scholarly journals Efficient Optomechanical Mode-Shape Mapping of Micromechanical Devices

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 880
Author(s):  
David Hoch ◽  
Kevin-Jeremy Haas ◽  
Leopold Moller ◽  
Timo Sommer ◽  
Pedro Soubelet ◽  
...  
Keyword(s):  
State Of The Art ◽  
Phase Lock Loop ◽  
Quality Data ◽  
High Quality Data ◽  
Silicon Nitride Membrane ◽  
Individual Contributions ◽  
Alternative Approaches ◽  
Shape Mapping ◽  
Degenerate Modes ◽  
Micromechanical Devices

Visualizing eigenmodes is crucial in understanding the behavior of state-of-the-art micromechanical devices. We demonstrate a method to optically map multiple modes of mechanical structures simultaneously. The fast and robust method, based on a modified phase-lock loop, is demonstrated on a silicon nitride membrane and shown to outperform three alternative approaches. Line traces and two-dimensional maps of different modes are acquired. The high quality data enables us to determine the weights of individual contributions in superpositions of degenerate modes.

scholarly journals Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments

2019 ◽  
Author(s):  
Leroy Cronin ◽  
Vasilios Duros ◽  
Jonathan Grizou ◽  
Abhishek Sharma ◽  
Hessam Mehr ◽  
...  
Keyword(s):  
Self Assembly ◽  
State Of The Art ◽  
Chemical Space ◽  
Autonomous Systems ◽  
Quality Data ◽  
High Quality Data ◽  
Current State ◽  
Robot Teams ◽  
Substantial Progress ◽  
Inorganic Chemical

<div> <p>Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules so vast, only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving towards the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the chemical space by comparing collaboration between human experimenters with an algorithm-based search, against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na<sub>6</sub>[Mo<sub>120</sub>Ce<sub>6</sub>O<sub>366</sub>H<sub>12</sub>(H<sub>2</sub>O)<sub>78</sub>]·200H<sub>2</sub>O (<b>1</b>). We show that the robot-human teams are able to increase the prediction accuracy to 75.6±1.8%, from 71.8±0.3% with the algorithm alone and 66.3±1.8% from only the human experimenters demonstrating that human-robot teams beat robots or humans working alone.</p> </div>

scholarly journals New model atmosphere analyses of cool white dwarfs: a revised luminosity function and constraints on the age of the Galaxy

1997 ◽  
Vol 189 ◽  
pp. 429-432
Author(s):  
S.K. Leggett ◽  
P. Bergeron ◽  
Maria Teresa Ruiz
Keyword(s):  
White Dwarfs ◽  
Luminosity Function ◽  
State Of The Art ◽  
Model Atmosphere ◽  
Quality Data ◽  
High Quality Data ◽  
The Galaxy ◽  
Effective Temperatures ◽  
Cool White Dwarfs ◽  
Turn Over

We have obtained new photometric and spectroscopic data for a large sample of cool white dwarfs. These data have been analysed with state-of-the-art model atmospheres and effective temperatures and atmospheric compositions have been determined (Bergeron, Ruiz & Leggett 1997). Radii and masses have also been obtained for those stars with accurate parallax measurements. These high quality data and models allow us to produce an improved cool white dwarf luminosity function based on the Liebert, Dahn & Monet (1988) proper motion sample. The turn-over seen at the faint end of this luminosity function, combined with theoretical cooling sequences, enable us to constrain the age of the local region of the Galaxy.

scholarly journals Dialog State Tracking with Reinforced Data Augmentation

2020 ◽  
Vol 34 (05) ◽  
pp. 9474-9481
Author(s):  
Yichun Yin ◽  
Lifeng Shang ◽  
Xin Jiang ◽  
Xiao Chen ◽  
Qun Liu
Keyword(s):  
Data Augmentation ◽  
State Of The Art ◽  
The State ◽  
Training Data ◽  
Quality Data ◽  
Specific Context ◽  
High Quality ◽  
High Quality Data ◽  
Fine Grained ◽  
State Tracking

Neural dialog state trackers are generally limited due to the lack of quantity and diversity of annotated training data. In this paper, we address this difficulty by proposing a reinforcement learning (RL) based framework for data augmentation that can generate high-quality data to improve the neural state tracker. Specifically, we introduce a novel contextual bandit generator to learn fine-grained augmentation policies that can generate new effective instances by choosing suitable replacements for specific context. Moreover, by alternately learning between the generator and the state tracker, we can keep refining the generative policies to generate more high-quality training data for neural state tracker. Experimental results on the WoZ and MultiWoZ (restaurant) datasets demonstrate that the proposed framework significantly improves the performance over the state-of-the-art models, especially with limited training data.

scholarly journals Spectrum Fitting Code for LAMOST ExtraGAlactic Surveys (LEGAS)

2009 ◽  
Vol 5 (S262) ◽  
pp. 295-298
Author(s):  
Xu Kong ◽  
Shanshan Su
Keyword(s):  
Optical Fibers ◽  
State Of The Art ◽  
Spectral Synthesis ◽  
Quality Data ◽  
Data Bases ◽  
High Quality Data ◽  
New Era ◽  
Extragalactic Surveys ◽  
Spectrum Fitting ◽  
Very High

AbstractThe study of stellar populations in galaxies is entering a new era with the availability of large and high-quality data bases of both observed galactic spectra and state-of-the-art evolutionary synthesis models. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has a 4m primary, and a 5 degree field of view, accommodate as many as 4000 optical fibers. It can obtain the spectra of objects as faint as down to B = 20.5 mag with an exposure of 1.5 hour, promising a very high spectrum acquiring rate of ten-thousands of spectra per night. In this talk, we introduce the progress of the LAMOST project, its surveys and show the spectral synthesis code that we are developing for the LAMOST ExtraGAlactic Surveys (LEGAS).

scholarly journals Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments

2019 ◽  
Author(s):  
Leroy Cronin ◽  
Vasilios Duros ◽  
Jonathan Grizou ◽  
Abhishek Sharma ◽  
Hessam Mehr ◽  
...  
Keyword(s):  
Self Assembly ◽  
State Of The Art ◽  
Chemical Space ◽  
Autonomous Systems ◽  
Quality Data ◽  
High Quality Data ◽  
Current State ◽  
Robot Teams ◽  
Substantial Progress ◽  
Inorganic Chemical

<div> <p>Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules so vast, only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving towards the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the chemical space by comparing collaboration between human experimenters with an algorithm-based search, against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na<sub>6</sub>[Mo<sub>120</sub>Ce<sub>6</sub>O<sub>366</sub>H<sub>12</sub>(H<sub>2</sub>O)<sub>78</sub>]·200H<sub>2</sub>O (<b>1</b>). We show that the robot-human teams are able to increase the prediction accuracy to 75.6±1.8%, from 71.8±0.3% with the algorithm alone and 66.3±1.8% from only the human experimenters demonstrating that human-robot teams beat robots or humans working alone.</p> </div>

scholarly journals The state of the art in helioseismic ground-based experiments

1997 ◽  
Vol 181 ◽  
pp. 15-29
Author(s):  
Pere L. Pallé
Keyword(s):  
Solar Cycle ◽  
State Of The Art ◽  
Quality Data ◽  
Direct Impact ◽  
High Quality ◽  
High Quality Data ◽  
The Past ◽  
Long Time ◽  
Inversion Techniques ◽  
And Inversion

The new results obtained from the observation of solar oscillations over the past decade, have a direct impact on our knowledge of the Sun's interior. As a consequence, a great interest in helioseismology has arisen and is reflected in the development of new observational projects as well as new analyse and inversion techniques. In this review we will describe the present ground-based observational programmes, which, unlike the space ones, are mostly designed to produce high quality data over very long time spans (up to solar cycle time scales). The characteristics of the various observational programmes, single-site and network, will be described together with their performances, the main results obtained up to now, and some other logistical aspects.

What Can Digitization Do For Formulated Product Innovation and Development

2020 ◽  
Author(s):  
James McDonagh ◽  
William Swope ◽  
Richard L. Anderson ◽  
Michael Johnston ◽  
David J. Bray
Keyword(s):  
High Performance ◽  
Quality Data ◽  
High Quality Data ◽  
Base Level ◽  
Hybrid Approaches ◽  
Digital Ecosystem ◽  
Recent Developments ◽  
Chemical Simulation ◽  
High Level ◽  
Physical And Chemical

Digitization offers significant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the benefits could be significant. Recent developments of base level technologies such as artificial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could offer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators.<br>

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

scholarly journals Velocity Analysis Using Separated Diffractions for Lunar Penetrating Radar Obtained by Yutu-2 Rover

2021 ◽  
Vol 13 (7) ◽  
pp. 1387
Author(s):  
Chao Li ◽  
Jinhai Zhang
Keyword(s):  
Dielectric Permittivity ◽  
Lunar Regolith ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Quality Data ◽  
High Quality Data ◽  
Shallow Subsurface ◽  
Subsurface Structures ◽  
Ground Penetrating ◽  
Permittivity Model

The high-frequency channel of lunar penetrating radar (LPR) onboard Yutu-2 rover successfully collected high quality data on the far side of the Moon, which provide a chance for us to detect the shallow subsurface structures and thickness of lunar regolith. However, traditional methods cannot obtain reliable dielectric permittivity model, especially in the presence of high mix between diffractions and reflections, which is essential for understanding and interpreting the composition of lunar subsurface materials. In this paper, we introduce an effective method to construct a reliable velocity model by separating diffractions from reflections and perform focusing analysis using separated diffractions. We first used the plane-wave destruction method to extract weak-energy diffractions interfered by strong reflections, and the LPR data are separated into two parts: diffractions and reflections. Then, we construct a macro-velocity model of lunar subsurface by focusing analysis on separated diffractions. Both the synthetic ground penetrating radar (GPR) and LPR data shows that the migration results of separated reflections have much clearer subsurface structures, compared with the migration results of un-separated data. Our results produce accurate velocity estimation, which is vital for high-precision migration; additionally, the accurate velocity estimation directly provides solid constraints on the dielectric permittivity at different depth.

scholarly journals Redistribution Preferences, Inequality Information, and Partisan Motivated Reasoning in the United States

Societies ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 65
Author(s):  
Clem Brooks ◽  
Elijah Harter
Keyword(s):  
Party Identification ◽  
Motivated Reasoning ◽  
The United States ◽  
Quality Data ◽  
Historical Time ◽  
High Quality Data ◽  
Redistributive Policies ◽  
Election Studies ◽  
New Evidence ◽  
The U.S

In an era of rising inequality, the U.S. public’s relatively modest support for redistributive policies has been a puzzle for scholars. Deepening the paradox is recent evidence that presenting information about inequality increases subjects’ support for redistributive policies by only a small amount. What explains inequality information’s limited effects? We extend partisan motivated reasoning scholarship to investigate whether political party identification confounds individuals’ processing of inequality information. Our study considers a much larger number of redistribution preference measures (12) than past scholarship. We offer a second novelty by bringing the dimension of historical time into hypothesis testing. Analyzing high-quality data from four American National Election Studies surveys, we find new evidence that partisanship confounds the interrelationship of inequality information and redistribution preferences. Further, our analyses find the effects of partisanship on redistribution preferences grew in magnitude from 2004 through 2016. We discuss implications for scholarship on information, motivated reasoning, and attitudes towards redistribution.

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