Continual Cross-Dataset Adaptation in Road Surface Classification

Accurate road surface classification is crucial for autonomous vehicles (AVs) to optimize driving conditions, enhance safety, and enable advanced road mapping. However, deep learning models for road surface classification suffer from poor generalization when tested on unseen datasets. To update these models with new information, also the original training dataset must be taken into account, in order to avoid catastrophic forgetting. This is, however, inefficient if not impossible, e.g., when the data is collected in streams or large amounts. To overcome this limitation and enable fast and efficient cross-dataset adaptation, we propose to employ continual learning finetuning methods designed to retain past knowledge while adapting to new data, thus effectively avoiding forgetting. Experimental results demonstrate the superiority of this approach over naive finetuning, achieving performance close to fresh retraining. While solving this known problem, we also provide a general description of how the same technique can be adopted in other AV scenarios. We highlight the potential computational and economic benefits that a continual-based adaptation can bring to the AV industry, while also reducing greenhouse emissions due to unnecessary joint retraining.Comment: To be published in Proceedings of 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023

L\u2019insegnamento dell\u2019italiano in Sudafrica: un profilo dello stato dell\u2019arte e il caso della Universit\ue0 del KwaZulu Natal di Durban

Il contributo traccia un profilo storico dell'italiano in Sudafrica a partire dalla prima immigrazione fino al suo sviluppo attuale al momento di crisi che sta attraversando il suo insegnamento come conseguenza della politica linguistica dell'attuale governo sudafricano e della scarsa attenzione da parte di quello italiano. Viene tracciato un bilancio delle istituzioni di ogni ordine, grado e tipologia (pubbliche e private) in cui l'italiano viene insegnato nel paese oggi, con un focus particolare, a titolo esemplificativo, a quanto accade nella Universit\ue0 del KwaZulu Natal di Durban

Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?

Human movement analysis has become easier with the wide availability of motion capture systems. Inertial sensing has made it possible to capture human motion without external infrastructure, therefore allowing measurements in any environment. As high-quality motion capture data is available in large quantities, this creates possibilities to further simplify hardware setups, by use of data-driven methods to decrease the number of body-worn sensors. In this work, we contribute to this field by analyzing the capabilities of using either artificial neural networks (eager learning) or nearest neighbor search (lazy learning) for such a problem. Sparse orientation features, resulting from sensor fusion of only five inertial measurement units with magnetometers, are mapped to full-body poses. Both eager and lazy learning algorithms are shown to be capable of constructing this mapping. The full-body output poses are visually plausible with an average joint position error of approximately 7 cm, and average joint angle error of 7 ∘ . Additionally, the effects of magnetic disturbances typical in orientation tracking on the estimation of full-body poses was also investigated, where nearest neighbor search showed better performance for such disturbances

Improving Full-Body Pose Estimation from a Small Sensor Set Using Artificial Neural Networks and a Kalman Filter

Previous research has shown that estimating full-body poses from a minimal sensor set using a trained ANN without explicitly enforcing time coherence has resulted in output pose sequences that occasionally show undesired jitter. To mitigate such effect, we propose to improve the ANN output by combining it with a state prediction using a Kalman Filter. Preliminary results are promising, as the jitter effects are diminished. However, the overall error does not decrease substantially

Estimation of vertical ground reaction forces and sagittal knee kinematics during running using three inertial sensors

Analysis of running mechanics has traditionally been limited to a gait laboratory using either force plates or an instrumented treadmill in combination with a full-body optical motion capture system. With the introduction of inertial motion capture systems, it becomes possible to measure kinematics in any environment. However, kinetic information could not be provided with such technology. Furthermore, numerous body-worn sensors are required for a full-body motion analysis. The aim of this study is to examine the validity of a method to estimate sagittal knee joint angles and vertical ground reaction forces during running using an ambulatory minimal body-worn sensor setup. Two concatenated artificial neural networks were trained (using data from eight healthy subjects) to estimate the kinematics and kinetics of the runners. The first artificial neural network maps the information (orientation and acceleration) of three inertial sensors (placed at the lower legs and pelvis) to lower-body joint angles. The estimated joint angles in combination with measured vertical accelerations are input to a second artificial neural network that estimates vertical ground reaction forces. To validate our approach, estimated joint angles were compared to both inertial and optical references, while kinetic output was compared to measured vertical ground reaction forces from an instrumented treadmill. Performance was evaluated using two scenarios: training and evaluating on a single subject and training on multiple subjects and evaluating on a different subject. The estimated kinematics and kinetics of most subjects show excellent agreement (ρ > 0.99) with the reference, for single subject training. Knee flexion/extension angles are estimated with a mean RMSE 0.9) is still achieved for seven of the eight different evaluated subjects. The performance of multiple subject learning depends on the diversity in the training dataset, as differences in accuracy were found for the different evaluated subjects

Estimation of Vertical Ground Reaction Forces and Sagittal Knee Kinematics During Running Using Three Inertial Sensors

Analysis of running mechanics has traditionally been limited to a gait laboratory using either force plates or an instrumented treadmill in combination with a full-body optical motion capture system. With the introduction of inertial motion capture systems, it becomes possible to measure kinematics in any environment. However, kinetic information could not be provided with such technology. Furthermore, numerous body-worn sensors are required for a full-body motion analysis. The aim of this study is to examine the validity of a method to estimate sagittal knee joint angles and vertical ground reaction forces during running using an ambulatory minimal body-worn sensor setup. Two concatenated artificial neural networks were trained (using data from eight healthy subjects) to estimate the kinematics and kinetics of the runners. The first artificial neural network maps the information (orientation and acceleration) of three inertial sensors (placed at the lower legs and pelvis) to lower-body joint angles. The estimated joint angles in combination with measured vertical accelerations are input to a second artificial neural network that estimates vertical ground reaction forces. To validate our approach, estimated joint angles were compared to both inertial and optical references, while kinetic output was compared to measured vertical ground reaction forces from an instrumented treadmill. Performance was evaluated using two scenarios: training and evaluating on a single subject and training on multiple subjects and evaluating on a different subject. The estimated kinematics and kinetics of most subjects show excellent agreement (ρ>0.99) with the reference, for single subject training. Knee flexion/extension angles are estimated with a mean RMSE <5°. Ground reaction forces are estimated with a mean RMSE < 0.27 BW. Additionaly, peak vertical ground reaction force, loading rate and maximal knee flexion during stance were compared, however, no significant differences were found. With multiple subject training the accuracy of estimating discrete and continuous outcomes decreases, however, good agreement (ρ > 0.9) is still achieved for seven of the eight different evaluated subjects. The performance of multiple subject learning depends on the diversity in the training dataset, as differences in accuracy were found for the different evaluated subjects

Expanding the phenotypic spectrum of BCS1L-related mitochondrial disease

Publisher Copyright: © 2021 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological AssociationObjective: To delineate the full phenotypic spectrum of BCS1L-related disease, provide better understanding of the genotype–phenotype correlations and identify reliable prognostic disease markers. Methods: We performed a retrospective multinational cohort study of previously unpublished patients followed in 15 centres from 10 countries. Patients with confirmed biallelic pathogenic BCS1L variants were considered eligible. Clinical, laboratory, neuroimaging and genetic data were analysed. Patients were stratified into different groups based on the age of disease onset, whether homozygous or compound heterozygous for the c.232A>G (p.Ser78Gly) variant, and those with other pathogenic BCS1L variants. Results: Thirty-three patients were included. We found that growth failure, lactic acidosis, tubulopathy, hepatopathy and early death were more frequent in those with disease onset within the first month of life. In those with onset after 1 month, neurological features including movement disorders and seizures were more frequent. Novel phenotypes, particularly involving movement disorder, were identified in this group. The presence of the c.232A>G (p.Ser78Gly) variant was associated with significantly worse survival and exclusively found in those with disease onset within the first month of life, whilst other pathogenic BCS1L variants were more frequent in those with later symptom onset. Interpretation: The phenotypic spectrum of BCS1L-related disease comprises a continuum of clinical features rather than a set of separate syndromic clinical identities. Age of onset defines BCS1L-related disease clinically and early presentation is associated with poor prognosis. Genotype correlates with phenotype in the presence of the c.232A>G (p.Ser78Gly) variant.Peer reviewe

A three-dimensional model of human lung development and disease from pluripotent stem cells

Author ManuscriptRecapitulation of lung development from human pluripotent stem cells (hPSCs) in three dimensions (3D) would allow deeper insight into human development, as well as the development of innovative strategies for disease modelling, drug discovery and regenerative medicine. We report here the generation from hPSCs of lung bud organoids (LBOs) that contain mesoderm and pulmonary endoderm and develop into branching airway and early alveolar structures after xenotransplantation and in Matrigel 3D culture. Expression analysis and structural features indicated that the branching structures reached the second trimester of human gestation. Infection in vitro with respiratory syncytial virus, which causes small airway obstruction and bronchiolitis in infants, led to swelling, detachment and shedding of infected cells into the organoid lumens, similar to what has been observed in human lungs. Introduction of mutation in HPS1, which causes an early-onset form of intractable pulmonary fibrosis, led to accumulation of extracellular matrix and mesenchymal cells, suggesting the potential use of this model to recapitulate fibrotic lung disease in vitro. LBOs therefore recapitulate lung development and may provide a useful tool to model lung disease.NIH HL120046-01 (H.-W.S.), 1U01HL134760-01 (H.-W.S.) RO1 AI031971 (A.M.), and RO1 AI114736 (A.M.), as well as a sponsored research and agreement from Northern Biologics Inc. (H.-W.S.), and funding from the Thomas R Kully IPF Research Fund (H.-W.S.). RUES2-HPS1 cells were generated by the Columbia Stem Cell Core Facility. We thank NYULMC OCS Microscopy core C. Petzold and K. Dancel for their assistance with transmission electron microscopy. We thank M. Peeples (Ohio State University) for providing the original recombinant RSV. Flow cytometry was performed in the CCTI Flow Cytometry Core, supported in part by the Office of the Director, National Institutes of Health under awards S10RR027050 and S10OD02005