The Plum Group, Inc. (DBA Plum Voice) is a company. Plum is headquartered in New York City with offices in Boston and Denver. == History == Plum Voice, founded in 2000 as The Plum Group, Inc., was incorporated to create technologies for personalized audio communication. By 2001, Plum had commercialized the open-standard Plum VoiceXML IVR platform which facilitated the creation of dynamic telecom applications. 2001 - Commercial launch of Plum VoiceXML IVR platform for customer-premises deployment 2002 - Launch of Plum Voice Hosting Centers for 24x7x365 managed IVR hosting 2004 - Plum Voice application suite receives a "Product of the Year" award from Customer Interactions magazine 2008 - Plum Survey builder launched, a do-it-yourself IVR survey tool. 2010 - Plum launched QuickFuse, a web-based rapid development platform used to create voice applications. 2013 - Plum launched VoiceTrends, an analytics and reporting toolkit designed specifically for voice applications. Plum achieves PCI-DSS Level 1. 2015 - Plum launched Plum Insight, a multi-channel (voice, web, mobile) survey platform. Plum achieves HIPAA compliance. 2016 - Plum launched a new version of QuickFuse called Fuse+. 2020 - Plum sunsets QuickFuse, rebrands Fuse+ as Plum Fuse.
Amália (LLM)
Amália is a Portuguese large language model (LLM) announced in November 2024 by the Portuguese Prime-Minister Luís Montenegro. Its final version is expected to be launched in 2026. It is being developed by Center for Responsible AI (Centro para a AI Responsável) and by the research centers of NOVA School of Science and Technology and Instituto Superior Técnico. == History == In 2024 it was announced that the Portuguese Agency for Administrative Modernization (Agência para a Modernização Administrativa) transpose this LLM to Portuguese Public Administration. According to Paulo Dimas (CEO of the Center for Responsible AI) the three fundamental points of this LLM project are the linguistic variant (European Portuguese), cultural representation and data protection. In April 2025 it was announced that Amália had entered beta phase with an improved version being expected to be launched in September 2025. The beta version released in September is available only to the Public Administration, but the website launched in October reiterates the final version will be an open model.
Auto-defrost
Auto-defrost, automatic defrost or self-defrosting is a technique which regularly defrosts the evaporator in a refrigerator or freezer. Appliances using this technique are often called frost free, frostless, or no-frost. == Mechanism == The defrost mechanism in a refrigerator heats the cooling element (evaporator coil) for a short period of time and melts the frost that has formed on it. The resulting water drains through a duct at the back of the unit. Defrosting is controlled by an electric or electronic timer. For every 6, 8, 10, 12 or 24 hours of compressor operation, it turns on a defrost heater for 15 minutes to half an hour. The defrost heater, having a typical power rating of 350W to 600W, is often mounted just below the evaporator in top and bottom-freezer models. It can also be located below and in the middle of the evaporator in side-by-side models. It may be protected from short circuits by means of fusible links. In older refrigerators, the timer runs continuously. In newer designs, the timer only runs while the compressor runs, so the longer the refrigerator door is closed, the less time the heater will run for and the more energy is saved. A defrost thermostat opens the heater circuit when the evaporator temperature rises above a preset temperature, 40°F (5°C) or more, thereby preventing excessive heating of the freezer compartment. The defrost timer is such that either the compressor or the defrost heater is on, but not both at the same time. Inside the freezer, air is circulated by means of one or more fans. In a typical design cold air from the freezer compartment is ducted to the fresh food compartment and circulated back into the freezer compartment. Air circulation helps sublimate any ice or frost that may form on frozen items in the freezer compartment. While defrosting, this fan is stopped to prevent heated-up air from reaching the food compartment. Instead of the normal cooling elements being embedded in the freezer liner, auto-defrost elements are behind or beneath the liner. This allows them to be heated for short periods of time to dispose of frost, without heating the contents of the freezer. Alternatively, some systems use the hot gas in the condenser to defrost the evaporator. This is done by means of a circuit that is cross-linked by a three-way valve. The hot gas quickly heats up the evaporator and defrosts it. This system is primarily used in commercial applications such as ice-cream displays. == Application == While this technique was originally applied to the refrigerator compartment, it was later used for freezer compartment as well. A combined refrigerator/freezer which applies self-defrosting to the refrigerator compartment only is usually called "partial frost free" or semi-automatic defrost (some brands call these "Auto Defrost" while Frigidaire referred to their semi-automatic models as "Cycla-Matic," Kelvinator often named these models as "Cyclic Defrost" ). These refrigerators usually have a pan underneath where water from the melted frost in the refrigerator section evaporates. Freezers with automatic defrosting and combined refrigerator/freezer units which also apply self defrosting to their freezer compartment are called "frost free". The latter usually feature an air connection between the two compartments with the air passage to the refrigerator compartment regulated by a damper. By this means, a controlled portion of the air coming from the freezer reaches the refrigerator. Some older models have no air circulation between their freezer and refrigerator sections. Instead, they use an independent cooling system (for example: an evaporator coil with a defrost heater and a circulating fan in the freezer and a cold-plate or open-coil evaporator in the refrigerator. "Frost-Free" refrigerator/freezer units usually use a heating element to defrost their evaporators, a pan to collect and evaporate water from the frost that melts from the cold plate and/or evaporator coil, a timer which turns off the compressor and turns on the defrost element usually from once to 4 times a day for periods usually ranging from 15 to 30 minutes, a defrost limiter thermostat that turns off the heating element before the temperature rises too much while the timer is still in its defrost phase. Some models also feature a drain heater to prevent ice from blocking the drain. Other early types of refrigerators also use hot gas defrost instead of electric heaters. These reverse the evaporator and condenser sides for the defrost cycle. Some newer refrigerator/freezer models have a computer that monitors how many times each door is opened and uses this data to control defrost scheduling thereby reducing power use. == Advantages == No need to manually defrost the frost buildup, therefore power consumption will not increase with time. Food packaging is easier to see. Most frozen food will not stick together. Smells are limited, especially in total frost-free appliances because the air always circulates. Better temperature management. == Disadvantages == The system can be more expensive to run when usage is high and if the fan continues or starts to run when the door is opened. A thermal cutout safety device is required to prevent overheating of the heating element. Increased electrical and mechanical complexity compared to a basic upright freezer or chest freezer, making it more prone to component failure. The temperature of the freezer contents rises during the defrosting cycles, especially if there is a light load in the freezer. This can cause "freezer burn" on articles placed in the freezer, from partially defrosting, then re-freezing On hot, humid days condensation will sometimes form around the refrigerator doors. Defrosting may not be completed by the time the defrost timer cycles back to normal operation (especially in hot, humid conditions with frequent door openings), leaving ice/frost on the evaporator coils. This condition can lead to "icing" which will interfere with the operation of the refrigerator. In laboratories, self-defrosting freezers must not be used to store certain delicate reagents such as enzymes, because the temperature cycling can degrade them. In addition, water can evaporate out of containers that do not have a very tight seal, altering the concentration of the reagents. Self-defrosting freezers should never be used to store flammable chemicals.
NeoPaint
NeoPaint is a raster graphics editor for Windows and MS-DOS. It supports several file formats including JPEG, GIF, BMP, PNG, and TIFF. The developer, NeoSoft, advertises NeoPaint as "being simple enough for use by children while remaining powerful enough for the purposes of advanced image editing". The first version, NeoPaint 1.0, was released in 1992 on floppy disks. It supported video modes ranging from 640x350 to 1024x768 and multiple fonts. NeoPaint 2.2 came out for MS-DOS 3.1 in 1993, with support of for 2, 16, or 256 color images in Hercules, EGA, VGA, and Super VGA modes. NeoPaint 3.1 was released in 1995 supporting 24-bit images and formats like PCX, TIFF and BMP. NeoPaint 3.2 was released in 1996. An updated version, NeoPaint 3.2a, supported the GIF file format. NeoPaint 3.2d was released in 1998. A Windows 95 version named NeoPaint for Windows v4.0 was released in 1999 supporting the PNG file format. On September 1, 2018 the program was rebranded as PixelNEO, becoming one of the VisualNEO software products. Formats such as JPEG 2000, ICO, CUR, PSD and RAW are supported.
Nona-binning
Nona-binning is a pixel binning technique used in high-resolution image sensors, primarily in smartphone cameras. The method is based on merging groups of nine neighbouring pixels arranged in a 3×3 pattern. This configuration allows a sensor with very small individual pixels to increase its effective light sensitivity when operating in low-light conditions, while still maintaining high nominal resolution in bright environments. == Overview == Nona-binning is most commonly implemented in sensors with a resolution of 108 megapixels and higher. As pixel counts grew, the physical dimensions of individual pixels continued to shrink, reducing the amount of light captured by each. The 3×3 binning structure enables a sensor to operate in two modes. In well-lit scenes, each pixel is processed separately, providing the full resolution of the sensor. In darker settings, nine pixels with identical colour filters are combined into a single output unit, increasing signal strength and reducing noise. == Technical principles == Unlike the traditional Bayer colour filter array, which alternates colours on a per-pixel basis, nona-binning uses a grouped layout. The sensor forms blocks of nine pixels with matching colour filters — typically within a Quad Bayer–derived arrangement extended to 3×3 regions. When operating in the binning mode, the sensor aggregates the charge generated by all nine pixels in each block. This increases effective sensitivity but lowers the final image resolution. When lighting conditions allow, the sensor returns to processing pixel data individually. == Applications == Nona-binning is primarily used in: Smartphone photography, particularly in devices equipped with sensors exceeding 100 megapixels. Low-light imaging, where increased sensitivity improves exposure stability and reduces noise. Computational photography systems, such as multi-frame processing and HDR capture. == Related technologies == Nona-binning belongs to the broader group of pixel-binning approaches used in modern sensors. Other implementations include Tetracell, which merges four pixels in a 2×2 block, and hexa-binning, which combines six pixels, though it is less common. All of these methods aim to balance the high nominal resolution of mobile sensors with the need for improved low-light performance.
Deep image prior
Deep image prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly initialized and used as prior to solve inverse problems such as noise reduction, super-resolution, and inpainting. Image statistics are captured by the structure of a convolutional image generator rather than by any previously learned capabilities. == Method == === Background === Inverse problems such as noise reduction, super-resolution, and inpainting can be formulated as the optimization task x ∗ = m i n x E ( x ; x 0 ) + R ( x ) {\displaystyle x^{}=min_{x}E(x;x_{0})+R(x)} , where x {\displaystyle x} is an image, x 0 {\displaystyle x_{0}} a corrupted representation of that image, E ( x ; x 0 ) {\displaystyle E(x;x_{0})} is a task-dependent data term, and R(x) is the regularizer. Deep neural networks learn a generator/decoder x = f θ ( z ) {\displaystyle x=f_{\theta }(z)} which maps a random code vector z {\displaystyle z} to an image x {\displaystyle x} . The image corruption method used to generate x 0 {\displaystyle x_{0}} is selected for the specific application. === Specifics === In this approach, the R ( x ) {\displaystyle R(x)} prior is replaced with the implicit prior captured by the neural network (where R ( x ) = 0 {\displaystyle R(x)=0} for images that can be produced by a deep neural networks and R ( x ) = + ∞ {\displaystyle R(x)=+\infty } otherwise). This yields the equation for the minimizer θ ∗ = a r g m i n θ E ( f θ ( z ) ; x 0 ) {\displaystyle \theta ^{}=argmin_{\theta }E(f_{\theta }(z);x_{0})} and the result of the optimization process x ∗ = f θ ∗ ( z ) {\displaystyle x^{}=f_{\theta ^{}}(z)} . The minimizer θ ∗ {\displaystyle \theta ^{}} (typically a gradient descent) starts from a randomly initialized parameters and descends into a local best result to yield the x ∗ {\displaystyle x^{}} restoration function. ==== Overfitting ==== A parameter θ may be used to recover any image, including its noise. However, the network is reluctant to pick up noise because it contains high impedance while useful signal offers low impedance. This results in the θ parameter approaching a good-looking local optimum so long as the number of iterations in the optimization process remains low enough not to overfit data. === Deep Neural Network Model === Typically, the deep neural network model for deep image prior uses a U-Net like model without the skip connections that connect the encoder blocks with the decoder blocks. The authors in their paper mention that "Our findings here (and in other similar comparisons) seem to suggest that having deeper architecture is beneficial, and that having skip-connections that work so well for recognition tasks (such as semantic segmentation) is highly detrimental." == Applications == === Denoising === The principle of denoising is to recover an image x {\displaystyle x} from a noisy observation x 0 {\displaystyle x_{0}} , where x 0 = x + ϵ {\displaystyle x_{0}=x+\epsilon } . The distribution ϵ {\displaystyle \epsilon } is sometimes known (e.g.: profiling sensor and photon noise) and may optionally be incorporated into the model, though this process works well in blind denoising. The quadratic energy function E ( x , x 0 ) = | | x − x 0 | | 2 {\displaystyle E(x,x_{0})=||x-x_{0}||^{2}} is used as the data term, plugging it into the equation for θ ∗ {\displaystyle \theta ^{}} yields the optimization problem m i n θ | | f θ ( z ) − x 0 | | 2 {\displaystyle min_{\theta }||f_{\theta }(z)-x_{0}||^{2}} . === Super-resolution === Super-resolution is used to generate a higher resolution version of image x. The data term is set to E ( x ; x 0 ) = | | d ( x ) − x 0 | | 2 {\displaystyle E(x;x_{0})=||d(x)-x_{0}||^{2}} where d(·) is a downsampling operator such as Lanczos that decimates the image by a factor t. === Inpainting === Inpainting is used to reconstruct a missing area in an image x 0 {\displaystyle x_{0}} . These missing pixels are defined as the binary mask m ∈ { 0 , 1 } H × V {\displaystyle m\in \{0,1\}^{H\times V}} . The data term is defined as E ( x ; x 0 ) = | | ( x − x 0 ) ⊙ m | | 2 {\displaystyle E(x;x_{0})=||(x-x_{0})\odot m||^{2}} (where ⊙ {\displaystyle \odot } is the Hadamard product). The intuition behind this is that the loss is computed only on the known pixels in the image, and the network is going to learn enough about the image to fill in unknown parts of the image even though the computed loss doesn't include those pixels. This strategy is used to remove image watermarks by treating the watermark as missing pixels in the image. === Flash–no-flash reconstruction === This approach may be extended to multiple images. A straightforward example mentioned by the author is the reconstruction of an image to obtain natural light and clarity from a flash–no-flash pair. Video reconstruction is possible but it requires optimizations to take into account the spatial differences. == Implementations == A reference implementation rewritten in Python 3.6 with the PyTorch 0.4.0 library was released by the author under the Apache 2.0 license: deep-image-prior A TensorFlow-based implementation written in Python 2 and released under the CC-SA 3.0 license: deep-image-prior-tensorflow A Keras-based implementation written in Python 2 and released under the GPLv3: machine_learning_denoising == Example == See Astronomy Picture of the Day (APOD) of 2024-02-18
JSGF
JSGF stands for Java Speech Grammar Format or the JSpeech Grammar Format (in a W3C Note). Developed by Sun Microsystems, it is a textual representation of grammars for use in speech recognition for technologies like XHTML+Voice. JSGF adopts the style and conventions of the Java programming language in addition to use of traditional grammar notations. The Speech Recognition Grammar Specification was derived from this specification. == Example == The following JSGF grammar will recognize the words coffee, tea, and milk.