890,376 research outputs found
The forgotten majority of computing have-nots
People have falsely regarded the computing working environment as dedicated to the confines of a building with availability of electricity, high-speed info-structure, and the latest computer technology. In reality, there are many computing have-nots in the real world living in challenged computing environments. Current computing curricula are designed to prepare graduates for more urban and best-case business scenarios where learning takes place within campus boundaries. To prepare computing graduates better to survive in harsh environments and to contribute meaningfully to society, their learning should also take place out of the classroom and into challenged computing environments where active and experiential learning could take place. Confronted with the harsh realities of life, students learn quickly to adapt themselves for survival and for their future career. Many generic skills can be reinforced here to make computing graduates more versatile, entrepreneurial, effective, and ever ready to face the real world
An integrated cryogenic optical modulator
Integrated electrical and photonic circuits (PIC) operating at cryogenic
temperatures are fundamental building blocks required to achieve scalable
quantum computing, and cryogenic computing technologies. Optical interconnects
offer better performance and thermal insulation than electrical wires and are
imperative for true quantum communication. Silicon PICs have matured for room
temperature applications but their cryogenic performance is limited by the
absence of efficient low temperature electro-optic (EO) modulation. While
detectors and lasers perform better at low temperature, cryogenic optical
switching remains an unsolved challenge. Here we demonstrate EO switching and
modulation from room temperature down to 4 K by using the Pockels effect in
integrated barium titanate (BaTiO3)-based devices. We report the nonlinear
optical (NLO) properties of BaTiO3 in a temperature range which has previously
not been explored, showing an effective Pockels coefficient of 200 pm/V at 4 K.
We demonstrate the largest EO bandwidth (30 GHz) of any cryogenic switch to
date, ultra-low-power tuning which is 10^9 times more efficient than thermal
tuning, and high-speed data modulation at 20 Gbps. Our results demonstrate a
missing component for cryogenic PICs. It removes major roadblocks for the
realisation of novel cryogenic-compatible systems in the field of quantum
computing and supercomputing, and for interfacing those systems with the real
world at room-temperature
The EPICS Software Framework Moves from Controls to Physics
The Experimental Physics and Industrial Control System (EPICS), is an open-source software framework for high-performance distributed control, and is at the heart of many of the world’s large accelerators and telescopes. Recently, EPICS has undergone a major revision, with the aim of better computing supporting for the next generation of machines and analytical tools. Many new data types, such as matrices, tables, images, and statistical descriptions, plus users’ own data types, now supplement the simple scalar and waveform types of the former EPICS. New computational architectures for scientific computing have been added for high-performance data processing services and pipelining. Python and Java bindings have enabled powerful new user interfaces. The result has been that controls are now being integrated with modelling and simulation, machine learning, enterprise databases, and experiment DAQs. We introduce this new EPICS (version 7) from the perspective of accelerator physics and review early adoption cases in accelerators around the world
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies
An algorithm that learns from a set of examples should ideally be able to
exploit the available resources of (a) abundant computing power and (b)
domain-specific knowledge to improve its ability to generalize. Connectionist
theory-refinement systems, which use background knowledge to select a neural
network's topology and initial weights, have proven to be effective at
exploiting domain-specific knowledge; however, most do not exploit available
computing power. This weakness occurs because they lack the ability to refine
the topology of the neural networks they produce, thereby limiting
generalization, especially when given impoverished domain theories. We present
the REGENT algorithm which uses (a) domain-specific knowledge to help create an
initial population of knowledge-based neural networks and (b) genetic operators
of crossover and mutation (specifically designed for knowledge-based networks)
to continually search for better network topologies. Experiments on three
real-world domains indicate that our new algorithm is able to significantly
increase generalization compared to a standard connectionist theory-refinement
system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file
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