Basic Enhancement Strategies When Using Bayesian Optimization for Hyperparameter Tuning of Deep Neural Networks

Compared to the traditional machine learning models, deep neural networks (DNN) are known to be highly sensitive to the choice of hyperparameters. While the required time and effort for manual tuning has been rapidly decreasing for the well developed and commonly used DNN architectures, undoubtedly DNN hyperparameter optimization will continue to be a major burden whenever a new DNN architecture needs to be designed, a new task needs to be solved, a new dataset needs to be addressed, or an existing DNN needs to be improved further. For hyperparameter optimization of general machine learning problems, numerous automated solutions have been developed where some of the most popular solutions are based on Bayesian Optimization (BO). In this work, we analyze four fundamental strategies for enhancing BO when it is used for DNN hyperparameter optimization. Specifically, diversification, early termination, parallelization, and cost function transformation are investigated. Based on the analysis, we provide a simple yet robust algorithm for DNN hyperparameter optimization - DEEP-BO (Diversified, Early-termination-Enabled, and Parallel Bayesian Optimization). When evaluated over six DNN benchmarks, DEEP-BO mostly outperformed well-known solutions including GP-Hedge, BOHB, and the speed-up variants that use Median Stopping Rule or Learning Curve Extrapolation. In fact, DEEP-BO consistently provided the top, or at least close to the top, performance over all the benchmark types that we have tested. This indicates that DEEP-BO is a robust solution compared to the existing solutions. The DEEP-BO code is publicly available at <uri>https://github.com/snu-adsl/DEEP-BO</uri>

Micro/Nano Hierarchical Super-Lyophobic Surfaces Against Gallium-Based Liquid Metal Alloy

Against a gallium-based liquid metal alloy, we report super-lyophobic surfaces that have micro/nano hierarchical structures made of polydimethylsiloxane (PDMS) and carbon nanotube (CNT) materials. The surface oxidation of the liquid metal is a challenging problem to be utilized for various applications, as it wets most surfaces. However, the micro/nano hierarchical structures of a fabricated-PDMS micro pillar array with dual-scale surface texturing and a grown-CNT surface enable one to minimize a contact area between the liquid metal droplet and the surface. Due to the low contact area, a large static contact angle and a low contact angle hysteresis are achieved, indicating super-lyophobic surfaces. Based on these super-lyophobic surfaces, the gallium-based liquid metal alloy can be more widely utilized for undeveloped applications that rely on the liquid metal’s mobility

Time-Newsweek Cover Story Game

Dixit and Nalebuff (1991) provided a simple example of how Time and Newsweek compete with each other through their cover story decisions. This paper goes beyond this example to specify the conditions under which the two competing magazines (Time and Newsweek) issue the same or different cover stories. The main result of this paper can be described as follows. The difference in relative market power and relative market size of story A to story B are critical in determining the cover story decision (business strategy). If the market size of potential story A relative to story B is sufficiently large, then both magazines may issue the same cover story. However, if the market size of potential story A relative to story B is not large enough, the relative market power (rather than the relative market size) becomes more relevant and both magazines may issue different cover stories. This paper provides empirical evidence that supports our hypothesis and shows how our finding is related to Hotelling’s paradox

DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech

The majority of current Text-to-Speech (TTS) datasets, which are collections of individual utterances, contain few conversational aspects. In this paper, we introduce DailyTalk, a high-quality conversational speech dataset designed for conversational TTS. We sampled, modified, and recorded 2,541 dialogues from the open-domain dialogue dataset DailyDialog inheriting its annotated attributes. On top of our dataset, we extend prior work as our baseline, where a non-autoregressive TTS is conditioned on historical information in a dialogue. From the baseline experiment with both general and our novel metrics, we show that DailyTalk can be used as a general TTS dataset, and more than that, our baseline can represent contextual information from DailyTalk. The DailyTalk dataset and baseline code are freely available for academic use with CC-BY-SA 4.0 license.Comment: 5 pages, 1 figures, 4 tables. Accepted to ICASSP 202

Satellite Dynamics Simulator Development Using Lie Group Variational Integrator

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90733/1/AIAA-2011-6430-719.pd

An Integration Avenue of Ground Monitoring Based on Wireless Sensor Networks

Since wireless sensor networks (WSNs) have a lot of potential capability to provide diverse services to human by monitoring things scattered in real world, they are envisioned as one of the core enabling technologies for ubiquitous computing which organizes and mediates both physical and social interactions anytime and anywhere. WSNs are being adopted in various fields and things in their zones are being monitored. However, existing WSNs are normally designed for observing special zones or regional things based on small-scale, low power, and short range technologies. Seamless system integration at a global scale is still in its infancy stage due to the lack of the fundamental integration technologies. In this paper, we present a global integration avenue of ground monitoring based on WSNs. The proposed avenue includes design, integration, and operational strategies of IP-WSN based territorial monitoring system to ensure compatibility, interoperability, and real-time. Specifically, we offer the standardization of sensing data formats using IP-WSN and database interfaces using EPC sensor network, which enable a spontaneous and systematic integration among the legacy WSN systems. Also, we categorize network topology according to topographic characteristics thereby helping deploy sensor nodes on the real environment. Therefore, the proposed technology would be a milestone for the practically deployable global territorial monitoring systems