The Dodo is an American online publisher focused on animals. The website was launched in January 2014 by Izzie Lerer, the daughter of media executive Kenneth Lerer, and journalist Kerry Lauerman. The Dodo has become one of the most popular Facebook publishers, garnering 1 billion video views from the social network in November 2015. The Dodo is headquartered in New York, New York. == History == The company—named after the first recorded species that humans drove to extinction—was founded by Lerer out of "a personal passion for the subject manner". Lerer has a PhD in animal studies with a focus on animal ethics and human relationships from Columbia University, launching the website after noticing the viral success of animal videos online but seeing no one "really owned the space." The Dodo's editorial and video production staff unionized with the Writers Guild of America, East in April 2018.
Caffe (software)
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. It is written in C++, with a Python interface. == History == Yangqing Jia created the Caffe project during his PhD at UC Berkeley, while working the lab of Trevor Darrell. The first version, called "DeCAF", made its first appearance in Spring 2013 when it was used for the ILSVRC challenge (later called ImageNet). The library was named Caffe and released to the public in December 2013. It reached end-of-support in 2018. It is hosted on GitHub. == Features == Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation. It supports CNN, RCNN, LSTM and fully-connected neural network designs. Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL. == Applications == Caffe is being used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Yahoo! has also integrated Caffe with Apache Spark to create CaffeOnSpark, a distributed deep learning framework. == Caffe2 == In April 2017, Facebook announced Caffe2, which included new features such as recurrent neural network (RNN). At the end of March 2018, Caffe2 was merged into PyTorch.
Mutation (evolutionary algorithm)
Mutation is a genetic operator used to maintain genetic diversity of the chromosomes of a population of an evolutionary algorithm (EA), including genetic algorithms in particular. It is analogous to biological mutation. The classic example of a mutation operator of a binary coded genetic algorithm (GA) involves a probability that an arbitrary bit in a genetic sequence will be flipped from its original state. A common method of implementing the mutation operator involves generating a random variable for each bit in a sequence. This random variable tells whether or not a particular bit will be flipped. This mutation procedure, based on the biological point mutation, is called single point mutation. Other types of mutation operators are commonly used for representations other than binary, such as floating-point encodings or representations for combinatorial problems. The purpose of mutation in EAs is to introduce diversity into the sampled population. Mutation operators are used in an attempt to avoid local minima by preventing the population of chromosomes from becoming too similar to each other, thus slowing or even stopping convergence to the global optimum. This reasoning also leads most EAs to avoid only taking the fittest of the population in generating the next generation, but rather selecting a random (or semi-random) set with a weighting toward those that are fitter. The following requirements apply to all mutation operators used in an EA: every point in the search space must be reachable by one or more mutations. there must be no preference for parts or directions in the search space (no drift). small mutations should be more probable than large ones. For different genome types, different mutation types are suitable. Some mutations are Gaussian, Uniform, Zigzag, Scramble, Insertion, Inversion, Swap, and so on. An overview and more operators than those presented below can be found in the introductory book by Eiben and Smith or in. == Bit string mutation == The mutation of bit strings ensue through bit flips at random positions. Example: The probability of a mutation of a bit is 1 l {\displaystyle {\frac {1}{l}}} , where l {\displaystyle l} is the length of the binary vector. Thus, a mutation rate of 1 {\displaystyle 1} per mutation and individual selected for mutation is reached. == Mutation of real numbers == Many EAs, such as the evolution strategy or the real-coded genetic algorithms, work with real numbers instead of bit strings. This is due to the good experiences that have been made with this type of coding. The value of a real-valued gene can either be changed or redetermined. A mutation that implements the latter should only ever be used in conjunction with the value-changing mutations and then only with comparatively low probability, as it can lead to large changes. In practical applications, the respective value range of the decision variables to be changed of the optimisation problem to be solved is usually limited. Accordingly, the values of the associated genes are each restricted to an interval [ x min , x max ] {\displaystyle [x_{\min },x_{\max }]} . Mutations may or may not take these restrictions into account. In the latter case, suitable post-treatment is then required as described below. === Mutation without consideration of restrictions === A real number x {\displaystyle x} can be mutated using normal distribution N ( 0 , σ ) {\displaystyle {\mathcal {N}}(0,\sigma )} by adding the generated random value to the old value of the gene, resulting in the mutated value x ′ {\displaystyle x'} : x ′ = x + N ( 0 , σ ) {\displaystyle x'=x+{\mathcal {N}}(0,\sigma )} In the case of genes with a restricted range of values, it is a good idea to choose the step size of the mutation σ {\displaystyle \sigma } so that it reasonably fits the range [ x min , x max ] {\displaystyle [x_{\min },x_{\max }]} of the gene to be changed, e.g.: σ = x max − x min 6 {\displaystyle \sigma ={\frac {x_{\text{max}}-x_{\text{min}}}{6}}} The step size can also be adjusted to the smaller permissible change range depending on the current value. In any case, however, it is likely that the new value x ′ {\displaystyle x'} of the gene will be outside the permissible range of values. Such a case must be considered a lethal mutation, since the obvious repair by using the respective violated limit as the new value of the gene would lead to a drift. This is because the limit value would then be selected with the entire probability of the values beyond the limit of the value range. The evolution strategy works with real numbers and mutation based on normal distribution. The step sizes are part of the chromosome and are subject to evolution together with the actual decision variables. === Mutation with consideration of restrictions === One possible form of changing the value of a gene while taking its value range [ x min , x max ] {\displaystyle [x_{\min },x_{\max }]} into account is the mutation relative parameter change of the evolutionary algorithm GLEAM (General Learning Evolutionary Algorithm and Method), in which, as with the mutation presented earlier, small changes are more likely than large ones. First, an equally distributed decision is made as to whether the current value x {\displaystyle x} should be increased or decreased and then the corresponding total change interval is determined. Without loss of generality, an increase is assumed for the explanation and the total change interval is then [ x , x max ] {\displaystyle [x,x_{\max }]} . It is divided into k {\displaystyle k} sub-areas of equal size with the width δ {\displaystyle \delta } , from which k {\displaystyle k} sub-change intervals of different size are formed: i {\displaystyle i} -th sub-change interval: [ x , x + δ ⋅ i ] {\displaystyle [x,x+\delta \cdot i]} with δ = ( x max − x ) k {\displaystyle \delta ={\frac {(x_{\text{max}}-x)}{k}}} and i = 1 , … , k {\displaystyle i=1,\dots ,k} Subsequently, one of the k {\displaystyle k} sub-change intervals is selected in equal distribution and a random number, also equally distributed, is drawn from it as the new value x ′ {\displaystyle x'} of the gene. The resulting summed probabilities of the sub-change intervals result in the probability distribution of the k {\displaystyle k} sub-areas shown in the adjacent figure for the exemplary case of k = 10 {\displaystyle k=10} . This is not a normal distribution as before, but this distribution also clearly favours small changes over larger ones. This mutation for larger values of k {\displaystyle k} , such as 10, is less well suited for tasks where the optimum lies on one of the value range boundaries. This can be remedied by significantly reducing k {\displaystyle k} when a gene value approaches its limits very closely. === Common properties === For both mutation operators for real-valued numbers, the probability of an increase and decrease is independent of the current value and is 50% in each case. In addition, small changes are considerably more likely than large ones. For mixed-integer optimization problems, rounding is usually used. == Mutation of permutations == Mutations of permutations are specially designed for genomes that are themselves permutations of a set. These are often used to solve combinatorial tasks. In the two mutations presented, parts of the genome are rotated or inverted. === Rotation to the right === The presentation of the procedure is illustrated by an example on the right: === Inversion === The presentation of the procedure is illustrated by an example on the right: === Variants with preference for smaller changes === The requirement raised at the beginning for mutations, according to which small changes should be more probable than large ones, is only inadequately fulfilled by the two permutation mutations presented, since the lengths of the partial lists and the number of shift positions are determined in an equally distributed manner. However, the longer the partial list and the shift, the greater the change in gene order. This can be remedied by the following modifications. The end index j {\displaystyle j} of the partial lists is determined as the distance d {\displaystyle d} to the start index i {\displaystyle i} : j = ( i + d ) mod | P 0 | {\displaystyle j=(i+d){\bmod {\left|P_{0}\right|}}} where d {\displaystyle d} is determined randomly according to one of the two procedures for the mutation of real numbers from the interval [ 0 , | P 0 | − 1 ] {\displaystyle \left[0,\left|P_{0}\right|-1\right]} and rounded. For the rotation, k {\displaystyle k} is determined similarly to the distance d {\displaystyle d} , but the value 0 {\displaystyle 0} is forbidden. For the inversion, note that i ≠ j {\displaystyle i\neq j} must hold, so for d {\displaystyle d} the value 0 {\displaystyle 0} must be excluded.
Extremal Ensemble Learning
Extremal Ensemble Learning (EEL) is a machine learning algorithmic paradigm for graph partitioning. EEL creates an ensemble of partitions and then uses information contained in the ensemble to find new and improved partitions. The ensemble evolves and learns how to form improved partitions through extremal updating procedure. The final solution is found by achieving consensus among its member partitions about what the optimal partition is. == Reduced-Network Extremal Ensemble Learning (RenEEL) == A particular implementation of the EEL paradigm is the Reduced-Network Extremal Ensemble Learning (RenEEL) scheme for partitioning a graph. RenEEL uses consensus across many partitions in an ensemble to create a reduced network that can be efficiently analyzed to find more accurate partitions. These better quality partitions are subsequently used to update the ensemble. An algorithm that utilizes the RenEEL scheme is currently the best algorithm for finding the graph partition with maximum modularity, which is an NP-hard problem.
Nearest centroid classifier
In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation. When applied to text classification using word vectors containing tfidf weights to represent documents, the nearest centroid classifier is known as the Rocchio classifier because of its similarity to the Rocchio algorithm for relevance feedback. An extended version of the nearest centroid classifier has found applications in the medical domain, specifically classification of tumors. == Algorithm == === Training === Given labeled training samples { ( x → 1 , y 1 ) , … , ( x → n , y n ) } {\displaystyle \textstyle \{({\vec {x}}_{1},y_{1}),\dots ,({\vec {x}}_{n},y_{n})\}} with class labels y i ∈ Y {\displaystyle y_{i}\in \mathbf {Y} } , compute the per-class centroids μ → ℓ = 1 | C ℓ | ∑ i ∈ C ℓ x → i {\displaystyle \textstyle {\vec {\mu }}_{\ell }={\frac {1}{|C_{\ell }|}}{\underset {i\in C_{\ell }}{\sum }}{\vec {x}}_{i}} where C ℓ {\displaystyle C_{\ell }} is the set of indices of samples belonging to class ℓ ∈ Y {\displaystyle \ell \in \mathbf {Y} } . === Prediction === The class assigned to an observation x → {\displaystyle {\vec {x}}} is y ^ = arg min ℓ ∈ Y ‖ μ → ℓ − x → ‖ {\displaystyle {\hat {y}}={\arg \min }_{\ell \in \mathbf {Y} }\|{\vec {\mu }}_{\ell }-{\vec {x}}\|} .
Common Image Generator Interface
The Common Image Generator Interface (CIGI) (pronounced sig-ee), is an on-the-wire data protocol that allows communication between an Image Generator and its host simulation. The interface is designed to promote a standard way for a host device to communicate with an image generator (IG) within the industry. CIGI enables plug-and-play by standard-compliant image generator vendors and reduces integration costs when upgrading visual systems. == Background == Most high-end simulators do not have everything running on a single machine the way popular home software flight simulators are currently implemented. The airplane model is run on one machine, normally referred to as the host, and the out the window visuals or scene graph program is run on another, usually referred to as an Image Generator (IG). Frequently there are multiple IGs required to display the surrounding environment created by a host. CIGI is the interface between the 'host' and the IGs. The main goal of CIGI is to capitalize on previous investments through the use of a common interface. CIGI is designed to assist suppliers and integrators of IG systems with ease of integration, code reuse, and overall cost reduction. In the past most image generators provided their own proprietary interface; every host had to implement that interface making changing image generators a costly ordeal. CIGI was created to standardize the interface between the host and the image generator so that little modification would be needed to switch image generators. The CIGI initiative was largely spearheaded by The Boeing Company during the early 21st century. The latest version of CIGI (CIGI 4.0) was developed by the Simulation Interoperability Standards Organization (SISO) in the form of SISO-STD-013-2014, Standard for Common Image Generator Interface (CIGI), Version 4.0, dated 22 August 2014. SISO-STD-013-2014 is freely available from SISO. == Definitions == Image generator – In this context an image generator consists of one or more rendering channels that produce an image that can be used to visualize an “Out-The-Window” scene, or images produced by various sensor simulations such as Infra-red, Day TV, electro-optical, and night vision. Host simulation – In this context a “Host” is the computational system that provides information about the device being simulated so that the image generator can portray the correct scenery to the user. This information is passed via CIGI to the image generator. == Maturation == CIGI 4 is the latest version of the standard as was approved by the Simulation Interoperability Standards Organization on August 22, 2014. CIGI became an international SISO standard known as SISO-STD-013-2014; which contains the CIGI version 4.0 Interface Control Document (ICD). CIGI 4.0 is the official standard, published by SISO. Previous versions of CIGI were spearheaded by Boeing include CIGI v3.3, in November 2008, v3.2 April 2006, v3.1 June 2004, v3 November 2003, v2 in March 2002, and the original (v1) in March 2001 == Protocol dependencies == Typically, CIGI uses UDP as its transport protocol, but CIGI does not require a specific transport mechanism, only packet definition conformance. CIGI traffic does not have a well known port; however, the use of ports 8004-8005 has been widely adopted by commercial image generator vendors implementations. == Development tools == === Host Emulator === The Host Emulator can be used as a surrogate to manipulate the interface when a simulation Host is not available. It is a Windows-based image generator Host application used to develop, integrate and test image generators that use the CIGI protocol. It provides a graphical user interface (GUI) for the creation, modification and deletion of entities; manipulation of views; control of environmental attributes and phenomena; and other host functions. The Host Emulator has several features that are useful for integration and testing. A free-flight mode allows for fixed-wing and rotorcraft flight, movement along entity axes and free rotation using a joystick or a joystick-like widget. Scripting and record/playback features support regression testing, demonstrations and other tasks needing exact reproduction of certain sequences of events. A packet-level snoop feature allows the user to examine the contents of CIGI messages, image generator response times and latencies. A Heartbeat Monitor Window shows a graphical timing history of the Image Generator's data frame rate. Other features include explicit packet creation, animation control, missile flyouts and a situation display window (Host Emulator 3.x only). === Multi-Purpose Viewer === The Multi-Purpose Viewer (MPV) provides the basic functionality expected of an Image Generator, such as loading and displaying a terrain database, displaying entities and so forth. The Multi-Purpose Viewer can be used as a surrogate to manipulate the interface when a real Image Generator is not available. The MPV is capable of operating with both the Windows and Linux operating systems. === CIGI Class Library === The CCL is an object-oriented software interface that automatically handles message composition and decomposition (i.e. packing, unpacking and byte swapping to the ICD specification) on both the Host and Image Generator sides of the interface. The CCL interprets Host or Image Generator messages based on compile time parameters. It also performs error handling and translation between different versions of CIGI. Each packet type has its own class. The individual packet members are accessed through packet class accessors. Outgoing messages are constructed by placing each packet into the outgoing buffer using a streaming operator. Incoming messages are parsed using callback or event-based mechanisms that supply the using program with fully populated packet objects. === Current tool suite === A set of CIGI development tools are managed and maintained by the SISO CIGI Product Support Group. The latest packages are available on SourceForge. Comments/Suggestions to the package can be directed to the SISO discussion board at: https://discussions.sisostds.org/index.htm?A0=SAC-PSG-CIGI Archived 2017-09-13 at the Wayback Machine === Wireshark === Wireshark is a free and open source packet analyzer. It is used for network troubleshooting, analysis, software and communications protocol development, and education. Wireshark provides a dissector for CIGI packets. As of October 2016, “The CIGI dissector is fully functional for CIGI version 2 and 3. Version 1 is not yet implemented.” === Older versions of CIGI === A CIGI Interface Control Document (ICD) and development suite is available in open source format. The tools, ICD, and accompanying user documentation can be found and downloaded from the CIGI sourceforge web site. The SourceForge version of the MPV is limited in its support of CIGI data packets and is intended to grow as needs arise. The MPV uses CIGI 3 as its interface, but the MPV is backward-compatible with earlier CIGI versions through the use of the CCL. The MPV uses the Open Scene Graph library to render a scene. The scene graph is manipulated according to the CIGI commands received from the Host via the CCL. The MPV itself is an application layer that consists of a small kernel leveraging heavily on a plug-in architecture for ease of maintainability and flexibility. An implementer can implement the interface from scratch, however a full suite of integration tools is available. These tools consist of three elements. The Host Emulator (HE), the Multi-Purpose Viewer (MPV), and the CIGI Class Library (CCL).
Oscillatory neural network
An oscillatory neural network (ONN) is an artificial neural network that uses coupled oscillators as neurons. Oscillatory neural networks are closely linked to the Kuramoto model, and are inspired by the phenomenon of neural oscillations in the brain. Oscillatory neural networks have been trained to recognize images. Complex-Valued Oscillatory network has also been shown to store and retrieve multidimensional aperiodic signals. An oscillatory autoencoder has also been demonstrated, which uses a combination of oscillators and rate-coded neurons. A neuron made of two coupled oscillators, one having a fixed and the other having a tunable natural frequency, has been shown able to run logic gates such as XOR that conventional sigmoid neurons cannot.