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William Sarjeant | External links | External links
Detailed life history on Wayback Archive
Wayback Archive of University website listing academic work
Obituary on Wayback Archive
Category:1935 births
Category:2002 deaths
Category:British expatriates in Canada
Category:Canadian male novelists
Category:Academics of the University of Sheffield
Category:Academic staff of the University of Saskatchewan
Category:English male novelists
Category:20th-century English novelists
Category:Canadian fantasy writers
Category:20th-century Canadian geologists
Category:20th-century Canadian male writers
Category:20th-century English male writers |
William Sarjeant | Table of Content | Use dmy dates, Writings, External links |
The Catherine Palace | # | Redirect Catherine Palace |
The Catherine Palace | Table of Content | # |
Battle of Blenau | # | redirect Battle of Bléneau |
Battle of Blenau | Table of Content | # |
Wartenberg | '''Wartenberg''' | Wartenberg may refer to: |
Wartenberg | Buildings | Buildings
Wartenberg castles, situated on the Wartenberg hill in the municipality of Muttenz near Basel
Wartenberg Castle built in the present day Kaiserslautern and destroyed in 1522; former seat of Counts of Wartenberg
Wartenberg station, an S-Bahn and railway station in the Lichtenberg district of Berlin
, German schloss built by Otto Wächter in Krakau |
Wartenberg | Places | Places
Wartenberg, Hesse in the district Vogelsbergkreis, Hesse, Germany
Wartenberg (Berlin), a locality in the borough of Lichtenberg in Berlin, Germany
Wartenberg, Bavaria in the district Erding, Upper Bavaria, Germany
Wartenberg (Swabian Jura), a mountain in Baden-Württemberg, Germany
Wartenberg am Rollberg, the German name of Stráž pod Ralskem, Czech Republic
The medieval County of Wartenberg, a fief of the Holy Roman Empire, mediatised to Kingdom of Westphalia in 1806 and subsequently to Prussia in 1814
Otyń, a town in Poland (German: Deutsch-Wartenberg)
Syców, a town in Poland (German: Polnisch-Wartenberg until 1888, then Groß-Wartenberg)
Chełm Dolny, a village in Poland
Jadowniki Bielskie, a village in Poland
Parsów, a village in Poland |
Wartenberg | People | People
Franz Wilhelm von Wartenberg (1593-1661), Count, Catholic clergy, Prince-Bishop of Minden, Osnabrück and Verden as well as Vicar Apostolic of the Archdiocese of Bremen
Ludolf von Wartenberg (born 1941), politician (CDU)
Robert Wartenberg (1887-1956), neurologist
Counts of Wartenberg, (since 1802 known as Counts of Wartenberg-Roth) an aristocratic family from Rhenish Hesse, Palatine and Upper Swabia
Counts of Wartenberg of the Wittelsbach dynasty, aristocratic title given to the descendants of Ferdinand of Bavaria (1550-1608)
, extinct aristocratic family from Bohemia |
Wartenberg | Other | Other
Wartenberg wheel, a medical device for neurological use |
Wartenberg | Table of Content | '''Wartenberg''', Buildings, Places, People, Other |
FC WIT Georgia | Infobox football club | FC WIT Georgia is a Georgian football team from Tbilisi. The team is sponsored by WIT Georgia Ltd (a subsidiary of the United States WIT, Inc.), a pet food, accessories, and human and veterinary pharmaceuticals import company. WIT stands for World Innovation Technologies. They play their home games at Mikheil Meskhi Stadium in Tbilisi.
In 2004, FC WIT Georgia won the Georgian Championship, qualifying them for the early stages of the UEFA Champions League. In 2009, they won the championship for the second time. In 2010, the team won the Georgian Cup for the first time. However, performance of WIT Georgia was faded after 2010–11 season and relegated to Pirveli Liga in 2014–15 season. In 2017–18, they finished second in the Erovnuli Liga 2 and secured promotion to the Erovnuli Liga. |
FC WIT Georgia | History | History
1997: Founded as FC WIT Georgia Tbilisi. |
FC WIT Georgia | Honours | Honours
Erovnuli Liga
Winners (2): 2003–04, 2009
Georgian Cup
Winners (1): 2010
Georgian Super Cup
Winners (1): 2009 |
FC WIT Georgia | Current squad | Current squad
As of 1 August 2023 |
FC WIT Georgia | European cups history | European cups history
Season Competition Round Country Team Home Away2000–01UEFA CupQRBeitar Jerusalem0–31–12001UEFA Intertoto Cup1RRied1–01–22RTroyes1–10–62002UEFA Intertoto Cup1RLokeren3–21–32003UEFA Intertoto Cup1RPasching2–10–12004–05UEFA Champions League1QRHB Tórshavn5–00–32QRWisła Kraków2–80–32005UEFA Intertoto Cup1RLombard-Papa0–11–22006–07UEFA Cup1QRArtmedia Petržalka2–10–22008–09UEFA Cup1QRSpartak Trnava1–02–22QRAustria ViennaX0–22009–10UEFA Champions League1QRMaribor0–01–32010–11UEFA Europa League2QRBaník Ostrava0–60–0 |
FC WIT Georgia | Managers | Managers
Elguja Gugushvili (1997–199?)
Sergo Kotrikadze (March 8, 1999 – 2001)
Nestor Mumladze (2006 – August 2009)
Merab Kochlashvili (August 2009 – 2009)
Gela Gomelauri (2009–2010)
Merab Kochlashvili (July 16, 2010–??)
Zurab Beridze (April 20, 2011 – March 11, 2012)
Merab Kochlashvili (March 2012–1?)
Zurab Beridze (April 1, 2013–1?)
Merab Kochlashvili (June 1, 2013–)
Tengiz Kobiashvili (2015–) |
FC WIT Georgia | References | References |
FC WIT Georgia | External links | External links
FC WIT Georgia results
WIT Georgia Tbilisi
Category:Association football clubs established in 1997
Category:1997 establishments in Georgia (country)
WIT Tbilisi |
FC WIT Georgia | Table of Content | Infobox football club, History, Honours, Current squad, European cups history, Managers, References, External links |
Ambedkar Samaj Party | Short description | Ambedkar Samaj Party (Ambedkar Society Party) is a political party in India, that fights for the rights of Dalits. The party is opposed to Hindu nationalism, which it sees as representing an upper caste minority. ASP claims that Bahujan Samaj Party has betrayed dalits through its alliance with Bharatiya Janata Party. The leader of ASP is Tej Singh.
Singh is also commander-in-chief of the Bahujan Swayam Sewak Sanghathan, a militant dalit organization. BSS was founded 1995.
In the Lok Sabha elections 2004 ASP had launched nine candidates from Uttar Pradesh. Tej Singh stood as candidate from Aligarh and got 1 054 votes (0,17%). |
Ambedkar Samaj Party | See also | See also
List of political parties in India |
Ambedkar Samaj Party | External links | External links
Bahujan Swayam Sewak Sanghathan
Category:Political parties in Uttar Pradesh
Category:1995 establishments in Uttar Pradesh
Category:Ambedkarite political parties
Category:Political parties established in 1995 |
Ambedkar Samaj Party | Table of Content | Short description, See also, External links |
Distance matrix | Short description | In mathematics, computer science and especially graph theory, a distance matrix is a square matrix (two-dimensional array) containing the distances, taken pairwise, between the elements of a set.Weyenberg, G., & Yoshida, R. (2015). Reconstructing the phylogeny: Computational methods. In Algebraic and Discrete Mathematical methods for modern Biology (pp. 293–319). Academic Press. Depending upon the application involved, the distance being used to define this matrix may or may not be a metric. If there are elements, this matrix will have size . In graph-theoretic applications, the elements are more often referred to as points, nodes or vertices. |
Distance matrix | Non-metric distance matrix | Non-metric distance matrix
In general, a distance matrix is a weighted adjacency matrix of some graph. In a network, a directed graph with weights assigned to the arcs, the distance between two nodes of the network can be defined as the minimum of the sums of the weights on the shortest paths joining the two nodes (where the number of steps in the path is bounded).Frank Harary, Robert Z. Norman and Dorwin Cartwright (1965) Structural Models: An Introduction to the Theory of Directed Graphs, pages 134–8, John Wiley & Sons This distance function, while well defined, is not a metric. There need be no restrictions on the weights other than the need to be able to combine and compare them, so negative weights are used in some applications. Since paths are directed, symmetry can not be guaranteed, and if negative-weight cycles exist the distance matrix may not be hollow (and in the absence of a bound on the step count, the matrix may be undefined).
An algebraic formulation of the above can be obtained by using the min-plus algebra. Matrix multiplication in this system is defined as follows: Given two matrices and , their distance product is defined as an matrix such that
Note that the off-diagonal elements that are not connected directly will need to be set to infinity or a suitable large value for the min-plus operations to work correctly. A zero in these locations will be incorrectly interpreted as an edge with no distance, cost, etc.
If is an matrix containing the edge weights of a graph, then (using this distance product) gives the distances between vertices using paths of length at most edges, and so is the distance matrix of the graph when the step count bound is set to k. If there are no loops of negative weight, will give the true distance matrix, with no bound, because removing repeated vertices from a path cannot lower its weight. On the other hand, if i and j are on a negative-weight loop, will decrease without bound as k increases.
An arbitrary graph on vertices can be modeled as a weighted complete graph on vertices by assigning a weight of one to each edge of the complete graph that corresponds to an edge of and infinity to all other edges. for this complete graph is the adjacency matrix of . The distance matrix of can be computed from as above; by contrast, if normal matrix multiplication is used, and unlinked vertices are represented with 0, would instead encode the number of paths between any two vertices of length exactly . |
Distance matrix | Metric distance matrix | Metric distance matrix
The value of a distance matrix formalism in many applications is in how the distance matrix can manifestly encode the metric axioms and in how it lends itself to the use of linear algebra techniques. That is, if with is a distance matrix for a metric distance, then
the entries on the main diagonal are all zero (that is, the matrix is a hollow matrix), i.e. for all ,
all the off-diagonal entries are positive ( if ), (that is, a non-negative matrix),
the matrix is a symmetric matrix (), and
for any and , for all (the triangle inequality). This can be stated in terms of tropical matrix multiplication
When a distance matrix satisfies the first three axioms (making it a semi-metric) it is sometimes referred to as a pre-distance matrix. A pre-distance matrix that can be embedded in a Euclidean space is called a Euclidean distance matrix. For mixed-type data that contain numerical as well as categorical descriptors, Gower's distance is a common alternative.
Another common example of a metric distance matrix arises in coding theory when in a block code the elements are strings of fixed length over an alphabet and the distance between them is given by the Hamming distance metric. The smallest non-zero entry in the distance matrix measures the error correcting and error detecting capability of the code. |
Distance matrix | Additive distance matrix | Additive distance matrix
An additive distance matrix is a special type of matrix used in bioinformatics to build a phylogenetic tree. Let be the lowest common ancestor between two species and , we expect . This is where the additive metric comes from. A distance matrix for a set of species is said to be additive if and only if there exists a phylogeny for such that:
Every edge in is associated with a positive weight
For every , equals the sum of the weights along the path from to in
For this case, is called an additive matrix and is called an additive tree. Below we can see an example of an additive distance matrix and its corresponding tree:
444x444px|Additive distance matrix (left) and its phylogeny tree (right)|center|frameless |
Distance matrix | Ultrametric distance matrix | Ultrametric distance matrix
The ultrametric distance matrix is defined as an additive matrix which models the constant molecular clock. It is used to build a phylogenetic tree. A matrix is said to be ultrametric if there exists a tree such that:
equals the sum of the edge weights along the path from to in
A root of the tree can be identified with the distance to all the leaves being the same
Here is an example of an ultrametric distance matrix with its corresponding tree:
frameless|395x395px|center |
Distance matrix | Bioinformatics | Bioinformatics
The distance matrix is widely used in the bioinformatics field, and it is present in several methods, algorithms and programs. Distance matrices are used to represent protein structures in a coordinate-independent manner, as well as the pairwise distances between two sequences in sequence space. They are used in structural and sequential alignment, and for the determination of protein structures from NMR or X-ray crystallography.
Sometimes it is more convenient to express data as a similarity matrix.
It is also used to define the distance correlation. |
Distance matrix | [[Sequence alignment]] | Sequence alignment
An alignment of two sequences is formed by inserting spaces in arbitrary locations along the sequences so that they end up with the same length and there are no two spaces at the same position of the two augmented sequences. One of the primary methods for sequence alignment is dynamic programming. The method is used to fill the distance matrix and then obtain the alignment. In typical usage, for sequence alignment a matrix is used to assign scores to amino-acid matches or mismatches, and a gap penalty for matching an amino-acid in one sequence with a gap in the other. |
Distance matrix | Global alignment | Global alignment
The Needleman–Wunsch algorithm used to calculate global alignment uses dynamic programming to obtain the distance matrix. |
Distance matrix | Local alignment | Local alignment
The Smith–Waterman algorithm is also dynamic programming based which consists also in obtaining the distance matrix and then obtain the local alignment. |
Distance matrix | Multiple sequence alignment | Multiple sequence alignment
Multiple sequence alignment is an extension of pairwise alignment to align several sequences at a time. Different MSA methods are based on the same idea of the distance matrix as global and local alignments.
Center star method. This method defines a center sequence which minimizes the distance between the sequence and any other sequence . Then it generates a multiple alignment for the set of sequences so that for every the alignment distance is the optimal pairwise alignment. This method has the characteristic that the computed alignment for whose sum-of-pair distance is at most twice the optimal multiple alignment.
Progressive alignment method. This heuristic method to create MSA first aligns the two most related sequences, and then it progressively aligns the next two most related sequences until all sequences are aligned.
There are other methods that have their own program due to their popularity:
ClustalW
MUSCLE
MAFFT
MANGO
And many more |
Distance matrix | MAFFT | MAFFT
Multiple alignment using fast Fourier transform (MAFFT) is a program with an algorithm based on progressive alignment, and it offers various multiple alignment strategies. First, MAFFT constructs a distance matrix based on the number of shared 6-tuples. Second, it builds the guide tree based on the previous matrix. Third, it clusters the sequences with the help of the fast Fourier transform and starts the alignment. Based on the new alignment, it reconstructs the guide tree and align again. |
Distance matrix | Phylogenetic analysis | Phylogenetic analysis
To perform phylogenetic analysis, the first step is to reconstruct the phylogenetic tree: given a collection of species, the problem is to reconstruct or infer the ancestral relationships among the species, i.e., the phylogenetic tree among the species. Distance matrix methods perform this activity. |
Distance matrix | Distance matrix methods | Distance matrix methods
Distance matrix methods of phylogenetic analysis explicitly rely on a measure of "genetic distance" between the sequences being classified, and therefore require multiple sequences as an input. Distance methods attempt to construct an all-to-all matrix from the sequence query set describing the distance between each sequence pair. From this is constructed a phylogenetic tree that places closely related sequences under the same interior node and whose branch lengths closely reproduce the observed distances between sequences. Distance-matrix methods may produce either rooted or unrooted trees, depending on the algorithm used to calculate them. Given species, the input is an distance matrix where is the mutation distance between species and . The aim is to output a tree of degree which is consistent with the distance matrix.
They are frequently used as the basis for progressive and iterative types of multiple sequence alignment. The main disadvantage of distance-matrix methods is their inability to efficiently use information about local high-variation regions that appear across multiple subtrees. Despite potential problems, distance methods are extremely fast, and they often produce a reasonable estimate of phylogeny. They also have certain benefits over the methods that use characters directly. Notably, distance methods allow use of data that may not be easily converted to character data, such as DNA–DNA hybridization assays.
The following are distance based methods for phylogeny reconstruction:
Additive tree reconstruction
UPGMA
Neighbor joining
Fitch–Margoliash |
Distance matrix | Additive tree reconstruction | Additive tree reconstruction
Additive tree reconstruction is based on additive and ultrametric distance matrices. These matrices have a special characteristic:
Consider an additive matrix . For any three species the corresponding tree is unique. Every ultrametric distance matrix is an additive matrix. We can observe this property for the tree below, which consists on the species .
center|frameless|Phylogenetic tree from 3 species
The additive tree reconstruction technique starts with this tree. And then adds one more species each time, based on the distance matrix combined with the property mentioned above. For example, consider an additive matrix and 5 species and . First we form an additive tree for two species and . Then we chose a third one, let's say and attach it to a point on the edge between and . The edge weights are computed with the property above. Next we add the fourth species to any of the edges. If we apply the property then we identify that should be attached to only one specific edge. Finally, we add following the same procedure as before. |
Distance matrix | UPGMA | UPGMA
The basic principle of UPGMA (Unweighted Pair Group Method with Arithmetic Mean) is that similar species should be closer in the phylogenetic tree. Hence, it builds the tree by clustering similar sequences iteratively. The method works by building the phylogenetic tree bottom up from its leaves. Initially, we have leaves (or singleton trees), each representing a species in . Those leaves are referred as clusters. Then, we perform iterations. In each iteration, we identify two clusters and with the smallest average distance and merge them to form a bigger cluster . If we suppose is ultrametric, for any cluster created by the UPGMA algorithm, is a valid ultrametric tree. |
Distance matrix | Neighbor joining | Neighbor joining
Neighbor is a bottom-up clustering method. It takes a distance matrix specifying the distance between each pair of sequences. The algorithm starts with a completely unresolved tree, whose topology corresponds to that of a star network, and iterates over the following steps until the tree is completely resolved and all branch lengths are known:
Based on the current distance matrix calculate the matrix (defined below).
Find the pair of distinct taxa i and j (i.e. with) for which has its lowest value. These taxa are joined to a newly created node, which is connected to the central node.
Calculate the distance from each of the taxa in the pair to this new node.
Calculate the distance from each of the taxa outside of this pair to the new node.
Start the algorithm again, replacing the pair of joined neighbors with the new node and using the distances calculated in the previous step. |
Distance matrix | Fitch–Margoliash | Fitch–Margoliash
The Fitch–Margoliash method uses a weighted least squares method for clustering based on genetic distance. Closely related sequences are given more weight in the tree construction process to correct for the increased inaccuracy in measuring distances between distantly related sequences. The least-squares criterion applied to these distances is more accurate but less efficient than the neighbor-joining methods. An additional improvement that corrects for correlations between distances that arise from many closely related sequences in the data set can also be applied at increased computational cost. |
Distance matrix | Data Mining and Machine Learning | Data Mining and Machine Learning |
Distance matrix | Data Mining | Data Mining
A common function in data mining is applying cluster analysis on a given set of data to group data based on how similar or more similar they are when compared to other groups. Distance matrices became heavily dependent and utilized in cluster analysis since similarity can be measured with a distance metric. Thus, distance matrix became the representation of the similarity measure between all the different pairs of data in the set. |
Distance matrix | Hierarchical clustering | Hierarchical clustering
A distance matrix is necessary for traditional hierarchical clustering algorithms which are often heuristic methods employed in biological sciences such as phylogeny reconstruction. When implementing any of the hierarchical clustering algorithms in data mining, the distance matrix will contain all pair-wise distances between every point and then will begin to create clusters between two different points or clusters based entirely on distances from the distance matrix.
If N be the number of points, the complexity of hierarchical clustering is:
Time complexity is due to the repetitive calculations done after every cluster to update the distance matrix
Space complexity is |
Distance matrix | Machine Learning | Machine Learning
Distance metrics are a key part of several machine learning algorithms, which are used in both supervised and unsupervised learning. They are generally used to calculate the similarity between data points: this is where the distance matrix is an essential element. The use of an effective distance matrix improves the performance of the machine learning model, whether it is for classification tasks or for clustering. |
Distance matrix | K-Nearest Neighbors | K-Nearest Neighbors
A distance matrix is utilized in the k-NN algorithm which is one of the slowest but simplest and most used instance-based machine learning algorithms that can be used both in classification and regression tasks. It is one of the slowest machine learning algorithms since each test sample's predicted result requires a fully computed distance matrix between the test sample and each training sample in the training set. Once the distance matrix is computed, the algorithm selects the K number of training samples that are the closest to the test sample to predict the test sample's result based on the selected set's majority (classification) or average (regression) value.
Prediction time complexity is , to compute the distance between each test sample with every training sample to construct the distance matrix where:
k = number of nearest neighbors selected
n = size of the training set
d = number of dimensions being used for the data
This classification focused model predicts the label of the target based on the distance matrix between the target and each of the training samples to determine the K-number of samples that are the closest/nearest to the target. |
Distance matrix | Computer Vision | Computer Vision
A distance matrix can be used in neural networks for 2D to 3D regression in image predicting machine learning models. |
Distance matrix | Information retrieval | Information retrieval |
Distance matrix | Distance matrices using Gaussian mixture distance | Distance matrices using Gaussian mixture distance
* Gaussian mixture distance for performing accurate nearest neighbor search for information retrieval. Under an established Gaussian finite mixture model for the distribution of the data in the database, the Gaussian mixture distance is formulated based on minimizing the Kullback-Leibler divergence between the distribution of the retrieval data and the data in database. In the comparison of performance of the Gaussian mixture distance with the well-known Euclidean and Mahalanobis distances based on a precision performance measurement, experimental results demonstrate that the Gaussian mixture distance function is superior in the others for different types of testing data.
Potential basic algorithms worth noting on the topic of information retrieval is Fish School Search algorithm an information retrieval that partakes in the act of using distance matrices in order for gathering collective behavior of fish schools. By using a feeding operator to update their weights
Eq. A:
Eq. B:
Stepvol defines the size of the maximum volume displacement preformed with the distance matrix, specifically using a Euclidean distance matrix. |
Distance matrix | Evaluation of the similarity or dissimilarity of Cosine similarity and Distance matrices | Evaluation of the similarity or dissimilarity of Cosine similarity and Distance matrices
none|thumb|Conversion formula between cosine similarity and Euclidean distance
* While the Cosine similarity measure is perhaps the most frequently applied proximity measure in information retrieval by measuring the angles between documents in the search space on the base of the cosine. Euclidean distance is invariant to mean-correction. The sampling distribution of a mean is generated by repeated sampling from the same population and recording of the sample means obtained. This forms a distribution of different means, and this distribution has its own mean and variance. For the data which can be negative as well as positive, the null distribution for cosine similarity is the distribution of the dot product of two independent random unit vectors. This distribution has a mean of zero and a variance of 1/n. While Euclidean distance will be invariant to this correction. |
Distance matrix | Clustering Documents | Clustering Documents
The implementation of hierarchical clustering with distance-based metrics to organize and group similar documents together will require the need and utilization of a distance matrix. The distance matrix will represent the degree of association that a document has with another document that will be used to create clusters of closely associated documents that will be utilized in retrieval methods of relevant documents for a user's query. |
Distance matrix | Isomap | Isomap
Isomap incorporates distance matrices to utilize geodesic distances to able to compute lower-dimensional embeddings. This helps to address a collection of documents that reside within a massive number of dimensions and empowers to perform document clustering. |
Distance matrix | Neighborhood Retrieval Visualizer (NeRV) | Neighborhood Retrieval Visualizer (NeRV)
An algorithm used for both unsupervised and supervised visualization that uses distance matrices to find similar data based on the similarities shown on a display/screen.
The distance matrix needed for Unsupervised NeRV can be computed through fixed input pairwise distances.
The distance matrix needed for Supervised NeRV requires formulating a supervised distance metric to be able to compute the distance of the input in a supervised manner. |
Distance matrix | Chemistry | Chemistry
The distance matrix is a mathematical object widely used in both graphical-theoretical (topological) and geometric (topographic) versions of chemistry. The distance matrix is used in chemistry in both explicit and implicit forms. |
Distance matrix | Interconversion mechanisms between two permutational isomers | Interconversion mechanisms between two permutational isomers
Distance matrices were used as the main approach to depict and reveal the shortest path sequence needed to determine the rearrangement between the two permutational isomers. |
Distance matrix | Distance Polynomials and Distance Spectra | Distance Polynomials and Distance Spectra
Explicit use of Distance matrices is required in order to construct the distance polynomials and distance spectra of molecular structures. |
Distance matrix | Structure-property model | Structure-property model
Implicit use of Distance matrices was applied through the use of the distance based metric Weiner number/Weiner Index which was formulated to represent the distances in all chemical structures. The Weiner number is equal to half-sum of the elements of the distance matrix.
thumb|Conversion formula between Weiner Number and Distance Matrix|none |
Distance matrix | Graph-theoretical Distance matrix | Graph-theoretical Distance matrix
Distance matrix in chemistry that are used for the 2-D realization of molecular graphs, which are used to illustrate the main foundational features of a molecule in a myriad of applications.
thumb|335x335px|Labeled tree representation of C6H14's carbon skeleton based on its distance matrix
Creating a label tree that represents the carbon skeleton of a molecule based on its distance matrix. The distance matrix is imperative in this application because similar molecules can have a myriad of label tree variants of their carbon skeleton. The labeled tree structure of hexane (C6H14) carbon skeleton that is created based on the distance matrix in the example, has different carbon skeleton variants that affect both the distance matrix and the labeled tree
Creating a labeled graph with edge weights, used in chemical graph theory, that represent molecules with hetero-atoms.
Le Verrier-Fadeev-Frame (LVFF) method is a computer oriented used to speed up the process of detecting the graph center in polycyclic graphs. However, LVFF requires the input to be a diagonalized distance matrix which is easily resolved by implementing the Householder tridiagonal-QL algorithm that takes in a distance matrix and returns the diagonalized distance needed for the LVFF method. |
Distance matrix | Geometric-Distance Matrix | Geometric-Distance Matrix
thumb|338x338px|Geometric distance matrix for 2,4-dimethylhexane
While the graph-theoretical distance matrix 2-D captures the constitutional features of the molecule, its three-dimensional (3D) character is encoded in the geometric-distance matrix. The geometric-distance matrix is a different type of distance matrix that is based on the graph-theoretical distance matrix of a molecule to represent and graph the 3-D molecule structure. The geometric-distance matrix of a molecular structure is a real symmetric matrix defined in the same way as a 2-D matrix. However, the matrix elements will hold a collection of shortest Cartesian distances between and in . Also known as topographic matrix, the geometric-distance matrix can be constructed from the known geometry of the molecule. As an example, the geometric-distance matrix of the carbon skeleton of 2,4-dimethylhexane is shown below: |
Distance matrix | Other Applications | Other Applications |
Distance matrix | Time Series Analysis | Time Series Analysis
Dynamic Time Warping distance matrices are utilized with the clustering and classification algorithms of a collection/group of time series objects. |
Distance matrix | Examples | Examples
For example, suppose these data are to be analyzed, where pixel Euclidean distance is the distance metric.
frame|none|Raw data
The distance matrix would be:
a b c d e f a 0 184 222 177 216 231 b 184 0 45 123 128 200 c 222 45 0 129 121 203 d 177 123 129 0 46 83 e 216 128 121 46 0 83 f 231 200 203 83 83 0
These data can then be viewed in graphic form as a heat map. In this image, black denotes a distance of 0 and white is maximal distance.
frame|none|Graphical View |
Distance matrix | See also | See also
Computer vision
Data clustering
Distance set
Hollow matrix
Min-plus matrix multiplication |
Distance matrix | References | References
Category:Metric geometry
Category:Bioinformatics
Category:Matrices (mathematics)
Category:Graph distance |
Distance matrix | Table of Content | Short description, Non-metric distance matrix, Metric distance matrix, Additive distance matrix, Ultrametric distance matrix, Bioinformatics, [[Sequence alignment]], Global alignment, Local alignment, Multiple sequence alignment, MAFFT, Phylogenetic analysis, Distance matrix methods, Additive tree reconstruction, UPGMA, Neighbor joining, Fitch–Margoliash, Data Mining and Machine Learning, Data Mining, Hierarchical clustering, Machine Learning, K-Nearest Neighbors, Computer Vision, Information retrieval, Distance matrices using Gaussian mixture distance, Evaluation of the similarity or dissimilarity of Cosine similarity and Distance matrices, Clustering Documents, Isomap, Neighborhood Retrieval Visualizer (NeRV), Chemistry, Interconversion mechanisms between two permutational isomers, Distance Polynomials and Distance Spectra, Structure-property model, Graph-theoretical Distance matrix, Geometric-Distance Matrix, Other Applications, Time Series Analysis, Examples, See also, References |
Wikipedia:MediaWiki order of page names | redirect | Unicode
The MediaWiki software uses Unicode alphabetical order when ordering names of articles (and other pages), such as seen when presenting alphabetized lists of articles on Category pages. Unicode alphabetical order is different from standard English alphabetization. The part of Unicode alphabetical order which concerns us is:
Note in particular that capital "Z" comes before lowercase "a", and "z" before "é". A blank space within a page name is treated as an underscore, and therefore comes after the capitals, and before the lower case letters. Sometimes a special character looks to the reader like a standard English-language Latin character, but has a special code anyway.
Since capital letters come before lower case letters, an article named "Happiness McGee" will come before "Happiness algorithm". They will both come before "Ātman (Hinduism)" which in turn will come before "δ-opioid receptor", unless ordering is altered by within the article (which is commonly done for articles which start with diacritic-added Latin characters (e.g. "Ā") but not for non-Latin characters (e.g. "δ")).
Controlling with DEFAULTSORT
Editors can control the alphabetical order of articles as they appear on Category pages (only) with the template. Thus, for an article named "¡Basta Ya!", writing {{DEFAULTSORT:Basta Ya}} inside the article will cause it to be alphabetically ordered with "B" articles on Category pages. (But the actual native name ("¡Basta Ya!") will appear to the reader on the Category page, even though grouped in with the "B"s, as DEFAULTSORT cannot control that). Similarly, {{DEFAULTSORT:Atman (Hinduism)}} will cause the article "Ātman (Hinduism)" to be ordered under "A", and so forth.
( does not affect alphabetical ordering except on Category pages. Thus, on Special:Allpages, which displays all articles but is not a Category page, "¡Basta Ya!" will still be ordered as beginning with "¡"; and any other alphabetical ordering done by the MediaWiki software will also be done in Unicode order.)
Articles beginning with a lowercase letter
The actual native names of articles cannot begin with a lowercase letter. The template is written inside articles such as "eBay" to make the article title to appear to the reader as starting with a lowercase "e". But the article will be ordered on Category pages under "E", and will appear to the reader on Category pages under its true native name as "EBay". (:Category:People with lower case names and pseudonyms has many examples.)
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Wikipedia:MediaWiki order of page names | Table of Content | redirect, See also, Notes |
Marian Otis Chandler | Short description | Marian Otis Chandler (July 1, 1866 – August 9, 1952) was the secretary and a director of the Times-Mirror Company, which published the Los Angeles Times. |
Marian Otis Chandler | Biography | Biography
She was born as Emma Marian Otis July 1, 1866, in Marietta, Ohio,"Private Funeral Set Today for Mrs. Harry Chandler," Los Angeles Times, August 11, 1952, page A-1 A library card is required to access this link. to Harrison Gray Otis (publisher) and Eliza Ann Wetherby. Marian had three sisters, Mabel, Lilian, and Esther (who died in infancy),{The Otis Family in America} and a brother, Harrison Gray (who died in infancy).{}
In 1894, Marian married Harry Chandler, who later became publisher of the Los Angeles Times. Marian and Harry raised eight children together, two from Harry's first marriage, and six of their own.Gwendolyn Garland Babcock, The Ancestry of Harry Chandler, http://www.babcockancestry.com/books/chandler/003harrychandler.shtml Norman Chandler (1899–1973), became publisher of the Times after his father's death.
After the death of her husband in 1944, Mrs. Chandler resigned as secretary; a month later she was elected chairman of the Times-Mirror board. She also was vice president of the Chandis Securities Company and vice-president of the Southwest Land Company and the Southwest Company. She was known for her numerous philanthropies.
She died on August 9, 1952, at her home in the Los Feliz foothills, Los Angeles, California., owned many years later by Father Yod. She was buried at Hollywood Forever Cemetery in Hollywood, California. She left seven children — Mrs. Roger Goodan (Alice May), Mrs. Earle E. Crowe (Constance), Mrs. John J. Garland (Helen), Mrs. James G. Boswell (Ruth), Norman Chandler. Philip Chandler and Harrison Chandler, as well as a sister, Mabel Otis Booth. |
Marian Otis Chandler | Legacy | Legacy
The community of Reseda, California, was originally named Marian, after Mrs. Chandler.Margaret Leslie Davis, Rivers in the Desert, Google e-book, page 91
A freighter ship built in 1917 (originally named War Flame but known as Empire Leopard when torpedoed and sunk November 2, 1942, by the German submarine U-402) was bought in 1929 by the Los Angeles Steamship Company and renamed Marian Otis Chandler, holding that name until it was sold again in 1939."Empire Leopard," uboat.net |
Marian Otis Chandler | References | References
Category:1866 births
Category:1952 deaths
Category:History of Los Angeles
Category:Los Angeles Times people
Category:Otis family
Category:People from San Marino, California
Category:Chandler family (publishing)
Category:People from Marietta, Ohio
Category:Burials at Hollywood Forever Cemetery |
Marian Otis Chandler | Table of Content | Short description, Biography, Legacy, References |
Universidad Autónoma Metropolitana | cleanup | The Metropolitan Autonomous University (Spanish: Universidad Autónoma Metropolitana) also known as UAM, is a Mexican public research university. Founded in 1974 with the support of then-President Luis Echeverria Alvarez, the institution aims to be closely linked to the social and human environment.
As an autonomous university, UAM is a public agency of the Mexican government.Article 3 of the Organic Law of the UAM It has five academic units located in Mexico City and Greater Mexico City: Azcapotzalco, in north, Iztapalapa, in east, Cuajimalpa, in west, Xochimilco, in south, and Lerma in State of Mexico.),
The institution is among the top academic universities in Mexico. In 2019, it ranked first among both public and private institution, was second in the number of full-time research professors with doctorates, according to the Comparative Study of Mexican Universities (Estudio Comparativo de Universidades Mexicanas); having the second largest number of built in National and Research System (Sistema Nacional de Investigadores) the second in having researchers at Level 3 of the same researchers. One of the leading universities in Mexico to submit the highest number of research. And the second institution to have publications in refereed journals, such as the Institute for Scientific Information, Latindex and journals included in the Index of Mexican Journals of Scientific and Technological Research of the National Council of Science and Technology (Consejo Nacional de Ciencia y Tecnología) and the second to have magazines within the Conacyt, it is also among the top four with the largest number of patents granted in Mexico. |
Universidad Autónoma Metropolitana | History | History
After the historic 1968 Tlatelolco massacre in Mexico concluded, and after other subsequent movements in favor of education and claim social improvements, the need for comprehensive education reform in Mexico was evident.
In 1973, during the administration of President Luis Echeverria Alvarez, the National Association of Universities and Institutions of Higher Education (ANUIES), he presented a document to the President noting the need to establish a new university in the metropolitan area Mexico City, taking into consideration issues such as increasing student demand and the increasing failure of existing universities to admit more students.
It then proposed that the nascent university project also constituted an opportunity to modernize higher education in the country. The expected characteristics of the new university were it to be public, metropolitan, independent, innovative addition to its educational and organizational terms. It is under such expectations that the law comes into force for the creation of the Autonomous Metropolitan University, on 1 January 1974. On 10 January of that year, he was appointed first Rector of the UAM architect Pedro Ramirez Vazquez.
The University is established since its creation three units, which are located in Iztapalapa, Azcapotzalco and Xochimilco, with the idea of promoting decentralization and allow the full development of each. Empirically, scientific research in Iztapalapa (UAM-I) Unit is located, the traditional careers such as civil engineering and architecture at the Azcapotzalco (UAM-A) unit and the area of health in the Xochimilco Unit (UAM -X). Subsequently, it was decided that their internal organization would be composed of divisions and academic departments, creating a contrast with the Schools and Colleges of existing universities. Each division would group different areas of knowledge and related disciplines each department, in order to give a flexible structure that prevents the lag that education has suffered in relation to the progress of science. The first Rector of the Iztapalapa Unit was Dr. Alonso Fernandez González and began operations on September 30, 1974.
In turn, the Rector of the Azcapotzalco Unit was Dr. Juan Casillas García de León, which opened its doors 11 November 1974. For the Xochimilco was Rector Dr. Ramon Villarreal Perez, initiating teaching also the November 11, 1974. Recently, the possibility of creating a new unit of the UAM was analyzed. The April 26, 2005 the Academic College of the institution approved the creation of the Cuajimalpa campus, appointing in June of that year Dr. Fresán Magdalena Orozco as the first Rector. The activities of the Unit officially pulled the September 14, 2005, using different locations, at first at the Universidad Iberoamericana. Then he took three temporary facilities: Baja California, Artifice and Constituents Constituents adding a fourth in 647. His final location was achieved in 2014 on the grounds of "Scorpio", ancient land of the Zona Especial Forestal y de Repoblación Bosques Industriales La Venta, which had not been built by the lack of legal certainty and the opposition of local people to Ocotal forest development in the sale of Cuajimalpa.:en:UAM Cuajimalpa (Universidad Autónoma Metropolitana) |
Universidad Autónoma Metropolitana | Campuses | Campuses
The university system has 5 units, with campuses located in different boroughs of Mexico City and in the adjacent state:
UAM Azcapotzalco — located in Azcapotzalco, northern Mexico City
UAM Iztapalapa — located in Iztapalapa, eastern Mexico City
UAM Cuajimalpa — located in Cuajimalpa, western Mexico City
UAM Xochimilco — located in Coyoacán, southern Mexico City.
UAM Lerma — located near Toluca, in the State of Mexico. |
Universidad Autónoma Metropolitana | Main office | Main office
UAM Rectoría is the main office of the university system. It is located in the south of the city, on Canal de Miramontes street near Xochimilco. |
Universidad Autónoma Metropolitana | Gallery | Gallery |
Universidad Autónoma Metropolitana | See also | See also |
Universidad Autónoma Metropolitana | Notes | Notes |
Universidad Autónoma Metropolitana | References | References
Category:Public universities and colleges in Mexico
Category:Universities and colleges established in 1974
Category:1974 establishments in Mexico
Category:Universities in Mexico City |
Universidad Autónoma Metropolitana | Table of Content | cleanup, History, Campuses, Main office, Gallery, See also, Notes, References |
TCP Wrappers | Short description | __NOTOC__
TCP Wrappers (also known as tcp_wrappers) is a host-based networking ACL system, used to filter network access to Internet Protocol servers on (Unix-like) operating systems such as Linux or BSD. It allows host or subnetwork IP addresses, names and/or ident query replies, to be used as tokens on which to filter for access control purposes.
The original code was written by Wietse Venema in 1990 to monitor a cracker's activities on the Unix workstations at the Department of Math and Computer Science at the Eindhoven University of Technology.TCP WRAPPER - Network monitoring, access control, and booby traps. by Wietse Venema (USENIX UNIX Security Symposium III, 1992) He maintained it until 1995, and on June 1, 2001, released it under its own BSD-style license.
The tarball includes a library named libwrap that implements the actual functionality. Initially, only services that were spawned for each connection from a super-server (such as inetd) got wrapped, utilizing the tcpd program. However most common network service daemons today can be linked against libwrap directly. This is used by daemons that operate without being spawned from a super-server, or when a single process handles multiple connections. Otherwise, only the first connection attempt would get checked against its ACLs.
When compared to host access control directives often found in daemons' configuration files, TCP Wrappers have the benefit of runtime ACL reconfiguration (i.e., services don't have to be reloaded or restarted) and a generic approach to network administration.
This makes it easy to use for anti-worm scripts, such as DenyHosts or Fail2ban, to add and expire client-blocking rules, when excessive connections and/or many failed login attempts are encountered.
While originally written to protect TCP and UDP accepting services, examples of usage to filter on certain ICMP packets exist too, such as 'pingd' – the userspace ping request responder.GNU/Linux Ping Daemon by route|daemon9 - Phrack Magazine Volume 8, Issue 52 January 26, 1998, article 07 |
TCP Wrappers | 1999 Trojan | 1999 Trojan
In January 1999, the distribution package at Eindhoven University of Technology (the primary distribution site until that day) was replaced by a modified version. The replacement contained a trojaned version of the software that would allow the intruder access to any server that it was installed on. The author spotted this within hours, upon which he relocated the primary distribution to his personal site.backdoored tcp wrapper source code, by Wietse Venema, on Bugtraq, Jan 21, 1999Announcement: Wietse's FTP site has moved, by Wietse Venema, on Bugtraq, Jan 21, 1999 |
TCP Wrappers | See also | See also
DNS-based blackhole list
Forward-confirmed reverse DNS
Firewall
IP blocking
Nullroute |
TCP Wrappers | References | References |
TCP Wrappers | External links | External links
TCP Wrappers source code
Softpanorama TCP Wrappers Information
Category:Unix network-related software
Category:BSD software
Category:Free security software
Wrapper
Category:Internet Protocol based network software |
TCP Wrappers | Table of Content | Short description, 1999 Trojan, See also, References, External links |
Betrayal at Krondor | Short description | Betrayal at Krondor is an MS-DOS-based role-playing video game developed by Dynamix and released by Sierra On-Line in the summer of 1993. Betrayal at Krondor takes place largely in Midkemia, the fantasy world developed by Raymond E. Feist in his Riftwar novels. The game is designed to resemble a book, separated into chapters and narrated in the third-person with a quick-save bookmark feature.
Although neither the dialog nor narrative were written by Feist himself, the game is considered canon, having been novelized as Krondor: The Betrayal five years later. Events in the game were also written into the Riftwar novels.
PyroTechnix completed a sequel, Return to Krondor, which was released by Sierra in 1998. Its protracted development experienced considerable delay, and the finished product was not nearly as warmly received as Betrayal.
GOG.com released an emulated version of Betrayal at Krondor for Microsoft Windows in 2010. |
Betrayal at Krondor | Gameplay | Gameplay
right|thumb|300px|The main interface of Betrayal at Krondor. The party is travelling east along a road.
Gameplay occurs mainly from a first-person perspective while traveling the overworld, dungeons, and caves, but switches to a third-person view during combat. The user interface is mouse-driven, with keyboard hotkeys for each action.
The game has two possible views, the 3D first-person view and the 2D top-down map view, where the player is represented with a triangular marker. The overworld is completely mapped, but other locations are automatically mapped in the top-down view as the player explores them. The player can also view the full map of Midkemia and see their location.
Each chapter's main plot usually takes place completely within one or two regions of the game world. However, the player is given enormous freedom to explore the world however they wish, with ample opportunity to perform optional sub-quests and enhance their characters' abilities, gain cash, upgrade weapons and armor, and so on. Only certain locations are accessible in each chapter, though the player is free to explore anywhere within those boundaries as well as take their time performing quests. While traveling, the party camps in the wilderness to rest and recover lost health and stamina, provided that there are no enemies in the vicinity. |
Betrayal at Krondor | Moredhel wordlock chests | Moredhel wordlock chests
One of the game's unique features is the large assortment of Moredhel wordlock chests, hidden throughout the land. These chests have combination locks with letters on each dial, and a riddle written upon them whose answer opens the chest. Wordlock chests can hold valuable items and equipment, as well as quest items essential to completing the game. If no member of the player's party can read Moredhel, the writing on the chest will appear untranslated, although it can still be opened by a brute force method of systematically cycling through each tumbler. The Moredhel alphabet is a character substitution of the A-Z alphabet. Gorath, a Moredhel, can read it, and casting the Union spell allows Patrus to do so for short periods of time. Wordlock chests containing essential quest items are relatively easy to find, either located near roads or their locations being disclosed through interaction with NPCs. However, "bonus" wordlock chests are typically hidden in areas where the player is not likely to travel unless they are assiduously searching for treasure. |
Betrayal at Krondor | Plot and dialogue | Plot and dialogue
The storyline is advanced primarily through literary cutscenes. Each chapter begins and ends with a cutscene, consisting of text, dialogue, and animations. The player meets various NPCs during their travels. Dialogue is text-based and some NPCs have their own pictures as well. Conversation is tree-based: in some cases, the player can choose between various dialogue keywords. This is used to get information, training, and items, sometimes for a price. |
Betrayal at Krondor | RPG system and player character development | RPG system and player character development
right|thumb|300px|Character sheet. Two skills - melee and crossbow accuracy - are emphasized, as indicated by the red pommels of the swords.
There are two or three characters in the adventuring party at any time. While the player meets various non-human characters during the game (including dwarves, elves, goblins, and dragons), five of the six player characters are human. The exception is Gorath, a dark elf. There are two classes of characters: fighters (Locklear, James, and Gorath) and magicians (Pug, Owyn, and Patrus). Fighters use swords and crossbows, while magicians use a staff. The only long-range attacks magicians are capable of are magic spells.
The character system is unique. The main character attributes are health, stamina, speed, and strength. Speed determines how many combat grid squares the character can move. Strength influences the amount of damage the character inflicts in mêlée combat. Spell-casting, swinging one's weapon, and combat damage first use up stamina. Once stamina is depleted, you begin to eat into health. Once it decreases, the character's skills (such as the crucial trait of weapon accuracy), as well as their movement speed, are adversely impacted.
In addition to attributes, each character has a set of skills expressed as percentages. Skills can be emphasized, causing them to improve faster, while de-emphasized skills improve more slowly. Unlike many other role-playing games, skills are improved by using them rather than through a leveling up system. For example, fixing weapons will improve the weaponcraft skill, which in turn will make the character more effective at fixing weapons in the future. Skills include defense, crossbow accuracy, mêlée weapon accuracy, spell-casting accuracy, enemy assessment, weapon and armor repair, barding, haggling, lockpicking, scouting for ambushes, and stealth. Some NPCs offer training in skills and items can increase skills permanently or temporarily.
Characters can acquire various status effects that affect their health or skills. Characters whose health drops to zero in combat are knocked out and acquire "near death" status, making them ineffective in combat, and their health recovery rate drops virtually to nil. If the health of the entire party drops to zero, the game will end. Improved healing rate is handled as a status effect as well, as are poisoning, drunkenness, sickness (from eating spoiled rations), and plague, which is an extremely serious condition that can reduce the party to moribund husks incapable of any productive action. |
Betrayal at Krondor | Magic | Magic
Spells are organized into six groups, grouped by magic symbol. Four groups of spells are combat spells and two groups are non-combat spells.
Spells first drain the caster's stamina and then health. Some spells have variable strength; the player can choose how much energy the spell consumes. Some combat spells also require that the target being within line of sight of the caster.
Spells are learned from scrolls that are found in caches or on enemies and can be bought from shops or NPCs throughout the world. |
Betrayal at Krondor | Items and inventory | Items and inventory
right|thumb|200px|Inventory screen.
The game features a wide variety of items, including equipment, food, treasure, and magical artifacts. Each item also has detailed background information available by right-clicking it.
The inventory management allows transferring items between the party characters. In the case of stacks of multiple items, there's also an option to distribute them evenly within the party. The game also manages money and keys independently.
Each weapon and type of armor has modifiers affecting its combat effectiveness, such as accuracy, damage, blessing, and racial modifiers. After combat, most weapons and armor must be kept in shape with a whetstone or armorer's hammer respectively. There are also items that enhance weapons and armor, such as the poisonous silverthorn or fiery naphtha.
Player characters must carry and eat rations every day or their health starts dropping. Rations are sold in taverns and can be found on enemies and in caches. Rations can also be poisoned or spoiled and will sicken characters if they eat it, adversely affecting their health, although careful inspection of all packages of questionable provenance will avoid this possibility. |
Betrayal at Krondor | Combat | Combat
left|thumb|300px|A battle scene. Gorath, marked with a green square on the grid, is having his turn; hovering mouse over the target marks it with a yellow square, and shows attack damage estimate on the bottom of the screen.
Combat is turn-based and takes place on a grid, similar to tactical role-playing games. The characters can move to a different location on the grid and if they can reach an enemy, can attack in the same move. There are two options for attacking: a thrust and a swing. The thrust is the default attack used when moving to attack an enemy. The swing often does more damage but is less accurate and uses up one point of health/stamina. Fighters can use crossbows and magicians can cast spells, but only if there are no enemy units in adjacent squares. The player can also rest, which regains health and stamina, defend against enemy mêlée attacks, or assess an enemy's capabilities.
Injured enemies may try to flee from combat, and will escape entirely and permanently from the player's grasp, unless they are killed or otherwise prevented them from reaching the upper edge of the battlefield. Defeated enemies remain on the ground after a battle, allowing the player to loot their remains. Salvaged items of little use to the party may be sold at shops in order to boost cash reserves.
The combat interface is also used to solve magical traps. Traps involve various types of hazards, such as fireball blasters and laser crystals, and the player either has to disable them using the objects provided or otherwise navigate through the trap and reach the top of the combat field.
Although the game uses a GUI, many actions can be performed using keys as well. There is a glitch (or intended hidden feature) that allows the player to make certain combinations of two moves in a single turn—one using the mouse and another using the keyboard—or rest twice by pressing 'R' before the turn begins and holding it through the turn. Computer opponents also seem to use this in some instances (like moving and defending in the same turn).
Another type of character-environment interaction, that could be considered a trap or a bonus, are the graveyards scattered around the landscape. The player is able to read the inscriptions on the gravestones (usually in the form of a short poetic eulogy), and then decide to dig up the grave (if someone has a shovel). Some graves reveal items and/or money, while others summon a ghost (in some cases, multiple ghosts), which must be fought using the standard combat interface. |
Betrayal at Krondor | Temples, stores and inns | Temples, stores and inns
Temples offer a variety of services including healing and blessing equipment. They also sell a relatively expensive teleportation service; the player is able to teleport between any temples they have already visited, with the price based on the distance traveled. This operates on the principle that a person visiting a new temple will memorize a unique pattern upon the wall, and by recalling this pattern at a different temple, be transported to the first temple with the aid of a priest.
Stores buy and sell various kinds of items; some also repair equipment. Inns and taverns allow characters to buy food and alcohol, gain information on local happenings, gamble (in some inns), talk to some NPCs, earn money by playing the lute, and sleep, which allows full healing of wounds and fatigue, whereas resting in the wilderness only restores 80% of health and stamina. |
Betrayal at Krondor | Characters | Characters |
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