다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted. Each of the sections describe recent successes as well as domain-specific methodology and challenges.

Machine learning and the physical sciences

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin-Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

/pdf/neuromorphic-silicon-neuron-circuits-3fphnmpvq9.pdf

Neuromorphic Silicon Neuron Circuits

https://www.cs.tufts.edu/comp/150AIH/pdf/NadkarniOC11.pdf

Natural language processing: an introduction.

https://dspace.stir.ac.uk/bitstream/1893/25490/1/affective-computing-review.pdf

A review of affective computing

The implementation of multi-carrier code division multiple access (MC-CDMA) receivers in digital hardware is considered. A low power algorithm is proposed which treats the received signal as a block of symbols, rather than processing the symbols individually. This reduces power by holding one input to the multiplier circuits used in the multi-carrier combiner multiplication constant for a number of clock cycles. This produces a 50% reduction in power consumption for a multi-user detection combiner circuit. This algorithm is also extended to the fast Fourier transform (FFT) block and allows an overall power drain reduction of 13% for the whole receiver. A software configurable version of the circuit, which allows a trade-off between power reduction and processing delay, is also described.

Low power receiver architectures for multi-carrier CDMA

Two multiplication schemes are investigated for the low power implementation of FIR filters through the reduction of switching activity within the multiplier section of the filters The schemes, which target single multiplier CMOS based DSP processors, are used with transpose direct form filter structure and their switching activities are compared

Low power multiplication schemes for single multiplier CMOS based FIR digital filter implementations

ESPACENET: A Joint Project for Evolvable and Reconfigurable Sensor Networks for Application to Aerospace-Based Monitoring and Diagnosis

We present an achromatic confocal laser scanning system capable of recording spectrally resolved fluorescence lifetime images (sFLIMs) at a rate of >8 frames per second (FPS) for a 128 x 128 image. This frame rate was achieved by optimizing the processing of lifetime calculations from previous results which demonstrated >4 FPS sFLIM imaging. The imaging system is achromatic for a spectral range of 400 - 900 nm, achieved by using reflective optics instead of a transmissive lens system, except for the primary objectives. Two excitation sources have been integrated into the system, 485 nm and 640 nm laser diodes with a pulse width of <70 ps and <90 ps respectively. Imaging is performed via a galvanometric mirror system which scans the laser beam over the sample with the ability to change the Field of View (FOV) on the fly. The collected fluorescence signal is focused into a multimode fiber via a second objective and recollimated onto a transmissive grating for spectral dispersion onto a novel complementary metal–oxide–semiconductor single photon avalanche diode (CMOS SPAD) line array sensor. This sensor can perform lifetime histogram generation on-chip and process over 16.5 Giga events/s enabling fast lifetime data acquisition. High speed sFLIM is demonstrated through imaging of convallaria majalis sections.

High speed spectral fluorescence lifetime imaging for life science applications

Several algorithms for performing cell delineation on a bit-serial and octet-parallel basis are considered. Four algorithms were implemented in total, two bit-serial and two octet-parallel, for the design of cell delineators through to netlist generation. Analysis is provided for post-synthesis designs detailing speed, area and power parameters for each implementation at an input data rate of 160 Mbps and 1280 Mbps.

Asynchronous transfer mode cell delineator implementations

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