107 research outputs found
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving
3D LiDAR scanners are playing an increasingly important role in autonomous
driving as they can generate depth information of the environment. However,
creating large 3D LiDAR point cloud datasets with point-level labels requires a
significant amount of manual annotation. This jeopardizes the efficient
development of supervised deep learning algorithms which are often data-hungry.
We present a framework to rapidly create point clouds with accurate point-level
labels from a computer game. The framework supports data collection from both
auto-driving scenes and user-configured scenes. Point clouds from auto-driving
scenes can be used as training data for deep learning algorithms, while point
clouds from user-configured scenes can be used to systematically test the
vulnerability of a neural network, and use the falsifying examples to make the
neural network more robust through retraining. In addition, the scene images
can be captured simultaneously in order for sensor fusion tasks, with a method
proposed to do automatic calibration between the point clouds and captured
scene images. We show a significant improvement in accuracy (+9%) in point
cloud segmentation by augmenting the training dataset with the generated
synthesized data. Our experiments also show by testing and retraining the
network using point clouds from user-configured scenes, the weakness/blind
spots of the neural network can be fixed
Counterexample-Guided Data Augmentation
We present a novel framework for augmenting data sets for machine learning
based on counterexamples. Counterexamples are misclassified examples that have
important properties for retraining and improving the model. Key components of
our framework include a counterexample generator, which produces data items
that are misclassified by the model and error tables, a novel data structure
that stores information pertaining to misclassifications. Error tables can be
used to explain the model's vulnerabilities and are used to efficiently
generate counterexamples for augmentation. We show the efficacy of the proposed
framework by comparing it to classical augmentation techniques on a case study
of object detection in autonomous driving based on deep neural networks
Personalized emotion recognition by personality-aware high-order learning of physiological signals
Due to the subjective responses of different subjects to physical stimuli, emotion recognition methodologies from physiological signals are increasingly becoming personalized. Existing works mainly focused on modeling the involved physiological corpus of each subject, without considering the psychological factors, such as interest and personality. The latent correlation among different subjects has also been rarely examined. In this article, we propose to investigate the influence of personality on emotional behavior in a hypergraph learning framework. Assuming that each vertex is a compound tuple (subject, stimuli), multi-modal hyper-graphs can be constructed based on the personality correlation among different subjects and on the physiological correlation among corresponding stimuli. To reveal the different importance of vertices, hyperedges, and modalities, we learn the weights for each of them. As the hypergraphs connect different subjects on the compound vertices, the emotions of multiple subjects can be simultaneously recognized. In this way, the constructed hypergraphs are vertex-weighted multi-modal multi-task ones. The estimated factors, referred to as emotion relevance, are employed for emotion recognition. We carry out extensive experiments on the ASCERTAIN dataset and the results demonstrate the superiority of the proposed method, as compared to the state-of-the-art emotion recognition approaches
Eugene – A Domain Specific Language for Specifying and Constraining Synthetic Biological Parts, Devices, and Systems
BACKGROUND: Synthetic biological systems are currently created by an ad-hoc, iterative process of specification, design, and assembly. These systems would greatly benefit from a more formalized and rigorous specification of the desired system components as well as constraints on their composition. Therefore, the creation of robust and efficient design flows and tools is imperative. We present a human readable language (Eugene) that allows for the specification of synthetic biological designs based on biological parts, as well as provides a very expressive constraint system to drive the automatic creation of composite Parts (Devices) from a collection of individual Parts. RESULTS: We illustrate Eugene's capabilities in three different areas: Device specification, design space exploration, and assembly and simulation integration. These results highlight Eugene's ability to create combinatorial design spaces and prune these spaces for simulation or physical assembly. Eugene creates functional designs quickly and cost-effectively. CONCLUSIONS: Eugene is intended for forward engineering of DNA-based devices, and through its data types and execution semantics, reflects the desired abstraction hierarchy in synthetic biology. Eugene provides a powerful constraint system which can be used to drive the creation of new devices at runtime. It accomplishes all of this while being part of a larger tool chain which includes support for design, simulation, and physical device assembly
- …