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Pandas (software)

Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. The name is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals, as well as a play on the phrase "Python data analysis". Wes McKinney started building what would become Pandas at AQR Capital while he was a researcher there from 2007 to 2010. The development of Pandas introduced into Python many comparable features of working with DataFrames that were established in the R programming language. The library is built upon another library, NumPy. == History == Developer Wes McKinney started working on Pandas in 2008 while at AQR Capital Management out of the need for a high performance, flexible tool to perform quantitative analysis on financial data. Before leaving AQR, he was able to convince management to allow him to open source the library in 2009. Another AQR employee, Chang She, joined the effort in 2012 as the second major contributor to the library. In 2015, Pandas signed on as a fiscally sponsored project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. == Data model == Pandas is built around data structures called Series and DataFrames. Data for these collections can be imported from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. === Series === A Series is a one-dimensional array-like object that stores a sequence of values together with an associated set of labels, called an index. It is built on top of NumPy's array and affords many similar functionalities, but instead of using implicit integer positions, a Series allows explicit index labels of many data types. A Series can be created from Python lists, dictionaries, or NumPy arrays. If no index is provided, pandas automatically assigns a default integer index ranging from 0 to n-1, where n is the number of items in the Series. A simple example with customized labels is: To access a value or list of values from a Series, use its index or list of indices: Series can be used arithmetically, as in the statement series_3 = series_1 + series_2. This will align data points with corresponding index values in series_1 and series_2 (similar to a join in relational algebra), then add them together to produce new values in series_3. A Series has various attributes, such as name (Series name), dtype (data type of values), shape (number of rows), values, and index. They can be used in many of the same operations as NumPy arrays, with additional methods for reindexing, label-based selection, and handling missing data. === DataFrame === A DataFrame is a two-dimensional, tabular data structure with labeled rows and columns. Each column is stored internally as a Series and may hold a different data type (numeric, string, boolean, etc.). DataFrames can be created by a variety of means, including dictionaries of lists, NumPy arrays, and external files such as CSV or Excel spreadsheets: To retrieve a DataFrame column as a Series, use either 1) the index (dict-like notation) or 2) the name of column if the name is a valid Python identifier (attribute-like access). DataFrames support operations such as column assignment, row and column deletion, label-based indexing with loc, position-based indexing with iloc, reshaping, grouping, and joining. Merge operations implement a subset of relational algebra and allow one-to-one, many-to-one, and many-to-many joins. Some common attributes of a DataFrame include dtypes (data type of each column), shape (dimensions of the DataFrame returned as a tuple with form (number of rows, number of columns)), index/columns (labels of the DataFrame's rows/columns, respectively, returned as an Index object), values (data in the DataFrame returned as a 2D array), and empty (returns True if the DataFrame is empty). === Index === Index objects hold metadata for Series and Dataframe objects, such as axis labels and names, and are automatically created from input data. By default, a pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can also use any NumPy data type, including floating point, timestamps, or strings. Indices are also immutable, which allows them to be safely shared across multiple objects. pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values. For example, if s is a Series, s['a'] will return the data point at index a. Unlike dictionary keys, index values are not guaranteed to be unique. If a Series uses the index value a for multiple data points, then s['a'] will instead return a new Series containing all matching values. A DataFrame's column names are stored and implemented identically to an index. As such, a DataFrame can be thought of as having two indices: one column-based and one row-based. Because column names are stored as an index, these are not required to be unique. If data is a Series, then data['a'] returns all values with the index value of a. However, if data is a DataFrame, then data['a'] returns all values in the column(s) named a. To avoid this ambiguity, Pandas supports the syntax data.loc['a'] as an alternative way to filter using the index. Pandas also supports the syntax data.iloc[n], which always takes an integer n and returns the nth value, counting from 0. This allows a user to act as though the index is an array-like sequence of integers, regardless of how it is actually defined. pandas also supports hierarchical indices with multiple values per data point through the "MultiIndex" class. MultiIndex objects allow a single DataFrame to represent multiple dimensions, similar to a pivot table in Microsoft Excel, where each level can optionally carry its own unique name. In practice, data with more than 2 dimensions is often represented using DataFrames with hierarchical indices, instead of the higher-dimension Panel and Panel4D data structures. == Functionality == pandas supports a variety of indexing and subsetting techniques, allowing data to be selected by label, index, or Boolean conditions. For example, df[df['col1'] > 5] will return all rows in the DataFrame df for which the value of the column col1 exceeds 5. The library also implements grouping operations based on the split-apply-combine approach, enabling users to aggregate, transform, or restructure data according to column values or functions applied to index labels. For example, df['col1'].groupby(df['col2']) groups the data in 'col1' by their values in 'col2', while df.groupby(lambda i: i % 2) groups all data in the whole DataFrame by whether their index is even. The library also provides extensive tools for transforming, filtering and summarizing data. Users may apply arbitrary functions to Series and DataFrames, and because the library is built on top of Numpy, most NumPy functions can be applied directly to pandas objects as well. The library also includes built-in operations for arithmetic operations, string processing, and descriptive statistics such as mean, median, and standard deviation. These built-in functions are designed to handle missing data, usually represented by the floating-point value NaN. In addition, pandas includes tools for reorganizing data into different structural formats, with methods that can reshape tabular data between "wide" and "long" formats and pivot values based on column labels. pandas also implements a flexible set of relational operations for combining datasets. For instance, merge() links row in DataFrames based on one or more shared keys or indices, supporting one-to-one, one-to-many, and many-to-many relationships in a manner analogous to join operations in relational databases like SQL. DataFrames can also be concatenated or stacked together along an axis through the concat() method, and overlapping data can be further spliced together using combine_first() to fill in missing values. Furthermore, the library includes specialized support for working with time-series data. Features include the ability to interpolate values and filter using a range of timestamps, such as data['1/1/2023':'2/2/2023'] , which will return all dates between January 1 and February 2. Missing values in time-series data are represented by a dedicated NaT (Not a Timestamp) object, instead of the NaN value it uses elsewhere. == Criticisms == Pandas has been criticized for its inefficiency. The entire dataset must be loaded in RAM, and the library does not optimize query plans or support parallel computing across multiple cores. Wes McKinney, the creator of Pandas, has recommended Apache Arrow as an alternative to address these performance concerns and ot
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ObjectVision

ObjectVision was a forms-based programming language and environment for Windows 3.x developed by Borland. The latest version, 2.1, was released in 1992. An ObjectVision application is composed by forms designed in a graphic way that contains objects and events to provide interactivity. Forms are connected together with logic in the form of decision trees. ObjectVision applications also can interact with databases using multiple engines, like Paradox and dBase. A finished project is saved as an OVD file, that is executed by an interpreted runtime that can be freely distributed. ObjectVision was not used broadly except in some niche segments, but the visual programming ideas were the basis for Borland Delphi.
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Quickly (software)

Quickly is a framework for creating software programs for a Linux distribution using Python, PyGTK, Glade Interface Designer and Desktop Couch. It then allows for easy publishing using bzr and Launchpad. Quickly is designed to speed up the start of new projects with the use of templates, not only for programs but for any type of project. These templates are used to automate project configuration and maintenance. Delegating into templates and not into a specific library allows projects created using Quickly not to require dependencies on any particular library or runtime of Quickly itself. The project was started by Rick Spencer after his frustration as a beginner Ubuntu developer. == Updates == Last available software update is on 2013-01-31 for Ubuntu 11.04.
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Foveated rendering

Foveated rendering is a rendering technique which uses an eye tracker integrated with a virtual reality headset to reduce the rendering workload by greatly reducing the image quality in the peripheral vision (outside of the zone gazed by the fovea). A less sophisticated variant called fixed foveated rendering doesn't utilise eye tracking and instead assumes a fixed focal point. == History == Research into foveated rendering dates back at least to 1991. At Tech Crunch Disrupt SF 2014, Fove unveiled a headset featuring foveated rendering. This was followed by a successful kickstarter in May 2015. At CES 2016, SensoMotoric Instruments (SMI) demoed a new 250 Hz eye tracking system and a working foveated rendering solution. It resulted from a partnership with camera sensor manufacturer Omnivision who provided the camera hardware for the new system. In July 2016, Nvidia demonstrated during SIGGRAPH a new method of foveated rendering claimed to be invisible to users. In February 2017, Qualcomm announced their Snapdragon 835 Virtual Reality Development Kit (VRDK) which includes foveated rendering support called Adreno Foveation. == Use == According to chief scientist Michael Abrash at Oculus, utilising foveated rendering in conjunction with sparse rendering and deep learning image reconstruction has the potential to require an order of magnitude fewer pixels to be rendered in comparison to a full image. Later, these results have been demonstrated and published. In December 2019, fixed foveated rendering support was added to the Oculus Quest SDK. A number of VR headsets have included on-board eye tracking to provide support for foveated rendering, including HTC's Vive Pro Eye (2019), Meta Quest Pro (2022), PlayStation VR2 (2023), and Apple Vision Pro (2024). In 2025, Valve announced the upcoming Steam Frame headset, which applies a variation of the technique known as "foveated streaming" for wireless streaming from a PC to the headset; the method similarly uses variance in bit rate, and is performed at the encoder level rather than the software level.
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Pulse-coupled networks

Pulse-coupled networks or pulse-coupled neural networks (PCNNs) are neural models proposed by modeling a cat's visual cortex, and developed for high-performance biomimetic image processing. In 1989, Eckhorn introduced a neural model to emulate the mechanism of cat's visual cortex. The Eckhorn model provided a simple and effective tool for studying small mammal’s visual cortex, and was soon recognized as having significant application potential in image processing. In 1994, Johnson adapted the Eckhorn model to an image processing algorithm, calling this algorithm a pulse-coupled neural network. The basic property of the Eckhorn's linking-field model (LFM) is the coupling term. LFM is a modulation of the primary input by a biased offset factor driven by the linking input. These drive a threshold variable that decays from an initial high value. When the threshold drops below zero it is reset to a high value and the process starts over. This is different than the standard integrate-and-fire neural model, which accumulates the input until it passes an upper limit and effectively "shorts out" to cause the pulse. LFM uses this difference to sustain pulse bursts, something the standard model does not do on a single neuron level. It is valuable to understand, however, that a detailed analysis of the standard model must include a shunting term, due to the floating voltages level in the dendritic compartment(s), and in turn this causes an elegant multiple modulation effect that enables a true higher-order network (HON). A PCNN is a two-dimensional neural network. Each neuron in the network corresponds to one pixel in an input image, receiving its corresponding pixel's color information (e.g. intensity) as an external stimulus. Each neuron also connects with its neighboring neurons, receiving local stimuli from them. The external and local stimuli are combined in an internal activation system, which accumulates the stimuli until it exceeds a dynamic threshold, resulting in a pulse output. Through iterative computation, PCNN neurons produce temporal series of pulse outputs. The temporal series of pulse outputs contain information of input images and can be used for various image processing applications, such as image segmentation and feature generation. Compared with conventional image processing means, PCNNs have several significant merits, including robustness against noise, independence of geometric variations in input patterns, capability of bridging minor intensity variations in input patterns, etc. A simplified PCNN called a spiking cortical model was developed in 2009. == Applications == PCNNs are useful for image processing, as discussed in a book by Thomas Lindblad and Jason M. Kinser. PCNNs have been used in a variety of image processing applications, including: image segmentation, pattern recognition, feature generation, face extraction, motion detection, region growing, image denoising and image enhancement Multidimensional pulse image processing of chemical structure data using PCNN has been discussed by Kinser, et al. They have also been applied to an all pairs shortest path problem.
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MetroHero

MetroHero is a semi-defunct real-time transit tracking and performance analysis application for the Washington Metro rapid transit system. Originally available on iOS, Android, and the web, it allows users to view live maps of all trains on a specific line, summary statistics relating to real-time system performance, and user feedback on current Metro conditions. The app launched in 2015, followed by ARIES for Transit, a related project from the same developers, and continued functioning until its original developers shut it down in 2023. Afterwards, forks of the application went live to allow for its continued public use, and the Washington Metropolitan Area Transit Authority (WMATA), Metro's operator, announced that it would launch a similar app. The app has been described by local news media as popular and well-liked among Washington, D.C.-area residents. == History and main development == MetroHero was initially developed by James and Jennifer Pizzurro, who both attended George Washington University and studied computer science. They said that they were inspired to create the app after experiencing train delays and searching for an app to track a train after boarding; such an app did not exist for the Washington Metro. The development of the app was not endorsed by WMATA, but it did use publicly available data from the agency. MetroHero launched as an Android application in September 2015, followed by the release of an iOS-compatible web app in December of that year. A standalone iOS app launched in April 2018, but the web app remained supported. By April 2018, MetroHero had approximately 13,000 monthly active users. James Pizzurro has stated that the app's intended audience was regular Metro commuters who wanted to communicate with each other about active problems, as opposed to tourists and riders who only wanted train time data. Throughout the application's development, the Pizzurros had been advocates for Metro's transparency with riders and the community by providing more high-quality data and taking on the feedback of developers. In particular, they criticized Metro's reluctance to uniquely identify individual train trips and its decision to obscure data under certain circumstances, which have posed problems for MetroHero's data collection. In addition to their work on MetroHero, the app's developers led or participated in other initiatives related to transit in the Greater Washington area. In 2019, MetroHero partnered with a local transit group to analyze Metrobus data and publish a "Metrobus Report Card", along with proposed goals and recommendations based on the report's findings. Based on this experience, MetroHero's developers began a sister project, the Adherence + Reliability + Integrity Evaluation System for Transit (ARIES for Transit), which displays data and issues grades for Washington- and Baltimore-area transit systems. Separately, James Pizzurro used MetroHero data to inform Rail Transit OPS, an independent Metro oversight group, and assist in its documentation of Metro system incidents. == Application == The MetroHero application uses several interfaces, including an overall dashboard and a live map, to display data to its users. On the dashboard, system-wide train summary data, such as the number of operating trains and headway adherence, is visible. The map offers a visual representation of all trains' positions throughout the system, filtered by line. Individual stations and trains can be selected to see ratings and comments provided by other users, including both positive and negative notes like cleanliness and crowdedness. Additionally, a list of train wait times is given, along with aggregate data like average wait time. Any train delays or service incidents are visible in the app. MetroHero uses several data sources for the various components of its application. Train positions and other operational data are provided by WMATA as part of its initiative to release open data for third-party developers. However, MetroHero's developers noted that the Metro-provided information is sometimes inaccurate and incomplete, thereby limiting the accuracy of MetroHero. The app also collects crowdsourced data from its users, who can report conditions in train cars and stations and add to reports sent by other people. Additionally, MetroHero parses data from Twitter feeds to learn about system incidents, including delays and fires. In addition to the web app, Android app, and iOS app, MetroHero's initial developers maintained automated social media accounts that alerted customers about Metro service; these accounts were discontinued upon the original app's eventual shutdown. MetroHero also hosts archived performance data for later review, a feature that is sometimes used after major incidents. == Shutdown and future == In February 2023, James Pizzurro announced that MetroHero would be shut down on July 1, 2023, citing "positive changes ... in the app landscape and in WMATA's data management and communication" and the costs and time associated with maintaining the app. Shortly before the application's end date, the Pizzurros shared MetroHero's source code on GitHub, which prompted others to fork the code and begin maintaining new instances of MetroHero to succeed the original app. The original website went offline on July 1, as planned. Historically, WMATA has not offered its own real-time map or similar service, citing other apps from third parties which accomplished the same task. However, on June 30, 2023, Randy Clarke, WMATA's general manager, announced that Metro would begin offering a similar service as MetroHero did. The app, initially named MetroMeter, was planned to begin operating in early July and would provide real-time information on trains, headways, and service schedules. Metro also noted its intentions to extend this service to Metrobus and MetroAccess. On July 20, Metro announced that the app had been renamed to MetroPulse and launched it in beta. MetroHero's other project, ARIES for Transit, was not affected by the shutdown. == Reception == MetroHero was generally well-received and has been recognized for its usage among Washington-area commuters. DCist called it one of the "most praised" Metro tracking apps, and WMATA publicly acknowledged its popularity when announcing its decision to establish MetroPulse. Chris Barnes, a member of the Metro Riders' Advisory Council, said that the app is considered important among riders because it fulfills a need for riders to have reliable and transparent transit information, albeit somewhat hindered by flaws in WMATA's data.
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List of ARM Cortex-M development tools

This is a list of development tools for 32-bit ARM Cortex-M-based microcontrollers, which consists of Cortex-M0, Cortex-M0+, Cortex-M1, Cortex-M3, Cortex-M4, Cortex-M7, Cortex-M23, Cortex-M33, Cortex-M35P, Cortex-M52, Cortex-M55, and Cortex-M85 cores. == Development toolchains == IDE, compiler, linker, debugger, flashing (in alphabetical order): Ac6 System Workbench for STM32 (based on Eclipse and the GNU GCC toolchain with direct support for all ST-provided evaluation boards, Eval, Discovery and Nucleo, debug with ST-LINK) ARM Development Studio 5 by ARM Ltd. Atmel Studio by Atmel (based on Visual Studio and GNU GCC Toolchain) Code Composer Studio by Texas Instruments CoIDE by CooCox (note - website dead since 2018) Crossware Development Suite for ARM by Crossware CrossWorks for ARM by Rowley Dave by Infineon. For XMC processors only. Includes project wizard, detailed register decoding and a code library still under development. DRT by SOMNIUM Technologies. Based on GCC toolchain and proprietary linker technology. Available as a plugin for Atmel Studio and an Eclipse-based IDE. EmBitz (formerly Em::Blocks) – free, fast (non-eclipse) IDE for ST-LINK (live data updates), OpenOCD, including GNU Tools for ARM and project wizards for ST, Atmel, EnergyMicro etc. Embeetle IDE - free, fast (non-eclipse) IDE. Works both on Linux and Windows. emIDE by emide – free Visual Studio Style IDE including GNU Tools for ARM GNU ARM Eclipse – A family of Eclipse CDT extensions and tools for GNU ARM development GNU Tools (aka GCC) for ARM Embedded Processors by ARM Ltd – free GCC for bare metal IAR Embedded Workbench for ARM by IAR Systems ICC by ImageCraft Keil MDK-ARM by Keil LPCXpresso by NXP (formerly Red Suite by Code Red Technologies) MikroC by mikroe – mikroC MULTI by Green Hills Software, for all Arm 7, 9, Cortex-M, Cortex-R, Cortex-A Ride and RKit for ARM by Raisonance SEGGER Embedded Studio for ARM by Segger. SEGGER Ozone by Segger. STM32CubeIDE by STMicroelectronics - Combines STCubeMX with TrueSTUDIO into a single Eclipse style package Sourcery CodeBench by Mentor Graphics TASKING VX-Toolset by Altium TrueSTUDIO by Atollic Visual Studio by Microsoft as IDE, with GNU Tools as compiler/linker – e.g. supported by VisualGDB VXM Design's Buildroot toolchain for Cortex. It integrates GNU toolchain, Nuttx, filesystem and debugger/flasher in one build. winIDEA/winIDEAOpen by iSYSTEM YAGARTO – free GCC (no longer supported) Code::Blocks (EPS edition) (debug with ST-LINK no GDB and no OpenOCD required) IDE for Arduino ARM boards Arduino – IDE for Atmel SAM3X (Arduino Due) Energia – Arduino IDE for Texas Instruments Tiva and CC3200 Notes: == Debugging tools == JTAG and/or SWD debug interface host adapters (in alphabetical order): Black Magic Probe by 1BitSquared. CMSIS-DAP by Mbed. Crossconnect by Rowley Associates. DSTREAM by ARM Holdings Green Hills Probe and SuperTrace Probe by Green Hills Software. iTAG by iSYSTEM. I-jet by IAR Systems. Jaguar by Crossware. J-Link by Segger Supports JTAG and SWD. Supports ARM7, ARM9, ARM11, Cortex-A, Cortex-M, Cortex-R, Renesas RX, Microchip PIC32. Eclipse plug-in available. Supports GDB, RDI, Ozone debuggers. J-Trace by Segger. Supports JTAG, SWD, and ETM trace on Cortex-M. JTAGjet by Signum. LPC-LINK by Embedded Artists (for NXP) This is only embedded on NXP LPCXpresso development boards. LPC-LINK 2 by NXP. This device can be reconfigured to support 3 different protocols: J-LINK by Segger, CMSIS-DAP by ARM, Redlink by Code Red. Multilink debug probes, Cyclone in-system programming/debugging interfaces, and a GDB Server plug-in for Eclipse-based ARM IDEs by PEmicro. OpenOCD open source GDB server supports a variety of JTAG probes OpenOCD Eclipse plug-in available in GNU ARM Eclipse Plug-ins. AK-OPENJTAG by Artekit (Open JTAG-compatible). AK-LINK by Artekit. PEEDI by RONETIX Debug Probe by Raspberry Pi. RLink by Raisonance. ST-LINK/V2 by STMicroelectronics The ST-LINK/V2 debugger embedded on STM32 Nucleo and Discovery development boards can be converted to SEGGER J-LINK protocol. TRACE32 Debugger and ETM/ITM Trace by Lauterbach. ULINK by Keil. Debugging tools and/or debugging plug-ins (in alphabetical order): Memfault Error Analysis for post mortem debugging Percepio Tracealyzer, RTOS trace visualizer (with Eclipse plugin). Segger SystemView, RTOS trace visualizer. == Real-time operating systems == Commonly referred to as RTOS: == C/C++ software libraries == The following are free C/C++ libraries: ARM Cortex libraries: Cortex Microcontroller Software Interface Standard (CMSIS) libopencm3 (formerly called libopenstm32) libmaple for STM32F1 chips LPCOpen for NXP LPC chips Alternate C standard libraries: Bionic libc, dietlibc, EGLIBC, glibc, klibc, musl, Newlib, uClibc FAT file system libraries: EFSL, FatFs, Petit FatFs Fixed-point math libraries: libfixmath, fixedptc, FPMLib Encryption libraries: Comparison of TLS implementations wolfSSL == Non-C/C++ computer languages and software libraries ==
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Inbox by Gmail

Inbox by Gmail was an email service developed by Google. Announced on a limited invitation-only basis on October 22, 2014, it was officially released to the public on May 28, 2015. Inbox was shut down by Google on April 2, 2019. Available on the web, and through mobile apps for Android and iOS, Inbox by Gmail aimed to improve email productivity and organization through several key features. Bundles gathered emails on the same topic together; highlighted surface key details from messages, reminders and assists; and a "snooze" functionality enabled users to control when specific information would appear. Updates to the service enabled an "undo send" feature; a "Smart Reply" feature that automatically generated short reply examples for certain emails; integration with Google Calendar for event organization, previews of newsletters; and a "Save to Inbox" feature that let users save links for later use. Inbox by Gmail received generally positive reviews. At its launch, it was called "minimalist and lovely, full of layers and easy to navigate", with features deemed helpful in finding the right messages—one reviewer noted that the service felt "a lot like the future of email". However, it also received criticism, particularly for a low density of information, algorithms that needed tweaking, and because the service required users to "give up the control" of organizing their own email, meaning that "Anyone who already has a system for organizing their emails will likely find themselves fighting Google's system". Google noted in March 2016 that 10% of all replies on mobile originated from Inbox's Smart Reply feature. Google announced it would discontinue Inbox by Gmail in March 2019, with many of its features integrated into Gmail proper. == Features == Inbox by Gmail scanned the user's incoming Gmail messages for information. It gathered email messages related to the same overall topic into an organized bundle, with a title describing the bundle's content. For example, flight tickets, car rentals, and hotel reservations were grouped under "Travel", giving the user an easier overview of emails. Users could also group emails together manually, to "teach" the Inbox how the user worked. The service highlighted key details and important information in messages, such as flight itineraries, event information, photos and documents. Inbox could retrieve updated information from the Internet, including the real-time status of flights and package deliveries. Users could set reminders to bring up important messages later. When a user needed particular information, Inbox could assist the user by displaying the necessary details. Where Inbox highlights information was not needed immediately, users could "snooze" a message or reminder, with options to make the information reappear at a later time or specific location. In June 2015, Google added an "Undo Send" feature to Inbox, giving the user 10 seconds to undo sending a message. In November 2015, Google added "Smart Reply" functionality to the mobile apps. With Smart Reply, Inbox determined which emails could be answered with a short reply, generating three example responses from which the user could select one with a single tap. Smart Reply (initially available only on the Android and iOS mobile apps) was added to the Inbox website in March 2016, Google announcing that "10% of all your replies on mobile already use Smart Reply". By May 2017, Google said Smart Reply was driving about 12% of replies in inbox on mobile. In April 2016, Google updated Inbox with three new features; Google Calendar event organization, newsletter previews, and a "Save to Inbox" functionality that let the user save links for later use, rather than having to email links to themselves. In December 2017, Google introduced an "Unsubscribe" card that let users easily unsubscribe from mailing lists. The card appeared for email messages (from specific senders) that the user had not opened for a month. A few popular Inbox by Gmail features were subsequently added to Gmail: "Snoozing" of emails Nudges: Gmail could move old messages back to the top of the inbox when it thought a follow up or reply might be required. Hover actions: Placing the mouse cursor over a certain part of the message could quickly effect an action, such as archiving, without its being opened. Smart reply: This feature employed boilerplate text to suggest appropriate replies. Google reportedly wished, at a time then to be decided, to add the "bundles" feature to Gmail, which at the time was available only in Inbox for Gmail. By March 2020, many Inbox features were still missing from Gmail. == Platforms == Inbox by Gmail was announced on a limited invitation-only basis on October 22, 2014, available on the web, and through the Android and iOS mobile operating systems. It was officially released to the public on May 28, 2015. == Reception == David Pierce of The Verge praised the service, writing that it was "minimalist and lovely, full of layers and easy to navigate. It's remarkably fast and smooth on all platforms, and far better on iOS than the Gmail app". However, he criticized the app's low density of information, with only a few emails visible on the screen at a time, making it "a bit of a challenge" for users who need to go through "hundreds of emails" every day. Although positive that "Inbox feels a lot like the future of email", Pierce wrote that there was "plenty of algorithm tweaking and design condensing to do", with particular attention needed on a "compact view" for denser view of information on the screen. Sarah Mitroff of CNET also praised Inbox, writing, "Not only is it visually appealing, it's also full of features that help you find every message you need, when you need it". She added that users must "give up the control" to organize their email, and that it "won't vibe with everyone", but admitted that "if you're willing ... the app will reward you with a smarter and cleaner inbox." Mitroff noted that, initially, users had to coach the app about which bundle was appropriate for certain emails, writing, "It's a tedious process at first, by [sic] in just a few days Inbox starts to get it right." Regarding any downsides of the service, Mitroff wrote that "Inbox has a built-in strategy for managing your emails that works best on its own. Anyone who already has a system for organizing their emails will likely find themselves fighting Google's system". == Discontinuation and legacy == Google ended the service in March 2019. Google called Inbox "a great place to experiment with new ideas" and noted that many of those ideas had been migrated to Gmail. The company wanted, going forward, to focus its resources on a single email system. Several services, like Shortwave, attempted to resurrect some of the features of Inbox by Gmail to attract its old users. Similarly, Inbox Reborn, an actively maintained browser extension developed by a team of volunteer developers from around the world since 2018, aims to recreate the core features and visual style of Inbox by Gmail within the standard Gmail interface. The project continues to focus on preserving functionalities such as email bundling and streamlined workflows to provide users with a familiar productivity experience. Afterwards, most people moved to Spark, Spike, or Newton. According to a product manager at Google, a "more focused approach" regarding email was the companies goal. This is likely the reason they moved away from Inbox.
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Rabbit r1

The Rabbit r1 is an artificial intelligence personal assistant device developed by the American technology startup Rabbit Inc and co-designed by Teenage Engineering. It was announced at the 2024 Consumer Electronics Show as a handheld device intended to perform digital tasks through voice commands, touch interaction, and web-based AI agents. The r1 was marketed around Rabbit's concept of a "large action model" (LAM), which the company described as software able to operate websites and services on behalf of users. The device runs rabbitOS, an operating system based on the Android Open Source Project. Its services have included AI search, image recognition, voice interaction, music playback, rideshare and food-ordering integrations, and later experimental web-agent features such as LAM Playground and teach mode. Initial reviews were largely negative, with reviewers criticizing the device's limited functionality, bugs, and unclear advantages over a smartphone. Critics also questioned Rabbit's claims after the r1 software was shown to run on an Android phone. Rabbit continued to issue software updates after launch, including rabbitOS 2 in September 2025, which introduced a redesigned card-based interface, gesture navigation, and a "creations" feature for generating small software tools and experiences on the device. Rabbit Inc was founded by Jesse Lyu Cheng. == Hardware == Display: A 2.88-inch touchscreen for interactive user input. Input: push-to-talk button to activate voice commands; scroll wheel; Gyroscope; Magnetometer; Accelerometer; GPS. Camera: 8 MP single camera, with a resolution of 3264x2448, allowing for the connected external AI to use computer vision. Audio: Equipped with a speaker and dual microphones for audio interaction. Connectivity: Supports Wi-Fi and cellular connections via a SIM card slot to access internet services. Processor: Runs on a 2.3GHz MediaTek Helio P35 processor. Memory: Contains 4GB of RAM for operational tasks. Storage: Offers 128GB of internal storage for data. Ports: Utilizes a USB-C port for charging and data connections. == Software == The Rabbit r1 runs rabbitOS, which is based on the Android Open Source Project (AOSP), specifically Android 13. Rabbit founder Jesse Lyu described rabbitOS as a "very bespoke AOSP" after reports that the r1's software could be run on a conventional Android phone. Rabbit described the r1 as using a large action model (LAM), a type of AI agent intended to perform tasks across software interfaces rather than only answer questions. At launch, the device supported a limited set of services, including AI search, vision features, music playback, and some third-party integrations. Perplexity.ai was one of the AI services used to answer user queries. In 2024, Rabbit released several software updates that added features and attempted to address early criticism of the device. In July 2024, the company launched "beta rabbit", an advanced search and conversation mode for more complex queries. In October 2024, it released LAM Playground, a web-based agent feature intended to let the r1 operate websites on behalf of users. Reviewers found the feature experimental; Android Authority reported that it could perform some navigation tasks but struggled with CAPTCHAs, loops, and unintended behavior. In November 2024, Rabbit introduced a beta "teach mode", which allowed users to demonstrate web-based tasks in the Rabbithole web portal and later ask the r1 to repeat them. The company described teach mode as experimental, and The Verge noted that Rabbit warned users that results could be unpredictable and that CAPTCHA-protected sites could cause problems. Rabbit released rabbitOS 2 in September 2025. The update redesigned the interface around a card-based layout, added additional touchscreen gestures, and introduced "creations", a feature that lets users generate simple software tools, games, and interfaces through natural-language prompts. Coverage of the update described it as a major software overhaul rather than new hardware. == Reception == === Funding === Rabbit raised $20 million in funding from Khosla Ventures, Synergis Capital and Kakao Investment in October 2023. The company announced an additional $10 million in funding in December 2023. === Sales === Following its announcement at the 2024 Consumer Electronics Show, 130,000 units were sold. On August 13, 2024, Rabbit announced that sales of r1 had expanded to the entire European Union (except Malta) and United Kingdom. On August 21, 2024, sales of r1 expanded to Singapore. === Reviews === The r1 was met with strong criticism immediately after Rabbit began shipping the device. Some reviews questioned what the device was able to do that a smartphone could not, while comparing it to the similar Humane Ai Pin. YouTuber Marques Brownlee called the device "barely reviewable". Android Authority's Mishaal Rahman managed to install Rabbit r1's software on a Pixel 6a smartphone, after a tipster shared an APK file. The Verge echoed the claims made by Rahman. In response, Lyu published statements confirming its use of Android, but denying that the r1 is an Android app. Mashable called its Vision features impressive, but said that "these praise-worthy features are overshadowed by buggy performance". Ars Technica wrote a blog post claiming "the company is blocking access from bootleg APKs". TechCrunch gave a slightly more positive review, calling the device a "fun peep at a possible future", but could not "advise anyone to buy one now." Shortly after the launch of r1, Rabbit began a weekly cadence of software updates to address much of the criticism from the early reviews, including "battery and GPS performance, time zone selection, and more". Digital Trends said the Magic Camera feature "takes the most mundane, ordinary, and badly composed photos and makes something fun and eye-catching from them." Mashable said the "beta rabbit" feature "makes Rabbit R1 more conversational and intelligent". Later coverage noted that Rabbit continued to update the r1 after its poorly received launch. The Verge reported in September 2024 that about 5,000 of roughly 100,000 purchasers were using the device at any given moment, citing Lyu, and described the product as having launched before it was ready. In 2025, coverage of rabbitOS 2 described the update as an attempt to reset the device's software experience after the criticism of its original release. == Controversies == === GAMA project === Rabbit Inc has garnered attention due to allegations surrounding its funding and the company's past projects. The company came under scrutiny when Stephen Findeisen, known as Coffeezilla on YouTube, published a video in May 2024, alleging that Rabbit Incorporation was "built on a scam". Rabbit Incorporation, initially named Cyber Manufacturing Co, rebranded just two months before launching the Rabbit R1. The company, under its former name, raised $6 million in November 2021 for a project called GAMA, described as a "Next Generation NFT Project." Jesse Lyu, the CEO of Rabbit Incorporation, referred to GAMA as a "fun little project." Coffeezilla, who investigates influencer scams, highlighted old Clubhouse recordings of Jesse Lyu discussing the GAMA project. In these recordings, Lyu emphasized the substantial funding behind GAMA and its potential to be a revolutionary, carbon-negative cryptocurrency. Coffeezilla questioned the whereabouts of the funds raised for GAMA, estimating that approximately $1 million in refunds to investors remained unresolved. He suggested that the rebranding to Rabbit Incorporation and the shift to developing the Rabbit R1 were attempts to divert from the GAMA project's issues. In response to Coffeezilla's inquiries, Rabbit Incorporation stated that the $6 million raised was used for the GAMA project. The company said that NFTs cannot be refunded unless the owner agrees to "burn" them on the blockchain. Rabbit Incorporation also said that the GAMA project was open-sourced and returned to the community, aligning with community feedback. They also mentioned that efforts to buy back NFTs were made to counteract malicious trading and maintain market stability. === Security === In June 2024, Engadget reported that the Rabbitude team, a community reverse engineering project, had gained access to the r1's codebase revealing that r1's software contained several hardcoded API keys in its code for ElevenLabs, Microsoft Azure, Yelp, and Google Maps, potentially allowing unauthorized access to r1 responses, including those containing the users' personal information. For a short time, Rabbit immediately began revoking and rotating those secrets and confirmed that the code was leaked by an employee who had "been terminated and remains under investigation". In July 2024, the company revealed that all user chats and device pairing data were logged on the r1 with no ability to delete them. This meant that lost or stolen devices could be used to extract user
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Scroll (web service)

Scroll was a subscription-based web service developed by Scroll Labs Inc., offering ad-free access to websites in exchange for a fee. Scroll was not an ad blocker; instead, it partnered directly with internet publishers who voluntarily removed ads from their sites for Scroll users in exchange for a portion of the subscription fee. In May 2021, Scroll was acquired by Twitter. In October 2021, Scroll sent out an email announcing its integration into Twitter Blue within 30 days. == Functionality == Scroll enabled users to browse websites that partnered with Scroll without encountering online advertising, in exchange for a subscription fee. Unlike ad blocker, which disable advertisements without compensating the publisher, Scroll sent a browser cookie indicating that the user was a subscriber. The Scroll software integrated into the website detected this cookie and served an ad-free version of the site. In exchange for disabling advertisements, partner websites received a portion of the subscription fee. As of January 2020, Scroll retained 30% of the subscription fee, with the remaining 70% distributed among publisher sites. Payments to sites were made individually by users based on their 'engagement and loyalty,' rather than from a single pool of all subscription revenue. Scroll did not grant subscribers access to partner sites behind a paywall; it only removed ads from the site if the user also paid the publication's subscription fee. == History == Scroll was founded in 2016 by former Chartbeat Chief Executive Tony Haile. Scroll raised US$3 million in its first round of funding in 2016, including investments from The New York Times, Uncork Capital, and Axel Springer SE. By October 2018, Scroll had raised US$10 million in funding. In 2018, Scroll signed its first partner websites, which included The Atlantic, Fusion Media Group, Business Insider, Slate, MSNBC, The Philadelphia Inquirer, and Talking Points Memo. In February 2019, Scroll acquired the social media curation app Nuzzel. The same month, Mozilla and Scroll announced a partnership to run a "test pilot" together, but did not go into details. Scroll entered beta testing in 2019 and launched to the general public on January 28, 2020. In March 2020, Mozilla started offering Scroll as part of its "Firefox Better Web" service bundle. In May 2021, Scroll was acquired by Twitter, with the future of Scroll cited as being uncertain. An email to customers announcing the change said, "Later this year, Scroll will become part of a wider Twitter subscription that will expand on and adapt our services and functionality".
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Web Dynpro

Web Dynpro (WD) is a web application technology developed by SAP SE that focuses on the development of server-side business applications. For modern releases (for instance as of NetWeaver 750, software layer SAP_UI) the user interface is rendered according to the HTML5 web standard. Since Netweaver 754 (software layer SAP_UI, ABAP Platform 1909) a touch enabled user interface is available. The newly released versions usually follow the SAP Fiori design principles. One of its main design features is that the user interface is defined in an entirely declarative manner. Web Dynpro applications can be developed using either the Java (Web Dynpro for Java, WDJ or WD4J) or ABAP (Web Dynpro ABAP, WDA or WD4A) development infrastructure. == Overview == The earliest version of Web Dynpro appeared in 2003 and was based on Java. This variant was released approximately 18 months before the ABAP variant. As of 2010, the Java variant of Web Dynpro was put into maintenance mode. WD follows a design architecture based on an interpretation of the MVC design pattern and uses a model driven development approach ("minimize coding, maximize design"). The Web Dynpro Framework is a server-side runtime environment into which many dedicated "hook methods" are available. The developer then places their own custom coding within these hook methods in order to implement the desired business functionality. These hook methods belong to one of the broad categories of either "life-cycle" and "round-trip"; that is, those methods that are concerned with the life-cycle of a software component (i.e. processing that takes place at start up and shut down etc.), and those methods that are concerned with processing the fixed sequence of events that take place during a client-initiated round trip to the server. Web Dynpro is aimed at the development of business applications that follow standardized UI principles, applications that connect to backend systems and which are scalable. Key Capabilities Declarative way of development: Web Dynpro offers a graphical and declarative means of UI development. UI controls, building blocks, views and windows are modeled, and the business logic can be coded separately. Separation of user interface and business logic: One advantage of Web Dynpro over SAP GUI is the separation between business logic and user interface, and the structured development process with less implementation effort. Support of stateful application: The state of the application is kept in the back-end. This leads to a reduced data transfer from ABAP server to browser and vice versa. Regarding Web Dynpro ABAP there is only one programming language (ABAP) and only one system necessary. Therefore, development can be easier and cost efficient.
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Teechart

TeeChart is a charting library for programmers, developed and managed by Steema Software of Girona, Catalonia, Spain. It is available as commercial and non-commercial software. TeeChart has been included in most Delphi and C++Builder products since 1997, and TeeChart Standard currently is part of Embarcadero RAD Studio 13 Florence. TeeChart Pro version is a commercial product that offers shareware releases for all of its formats. The TeeChart Charting Library offers charts, maps and gauges in versions for Delphi VCL/FMX, ActiveX, C# for Microsoft Visual Studio .NET. Full source code has always been available for all versions except the ActiveX version. TeeChart's user interface is translated into 38 languages. == History == The first version of TeeChart was authored in 1995 by David Berneda, co-founder of Steema, using the Borland Delphi Visual Component Library programming environment and TeeChart was first released as a shareware version and made available via Compuserve in the same year. It was written in the first version of Delphi VCL, as a 16-bit Charting Library named TeeChart version 1. The next version of TeeChart was released as a 32-bit library (Delphi 2 supported 32-bit compilation) but was badged as TeeChart VCL v3 to coincide with Borland's naming convention for inclusion on the toolbox palette of Borland Delphi v3 in 1997 and with C++ Builder v3 in 1998. It has been on the Delphi/C++ Builder toolbox palette ever since. The current version is Embarcadero RAD Studio 13 Florence. TeeChart's first ActiveX version named "version 3" too, to match the VCL version's nomenclature, was released in 1998. The version was optimised to work with Microsoft's Visual Studio v97 and v6.0 developer suites that include Visual Basic and Microsoft Visual C++ programming languages. Support for new programming environments followed with TeeChart's first native C# version for Microsoft Visual Studio .NET released in 2002 and TeeChart.Lite for .NET, a free charting component, released for Visual Studio.NET in 2003 and supporting too, Mono (programming). Steema Software released the first native TeeChart Java (programming language) version in 2006 and TeeChart's first native PHP version was released in 2009 and published as open-source in June 2010. Mobile versions of TeeChart, for Android (operating system) devices and Windows Phone 7 devices were released during the first half of 2011. In 2012 TeeChart extended functionality to iPhone/iPad and BlackBerry OS devices and a new JavaScript version was released in the same year to support HTML5 Canvas. In 2013 Steema launched TeeChart for .NET Chart for Windows Store applications and included support for Microsoft's Windows Phone 8 mobile platform. TeeChart for Xamarin.Forms written with 100% C# code and cross-platform support for .NET desktops, Windows Phone, iOS and Android was released in 2014. Also since 2014 Webforms charts now offers HTML5 interactivity. Steema launched TeeChart for Avalonia (software framework) in 2022 and in 2023 .NET_MAUI support was added to the TeeChart for .NET. == Usage == TeeChart is a general purpose charting component designed for use in differing ambits, offering a wide range of aesthetics to chart data. Generally TeeCharts published in the field, in areas where large amounts of data must be interpreted regularly, remain by designer choice in their simplest form to maximize the "data-ink ratio". Sloan Digital Sky Survey, SDSS Web Services' use for charting "Scientific .. plotting of online data" at The Virtual Observatory Spectrum Services reflects that approach. The SDSS chart authors choose to represent data using TeeChart's standard 2D line display. Speed is also a factor when choosing how to most effectively plot data. Realtime data, at frequencies of up to tens or hundreds of data points or more per second, require the most processor economic approach to charting. Computer processing time dedicated to the plotting of data needs to be as lightweight as possible, freeing-up computer tasks "to achieve real-time data acquisition, display and analysis". A critical and stated aspect of many data visualisation applications is the ability to offer interactivity to the user; NASA's document, the Orbital Debris Engineering Model Model ORDEM 3.0 - User's Guide, 2014, states that "The user may manipulate the graphs to zoom, pan, and copy to the clipboard and export to various file types" and Computer and Computing Technologies in Agriculture II, Volume 1, Daoliang, Li; Chunjiang, Zhao (2009), also using TeeChart, states "the properties at any point in the chart can be viewed moving the mouse over it". Writing about control education, Juha Lindfors states "The desired charting functionality (such as zooming and scaling) is achieved..". Charting applications have become increasingly 'onlined', made available either to a wider public or to a territorially remote userbase via networked applications. The World Wide Web (the Web) has become "by far, the most popular Internet protocol" to disseminate online applications. Most major IDEs now offer environments for web application developede aimed at browser hosted applications. Charting components, TeeChart among them, have adapted to provide models that work within a browser environment, often using static images and scripted layering techniques such as Ajax (programming) to offer a level of interactivity, improve response times and hide apparent delay from the user. Options to enrich client, browser-side processing flexibility are exploited by TeeChart libraries via modules that offer 'micro-environments' within the browser, such as the long established ActiveX technology, Adobe Flash, Microsoft Silverlight or Java Applets. Serverside environments offer too, a means to interact with browser based script to dynamically respond to charting requests. Joomla and CodeIgniter are host environments for TeeChart PHP and an example of an Embarcadero IntraWeb VCL designed application using TeeChart, is documented here. == Programmer reference == The Code Project includes a demo that uses TeeChart.Lite, called 'Self-Organizing Feature Maps (Kohonen maps)' written by Bashir Magomedovl and SourceForge includes a Database Stress and Monitor that also uses TeeChart.Lite. Books and information sources that include substantial sections about working with the Delphi version of TeeChart include "Mastering Delphi 6" by Marco Cantù, "C++ Builder 5 developer's guide", a video Delphi Tutorial on charting JPEG compression and support forums and reference pages at TeeChart Support Forums. Non-English language document sources include, in Czech "Myslíme v jazyku Delphi 7: knihovna zkušeného programátora" by Marco Cantù, and Chinese, Delphi 6, Delphi, and Delphi 5.
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Problem solving

Problem solving is the process of achieving a goal by overcoming obstacles, a frequent part of most activities. Problems in need of solutions range from simple personal tasks (e.g. how to get from point A to B) to complex issues in business and technical fields. The former is an example of simple problem solving (SPS) addressing one issue, whereas the latter is complex problem solving (CPS) with multiple interrelated obstacles. Another classification of problem-solving tasks is into well-defined problems with specific obstacles and goals, and ill-defined problems in which the current situation is troublesome but it is not clear what kind of resolution to aim for. Similarly, one may distinguish formal or fact-based problems requiring psychometric intelligence, versus socio-emotional problems which depend on the changeable emotions of individuals or groups, such as tactful behavior, fashion, or gift choices. Solutions require sufficient resources and knowledge to attain the goal. Professionals such as lawyers, doctors, programmers, and consultants are largely problem solvers for issues that require technical skills and knowledge beyond general competence. Many businesses have found profitable markets by recognizing a problem and creating a solution: the more widespread and inconvenient the problem, the greater the opportunity to develop a scalable solution. There are many specialized problem-solving techniques and methods in fields such as science, engineering, business, medicine, mathematics, computer science, philosophy, and social organization. The mental techniques to identify, analyze, and solve problems are studied in psychology and cognitive sciences. Also widely researched are the mental obstacles that prevent people from finding solutions; problem-solving impediments include confirmation bias, mental set, and functional fixedness. == Definition == The term problem solving has a slightly different meaning depending on the discipline. For instance, it is a mental process in psychology and a computerized process in computer science. There are two different types of problems: ill-defined and well-defined; different approaches are used for each. Well-defined problems have specific end goals and clearly expected solutions, while ill-defined problems do not. Well-defined problems allow for more initial planning than ill-defined problems. Solving problems sometimes involves dealing with pragmatics (the way that context contributes to meaning) and semantics (the interpretation of the problem). The ability to understand what the end goal of the problem is, and what rules could be applied, represents the key to solving the problem. Sometimes a problem requires abstract thinking or coming up with a creative solution. Problem solving has two major domains: mathematical problem solving and personal problem solving. Each concerns some difficulty or barrier that is encountered. === Psychology === Problem solving in psychology refers to the process of finding solutions to problems encountered in life. Solutions to these problems are usually situation- or context-specific. The process starts with problem finding and problem shaping, in which the problem is discovered and simplified. The next step is to generate possible solutions and evaluate them. Finally a solution is selected to be implemented and verified. Problems have an end goal to be reached; how you get there depends upon problem orientation (problem-solving coping style and skills) and systematic analysis. Mental health professionals study the human problem-solving processes using methods such as introspection, behaviorism, simulation, computer modeling, and experiment. Social psychologists look into the person-environment relationship aspect of the problem and independent and interdependent problem-solving methods. Problem solving has been defined as a higher-order cognitive process and intellectual function that requires the modulation and control of more routine or fundamental skills. Empirical research shows many different strategies and factors influence everyday problem solving. Rehabilitation psychologists studying people with frontal lobe injuries have found that deficits in emotional control and reasoning can be re-mediated with effective rehabilitation and could improve the capacity of injured persons to resolve everyday problems. Interpersonal everyday problem solving is dependent upon personal motivational and contextual components. One such component is the emotional valence of "real-world" problems, which can either impede or aid problem-solving performance. Researchers have focused on the role of emotions in problem solving, demonstrating that poor emotional control can disrupt focus on the target task, impede problem resolution, and lead to negative outcomes such as fatigue, depression, and inertia. In conceptualization,human problem solving consists of two related processes: problem orientation, and the motivational/attitudinal/affective approach to problematic situations and problem-solving skills. People's strategies cohere with their goals and stem from the process of comparing oneself with others. === Cognitive sciences === Among the first experimental psychologists to study problem solving were the Gestaltists in Germany, such as Karl Duncker in The Psychology of Productive Thinking (1935). Perhaps best known is the work of Allen Newell and Herbert A. Simon. Experiments in the 1960s and early 1970s asked participants to solve relatively simple, well-defined, but not previously seen laboratory tasks. These simple problems, such as the Tower of Hanoi, admitted optimal solutions that could be found quickly, allowing researchers to observe the full problem-solving process. Researchers assumed that these model problems would elicit the characteristic cognitive processes by which more complex "real world" problems are solved. An outstanding problem-solving technique found by this research is the principle of decomposition. === Computer science === Much of computer science and artificial intelligence involves designing automated systems to solve a specified type of problem: to accept input data and calculate a correct or adequate response, reasonably quickly. Algorithms are recipes or instructions that direct such systems, written into computer programs. Steps for designing such systems include problem determination, heuristics, root cause analysis, de-duplication, analysis, diagnosis, and repair. Analytic techniques include linear and nonlinear programming, queuing systems, and simulation. A large, perennial obstacle is to find and fix errors in computer programs: debugging. === Logic === Formal logic concerns issues like validity, truth, inference, argumentation, and proof. In a problem-solving context, it can be used to formally represent a problem as a theorem to be proved, and to represent the knowledge needed to solve the problem as the premises to be used in a proof that the problem has a solution. The use of computers to prove mathematical theorems using formal logic emerged as the field of automated theorem proving in the 1950s. It included the use of heuristic methods designed to simulate human problem solving, as in the Logic Theory Machine, developed by Allen Newell, Herbert A. Simon and J. C. Shaw, as well as algorithmic methods such as the resolution principle developed by John Alan Robinson. In addition to its use for finding proofs of mathematical theorems, automated theorem-proving has also been used for program verification in computer science. In 1958, John McCarthy proposed the advice taker, to represent information in formal logic and to derive answers to questions using automated theorem-proving. An important step in this direction was made by Cordell Green in 1969, who used a resolution theorem prover for question-answering and for such other applications in artificial intelligence as robot planning. The resolution theorem-prover used by Cordell Green bore little resemblance to human problem solving methods. In response to criticism of that approach from researchers at MIT, Robert Kowalski developed logic programming and SLD resolution, which solves problems by problem decomposition. He has advocated logic for both computer and human problem solving and computational logic to improve human thinking. === Engineering === When products or processes fail, problem solving techniques can be used to develop corrective actions that can be taken to prevent further failures. Such techniques can also be applied to a product or process prior to an actual failure event—to predict, analyze, and mitigate a potential problem in advance. Techniques such as failure mode and effects analysis can proactively reduce the likelihood of problems. In either the reactive or the proactive case, it is necessary to build a causal explanation through a process of diagnosis. In deriving an explanation of effects in terms of causes, abduction generates new ideas or hypothes
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Chirplet transform

In signal processing, the chirplet transform is an inner product of an input signal with a family of analysis primitives called chirplets. Similar to the wavelet transform, chirplets are usually generated from (or can be expressed as being from) a single mother chirplet (analogous to the so-called mother wavelet of wavelet theory). == Definitions == The term chirplet transform was coined by Steve Mann, as the title of the first published paper on chirplets. The term chirplet itself (apart from chirplet transform) was also used by Steve Mann, Domingo Mihovilovic, and Ronald Bracewell to describe a windowed portion of a chirp function. In Mann's words: A wavelet is a piece of a wave, and a chirplet, similarly, is a piece of a chirp. More precisely, a chirplet is a windowed portion of a chirp function, where the window provides some time localization property. In terms of time–frequency space, chirplets exist as rotated, sheared, or other structures that move from the traditional parallelism with the time and frequency axes that are typical for waves (Fourier and short-time Fourier transforms) or wavelets. The chirplet transform thus represents a rotated, sheared, or otherwise transformed tiling of the time–frequency plane. Although chirp signals have been known for many years in radar, pulse compression, and the like, the first published reference to the chirplet transform described specific signal representations based on families of functions related to one another by time–varying frequency modulation or frequency varying time modulation, in addition to time and frequency shifting, and scale changes. In that paper, the Gaussian chirplet transform was presented as one such example, together with a successful application to ice fragment detection in radar (improving target detection results over previous approaches). The term chirplet (but not the term chirplet transform) was also proposed for a similar transform, apparently independently, by Mihovilovic and Bracewell later that same year. == Applications == The first practical application of the chirplet transform was in water-human-computer interaction (WaterHCI) for marine safety, to assist vessels in navigating through ice-infested waters, using marine radar to detect growlers (small iceberg fragments too small to be visible on conventional radar, yet large enough to damage a vessel). Other applications of the chirplet transform in WaterHCI include the SWIM (Sequential Wave Imprinting Machine). More recently other practical applications have been developed, including image processing (e.g. where there is periodic structure imaged through projective geometry), as well as to excise chirp-like interference in spread spectrum communications, in EEG processing, and Chirplet Time Domain Reflectometry. == Extensions == The warblet transform is a particular example of the chirplet transform introduced by Mann and Haykin in 1992 and now widely used. It provides a signal representation based on cyclically varying frequency modulated signals (warbling signals).
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LemonStand

LemonStand was a Canadian e-commerce company headquartered in Vancouver, British Columbia, that developed cloud-based computer software for online retailers. LemonStand was shut down on June 5, 2019. == History == LemonStand Version 1 was launched on July 28, 2001. It is written in the PHP programming language. Version 1 was released as an on-premises proprietary licensed software, and the commercial license was not free. However, there was a free trial license available. June 2012, LemonStand raised seed funding from the BDC Venture Capital, and a group of angel investors. December 20, 2013, a cloud-based SaaS version of the LemonStand eCommerce platform was released publicly. May 9, 2014, LemonStand and Payfirma, a payments processing company, partnered to provide integrated services for online retailers. May 3, 2016, LemonStand raised funding from BDC Venture Capital and Silicon Valley–based angel investors. March 5, 2019, LemonStand announced their intention to shut down on June 5, 2019. LemonStand was quietly acquired by Mailchimp at the end of February. == Pricing == LemonStand offered three levels of service plans. LemonStand did not charge any transaction fees.
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