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 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 Integrated Simulation Environment for Unmanned Autonomous Systems—Towards a Conceptual Framework

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
M. G. Perhinschi ◽  
M. R. Napolitano ◽  
S. Tamayo
Keyword(s):  
Conceptual Framework ◽  
State Of The Art ◽  
Autonomous Systems ◽  
Simulation Environment ◽  
Integrated Simulation ◽  
Systems Perspective ◽  
Future Developments ◽  
Current State ◽  
And Performance ◽  
Testing And Evaluation

The paper initiates a comprehensive conceptual framework for an integrated simulation environment for unmanned autonomous systems (UAS) that is capable of supporting the design, analysis, testing, and evaluation from a “system of systems” perspective. The paper also investigates the current state of the art of modeling and performance assessment of UAS and their components and identifies directions for future developments. All the components of a comprehensive simulation environment focused on the testing and evaluation of UAS are identified and defined through detailed analysis of current and future required capabilities and performance. The generality and completeness of the simulation environment is ensured by including all operational domains, types of agents, external systems, missions, and interactions between components. The conceptual framework for the simulation environment is formulated with flexibility, modularity, generality, and portability as key objectives. The development of the conceptual framework for the UAS simulation reveals important aspects related to the mechanisms and interactions that determine specific UAS characteristics including complexity, adaptability, synergy, and high impact of artificial and human intelligence on system performance and effectiveness.

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.

Testing of Autonomous Systems - Challenges and Current State-of-the-Art

2016 ◽  
Vol 26 (1) ◽  
pp. 571-584 ◽  
Author(s):  
Philipp Helle ◽  
Wladimir Schamai ◽  
Carsten Strobel
Keyword(s):  
State Of The Art ◽  
Autonomous Systems ◽  
Current State

scholarly journals Global-local attention for emotion recognition

2021 ◽  
Author(s):  
Nhat Le ◽  
Khanh Nguyen ◽  
Anh Nguyen ◽  
Bac Le
Keyword(s):  
Emotion Recognition ◽  
State Of The Art ◽  
Contextual Information ◽  
Research Area ◽  
The Past ◽  
Current State ◽  
Substantial Progress ◽  
Human Emotions ◽  
Active Research ◽  
Active Research Area

AbstractHuman emotion recognition is an active research area in artificial intelligence and has made substantial progress over the past few years. Many recent works mainly focus on facial regions to infer human affection, while the surrounding context information is not effectively utilized. In this paper, we proposed a new deep network to effectively recognize human emotions using a novel global-local attention mechanism. Our network is designed to extract features from both facial and context regions independently, then learn them together using the attention module. In this way, both the facial and contextual information is used to infer human emotions, therefore enhancing the discrimination of the classifier. The intensive experiments show that our method surpasses the current state-of-the-art methods on recent emotion datasets by a fair margin. Qualitatively, our global-local attention module can extract more meaningful attention maps than previous methods. The source code and trained model of our network are available at https://github.com/minhnhatvt/glamor-net.

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 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 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 A Modular Programmable Inorganic Cluster Discovery Robot for the Discovery and Synthesis of Polyoxometalates

2020 ◽  
Author(s):  
Daniel Salley ◽  
Graham Keenan ◽  
De-Liang Long ◽  
Nicola Bell ◽  
Leroy Cronin
Keyword(s):  
Self Assembly ◽  
Chemical Space ◽  
Large Set ◽  
Materials Chemistry ◽  
Ambient Conditions ◽  
Synthetic Strategy ◽  
Multiple Reactions ◽  
Inorganic Chemical ◽  
The Many ◽  
Mixed Transition

<p>The exploration of complex multi-component chemical reactions leading to new clusters, where discovery requires both molecular self-assembly and crystallization, is a major challenge. This is because the systematic approach required for an experimental search is limited when the number of parameters in a chemical space becomes too large, restricting both exploration, and reproducibility. Herein, we present a synthetic strategy to systematically search a very large set of potential reactions, using an inexpensive, high-throughput platform; modular in terms of both hardware and software, and capable of running multiple reactions with in-line analysis; for the automation of inorganic and materials chemistry. The platform has been used to explore several inorganic chemical spaces to discover new, and reproduce known, tungsten-based, mixed transition-metal polyoxometalate clusters, giving a digital code allowing the easy repeat synthesis of the clusters. Among the many species identified in this work, most significantly is the discovery of a novel, purely inorganic W<sub>24</sub>Fe<sup>III</sup>-superoxide cluster formed under ambient conditions. The Modular Wheel Platform (MWP) was then employed to undertake two chemical space explorations producing compounds [1-4]: (C<sub>2</sub>H<sub>8</sub>N)<sub>10</sub>Na<sub>2</sub>[H<sub>6</sub>Fe(O<sub>2</sub>)W<sub>24</sub>O<sub>82</sub>(H<sub>2</sub>O)<sub>25</sub>] (1, {W<sub>24</sub>Fe}), (C<sub>2</sub>H<sub>8</sub>N)<sub>72</sub>Na<sub>16</sub>[H<sub>16</sub>Co<sub>8</sub>W<sub>200</sub>O<sub>660</sub>(H<sub>2</sub>O)<sub>40</sub>] (2, {W<sub>200</sub>Co<sub>8</sub>}), (C<sub>2</sub>H<sub>8</sub>N)<sub>72</sub>Na<sub>16</sub>[H<sub>16</sub>Ni<sub>8</sub>W<sub>200 </sub>O<sub>660-</sub>(H<sub>2</sub>O)<sub>40</sub>] (3, {W<sub>200</sub>Ni<sub>8</sub>}) and (C<sub>2</sub>H<sub>8</sub>N)<sub>14</sub>[H<sub>26</sub>W<sub>34</sub>V<sub>4</sub>O<sub>130</sub>] (4, {W<sub>34</sub>V<sub>4</sub>}), along with many other known species, for example simple Keggin clusters and 1D {W<sub>11</sub>M<sup>2+</sup>} chains. <b></b></p>

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.

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