeover negotiations with EMC Corporation, initially a strategic investor in StoredIQ, however, the company rejected the approach, leaving EMC to acquire a competitor. The company published a whitepaper titled The Truth About Big Data. This promotion combined with StoredIQ's patented technology led to IBM selecting StoredIQ as the basis for some products.

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  Physicalization

  Physicalization of computer hardware (the opposite of virtualization), is a way to place multiple physical machines in a rack unit. It can be a way to reduce hardware costs, since in some cases, server processors cost more per core than energy efficient laptop processors, which may make up for added cost of board level integration. While Moore's law makes increasing integration less expensive, some jobs require much I/O bandwidth, which may be less expensive to provide using many less-integrated processors. Applications and services that are I/O bound are likely to benefit from such physicalized environments. This ensures that each operating system instance is running on a processor that has its own network interface card, host bus and I/O sub-system unlike in the case of a multi-core servers where a single I/O sub-system is shared between all the cores / VMs.

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  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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  Virtual data room

  A virtual data room (sometimes called a VDR or Deal Room) is an online repository of information that is used for the storing and distribution of documents. In many cases, a virtual data room is used to facilitate the due diligence process during an M&A transaction, loan syndication, or private equity and venture capital transactions. This due diligence process has traditionally used a physical data room to accomplish the disclosure of documents. For reasons of cost, efficiency and security, virtual data rooms have widely replaced the more traditional physical data room. A virtual data room is an extranet to which the bidders and their advisers are given access via the internet. An extranet is essentially a website with limited controlled access, using a secure log-on supplied by the vendor, which can be disabled at any time, by the vendor, if a bidder withdraws. Much of the information released is confidential and restrictions are applied to the viewer's ability to release this to third parties (by means of forwarding, copying or printing). This can be effectively applied to protect the data using digital rights management. The virtual data room provides access to secure documents for authorized users through a dedicated web site, or through secure agent applications. In the process of mergers and acquisitions the data room is set up as part of the central repository of data relating to companies or divisions being acquired or sold. The data room enables the interested parties to view information relating to the business in a controlled environment where confidentiality can be preserved. Conventionally this was achieved by establishing a supervised, physical data room in secure premises with controlled access. In most cases, with a physical data room, only one bidder team can access the room at a time. A virtual data room is designed to have the same advantages as a conventional data room (controlling access, viewing, copying and printing, etc.) with fewer disadvantages. Due to their increased efficiency, many businesses and industries have moved to using virtual data rooms instead of physical data rooms. In 2006, a spokesperson for a company which sets up virtual deal rooms was reported claiming that the process reduced the bidding process by about thirty days compared to physical data rooms. In the process of startup fundraising, a virtual data room is set up to be a central location for key data, documents, and financials. These are shared with venture capital and angel investors and allows them to streamline due diligence. == Application == Any business dealing with private data can apply VDRs when secure transaction processing is required. This includes financial institutions that need to negotiate confidential customer information without involving third parties. VDRs have traditionally been used for IPOs and real estate asset management. Technology companies may use them to exchange and review code or confidential data needed for operations. The same is true for clients, who entrust their valuable code only to the most qualified people in the organisation. The code is not something that can be printed out and brought in a folder. It resides on a computer and must be used together. VDR can find application in any business that manages data in the form of documents, especially law firms, financial advisers or the B2B sector. The latter work with documents that must always be handled and controlled confidentially, and it is difficult to store them securely when they are on a server that other people can access. In addition, in B2B, it is important to close the deal as quickly as possible: the average sales cycle is one to three months. VDR can be compared to a locked filing cabinet where all those folders and documents are kept. It automates the mathematics of pricing to prevent revenue leakage, and initially integrates CRM to ensure accurate synchronisation of all account data, which is important for B2B in particular and sales in general. While virtual data rooms offer many advantages, they are not suitable for every industry. For example, some governments may decide to continue using physical data rooms for highly confidential information sharing. The damage from potential cyberattacks and data breaches exceeds the benefits offered by virtual data rooms. In such cases, the use of VDRs is not considered. Data breaches have particularly affected the US healthcare system from March 2021 to March 2022 - according to IBM Security the cost of the breach was a record high of $10.1 million.

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  Operational database

  Operational database management systems (also referred to as OLTP databases or online transaction processing databases), are used to update data in real-time. These types of databases allow users to do more than simply view archived data. Operational databases allow you to modify that data (add, change or delete data), doing it in real-time. OLTP databases provide transactions as main abstraction to guarantee data consistency that guarantee the so-called ACID properties. Basically, the consistency of the data is guaranteed in the case of failures and/or concurrent access to the data. == History == Since the early 1990s, the operational database software market has been largely taken over by SQL engines. In 2014, the operational DBMS market (formerly OLTP) was evolving dramatically, with new, innovative entrants and incumbents supporting the growing use of unstructured data and NoSQL DBMS engines, as well as XML databases and NewSQL databases. NoSQL databases typically have focused on scalability and have renounced to data consistency by not providing transactions as OLTP system do. Operational databases are increasingly supporting distributed database architecture that can leverage distribution to provide high availability and fault tolerance through replication and scale out ability. The growing role of operational databases in the IT industry is moving fast from legacy databases to real-time operational databases capable to handle distributed web and mobile demand and to address Big data challenges. Recognizing this, Gartner started to publish the Magic Quadrant for Operational Database Management Systems in October 2013. == List of operational databases == Notable operational databases include: == Use in business == Operational databases are used to store, manage and track real-time business information. For example, a company might have an operational database used to track warehouse/stock quantities. As customers order products from an online web store, an operational database can be used to keep track of how many items have been sold and when the company will need to reorder stock. An operational database stores information about the activities of an organization, for example customer relationship management transactions or financial operations, in a computer database. Operational databases allow a business to enter, gather, and retrieve large quantities of specific information, such as company legal data, financial data, call data records, personal employee information, sales data, customer data, data on assets and many other information. An important feature of storing information in an operational database is the ability to share information across the company and over the Internet. Operational databases can be used to manage mission-critical business data, to monitor activities, to audit suspicious transactions, or to review the history of dealings with a particular customer. They can also be part of the actual process of making and fulfilling a purchase, for example in e-commerce. == Data warehouse terminology == In data warehousing, the term is even more specific: the operational database is the one which is accessed by an operational system (for example a customer-facing website or the application used by the customer service department) to carry out regular operations of an organization. Operational databases usually use an online transaction processing database which is optimized for faster transaction processing (create, read, update and delete operations). An operational database is the source for a data warehouse. Data from an operational database can be loaded into an operational data store at a data warehouse before the data is processed into the data warehouse.

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  Passenger drone

  A passenger drone is an autonomous aircraft that is designed to carry a small number of passengers to a destination. In 2021, Ehang, a technology company based in Guangzhou, China, developed the Ehang 184, the world's first passenger drone. == History == Unmanned aerial vehicles were first introduced in World War 1, when Britain first developed the Aerial Target, an aircraft controlled remotely through radio signals. A year later in the United States, testing of Kettering Bug, a 12-foot long biplane attached with a bomb and that launched via a “slingshot-like rail”, was also under progress. Both of their unreliable test results and their possibility of endangering friendly troops in deployment caused neither aircraft to be used during the war. Production of UAVs continued after World War I and into World War II and the Vietnam War, where they would be invaluable in assisting with training as well as reconnaissance. Late 20th century also saw the proposition and development of unique methods of travel, including personal jetpacks and even flying cars. While the previously mentioned are not drones, they serve as a precursor and foundation for the passenger drones of today. The first passenger drone was unveiled on January 6 of 2016 at the international Consumer Electronics Show (CES) in Las Vegas. Produced by Ehang, a Chinese company based in Guangzhou, the 184 was a one passenger drone equipped with four propellers that could fly for approximately 23 minutes at a top speed of 63 mph. Since then, many new companies have entered the market, but none yet have been accessible by the public. == Technological development == Since 2013, improvements in designs to wing structures have contributed to the economic feasibility of passenger drones. New structural advancements, such as the flapping-wing propulsion system based on the mechanisms of birds’ wings, are more available as they have proven their capabilities in laboratory testing. As of September 29th, 2015, most market-ready drones are delivery drones with a carrying capacity limited to small packages - with a typical max capacity of under 5 pounds. However, while the technology exists for drones with larger carrying capacities, specifically those capable of carrying multiple humans, the execution of this technology is not yet market accessible. This capacity limit must be addressed for passenger drones; given current designs strive to carry a maximum of 5 people. However, some estimates believe that passengers drones could become a reality, specifically for paid transportation and emergency purposes, as early as 2026. With implementation of this technology, there could be significant effects on ground traffic including reducing gridlock in heavily congested areas and conserving up to 15% of the fuel currently used in heavy traffic patterns. However, extensive growth of the passenger drone market also risks clouding the low-altitude airspace and causing new safety risks. However, this concern is being addressed by recent advancements in the Internet of Drones (IoD) which links drones together to ensure appropriate pathing and reduce mid-air collisions. While this brings additional security issues, including maintaining reliable communication channels in the case of technological failure, researchers hope that this will help reduce crashes that can result in damage to passengers, buildings, and people in and around the airspace. == Notable companies == Ehang is a Chinese company that has developed numerous drones including passenger plane Ehang 184. EHang 184 was their first model, developed as an eight dual rotor wing blade drone that can carry two passengers. The model was retired in 2020 and is replaced by the Ehang 216. Ehang also released a one passenger drone, Ehang 116. Ehang in 2021 unveiled the model VT-30. VT-30 is designed to have eight dual rotor wing blades to complement its fixed wing platform. Flyastro, a Texas-based drone company, developed the Astro ALTA, with two and four person passenger models. The company is known for being the first to develop a solar-powered airplane. The development team initially began with the model, Elroy. It was a two passenger drone with similar design to the ALTA. Once flight was achieved, the model Astro ALTA began development. Joby Aviation is a California based company that has developed a five passenger drone, with one seat for the pilot. The company expects to complete its FAA certification process 2022. Joby in 2020 acquired a 75 million dollar investment from service provider Uber Technologies Inc., leading to Uber Elevate and Expands partnership. Archer Aviation is a California-based company that has developed a two passenger model called Maker. It has fixed wings with twelve rotor wings. Archer is developing five person model. United Airlines has partnered with Archer for commercial sale of the model, Maker. Maker is expected to be released within Los Angeles and Miami by 2024. CityAirbus is a drone project developed by Airbus, a European multinational aerospace company, based in the Netherlands. CityAirbus has developed a four- person passenger drone with fixed wings that include rotor wing blades. Its expected certification for public flight is in 2025. Boeing, an American multinational aviation corporation is developing a passenger drone model called the Passenger Air Vehicle (PAV). The model is a fixed wing with eight rotor blade wings attached onto a platform underneath the base structure. This model can hold two passengers and still is in development. Volocopter is a German aircraft manufacturer that is developing a passenger drone called Volocity. The model consist of eighteen rotor wings above the cockpit on a circular ring. Japan Airlines, an investor of Volocopter plans to have public test in Japan as early as 2023. == Future use == === Potential benefits === Passenger drones can greatly reduce the time for travel. As passenger drones flight paths are not restricted by conventional roads, the travel distance is shortened. Current ventures such as Joby Aviation, after acquiring Uber Air, plan to take advantage of this technology in the form of air taxis. Other potential benefits include the use of passenger drones by emergency services such as search and rescue missions and the delivery of life saving goods. Companies like Ehang have already begun using passenger drones as emergency vehicles as a response to the potential river collapses during the flood season in China. === Concerns === Passenger and air traffic safety remains at the forefront of concerns. Regulations for air traffic centered around passenger drones are still underway and would continue to develop with increasing use cases for passenger drones. Remote security threats on commercial drones such as Man-In-The-Middle (MITM) attack have also exposed the vulnerabilities in current drone systems. Among American adults, 54 percent say that they would feel unsafe flying inside a passenger drone. Passenger drones can be very noisy; a single passenger drone such as Joby Aviation’s all-electric vertical take-off and landing (“eVTOL”) aircraft has an estimated noise production of 70 decibels (dB), a noise level equating to “loud traffic”.

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  Aidoc

  Aidoc Medical is an Israeli technology company that develops computer-aided simple triage and notification systems. Aidoc has obtained U.S. Food and Drug Administration and CE mark approval for its stroke, pulmonary embolism, cervical fracture, intracranial hemorrhage, intra-abdominal free gas, and incidental pulmonary embolism algorithms. Aidoc algorithms are in use in more than 900 hospitals and imaging centers, including Montefiore Nyack Hospital, LifeBridge Health, LucidHealth, Yale New Haven Hospital, Cedars-Sinai Medical Center, University of Rochester Medical Center, and Sheba Medical Center. == History == Aidoc was founded in 2016 by Elad Walach as the CEO, Michael Braginsky as the CTO and Guy Reiner as the VP. In April 2017, the company raised $7M, led by TLV Partners, and in April 2019, the company raised another $27M, led by Square Peg capital. There have been several additional rounds of funding as well, bringing Aidoc's total investment to $370M as of July 2025. In August 2018, Aidoc gained FDA clearance for its intracranial hemorrhage system, and in May 2019 it received clearance for the pulmonary embolism system. In January 2020, the system for detecting large-vessel occlusions (LVOs) in head CTA examinations obtained FDA clearance. In October 2024, it was reported that Aidoc is working with NVIDIA to develop a framework for deployment and integration of artificial intelligence tools in healthcare. The Blueprint for Resilient Integration and Deployment of Guided Excellence (BRIDGE) is a guideline to facilitate AI adoption in the healthcare industry. == Products and market == Aidoc has developed a suite of artificial intelligence products that flag both time-sensitive and time-consuming (for the radiologist) abnormalities across the body. The algorithms are developed with large quantities of data to provide diagnostic aid for a broad set of pathologies. The company offers an array of algorithms that span across the body, including for intracranial hemorrhage, spine fractures (C, T & L), free air in the abdomen, pulmonary embolism, and more. It developed "Always-on AI", a term coined by Elad Walach that refers to a type of artificial intelligence that is "Always-on—constantly running in the background and automatically analyzing medical imaging data, identifying urgent findings, and sparing radiologists from "drowning" in vast amounts of irrelevant data. Aidoc's solutions cover medical conditions prevalent in all settings (ED/inpatient/outpatient), including level 1 trauma centers, outpatient imaging centers, teleradiology groups and, are set up in over 200 medical centers worldwide. Notable customers include the University of Rochester Medical Center and Global Diagnostics Australia. Aidoc announced in 2024 that its new Clinical AI Reasoning Engine (CARE1) had been submitted for FDA approval. In September 2025 Aidoc received a "Breakthrough Device Designation" from the FDA for a new multi-triage solution that spans numerous acute findings in CT scans. Aidoc's CARE1 foundation model was the basis of the workflow on which the designation was made, enabling simultaneous coverage of multiple pathologies. This new designation allows parallel FDA review of multiple indications under a single submission. In April 2026, Aidoc raised million in a Series E funding round led by Growth Equity at Goldman Sachs Alternatives, with participation from General Catalyst and NVentures. The financing brought the company's total funding to over million. == Clinical Research == A clinical study on Aidoc’ accuracy of deep convolutional neural networks for the detection of pulmonary embolism (PE) on CT pulmonary angiograms (CTPAs) was performed by the University Hospital of Basel and presented at the European Congress of Radiology, showing that the Aidoc algorithm reached 93% sensitivity and 95% specificity. Clinical research has also been performed to test the diagnostic performance of Aidoc's deep learning-based triage system for the flagging of acute findings in abdominal computed tomography (CT) examinations. Overall, the algorithm achieved 93% sensitivity (91/98, 7 false negatives) and 97% specificity (93/96, 3 false-positive) in the detection of acute abdominal findings. Additional clinical research on Aidoc's Intracranial hemorrhage algorithm accuracy was presented at the European Congress of Radiology by Antwerp University Hospital, evaluating the use of its deep learning algorithm for the detection of intracranial hemorrhage on non-contrast enhanced CT of the brain. The University of Washington completed a study on the accuracy of Aidoc's intracranial hemorrhage algorithm.

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  Sequential algorithm

  In computer science, a sequential algorithm or serial algorithm is an algorithm that is executed sequentially – once through, from start to finish, without other processing executing – as opposed to concurrently or in parallel. The term is primarily used to contrast with concurrent algorithm or parallel algorithm; most standard computer algorithms are sequential algorithms, and not specifically identified as such, as sequentialness is a background assumption. Concurrency and parallelism are in general distinct concepts, but they often overlap – many distributed algorithms are both concurrent and parallel – and thus "sequential" is used to contrast with both, without distinguishing which one. If these need to be distinguished, the opposing pairs sequential/concurrent and serial/parallel may be used. "Sequential algorithm" may also refer specifically to an algorithm for decoding a convolutional code.

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