1,100 research outputs found
Quasar x-ray spectra revisited
A sample of 45 quasars observed by the Imaging Proportional Counter (IPC) on the Einstein satellite is used to re-examine the relationship between the soft (0.2-3.5 keV) X-ray energy index and radio-loudness. We found the following: (1) the tendency for radio-loud quasars to have systematically flatter X-ray slopes than radio-quiet quasars (RQQ's) is confirmed with the soft X-ray excess having negligible effect; (2) there is a tendency for the flatness of the X-ray slope to correlate with radio core-dominance for radio-loud quasars, suggesting that a component of the X-ray emission is relativistically beamed; (3) for the RQQ's the soft X-ray slopes, with a mean of approximately 1.0, are consistent with the slopes found at higher energies (2-10 keV) although steeper than those observed for Seyfert 1 galaxies (also 2-10 keV) where the reflection model gives a good fit to the data; (4) the correlation of FeII emission line strength with X-ray energy index is confirmed for radio-quiet quasars using a subset of 18 quasars. The radio-loud quasars show no evidence for a correlation. This relation suggests a connection between the ionizing continuum and the line emission from the broad emission line region (BELR) of radio-quiet quasars, but in the opposite sense to that predicted by current photoionization models; and (5) the correlations of X-ray slope with radio core dominance and FeII equivalent width within the radio-loud and radio-quiet sub-classes respectively imply that the observed wide range of X-ray spectral slopes is real rather than due to the large measuring uncertainties for individual objects
SIMPEL: Circuit model for photonic spike processing laser neurons
We propose an equivalent circuit model for photonic spike processing laser
neurons with an embedded saturable absorber---a simulation model for photonic
excitable lasers (SIMPEL). We show that by mapping the laser neuron rate
equations into a circuit model, SPICE analysis can be used as an efficient and
accurate engine for numerical calculations, capable of generalization to a
variety of different laser neuron types found in literature. The development of
this model parallels the Hodgkin--Huxley model of neuron biophysics, a circuit
framework which brought efficiency, modularity, and generalizability to the
study of neural dynamics. We employ the model to study various
signal-processing effects such as excitability with excitatory and inhibitory
pulses, binary all-or-nothing response, and bistable dynamics.Comment: 16 pages, 7 figure
Integrated Photonic Tensor Processing Unit for a Matrix Multiply: a Review
The explosion of artificial intelligence and machine-learning algorithms,
connected to the exponential growth of the exchanged data, is driving a search
for novel application-specific hardware accelerators. Among the many, the
photonics field appears to be in the perfect spotlight for this global data
explosion, thanks to its almost infinite bandwidth capacity associated with
limited energy consumption. In this review, we will overview the major
advantages that photonics has over electronics for hardware accelerators,
followed by a comparison between the major architectures implemented on
Photonics Integrated Circuits (PIC) for both the linear and nonlinear parts of
Neural Networks. By the end, we will highlight the main driving forces for the
next generation of photonic accelerators, as well as the main limits that must
be overcome
Dynamical laser spike processing
Novel materials and devices in photonics have the potential to revolutionize
optical information processing, beyond conventional binary-logic approaches.
Laser systems offer a rich repertoire of useful dynamical behaviors, including
the excitable dynamics also found in the time-resolved "spiking" of neurons.
Spiking reconciles the expressiveness and efficiency of analog processing with
the robustness and scalability of digital processing. We demonstrate that
graphene-coupled laser systems offer a unified low-level spike optical
processing paradigm that goes well beyond previously studied laser dynamics. We
show that this platform can simultaneously exhibit logic-level restoration,
cascadability and input-output isolation---fundamental challenges in optical
information processing. We also implement low-level spike-processing tasks that
are critical for higher level processing: temporal pattern detection and stable
recurrent memory. We study these properties in the context of a fiber laser
system, but the addition of graphene leads to a number of advantages which stem
from its unique properties, including high absorption and fast carrier
relaxation. These could lead to significant speed and efficiency improvements
in unconventional laser processing devices, and ongoing research on graphene
microfabrication promises compatibility with integrated laser platforms.Comment: 13 pages, 7 figure
New faecal tests for colorectal cancer screening: is tumour pyruvate kinase M2 one of the options?
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
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