Michael Justin Kearns is an American computer scientist, professor and National Center Chair at the University of Pennsylvania, the founding director of Penn's Singh Program in Networked & Social Systems Engineering (NETS), the founding director of Warren Center for Network and Data Sciences, and also holds secondary appointments in Penn's Wharton School and department of Economics. He is a leading researcher in computational learning theory and algorithmic game theory, and interested in machine learning, artificial intelligence, computational finance, algorithmic trading, computational social science and social networks. He previously led the Advisory and Research function in Morgan Stanley's Artificial Intelligence Center of Excellence team, and is currently an Amazon Scholar within Amazon Web Services. == Biography == Kearns was born into an academic family, where his father David R Kearns is Professor Emeritus at University of California, San Diego in chemistry, who won Guggenheim Fellowship in 1969, and his uncle Thomas R. Kearns is Professor Emeritus at Amherst College in Philosophy and Law, Jurisprudence, and Social Thought. His paternal grandfather Clyde W. Kearns was a pioneer in insecticide toxicology and was a professor at University of Illinois at Urbana–Champaign in Entomology, and his maternal grandfather Chen Shou-Yi (1899–1978) was a professor at Pomona College in history and literature, who was born in Canton (Guangzhou, China) into a family noted for their scholarship and educational leadership. Kearns received his B.S. degree at the University of California at Berkeley in math and computer science in 1985, and Ph.D. in computer science from Harvard University in 1989, under the supervision of Turing Award winner Leslie Valiant. His doctoral dissertation was The Computational Complexity of Machine Learning, later published by MIT press as part of the ACM Doctoral Dissertation Award Series in 1990. Before joining AT&T Bell Labs in 1991, he continued with postdoctoral positions at the Laboratory for Computer Science at MIT hosted by Ronald Rivest, and at the International Computer Science Institute (ICSI) in UC Berkeley hosted by Richard M. Karp, both of whom are Turing Award winners. Kearns is currently a full professor and National Center Chair at the University of Pennsylvania, where his appointment is split across the Department of Computer and Information Science, and Statistics and Operations and Information Management in the Wharton School. Prior to joining the Penn faculty in 2002, he spent a decade (1991–2001) in AT&T Labs and Bell Labs, including as head of the AI department with colleagues including Michael L. Littman, David A. McAllester, and Richard S. Sutton; Secure Systems Research department; and Machine Learning department with members such as Michael Collins and the leader Fernando Pereira. Other AT&T Labs colleagues in Algorithms and Theoretical Computer Science included Yoav Freund, Ronald Graham, Mehryar Mohri, Robert Schapire, and Peter Shor, as well as Sebastian Seung, Yann LeCun, Corinna Cortes, and Vladimir Vapnik (the V in VC dimension). Kearns was named Fellow of the Association for Computing Machinery (2014) for contributions to machine learning, and a fellow of the American Academy of Arts and Sciences (2012). His former graduate students and postdoctoral visitors include Ryan W. Porter, John Langford, and Jennifer Wortman Vaughan. Kearns' work has been reported by media, such as MIT Technology Review (2014) Can a Website Help You Decide to Have a Kid?, Bloomberg News (2014) Schneiderman (and Einstein) Pressure High-Speed Trading and NPR audio (2012) Online Education Grows Up, And For Now, It's Free. == Academic life == === Computational learning theory === Kearns and Umesh Vazirani published An introduction to computational learning theory, which has been a standard text on computational learning theory since it was published in 1994. === Weak learnability and the origin of Boosting algorithms === The question "is weakly learnability equivalent to strong learnability?" posed by Kearns and Valiant (Unpublished manuscript 1988, ACM Symposium on Theory of Computing 1989) is the origin of boosting machine learning algorithms, which got a positive answer by Robert Schapire (1990, proof by construction, not practical) and Yoav Freund (1993, by voting, not practical) and then they developed the practical AdaBoost (European Conference on Computational Learning Theory 1995, Journal of Computer and System Sciences 1997), an adaptive boosting algorithm that won the prestigious Gödel Prize (2003). == Honors and awards == 2021. Member of the U. S. National Academy of Sciences. 2014. ACM Fellow. For contributions to machine learning, artificial intelligence, and algorithmic game theory and computational social science. 2012. American Academy of Arts and Sciences Fellow. == Selected works == 2019. The Ethical Algorithm: The Science of Socially Aware Algorithm Design. (with Aaron Roth). Oxford University Press. 1994. An introduction to computational learning theory. (with Umesh Vazirani). MIT press. Widely used as a text book in computational learning theory courses. 1990. The computational complexity of machine learning. MIT press. Based on his 1989 doctoral dissertation; ACM Doctoral Dissertation Award Series in 1990 Archived 2014-11-03 at the Wayback Machine 1989. Cryptographic limitations on learning Boolean formulae and finite automata. (with Leslie Valiant) Proceedings of the twenty-first annual ACM symposium on Theory of computing (STOC'89). The open question: is weakly learnability equivalent to strong learnability?; The origin of boosting algorithms; Important publication in machine learning.
3D-Coat
3DCoat is a commercial digital sculpting program from Pilgway designed to create free-form organic and hard surfaced 3D models, with tools which enable users to sculpt, add polygonal topology (automatically or manually), create UV maps (automatically or manually), texture the resulting models with natural painting tools, and render static images or animated "turntable" movies. The program can also be used to modify imported 3D models from a number of commercial 3D software products by means of plugins called Applinks. Imported models can be converted into voxel objects for further refinement and for adding high resolution detail, complete UV unwrapping and mapping, as well as adding PBR textures for displacement, bump maps, specular and diffuse color maps. A live connection to a chosen external 3D application can be established through the Applink pipeline, allowing for the transfer of model and texture information. 3DCoat specializes in voxel sculpting and polygonal sculpting using dynamic patch tessellation technology and polygonal sculpting tools. It includes "auto-retopology", a proprietary skinning algorithm which generates a polygonal mesh skin over any voxel sculpture, composed primarily of quadrangles.
Content adaptation
Content adaptation is the action of transforming content to adapt to device capabilities. Content adaptation is usually related to mobile devices, which require special handling because of their limited computational power, small screen size, and constrained keyboard functionality. Content adaptation could roughly be divided to two fields: Media content adaptation that adapts media files. Browsing content adaptation that adapts a website to mobile devices. == Browsing content adaptation == Advances in the capabilities of small, mobile devices such as mobile phones (cell phones) and Personal Digital Assistants have led to an explosion in the number of types of device that can now access the Web. Some commentators refer to the Web that can be accessed from mobile devices as the Mobile Web. The sheer number and variety of Web-enabled devices poses significant challenges for authors of websites who want to support access from mobile devices. The W3C Device Independence Working Group described many of the issues in its report Authoring Challenges for Device Independence. Content adaptation is one approach to a solution. Rather than requiring authors to create pages explicitly for each type of device that might request them, content adaptation transforms an author's materials automatically. For example, content might be converted from a device-independent markup language, such as XDIME, an implementation of the W3C's DIAL specification, into a form suitable for the device, such as XHTML Basic, C-HTML, or WML. Similarly, a suitable device-specific CSS style sheet or a set of in-line styles might be generated from abstract style definitions. Likewise, a device specific layout might be generated from abstract layout definitions. Once created, the device-specific materials form the response returned to the device from which the request was made. Another way is to use the latest trend responsive design based on CSS, covered in this article (RWD). Content adaptation requires a processor that performs the selection, modification, and generation of materials to form the device-specific result. IBM's Websphere Everyplace Mobile Portal (WEMP), BEA Systems' WebLogic Mobility Server, Morfeo's MyMobileWeb, and Apache Cocoon are examples of such processors. Wurfl and WALL are popular open source tools for content adaptation. WURFL is an XML-based Device Description Repository with APIs to access the data in Java and PHP (and other popular programming languages). WALL (Wireless Abstraction Library) lets a developer author mobile pages which look like plain HTML, but converts them to WML, C-HTML, or XHTML Mobile Profile, depending on the capabilities of the device from which the HTTP request originates. GreasySpoon lets the developer build plugins for content editing, in JavaScript, Ruby (programming language), and more, just like the Firefox application GreaseMonkey. Alembik (Media Transcoding Server) is a Java (J2EE) application providing transcoding services for variety of clients and for different media types (image, audio, video, etc.). It is fully compliant with OMA's Standard Transcoder Interface specification and is distributed under the LGPL open source license. In 2007, the first large scale carrier-grade deployments of content transformation, on existing mass-market handsets, with no software download required, were deployed by Vodafone in the UK and globally for Yahoo! oneSearch, using the Novarra Vision solution. Novarra's content adaptation solution had been used in enterprise intranet deployments as early as 2003 (at that time, the platform was named "Engines for Wireless Data"). InfoGin, the 9-year-old content-adaptation company with customers like Vodafone, Orange, Telefónica and PCCW. The patented "Web to Mobile adaptation", Mobile Matrix Transcoder, Multimedia and Documents transcoders, Video adaptation supporte. Launched in 2007, Bytemobile's Web Fidelity Service was another carrier-grade, commercial infrastructure solution, which provided wireless content adaptation to mobile subscribers on their existing mass-market handsets, with no client download required.
Randomized benchmarking
Randomized benchmarking is an experimental method for measuring the average error rates of quantum computing hardware platforms. The protocol estimates the average error rates by implementing long sequences of randomly sampled quantum gate operations. Randomized benchmarking is the industry-standard protocol used by quantum hardware developers such as IBM and Google to test the performance of the quantum operations. The original theory of randomized benchmarking, proposed by Joseph Emerson and collaborators, considered the implementation of sequences of Haar-random operations, but this had several practical limitations. The now-standard protocol for randomized benchmarking (RB) relies on uniformly random Clifford operations, as proposed in 2006 by Dankert et al. as an application of the theory of unitary t-designs. In current usage randomized benchmarking sometimes refers to the broader family of generalizations of the 2005 protocol involving different random gate sets that can identify various features of the strength and type of errors affecting the elementary quantum gate operations. Randomized benchmarking protocols are an important means of verifying and validating quantum operations and are also routinely used for the optimization of quantum control procedures. == Overview == Randomized benchmarking offers several key advantages over alternative approaches to error characterization. For example, the number of experimental procedures required for full characterization of errors (called tomography) grows exponentially with the number of quantum bits (called qubits). This makes tomographic methods impractical for even small systems of just 3 or 4 qubits. In contrast, randomized benchmarking protocols are the only known approaches to error characterization that scale efficiently as number of qubits in the system increases. Thus RB can be applied in practice to characterize errors in arbitrarily large quantum processors. Additionally, in experimental quantum computing, procedures for state preparation and measurement (SPAM) are also error-prone, and thus quantum process tomography is unable to distinguish errors associated with gate operations from errors associated with SPAM. In contrast, RB protocols are robust to state-preparation and measurement errors Randomized benchmarking protocols estimate key features of the errors that affect a set of quantum operations by examining how the observed fidelity of the final quantum state decreases as the length of the random sequence increases. If the set of operations satisfies certain mathematical properties, such as comprising a sequence of twirls with unitary two-designs, then the measured decay can be shown to be an invariant exponential with a rate fixed uniquely by features of the error model. == History == Randomized benchmarking was proposed in Scalable noise estimation with random unitary operators, where it was shown that long sequences of quantum gates sampled uniformly at random from the Haar measure on the group SU(d) would lead to an exponential decay at a rate that was uniquely fixed by the error model. Emerson, Alicki and Zyczkowski also showed, under the assumption of gate-independent errors, that the measured decay rate is directly related to an important figure of merit, the average gate fidelity and independent of the choice of initial state and any errors in the initial state, as well as the specific random sequences of quantum gates. This protocol applied for arbitrary dimension d and an arbitrary number n of qubits, where d=2n. The SU(d) RB protocol had two important limitations that were overcome in a modified protocol proposed by Dankert et al., who proposed sampling the gate operations uniformly at random from any unitary two-design, such as the Clifford group. They proved that this would produce the same exponential decay rate as the random SU(d) version of the protocol proposed in Emerson et al.. This follows from the observation that a random sequence of gates is equivalent to an independent sequence of twirls under that group, as conjectured in and later proven in. This Clifford-group approach to Randomized Benchmarking is the now standard method for assessing error rates in quantum computers. A variation of this protocol was proposed by NIST in 2008 for the first experimental implementation of an RB-type for single qubit gates. However, the sampling of random gates in the NIST protocol was later proven not to reproduce any unitary two-design. The NIST RB protocol was later shown to also produce an exponential fidelity decay, albeit with a rate that depends on non-invariant features of the error model In recent years a rigorous theoretical framework has been developed for Clifford-group RB protocols to show that they work reliably under very broad experimental conditions. In 2011 and 2012, Magesan et al. proved that the exponential decay rate is fully robust to arbitrary state preparation and measurement errors (SPAM). They also proved a connection between the average gate fidelity and diamond norm metric of error that is relevant to the fault-tolerant threshold. They also provided evidence that the observed decay was exponential and related to the average gate fidelity even if the error model varied across the gate operations, so-called gate-dependent errors, which is the experimentally realistic situation. In 2018, Wallman and Dugas et al., showed that, despite concerns raised in, even under very strong gate-dependence errors the standard RB protocols produces an exponential decay at a rate that precisely measures the average gate-fidelity of the experimentally relevant errors. The results of Wallman. in particular proved that the RB error rate is so robust to gate-dependent errors models that it provides an extremely sensitive tool for detecting non-Markovian errors. This follows because under a standard RB experiment only non-Markovian errors (including time-dependent Markovian errors) can produce a statistically significant deviation from an exponential decay The standard RB protocol was first implemented for single qubit gate operations in 2012 at Yale on a superconducting qubit. A variation of this standard protocol that is only defined for single qubit operations was implemented by NIST in 2008 on a trapped ion. The first implementation of the standard RB protocol for two-qubit gates was performed in 2012 at NIST for a system of two trapped ions
CodePen
CodePen is an online community for testing and showcasing user-created HTML, CSS and JavaScript code snippets. It functions as an online code editor and open-source learning environment, where developers can create code snippets, called "pens," and test them. It was founded in 2012 by full-stack developers Alex Vazquez and Tim Sabat and front-end designer Chris Coyier. Its employees work remotely, rarely all meeting together in person. CodePen is a large community for web designers and developers to showcase their coding skills, with an estimated 330,000 registered users and 14.16 million monthly visitors.
Closest point method
The closest point method (CPM) is an embedding method for solving partial differential equations on surfaces. The closest point method uses standard numerical approaches such as finite differences, finite element or spectral methods in order to solve the embedding partial differential equation (PDE) which is equal to the original PDE on the surface. The solution is computed in a band surrounding the surface in order to be computationally efficient. In order to extend the data off the surface, the closest point method uses a closest point representation. This representation extends function values to be constant along directions normal to the surface. == Definitions == Closest Point function: Given a surface S , c p ( x ) {\displaystyle {\mathcal {S}},cp(\mathbf {x} )} refers to a (possibly non-unique) point belonging to S {\displaystyle {\mathcal {S}}} , which is closest to x {\displaystyle \mathbf {x} } [SE]. Closest point extension: Let S {\displaystyle {\mathcal {S}}} , be a smooth surface in R d {\displaystyle \mathbb {R} ^{d}} . The closest point extension of a function u : S → R {\displaystyle u:{\mathcal {S}}\rightarrow \mathbb {R} } , to a neighborhood Ω {\displaystyle \Omega } of S {\displaystyle {\mathcal {S}}} , is the function v : Ω → R {\displaystyle v:\Omega \rightarrow \mathbb {R} } , defined by v ( x ) = u ( c p ( x ) ) {\displaystyle v(\mathbf {x} )=u(cp(\mathbf {x} ))} . == Closest point method == Initialization consists of these steps [EW]: If it is not already given, a closest point representation of the surface is constructed. A computational domain is chosen. Typically this is a band around the surface. Replace surface gradients by standard gradients in R 3 {\displaystyle \mathbb {R} ^{3}} . Solution is initialized by extending the initial surface data on to the computational domain using the closest point function. After initialization, alternate between the following two steps: Using the closest point function, extend the solution off the surface to the computational domain. Compute the solution to the embedding PDE on a Cartesian mesh in the computational domain for one time step. == Banding == The surface PDE is extended into R 3 {\displaystyle \mathbb {R} ^{3}} however it is only necessary to solve this new PDE near the surface. Hence, we solve the PDE in a band surrounding the surface for efficient computational purposes. Ω c x : ‖ x − c p ( x ) ‖ 2 ≤ λ {\displaystyle \Omega _{c}{x:\|x-cp(x)\|_{2}\leq \lambda }} where λ {\displaystyle \lambda } is the bandwidth. == Example: Heat equation on a circle == Using initial profile u S ( θ , t ) = sin ( θ ) {\displaystyle u_{S}(\theta ,t)=\sin(\theta )} leads to the solution u S ( θ , t ) = exp ( − t ) sin ( θ ) {\displaystyle u_{S}(\theta ,t)=\exp(-t)\sin(\theta )} for the heat equation. Forward Euler time-stepping is used with relation Δ t = 0.1 Δ x 2 {\displaystyle \Delta t=0.1\Delta x^{2}} and degree-four interpolation polynomials for the interpolations. Second-order centered differences are used for the spatial discretization. The CPM results in the expected second order error in the solution u {\displaystyle u} . == Applications == The closest point method can be applied to various PDEs on surfaces. Reaction–diffusion problems on point clouds [RD], eigenvalue problems [EV], and level set equations [LS] are a few examples.
Outline of electronics
The following outline is provided as an overview of and topical guide to electronics: Electronics – branch of physics, engineering and technology dealing with electrical circuits that involve active semiconductor components and associated passive interconnection technologies. == Branches == === Classical electronics === Analog electronics Digital electronics Electronic instrumentation Electronic engineering Microelectronics Optoelectronics Power electronics Printed electronics Semiconductor technology Schematic capture Thermal management Automation Electronics === Advanced topics === Atomtronics Bioelectronics Failure modes of electronics Flexible electronics Low-power electronics Microelectromechanical systems (MEMS) Molecular electronics Nanoelectronics Organic electronics Photonics Piezotronics Quantum electronics Spintronics === History of electronics === History of electronic engineering History of radar History of radio History of television == General concepts == === Data converters === Analog-to-digital converters (ADC) Aliasing Successive approximation ADC Dual-slope ADC Quantization Sensor resolution Sampling Delta-sigma ADC Digital-to-analog converters (DAC) Digital potentiometer Binary weighted resistor converter Charge distribution DAC Pulse width modulator Reconstruction filter The R2R ladder === Digital electronics === Binary decision diagrams Boolean algebra Combinational logic Counters (digital) De Morgan's laws Digital circuit Formal verification Karnaugh maps Logic families Logic gate Logic minimization Logic simulation Logic synthesis Registers Sequential logic State machines Truth tables Transparent latch === Electrical element/discretes === Passive elements: Capacitor Inductor Memristor Resistor Transformer Active elements: Diode Zener diode Light-emitting diode PIN diode Schottky diode Avalanche diode Laser diode Microcontroller Operational amplifier Thyristor DIAC TRIAC IGBT Transistor Bipolar transistor (BJT) Field effect transistor (FET) Darlington transistor Other components Aural devices Battery (electricity) Crystal oscillator Electromechanical devices Sensors Surface acoustic wave (SAW) === Electronics analysis === Electronic packaging Electronic circuit simulation Electronic design automation Electronic noise Mathematical methods in electronics Thermal management of electronic devices and systems === Electronic circuits === Amplifiers Differential amplifiers Feedback amplifiers Power amplifiers Comparators Converters Filters Active filters Passive filters Digital filters Oscillators Phase-locked loops Timers === Electronic equipment === Air conditioner Breathalyzer Central heating Clothes dryer Computer/Notebook Dishwasher Freezer Home robot Home entertainment system Information technologies Cooker Microwave oven Refrigerator Robotic vacuum cleaner Tablet Telephone Water heater Washing machine === Television === Analog television History of television Television show Television broadcaster Timeline of the introduction of television in countries Mechanical television Color television Digital television Digital television transition Smart television Streaming television Internet Protocol television 3D television Terrestrial television ==== Television broadcasting ==== === Electronic instrumentation === Ammeter Capacitance meter Distortionmeter Electric energy meter LCR meter Microwave power meter Multimeter Network analyzer Ohmmeter Oscilloscope Psophometer Q meter Signal analyzer Signal generator Spectrum analyzer Transistor tester Tube tester Wattmeter Vectorscope Video signal generator Voltmeter VU meter === Memory technology === Flash memory Hard drive systems Optical storage Probe Storage Programmable read-only memory Read-only memory Solid-state drive (SSD) Volatile memory === Microcontrollers === Features Analog-to-digital converter Central processing unit (CPU) Clock generator (Quartz timing crystal, resonator or RC circuit) Debugging support Digital-to-analog converters Discrete input and output bits In-circuit programming Non-volatile memory (ROM, EPROM, EEPROM or Flash) Peripherals (Timers, event counters, PWM generators, and watchdog) Serial interface (Input/output such as serial ports (UARTs)) Serial communications (I²C, Serial Peripheral Interface and Controller Area Network) Volatile memory (RAM) 8-bit microcontroller families: AVR - PIC - COP8 - MCS-48 - MCS-51 - Z8 - eZ80 - HC08 - HC11 - H8 - PSoC Some notable suppliers: ARM Atmel Cypress Semiconductor Freescale Intel MIPS Microchip Technology NXP Semiconductors Parallax Propeller PowerPC Rabbit 2000 Renesas RX, V850 Silicon Laboratories STMicroelectronics Texas Instruments Toshiba TLCS === Optoelectronics === Optical fiber Optical properties Optical receivers Optical system design Optical transmitters === Physical laws === Ampère's law Coulomb's law Faraday's law of induction/Faraday-Lenz law Gauss's law Kirchhoff's circuit laws Current law Voltage law Maxwell's equations Gauss's law Faraday's law of induction Ampère's law Ohm's law === Power electronics === Power Devices Gate turn-off thyristor MOS-controlled thyristor (MCT) Power BJT/MOSFET Static induction devices Electric power conversion DC to DC DC to DC converter Voltage stabiliser Linear regulator AC to DC Rectifier Mains power supply unit (PSU) Switched-mode power supply DC to AC Inverter AC to AC Cycloconverter Transformer Variable frequency transformer Voltage converter Voltage regulator Power applications Automotive applications Capacitor charging applications Electronic ballasts Energy harvesting technologies Flexible AC transmission systems (FACTS) High frequency inverters HVDC transmission Motor controller Photovoltaic system Conversion Power factor correction circuits Power supply Renewable energy sources Switching power converters Uninterruptible power supply Wind power === Programmable devices === Application-specific integrated circuit (ASIC) Complex programmable logic device (CPLD) Erasable programmable logic device (EPLD) Simple programmable logic device (SPLD) Macrocell array Programmable array logic (PAL) Programmable logic array (PLA) Programmable logic device (PLD) Field-programmable gate array (FPGA) VHSIC Hardware Description Language (VHDL) Verilog Hardware Description Language Some notable suppliers: Altera - Atmel - Cypress Semiconductor - Lattice Semiconductor - Xilinx === Semiconductors theory === Properties Bipolar junction transistors Capacitance voltage profiling Charge carrier Charge-transfer complex Deep-level transient spectroscopy Depletion region Density of states Diode modelling Direct band gap Electronic band structure Energy level Exciton Field-effect transistors Metal–semiconductor junction MOSFETs N-type semiconductor Organic semiconductors P–n junction P-type semiconductor Photoelectric effect Quantum tunneling Semiconductor chip Semiconductor detector Solar cell Transistor model Thin film Tight-binding model Device Fabrication Semiconductor device fabrication Semiconductor industry Semiconductor consolidation == Applications == Audio electronics Automotive electronics Avionics Control Systems Consumer electronics Data acquisition E-health Electronic book Electronics industry Electronic warfare Embedded systems Home automation Integrated circuits Marine electronics Microwave technology Military electronics Multimedia Nuclear electronics Open hardware Radar and Radionavigation Radio electronics Terahertz technology Video hardware Wired and Wireless Communications