In computer graphics, depth peeling is an exact multipass method of order-independent transparency that extracts transparent fragments into depth layers and composites those layers in depth order. Depth peeling has the advantage of being able to generate correct results even for complex images containing intersecting transparent objects. == Method == Depth peeling works by rendering the image multiple times. Depth peeling uses two Z buffers, one that works conventionally, and one that is not modified, and sets the minimum distance at which a fragment can be drawn without being discarded. For each pass, the previous pass' conventional Z-buffer is used as the minimal Z-buffer, so each pass removes already-captured nearer fragments and draws the next depth layer behind them. The resulting images can then be composited in depth order to form a single image. A major drawback of classical depth peeling is performance: it requires one geometry pass per peeled layer, so scenes with high depth complexity require many passes that each re-rasterize the transparent geometry. Later variants reduce the number of passes by peeling multiple layers or both front and back layers in a pass. Dual depth peeling reduces the geometry-pass count from N to N/2+1 by peeling one layer from the front and one from the back in each pass, while multi-layer depth peeling peels several layers per pass and reported up to an 8x speed-up in RGBA8 settings.
Wetware (brain)
Wetware is a term drawn from the computer-related idea of hardware or software, but applied to biological life forms. == Usage == The prefix "wet" is a reference to the water found in living creatures. Wetware is used to describe the elements equivalent to hardware and software found in a person, especially the central nervous system (CNS) and the human mind. The term wetware finds use in works of fiction, in scholarly publications and in popularizations. The "hardware" component of wetware concerns the bioelectric and biochemical properties of the CNS, specifically the brain. If the sequence of impulses traveling across the various neurons are thought of symbolically as software, then the physical neurons would be the hardware. The amalgamated interaction of this software and hardware is manifested through continuously changing physical connections, and chemical and electrical influences that spread across the body. The process by which the mind and brain interact to produce the collection of experiences that we define as self-awareness is in question. == History == Although the exact definition has shifted over time, the term Wetware and its fundamental reference to "the physical mind" has been around at least since the mid-1950s. Mostly used in relatively obscure articles and papers, it was not until the heyday of cyberpunk, however, that the term found broad adoption. Among the first uses of the term in popular culture was the Bruce Sterling novel Schismatrix (1985) and the Michael Swanwick novel Vacuum Flowers (1987). Rudy Rucker references the term in a number of books, including one entitled Wetware (1988): ... all sparks and tastes and tangles, all its stimulus/response patterns – the whole bio-cybernetic software of mind. Rucker did not use the word to simply mean a brain, nor in the human-resources sense of employees. He used wetware to stand for the data found in any biological system, analogous perhaps to the firmware that is found in a ROM chip. In Rucker's sense, a seed, a plant graft, an embryo, or a biological virus are all wetware. DNA, the immune system, and the evolved neural architecture of the brain are further examples of wetware in this sense. Rucker describes his conception in a 1992 compendium The Mondo 2000 User's Guide to the New Edge, which he quotes in a 2007 blog entry. Early cyber-guru Arthur Kroker used the term in his blog. With the term getting traction in trendsetting publications, it became a buzzword in the early 1990s. In 1991, Dutch media theorist Geert Lovink organized the Wetware Convention in Amsterdam, which was supposed to be an antidote to the "out-of-body" experiments conducted in high-tech laboratories, such as experiments in virtual reality. Timothy Leary, in an appendix to Info-Psychology originally written in 1975–76 and published in 1989, used the term wetware, writing that "psychedelic neuro-transmitters were the hot new technology for booting-up the 'wetware' of the brain". Another common reference is: "Wetware has 7 plus or minus 2 temporary registers." The numerical allusion is to a classic 1957 article by George A. Miller, The magical number 7 plus or minus two: some limits in our capacity for processing information, which later gave way to Miller's law.
Sinewave synthesis
Sinewave synthesis, or sine wave speech, is a technique for synthesizing speech by replacing the formants (main bands of energy) with pure tone whistles. The first sinewave synthesis program (SWS) for the automatic creation of stimuli for perceptual experiments was developed by Philip Rubin at Haskins Laboratories in the 1970s. This program was subsequently used by Robert Remez, Philip Rubin, David Pisoni, and other colleagues to show that listeners can perceive continuous speech without traditional speech cues, i.e., pitch, stress, and intonation. This work paved the way for a view of speech as a dynamic pattern of trajectories through articulatory-acoustic space.
Vague set
In mathematics, vague sets are an extension of fuzzy sets. In a fuzzy set, each object is assigned a single value in the interval [0,1] reflecting its grade of membership. This single value does not allow a separation of evidence for membership and evidence against membership. Gau et al. proposed the notion of vague sets, where each object is characterized by two different membership functions: a true membership function and a false membership function. This kind of reasoning is also called interval membership, as opposed to point membership in the context of fuzzy sets. == Mathematical definition == A vague set V {\displaystyle V} is characterized by its true membership function t v ( x ) {\displaystyle t_{v}(x)} its false membership function f v ( x ) {\displaystyle f_{v}(x)} with 0 ≤ t v ( x ) + f v ( x ) ≤ 1 {\displaystyle 0\leq t_{v}(x)+f_{v}(x)\leq 1} The grade of membership for x is not a crisp value anymore, but can be located in [ t v ( x ) , 1 − f v ( x ) ] {\displaystyle [t_{v}(x),1-f_{v}(x)]} . This interval can be interpreted as an extension to the fuzzy membership function. The vague set degenerates to a fuzzy set, if 1 − f v ( x ) = t v ( x ) {\displaystyle 1-f_{v}(x)=t_{v}(x)} for all x. The uncertainty of x is the difference between the upper and lower bounds of the membership interval; it can be computed as ( 1 − f v ( x ) ) − t v ( x ) {\displaystyle (1-f_{v}(x))-t_{v}(x)} .
Tensor network
Tensor networks or tensor network states are a class of variational wave functions used in the study of many-body quantum systems and fluids. Tensor networks extend one-dimensional matrix product states to higher dimensions while preserving some of their useful mathematical properties. The wave function is encoded as a tensor contraction of a network of individual tensors. The structure of the individual tensors can impose global symmetries on the wave function (such as antisymmetry under exchange of fermions) or restrict the wave function to specific quantum numbers, like total charge, angular momentum, or spin. It is also possible to derive strict bounds on quantities like entanglement and correlation length using the mathematical structure of the tensor network. This has made tensor networks useful in theoretical studies of quantum information in many-body systems. They have also proved useful in variational studies of ground states, excited states, and dynamics of strongly correlated many-body systems. == Diagrammatic notation == In general, a tensor network diagram (Penrose diagram) can be viewed as a graph where nodes (or vertices) represent individual tensors, while edges represent summation over an index. Free indices are depicted as edges (or legs) attached to a single vertex only. Sometimes, there is also additional meaning to a node's shape. For instance, one can use trapezoids for unitary matrices or tensors with similar behaviour. This way, flipped trapezoids would be interpreted as complex conjugates to them. == History == Foundational research on tensor networks began in 1971 with a paper by Roger Penrose. In "Applications of negative dimensional tensors" Penrose developed tensor diagram notation, describing how the diagrammatic language of tensor networks could be used in applications in physics. In 1992, Steven R. White developed the density matrix renormalization group (DMRG) for quantum lattice systems. The DMRG was the first successful tensor network and associated algorithm. In 2002, Guifré Vidal and Reinhard Werner attempted to quantify entanglement, laying the groundwork for quantum resource theories. This was also the first description of the use of tensor networks as mathematical tools for describing quantum systems. In 2004, Frank Verstraete and Ignacio Cirac developed the theory of matrix product states, projected entangled pair states, and variational renormalization group methods for quantum spin systems. In 2006, Vidal developed the multi-scale entanglement renormalization ansatz (MERA). In 2007 he developed entanglement renormalization for quantum lattice systems. In 2010, Ulrich Schollwock developed the density-matrix renormalization group for the simulation of one-dimensional strongly correlated quantum lattice systems. In 2014, Román Orús introduced tensor networks for complex quantum systems and machine learning, as well as tensor network theories of symmetries, fermions, entanglement and holography. == Connection to machine learning == Tensor networks have been adapted for supervised learning, taking advantage of similar mathematical structure in variational studies in quantum mechanics and large-scale machine learning. This crossover has spurred collaboration between researchers in artificial intelligence and quantum information science. In June 2019, Google, the Perimeter Institute for Theoretical Physics, and X (company), released TensorNetwork, an open-source library for efficient tensor calculations. The main interest in tensor networks and their study from the perspective of machine learning is to reduce the number of trainable parameters (in a layer) by approximating a high-order tensor with a network of lower-order ones. Using the so-called tensor train technique (TT), one can reduce an N-order tensor (containing exponentially many trainable parameters) to a chain of N tensors of order 2 or 3, which gives us a polynomial number of parameters.
CineAsset
CineAsset was a complete mastering software suite by Doremi Labs that could create and playback encrypted (Pro version) and unencrypted DCI compliant packages from virtually any source. CineAsset included a separate "Editor" application for generating Digital Cinema Packages (DCPs). CineAsset Pro added the ability to generate encrypted DCPs and Key Delivery Messages (KDMs) for any encrypted content in the database. It has since been discontinued, along with CineAsset Player. == Features == == Supported formats == === Input === Source: ==== Containers ==== AVI MOV MXF MPG TS WMV M2TS MTS MP4 MKV ==== Video Codecs ==== JPEG2000 ProRes 422 DNxHD® YUV Uncompressed 8-10 bits DIVX® XVID® MPEG4 AVC / H-264 VC-1 MPEG2 ==== Image Sequences ==== BMP TIFF TGA DPX JPG J2C ==== Audio Files ==== WAV MP3 WMA MP2 === Output === Source: ==== JPEG2000 ==== 2D and 3D at up to 4K resolution Bit Rate: 50–250 Mbit/s (500 Mbit/s for frame rates above 30 fps) Speed: Faster than real-time processing when using optional render nodes ==== MPEG2 ==== I-Only or Long GOP 1080p up to 80 Mbit/s ==== H264 ==== 1080p up to 50 Mbit/s ==== VC1 ==== DCP wrapping only (no transcode)
Woken Furies
Woken Furies (2005) is a science fiction novel by British writer Richard Morgan. It is the third novel featuring the anti-hero Takeshi Kovacs and is the sequel to Broken Angels. This addition to the series casts light upon Kovacs' early life providing information on his post-envoy activities. Morgan's official website and interviews suggest that Woken Furies could be the last Kovacs novel, although in 2018 (before Netflix cancelled the show) Morgan stated that the Netflix adaptation has "kind of woken it all up again" after all these years, making him possibly reconsider being done with Kovacs. == Plot == Takeshi Kovacs finds himself in a new "sleeve," or human body, back on his home planet of Harlan's World. He is on the run after making numerous attacks against the Knights of the New Revelation, an extremist religious order responsible for the death of his lost love and her daughter. Because she had violated tenets about resleeving, her executioners dropped her and her daughter's cortical stacks in the sea, effectively preventing them from being resleeved (into new bodies). While trying to secure passage after his most recent attack, Kovacs saves a woman named Sylvie from a group of religious zealots. In return, she allows him to take refuge with her mercenary "deCom" crew as they head out to decommission sentient military hardware that has run amok on the island of New Hokkaido (AKA New Hok). Sylvie is the "command head" of her crew, co-ordinating them during missions by using her biologically implanted circuitry and software. During one of these missions, Sylvie collapses, regains consciousness, and Kovacs realizes that her personality seems to have been replaced by that of long-dead revolutionary leader Quellcrist Falconer. Harlan's World is surrounded by automated "orbitals" which target flying objects, such as vehicles, with high-energy beam weapons known as "angelfire"; Falconer is believed to have died without a backup of her cortical stack when her getaway aircraft was destroyed by angelfire 300 years prior. When Sylvie's crew returns from New Hok, they discover a younger version of Kovacs has been illegally duplicated into a different body (AKA "double sleeved") and is hunting them on behalf of the Harlan family that rules the planet. Most of Sylvie's crew is killed and Sylvie/Quellcrist is captured. Kovacs schemes to rescue Sylvie by approaching old criminal associates of his, the Little Blue Bugs. The Little Blue Bugs mount a semi-successful attack on a Harlan fortress and rescue Sylvie/Quellcrist. Hiding from Harlan forces in a floating base, the neo-Quellists are sold out by its owner and recaptured. An assault by Kovacs and a single UN Envoy on the base ends badly when Kovacs is betrayed by the Envoy who was actually embedded with several colleagues. However, Sylvie/Quellcrist has established a connection with the orbitals and calls down angelfire, eliminating their captors. The younger Kovacs is killed in the aftermath. Sylvie explains that angelfire is a destructive recording device. Thus, in destroying Quellcrist and the helicopter carrying her, it copied her. When the technology of the deCom crews advanced far enough, her persona was able to insert itself into Sylvie's implants and co-exist in her body. The novel ends with Kovacs, Virginia Vidaura, and Sylvie/Quellcrist waiting to see if they can use Sylvie/Quellcrist's newfound connection to the orbitals and the expansion of a long-dormant genetic virus to turn the population against the ruling oligarchy.