208 research outputs found
Distributed Averaging via Lifted Markov Chains
Motivated by applications of distributed linear estimation, distributed
control and distributed optimization, we consider the question of designing
linear iterative algorithms for computing the average of numbers in a network.
Specifically, our interest is in designing such an algorithm with the fastest
rate of convergence given the topological constraints of the network. As the
main result of this paper, we design an algorithm with the fastest possible
rate of convergence using a non-reversible Markov chain on the given network
graph. We construct such a Markov chain by transforming the standard Markov
chain, which is obtained using the Metropolis-Hastings method. We call this
novel transformation pseudo-lifting. We apply our method to graphs with
geometry, or graphs with doubling dimension. Specifically, the convergence time
of our algorithm (equivalently, the mixing time of our Markov chain) is
proportional to the diameter of the network graph and hence optimal. As a
byproduct, our result provides the fastest mixing Markov chain given the
network topological constraints, and should naturally find their applications
in the context of distributed optimization, estimation and control
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
We study contextual linear bandit problems under uncertainty on features;
they are noisy with missing entries. To address the challenges from the noise,
we analyze Bayesian oracles given observed noisy features. Our Bayesian
analysis finds that the optimal hypothesis can be far from the underlying
realizability function, depending on noise characteristics, which is highly
non-intuitive and does not occur for classical noiseless setups. This implies
that classical approaches cannot guarantee a non-trivial regret bound. We thus
propose an algorithm aiming at the Bayesian oracle from observed information
under this model, achieving regret bound with respect to
feature dimension and time horizon . We demonstrate the proposed
algorithm using synthetic and real-world datasets.Comment: 30 page
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Development of Opensource-based Photogrammetric UAV System Using Smart Camera
Normally, aero photography using UAV uses about 430MHz bandwidth RF (radio frequency) modem and navigates and remotely controls through the connection between UAV and ground control system. When using the exhausting method, it has communication range of 1-2km with frequent cross line and since wireless communication sends information using radio wave as a carrier, it has 10mW of signal strength limitation which gave restraints on life my distance communication. The purpose of research is to use communication technologies such as LTE (long-term evolution) of smart camera, Bluetooth, Wi-Fi and other communication modules and cameras that can transfer data to design and develop automatic shooting system that acquires images to UAV at the necessary locations. We conclude that the Photogrammetric UAV system using Smart Camera can not only film images with just one smart camera but also connects UAV system and ground control system together and also able to obtain real-time 3D location information and 3D position information using UAV system, GPS, a gyroscope, an accelerometer, and magnetic measuring sensor which will allow us to use real-time position of the UAV and correction work through non-datum aero triangulation
Effect of charge-transfer complex on the energy level alignment between graphene and organic molecules
We performed density-functional theory calculations to study the electronic structures at the interfaces between graphene and organic molecules that have been used in organic light-emitting diodes. In terms of work function, graphene itself is not favorable as either anode or cathode for commonly used electron or hole transport molecular species. However, the formation of charge transfer complex on the chemically inert sp(2) carbon surface can provide a particular advantage. Unlike metal surfaces, the graphene surface remains non-bonded to electron-accepting molecules even after electron transfer, inducing an improved Fermi-level alignment with the highest-occupied-molecular-orbital level of the hole-injecting-layer molecules.open1
Time Is MattEr: Temporal Self-supervision for Video Transformers
Understanding temporal dynamics of video is an essential aspect of learning
better video representations. Recently, transformer-based architectural designs
have been extensively explored for video tasks due to their capability to
capture long-term dependency of input sequences. However, we found that these
Video Transformers are still biased to learn spatial dynamics rather than
temporal ones, and debiasing the spurious correlation is critical for their
performance. Based on the observations, we design simple yet effective
self-supervised tasks for video models to learn temporal dynamics better.
Specifically, for debiasing the spatial bias, our method learns the temporal
order of video frames as extra self-supervision and enforces the randomly
shuffled frames to have low-confidence outputs. Also, our method learns the
temporal flow direction of video tokens among consecutive frames for enhancing
the correlation toward temporal dynamics. Under various video action
recognition tasks, we demonstrate the effectiveness of our method and its
compatibility with state-of-the-art Video Transformers.Comment: Accepted to ICML 2022. Code is available at
https://github.com/alinlab/temporal-selfsupervisio
Experimental Study on Electromagnetic Forming of High Strength Steel Sheets with Different Dimensions of Aluminum Driver Plate
Recently, the potential of the electromagnetic forming process has been introduced to
form the shallow longitudinal reinforcement ribs in the lateral walls of roll formed parts,
made of high strength steel sheets of 340MPa tensile stress grade [1]. However, it seems
that the application may not be easy for high strength steel sheet because of its high
tensile strength and low electric conductivity. In order to overcome this difficulty, aluminum
driver plate could be considered to enhance the formability of high strength steel sheets in
the electromagnetic forming process. In this paper, in order to investigate the effect of
aluminum driver plate on forming height of high strength steel sheet in electromagnetic
forming process, DP780 workpiece sheets were formed into a hemi elliptical protrusion
shape with Al1050 driver plate of various thicknesses and sizes. Experiments were
performed with a flat spiral coil actuator connected to an electromagnetic forming system.
The results, the aluminum driver plate helps to increase the forming height of high
strength steel sheets. In addition, the forming height of high strength steel sheet increases
as the thickness and size of a driver plate increases
Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
Reducing bit-widths of activations and weights of deep networks makes it
efficient to compute and store them in memory, which is crucial in their
deployments to resource-limited devices, such as mobile phones. However,
decreasing bit-widths with quantization generally yields drastically degraded
accuracy. To tackle this problem, we propose to learn to quantize activations
and weights via a trainable quantizer that transforms and discretizes them.
Specifically, we parameterize the quantization intervals and obtain their
optimal values by directly minimizing the task loss of the network. This
quantization-interval-learning (QIL) allows the quantized networks to maintain
the accuracy of the full-precision (32-bit) networks with bit-width as low as
4-bit and minimize the accuracy degeneration with further bit-width reduction
(i.e., 3 and 2-bit). Moreover, our quantizer can be trained on a heterogeneous
dataset, and thus can be used to quantize pretrained networks without access to
their training data. We demonstrate the effectiveness of our trainable
quantizer on ImageNet dataset with various network architectures such as
ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve
the state-of-the-art accuracy
Spin cast ferroelectric beta poly(vinylidene fluoride) thin films via rapid thermal annealing
We describe a method of fabricating ferroelectric beta-type poly(vinylidene fluoride) (PVDF) thin films on Au substrate by the humidity controlled spin casting combined with rapid thermal treatment. Our method produces thin uniform ferroelectric PVDF film with ordered beta crystals consisting of characteristic needlelike microdomains. A capacitor with a 160 nm thick ferroelectric PVDF film exhibits the remanent polarization and coercive voltage of similar to 7.0 mu C/cm(2) and 8 V, respectively, with the temperature stability of up to 160 degrees C. A ferroelectric field effect transistor also shows a drain current bistablility of 100 at zero gate voltage with +/- 20 V gate voltage sweep. (C) 2008 American Institute of Physicsopen485
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