Temporal resolution (TR) refers to the discrete resolution of a measurement with respect to time. It is defined as the amount of time needed to revisit and acquire data for the same location. When applied to remote sensing, this amount of time is influenced by the sensor platform's orbital characteristics and the features of the sensor itself. The temporal resolution is low when the revisiting delay is high and vice versa. Temporal resolution is typically expressed in days. == Physics == Often there is a trade-off between the temporal resolution of a measurement and its spatial resolution, due to Heisenberg's uncertainty principle. In some contexts, such as particle physics, this trade-off can be attributed to the finite speed of light and the fact that it takes a certain period of time for the photons carrying information to reach the observer. In this time, the system might have undergone changes itself. Thus, the longer the light has to travel, the lower the temporal resolution. == Technology == === Computing === In another context, there is often a tradeoff between temporal resolution and computer storage. A transducer may be able to record data every millisecond, but available storage may not allow this, and in the case of 4D PET imaging the resolution may be limited to several minutes. === Electronic displays === In some applications, temporal resolution may instead be equated to the sampling period, or its inverse, the refresh rate, or update frequency in Hertz, of a TV, for example. The temporal resolution is distinct from temporal uncertainty. This would be analogous to conflating image resolution with optical resolution. One is discrete, the other, continuous. The temporal resolution is a resolution somewhat the 'time' dual to the 'space' resolution of an image. In a similar way, the sample rate is equivalent to the pixel pitch on a display screen, whereas the optical resolution of a display screen is equivalent to temporal uncertainty. Note that both this form of image space and time resolutions are orthogonal to measurement resolution, even though space and time are also orthogonal to each other. Both an image or an oscilloscope capture can have a signal-to-noise ratio, since both also have measurement resolution. === Oscilloscopy === An oscilloscope is the temporal equivalent of a microscope, and it is limited by temporal uncertainty the same way a microscope is limited by optical resolution. A digital sampling oscilloscope has also a limitation analogous to image resolution, which is the sample rate. A non-digital non-sampling oscilloscope is still limited by temporal uncertainty. The temporal uncertainty can be related to the maximum frequency of continuous signal the oscilloscope could respond to, called the bandwidth and given in Hertz. But for oscilloscopes, this figure is not the temporal resolution. To reduce confusion, oscilloscope manufacturers use 'Sa/s' instead of 'Hz' to specify the temporal resolution. Two cases for oscilloscopes exist: either the probe settling time is much shorter than the real time sampling rate, or it is much larger. The case where the settling time is the same as the sampling time is usually undesirable in an oscilloscope. It is more typical to prefer a larger ratio either way, or if not, to be somewhat longer than two sample periods. In the case where it is much longer, the most typical case, it dominates the temporal resolution. The shape of the response during the settling time also has as strong effect on the temporal resolution. For this reason probe leads usually offer an arrangement to 'compensate' the leads to alter the trade off between minimal settling time, and minimal overshoot. If it is much shorter, the oscilloscope may be prone to aliasing from radio frequency interference, but this can be removed by repeatedly sampling a repetitive signal and averaging the results together. If the relationship between the 'trigger' time and the sample clock can be controlled with greater accuracy than the sampling time, then it is possible to make a measurement of a repetitive waveform with much higher temporal resolution than the sample period by upsampling each record before averaging. In this case the temporal uncertainty may be limited by clock jitter.
Winner-take-all in action selection
Winner-take-all is a computer science concept that has been widely applied in behavior-based robotics as a method of action selection for intelligent agents. Winner-take-all systems work by connecting modules (task-designated areas) in such a way that when one action is performed it stops all other actions from being performed, so only one action is occurring at a time. The name comes from the idea that the "winner" action takes all of the motor system's power. == History == In the 1980s and 1990s, many roboticists and cognitive scientists were attempting to find speedier and more efficient alternatives to the traditional world modeling method of action selection. In 1982, Jerome A. Feldman and D.H. Ballard published the "Connectionist Models and Their Properties", referencing and explaining winner-take-all as a method of action selection. Feldman's architecture functioned on the simple rule that in a network of interconnected action modules, each module will set its own output to zero if it reads a higher input than its own in any other module. In 1986, Rodney Brooks introduced behavior-based artificial intelligence. Winner-take-all architectures for action selection soon became a common feature of behavior-based robots, because selection occurred at the level of the action modules (bottom-up) rather than at a separate cognitive level (top-down), producing a tight coupling of stimulus and reaction. == Types of winner-take-all architectures == === Hierarchy === In the hierarchical architecture, actions or behaviors are programmed in a high-to-low priority list, with inhibitory connections between all the action modules. The agent performs low-priority behaviors until a higher-priority behavior is stimulated, at which point the higher behavior inhibits all other behaviors and takes over the motor system completely. Prioritized behaviors are usually key to the immediate survival of the agent, while behaviors of lower priority are less time-sensitive. For example, "run away from predator" would be ranked above "sleep." While this architecture allows for clear programming of goals, many roboticists have moved away from the hierarchy because of its inflexibility. === Heterarchy and fully distributed === In the heterarchy and fully distributed architecture, each behavior has a set of pre-conditions to be met before it can be performed, and a set of post-conditions that will be true after the action has been performed. These pre- and post-conditions determine the order in which behaviors must be performed and are used to causally connect action modules. This enables each module to receive input from other modules as well as from the sensors, so modules can recruit each other. For example, if the agent's goal were to reduce thirst, the behavior "drink" would require the pre-condition of having water available, so the module would activate the module in charge of "find water". The activations organize the behaviors into a sequence, even though only one action is performed at a time. The distribution of larger behaviors across modules makes this system flexible and robust to noise. Some critics of this model hold that any existing set of division rules for the predecessor and conflictor connections between modules produce sub-par action selection. In addition, the feedback loop used in the model can in some circumstances lead to improper action selection. === Arbiter and centrally coordinated === In the arbiter and centrally coordinated architecture, the action modules are not connected to each other but to a central arbiter. When behaviors are triggered, they begin "voting" by sending signals to the arbiter, and the behavior with the highest number of votes is selected. In these systems, bias is created through the "voting weight", or how often a module is allowed to vote. Some arbiter systems take a different spin on this type of winner-take-all by using a "compromise" feature in the arbiter. Each module is able to vote for or against each smaller action in a set of actions, and the arbiter selects the action with the most votes, meaning that it benefits the most behavior modules. This can be seen as violating the general rule against creating representations of the world in behavior-based AI, established by Brooks. By performing command fusion, the system is creating a larger composite pool of knowledge than is obtained from the sensors alone, forming a composite inner representation of the environment. Defenders of these systems argue that forbidding world-modeling puts unnecessary constraints on behavior-based robotics, and that agents benefits from forming representations and can still remain reactive.
NNDB
The Notable Names Database (NNDB) is an online database of biographical details of over 40,000 people. Soylent Communications, a sole proprietorship that also hosted the later defunct Rotten.com, describes NNDB as an "intelligence aggregator" of noteworthy persons, highlighting their interpersonal connections. The Rotten.com domain was registered in 1996 by former Apple and Netscape software engineer Thomas E. Dell, who was also known by his internet alias, "Soylent". == Entries == Each entry has an executive summary followed by a brief narrative about their life. It also lists date and cause of death if deceased. Businesspeople and government officials are listed with chronologies of their posts, positions, and board memberships. As of 2022, the site is no longer updated. == NNDB Mapper == The NNDB Mapper, a visual tool for exploring connections between people, was made available in May 2008. It required Adobe Flash 7.
Hekaton (database)
Hekaton (also known as SQL Server In-Memory OLTP) is an in-memory database for OLTP workloads built into Microsoft SQL Server. Hekaton was designed in collaboration with Microsoft Research and was released in SQL Server 2014. Traditional RDBMS systems were designed when memory resources were expensive, and were optimized for disk storage. Hekaton is instead optimized for a working set stored entirely in main memory, but is still accessible via T-SQL like normal tables. It is fundamentally different from the "DBCC PINTABLE" feature in earlier SQL Server versions. Hekaton was announced at the Professional Association for SQL Server (PASS) conference 2012.
Paprika (app)
Paprika is an app and website that helps users organize recipes, produce meal plans, and create grocery lists. The app is available for Android, iOS, macOS, and Windows devices. == Overview == The app allows users to import recipes from various sources, including websites and other apps. The app also allows users to automatically generate meal plans, which are also customizable, in order to achieve specific objectives such as weight loss, muscle gain, adherence to various dietary preferences, or personal taste. The app is also capable of generating grocery lists based on the daily or weekly meal plans chosen by the user. All the recipes, menus, and grocery lists of each user are accessible from smartphones, tablets, and computers. The app is part of a broader category of mobile apps focused on meal planning, recipe management, and shopping list automation, which have grown in popularity with the expansion of smartphone usage and digital cooking tools. == History == Paprika Recipe Manager for iPad version 1.0 was initially released in September 2010 by Hindsight LLC. Paprika 2.0 was released for iPhone and iPad in November 2013, and Paprika 3.0 was released for iOS and macOS in November 2017. == Reception == Paprika has been featured in technology and lifestyle publications as a recipe management and meal planning application. Coverage has noted features such as importing recipes from websites, ingredient scaling, and cross-platform synchronization. The app has also appeared in lists of cooking and meal planning tools published by outlets including The Verge and The Kitchn.
Language identification
In natural language processing, language identification or language guessing is the problem of determining which natural language a given content is in. Computational approaches to this problem view it as a special case of text categorization, solved with various statistical methods. == Overview == === Logical approach === A common non-statistical intuitive approach (though highly uncertain) is to look for common letter combinations, or distinctive diacritics or punctuation. === Statistical approach === There are several statistical approaches to language identification. An older statistical method by Grefenstette was based on the frequency of short n-grams, which are often function morphemes. For example, "ing" is more common in English than in French, while the sequence "que" is more common in French. Given a new page found on the Web, one counts the number of occurrences of each such short sequence and picks the language whose frequency table it matches the most. One technique is to compare the compressibility of the text to the compressibility of texts in a set of known languages. This approach is known as mutual information based distance measure. The same technique can also be used to empirically construct family trees of languages which closely correspond to the trees constructed using historical methods. Mutual information based distance measure is essentially equivalent to more conventional model-based methods and is not generally considered to be either novel or better than simpler techniques. Another technique, as described by Cavnar and Trenkle (1994) and Dunning (1994) is to create a language n-gram model from a "training text" for each of the languages. These models can be based on characters (Cavnar and Trenkle) or encoded bytes (Dunning); in the latter, language identification and character encoding detection are integrated. Then, for any piece of text needing to be identified, a similar model is made, and that model is compared to each stored language model. The most likely language is the one with the model that is most similar to the model from the text needing to be identified. This approach can be problematic when the input text is in a language for which there is no model. In that case, the method may return another, "most similar" language as its result. Also problematic for any approach are pieces of input text that are composed of several languages, as is common on the Web. As of 2025, a commonly used baseline method is via the fastText library, which has comparable classification accuracy as deep learning techniques, but much faster. == Identifying similar languages == One of the great bottlenecks of language identification systems is to distinguish between closely related languages. Similar languages like Bulgarian and Macedonian or Indonesian and Malay present significant lexical and structural overlap, making it challenging for systems to discriminate between them. In 2014 the DSL shared task has been organized providing a dataset (Tan et al., 2014) containing 13 different languages (and language varieties) in six language groups: Group A (Bosnian, Croatian, Serbian), Group B (Indonesian, Malaysian), Group C (Czech, Slovak), Group D (Brazilian Portuguese, European Portuguese), Group E (Peninsular Spanish, Argentine Spanish), Group F (American English, British English). The best system reached performance of over 95% results (Goutte et al., 2014). Results of the DSL shared task are described in Zampieri et al. 2014. == Software == Apache OpenNLP includes char n-gram based statistical detector and comes with a model that can distinguish 103 languages Apache Tika contains a language detector for 18 languages
World Database of Happiness
The World Database of Happiness is a web-based archive of research findings on subjective appreciation of life, based in the Erasmus Happiness Economics Research Organization of the Erasmus University Rotterdam in The Netherlands. The database contains both an overview of scientific publications on happiness and a digest of research findings. Happiness is defined as the degree to which an individual judges the quality of his or her life as a whole favorably. Two 'components' of happiness are distinguished: hedonic level of affect (the degree to which pleasant affect dominates) and contentment (perceived realization of wants). == Aims == The World Database of Happiness is a tool to quickly acquire an overview on the ever-growing stream of research findings on happiness Medio 2023 the database covered some 16,000 scientific publications on happiness, from which were extracted 23,000 distributional findings (on how happy people are) and another 24,000 correlational findings (on factors associated with more and less happiness). The first findings date from 1915. == Technique == The World Database of Happiness is a ‘findings archive’, which consists of electronic ‘finding pages’ on which separate research results are described in a standard format and terminology. These finding pages can be selected on various characteristics, such as population studies, the measure of happiness used and observed co-variates. All finding-pages have a specific internet address to which links can be made in scientific review papers or policy recommendations. This allows a concise presentation of many findings in a table, while providing readers with access to detail. == Scientific use == The Database has been cited in 254 scientific papers, for example to access under what conditions economic growth enhances average happiness or to show that rising mean happiness at first raises happiness inequality, but further rise will diminish these differences, or that healthy eating is associated with more happiness, even after controlling for the effect on health Another finding is that relative simple happiness training techniques raise happiness by some 5% == Popular use == The World Database of Happiness is often used by popular media to make lists of the happiest countries around the globe. An example is the Happy Planet Index, which aims to chart sustainable happiness all over the world by combining data on longevity, happiness and the size of the ecological footprint of citizens. == Strengths and weaknesses == The database has a clear conceptual focus, it includes only research findings on subjective enjoyment of one's life as a whole. Thereby it evades the Babel that has haunted the study of happiness for ages. The other side of that coin is that much interesting research is left out. The findings are reported with technical details about measurement and statistical analysis. This detail is welcomed by scholars, but makes the information difficult to digest for lay-persons. Still another limitation is that the determinants of happiness appear to vary considerably across persons and situations, which make it hard to draw general conclusions about the causes of happiness. What is clear is that poor health, separation, unemployment and lack of social contact are all strongly negatively associated with happiness. Another problem for the World database of happiness is that the studies on happiness increase with such a high rate that it gets increasingly difficult to offer a complete overview of all research findings. A further concern is that the Database of Happiness is exclusively focused on hedonic happiness (feeling good) and not on mature happiness that might exist in the face of suffering