380 research outputs found
High Speed Human Action Recognition using a Photonic Reservoir Computer
The recognition of human actions in videos is one of the most active research
fields in computer vision. The canonical approach consists in a more or less
complex preprocessing stages of the raw video data, followed by a relatively
simple classification algorithm. Here we address recognition of human actions
using the reservoir computing algorithm, which allows us to focus on the
classifier stage. We introduce a new training method for the reservoir
computer, based on "Timesteps Of Interest", which combines in a simple way
short and long time scales. We study the performance of this algorithm using
both numerical simulations and a photonic implementation based on a single
non-linear node and a delay line on the well known KTH dataset. We solve the
task with high accuracy and speed, to the point of allowing for processing
multiple video streams in real time. The present work is thus an important step
towards developing efficient dedicated hardware for video processing
TransNet: A Transfer Learning-Based Network for Human Action Recognition
Human action recognition (HAR) is a high-level and significant research area
in computer vision due to its ubiquitous applications. The main limitations of
the current HAR models are their complex structures and lengthy training time.
In this paper, we propose a simple yet versatile and effective end-to-end deep
learning architecture, coined as TransNet, for HAR. TransNet decomposes the
complex 3D-CNNs into 2D- and 1D-CNNs, where the 2D- and 1D-CNN components
extract spatial features and temporal patterns in videos, respectively.
Benefiting from its concise architecture, TransNet is ideally compatible with
any pretrained state-of-the-art 2D-CNN models in other fields, being
transferred to serve the HAR task. In other words, it naturally leverages the
power and success of transfer learning for HAR, bringing huge advantages in
terms of efficiency and effectiveness. Extensive experimental results and the
comparison with the state-of-the-art models demonstrate the superior
performance of the proposed TransNet in HAR in terms of flexibility, model
complexity, training speed and classification accuracy
A programmable chemical computer with memory and pattern recognition
Current computers are limited by the von Neumann bottleneck, which constrains the throughput between the processing unit and the memory. Chemical processes have the potential to scale beyond current computing architectures as the processing unit and memory reside in the same space, performing computations through chemical reactions, yet their lack of programmability limits them. Herein, we present a programmable chemical processor comprising of a 5 by 5 array of cells filled with a switchable oscillating chemical (Belousov–Zhabotinsky) reaction. Each cell can be individually addressed in the ‘on’ or ‘off’ state, yielding more than 2.9 × 1017 chemical states which arise from the ability to detect distinct amplitudes of oscillations via image processing. By programming the array of interconnected BZ reactions we demonstrate chemically encoded and addressable memory, and we create a chemical Autoencoder for pattern recognition able to perform the equivalent of one million operations per second
Automated design of complex dynamic systems
Several fields of study are concerned with uniting the concept of computation with that of the design of physical systems. For example, a recent trend in robotics is to design robots in such a way that they require a minimal control effort. Another example is found in the domain of photonics, where recent efforts try to benefit directly from the complex nonlinear dynamics to achieve more efficient signal processing. The underlying goal of these and similar research efforts is to internalize a large part of the necessary computations within the physical system itself by exploiting its inherent non-linear dynamics. This, however, often requires the optimization of large numbers of system parameters, related to both the system's structure as well as its material properties. In addition, many of these parameters are subject to fabrication variability or to variations through time. In this paper we apply a machine learning algorithm to optimize physical dynamic systems. We show that such algorithms, which are normally applied on abstract computational entities, can be extended to the field of differential equations and used to optimize an associated set of parameters which determine their behavior. We show that machine learning training methodologies are highly useful in designing robust systems, and we provide a set of both simple and complex examples using models of physical dynamical systems. Interestingly, the derived optimization method is intimately related to direct collocation a method known in the field of optimal control. Our work suggests that the application domains of both machine learning and optimal control have a largely unexplored overlapping area which envelopes a novel design methodology of smart and highly complex physical systems
Intelligent Computing: The Latest Advances, Challenges and Future
Computing is a critical driving force in the development of human
civilization. In recent years, we have witnessed the emergence of intelligent
computing, a new computing paradigm that is reshaping traditional computing and
promoting digital revolution in the era of big data, artificial intelligence
and internet-of-things with new computing theories, architectures, methods,
systems, and applications. Intelligent computing has greatly broadened the
scope of computing, extending it from traditional computing on data to
increasingly diverse computing paradigms such as perceptual intelligence,
cognitive intelligence, autonomous intelligence, and human-computer fusion
intelligence. Intelligence and computing have undergone paths of different
evolution and development for a long time but have become increasingly
intertwined in recent years: intelligent computing is not only
intelligence-oriented but also intelligence-driven. Such cross-fertilization
has prompted the emergence and rapid advancement of intelligent computing.
Intelligent computing is still in its infancy and an abundance of innovations
in the theories, systems, and applications of intelligent computing are
expected to occur soon. We present the first comprehensive survey of literature
on intelligent computing, covering its theory fundamentals, the technological
fusion of intelligence and computing, important applications, challenges, and
future perspectives. We believe that this survey is highly timely and will
provide a comprehensive reference and cast valuable insights into intelligent
computing for academic and industrial researchers and practitioners
Understanding Quantum Technologies 2022
Understanding Quantum Technologies 2022 is a creative-commons ebook that
provides a unique 360 degrees overview of quantum technologies from science and
technology to geopolitical and societal issues. It covers quantum physics
history, quantum physics 101, gate-based quantum computing, quantum computing
engineering (including quantum error corrections and quantum computing
energetics), quantum computing hardware (all qubit types, including quantum
annealing and quantum simulation paradigms, history, science, research,
implementation and vendors), quantum enabling technologies (cryogenics, control
electronics, photonics, components fabs, raw materials), quantum computing
algorithms, software development tools and use cases, unconventional computing
(potential alternatives to quantum and classical computing), quantum
telecommunications and cryptography, quantum sensing, quantum technologies
around the world, quantum technologies societal impact and even quantum fake
sciences. The main audience are computer science engineers, developers and IT
specialists as well as quantum scientists and students who want to acquire a
global view of how quantum technologies work, and particularly quantum
computing. This version is an extensive update to the 2021 edition published in
October 2021.Comment: 1132 pages, 920 figures, Letter forma
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