82 research outputs found
Apparatus and Method of Fabricating Directional Fiber Optic Taps, Sensors and Other Devices with Variable Angle Output
An apparatus and method for fabricating directional fiber optic taps having a variety of output angles. The taps can be used to monitor losses due to misalignment of the fiber or losses due to bending and straining of the fiber. The apparatus and method can also be used to fabricate taps which filter out higher order modes in a fiber. The apparatus and method can also be used to produce taps which can be used in a position or refractive index measuring system
Optimal Gaussian measurements for phase estimation in single-mode Gaussian metrology
The central issue in quantum parameter estimation is to find out the optimal
measurement setup that leads to the ultimate lower bound of an estimation
error. We address here a question of whether a Gaussian measurement scheme can
achieve the ultimate bound for phase estimation in single-mode Gaussian
metrology that exploits single-mode Gaussian probe states in a Gaussian
environment. We identify three types of optimal Gaussian measurement setups
yielding the maximal Fisher information depending on displacement, squeezing,
and thermalization of the probe state. We show that the homodyne measurement
attains the ultimate bound for both displaced thermal probe states and squeezed
vacuum probe states, whereas for the other single-mode Gaussian probe states,
the optimized Gaussian measurement cannot be the optimal setup, although they
are sometimes nearly optimal. We then demonstrate that the measurement on the
basis of the product quadrature operators XP+PX, i.e., a non-Gaussian
measurement, is required to be fully optimal.Comment: 13 pages, 6 figure
From technological development to social advance: A review of Industry 4.0 through machine learning
Industry 4.0 has attracted considerable interest from firms, governments, and individuals as the new concept of future computer, industrial, and social systems. However, the concept has yet to be fully explored in the scientific literature. Given the topic's broad scope, this work attempts to understand and clarify Industry 4.0 by analyzing 660 journal papers and 3,901 news articles through text mining with unsupervised machine learning algorithms. Based on the results, this work identifies 31 research and application issues related to Industry 4.0. These issues are categorized and described within a five-level hierarchy: 1) infrastructure development for connection, 2) artificial intelligence development for data-driven decision making, 3) system and process optimization, 4) industrial innovation, and 5) social advance. Further, a framework for convergence in Industry 4.0 is proposed, featuring six dimensions: connection, collection, communication, computation, control, and creation. The research outcomes are consistent with and complementary to existing relevant discussion and debate on Industry 4.0, which validates the utility and efficiency of the data-driven approach of this work to support experts??? insights on Industry 4.0. This work helps establish a common ground for understanding Industry 4.0 across multiple disciplinary perspectives, enabling further research and development for industrial innovation and social advance
INSTA-BNN: Binary Neural Network with INSTAnce-aware Threshold
Binary Neural Networks (BNNs) have emerged as a promising solution for
reducing the memory footprint and compute costs of deep neural networks. BNNs,
on the other hand, suffer from information loss because binary activations are
limited to only two values, resulting in reduced accuracy. To improve the
accuracy, previous studies have attempted to control the distribution of binary
activation by manually shifting the threshold of the activation function or
making the shift amount trainable. During the process, they usually depended on
statistical information computed from a batch. We argue that using statistical
data from a batch fails to capture the crucial information for each input
instance in BNN computations, and the differences between statistical
information computed from each instance need to be considered when determining
the binary activation threshold of each instance. Based on the concept, we
propose the Binary Neural Network with INSTAnce-aware threshold (INSTA-BNN),
which decides the activation threshold value considering the difference between
statistical data computed from a batch and each instance. The proposed
INSTA-BNN outperforms the baseline by 2.5% and 2.3% on the ImageNet
classification task with comparable computing cost, achieving 68.0% and 71.7%
top-1 accuracy on ResNet-18 and MobileNetV1 based models, respectively.Comment: 19 pages, 7 figures; excluded axessibility packag
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