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Category: robotics/AI – Page 1320
If you enjoy building things
Posted in robotics/AI
fixing things, and being creative in the sense of making something new, thatâs very much a robotics mind set. â Electrical Engineer Cassie answers your questions about making, using, and imagining robots.
Have a question for our team? Let us know in the comments using #AskARoboticist.
Click on photo to start video.
to adapt to constant changes in customersâ homes, Astro uses a deep-learning model to produce invariant representations of visual data, estimates sensor reliability to guide the fusion of sensor data, and prunes and compresses map representations: https://amzn.to/36qQUZo
A new deep learning algorithm is able to quantify arousal and awareness in humans at the same time.
#consc⊠See more.
Summary: A new deep learning algorithm is able to quantify arousal and awareness in humans at the same time.
Source: CORDIS
Teledyne FLIR Defense has announced the partnership with MFE Inspection Solutions to integrate the FLIR MUVE C360 multi-gas detector on Boston Dynamicsâ Spot robot and commercial unmanned aerial systems (UAS). The integrated solutions will enable remote monitoring of chemical threats in industrial and public safety applications.
The compact multi-gas detector can detect and classify airborne gas or chemical hazards, allowing inspection personnel to perform their job more safely and efficiently with integrated remote sensing capabilities from both the air and ground.
MUVE C360 is designed to operate on Boston DynamicsâSpot mobile robot, which can autonomously inspect dangerous, inaccessible, or remote environments. It is also compatible with common commercial UAS systems, which allow operators to fly the C360 into a scene to perform hazard assessments in real-time.
Next, we aimed to determine whether the model type, i.e., a linear regression vs. a neural network, would significantly impact the performance. We, therefore, compared the aforementioned linear models with the neural network AltumAge using the same set of features. AltumAge outperformed the respective linear model with Horvathâs 353 CpG sites (MAE = 2.425 vs. 3.011, MSE = 32.732 vs. 46.867) and ElasticNet-selected 903 CpG sites (MAE = 2.302 vs. 2.621, MSE = 30.455 vs. 39.198). This result shows that AltumAge outperforms linear models given the same training data and set of features.
Lastly, to compare the effect of the different sets of CpG sites, we trained AltumAge with all 20,318 CpG sites available and compared the results from the smaller sets of CpG sites obtained above. There is a gradual improvement in performance for AltumAge by expanding the feature set from Horvathâs 353 sites (MAE = 2.425, MSE = 32.732) to 903 ElasticNet-selected CpG sites (MAE = 2.302, MSE = 30.455) to all 20,318 CpG sites (MAE = 2.153, MSE = 29.486). This result suggests that the expanded feature set helps improve the performance, likely because relevant information in the epigenome is not entirely captured by the CpG sites selected by an ElasticNet model.
Overall, these results indicate that even though more data samples lower the prediction error, AltumAgeâs performance improvement is greater than the increased data effect. Indeed, the lower error of AltumAge when compared to the ElaticNet is robust to other data splits (Alpaydinâs Combined 5x2cv F test p-value = 9.71eâ5).
Discovering a systemâs causal relationships and structure is a crucial yet challenging problem in scientific disciplines ranging from medicine and biology to economics. While researchers typically adopt the graphical formalism of causal Bayesian networks (CBNs) to induce a graph structure that best describes these relationships, such unsupervised score-based approaches can quickly lead to prohibitively heavy computation burdens.
A research team from DeepMind, Mila â University of Montreal and Google Brain challenges the conventional causal induction approach in their new paper Learning to Induce Causal Structure, proposing a neural network architecture that learns the graph structure of observational and/or interventional data via supervised training on synthetic graphs. The teamâs proposed Causal Structure Induction via Attention (CSIvA) method effectively makes causal induction a black-box problem and generalizes favourably to new synthetic and naturalistic graphs.
The team summarizes their main contributions as: