Over-the-air updateAn over-the-air update (or OTA update), also known as over-the-air programming (or OTA programming), is an update to an embedded system that is delivered through a wireless network, such as Wi-Fi or a cellular network. These embedded systems include mobile phones, tablets, set-top boxes, cars and telecommunications equipment. OTA updates for cars and internet of things devices can also be called firmware over-the-air (FOTA). Various components may be updated OTA, including the device's operating system, applications, configuration settings, or parameters like encryption keys.
Android-x86Android-x86 was an open source project that made an unofficial porting of the Android mobile operating system developed by the Open Handset Alliance to run on devices powered by x86 processors, rather than RISC-based ARM chips. Developers Chih-Wei Huang and Yi Sun originated the project in 2009. The project began as a series of patches to the Android source code to enable Android to run on various netbooks, tablets and ultra-mobile PCs. The OS is based on the Android Open Source Project (AOSP) with some modifications and improvements.
Performance engineeringPerformance engineering encompasses the techniques applied during a systems development life cycle to ensure the non-functional requirements for performance (such as throughput, latency, or memory usage) will be met. It may be alternatively referred to as systems performance engineering within systems engineering, and software performance engineering or application performance engineering within software engineering.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Stochastic gradient descentStochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated from the entire data set) by an estimate thereof (calculated from a randomly selected subset of the data).
Evolutionary computationIn computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated.
HypercomputationHypercomputation or super-Turing computation is a set of models of computation that can provide outputs that are not Turing-computable. For example, a machine that could solve the halting problem would be a hypercomputer; so too would one that can correctly evaluate every statement in Peano arithmetic. The Church–Turing thesis states that any "computable" function that can be computed by a mathematician with a pen and paper using a finite set of simple algorithms, can be computed by a Turing machine.
Self-supervised learningSelf-supervised learning (SSL) is a paradigm in machine learning for processing data of lower quality, rather than improving ultimate outcomes. Self-supervised learning more closely imitates the way humans learn to classify objects. The typical SSL method is based on an artificial neural network or other model such as a decision list. The model learns in two steps. First, the task is solved based on an auxiliary or pretext classification task using pseudo-labels which help to initialize the model parameters.
Protein structure predictionProtein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its secondary and tertiary structure from primary structure. Structure prediction is different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by computational biology; and it is important in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes).
Computational scienceComputational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science that uses advanced computing capabilities to understand and solve complex physical problems. This includes Algorithms (numerical and non-numerical): mathematical models, computational models, and computer simulations developed to solve sciences (e.
