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 $\tilde{O}(d\sqrt{T})$ regret bound with respect to feature dimension $d$ and time horizon $T$. We demonstrate the proposed algorithm using synthetic and real-world datasets.Comment: 30 page

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