Efficient Deep Learning of Robust Policies from MPC using Imitation and Tube-Guided Data Augmentation

Imitation Learning (IL) has been increasingly employed to generate computationally efficient policies from task-relevant demonstrations provided by Model Predictive Control (MPC). However, commonly employed IL methods are often data- and computationally-inefficient, as they require a large number of MPC demonstrations, resulting in long training times, and they produce policies with limited robustness to disturbances not experienced during training. In this work, we propose an IL strategy to efficiently compress a computationally expensive MPC into a Deep Neural Network (DNN) policy that is robust to previously unseen disturbances. By using a robust variant of the MPC, called Robust Tube MPC (RTMPC), and leveraging properties from the controller, we introduce a computationally-efficient Data Aggregation (DA) method that enables a significant reduction of the number of MPC demonstrations and training time required to generate a robust policy. Our approach opens the possibility of zero-shot transfer of a policy trained from a single MPC demonstration collected in a nominal domain, such as a simulation or a robot in a lab/controlled environment, to a new domain with previously-unseen bounded model errors/perturbations. Numerical and experimental evaluations performed using linear and nonlinear MPC for agile flight on a multirotor show that our method outperforms strategies commonly employed in IL (such as DAgger and DR) in terms of demonstration-efficiency, training time, and robustness to perturbations unseen during training.Comment: Under review. arXiv admin note: text overlap with arXiv:2109.0991

Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation

The deployment of agile autonomous systems in challenging, unstructured environments requires adaptation capabilities and robustness to uncertainties. Existing robust and adaptive controllers, such as the ones based on MPC, can achieve impressive performance at the cost of heavy online onboard computations. Strategies that efficiently learn robust and onboard-deployable policies from MPC have emerged, but they still lack fundamental adaptation capabilities. In this work, we extend an existing efficient IL algorithm for robust policy learning from MPC with the ability to learn policies that adapt to challenging model/environment uncertainties. The key idea of our approach consists in modifying the IL procedure by conditioning the policy on a learned lower-dimensional model/environment representation that can be efficiently estimated online. We tailor our approach to the task of learning an adaptive position and attitude control policy to track trajectories under challenging disturbances on a multirotor. Our evaluation is performed in a high-fidelity simulation environment and shows that a high-quality adaptive policy can be obtained in about $1.3$ hours. We additionally empirically demonstrate rapid adaptation to in- and out-of-training-distribution uncertainties, achieving a $6.1$ cm average position error under a wind disturbance that corresponds to about $50\%$ of the weight of the robot and that is $36\%$ larger than the maximum wind seen during training.Comment: 8 pages, 6 figure

Neoadjuvant therapy for breast cancer

Objective: To evaluate the frequency of neoadjuvant therapy (NT) in women with stage I–III breast cancer in Italy and whether it is influenced by biological characteristics, screening history, and geographic area. Methods: Data from the High Resolution Study conducted in 7 Italian cancer registries were used; they are a representative sample of incident cancers in the study period (2009–2013). Included were 3546 women aged <85 years (groups <50, 50–69, 70–64, and 75+) with stage I–III breast cancer at diagnosis who underwent surgery. Women were classified as receiving NT if they received chemotherapy, target therapy, and/or hormone therapy before the first surgical treatment. Logistic models were built to test the association with biological and contextual variables. Results: Only 8.2% of women (290 cases) underwent NT; the treatment decreases with increasing age (14.5% in age <50 and 2.2% in age 75+), is more frequent in women with negative receptors (14.8%), HER2-positive (15.7%), and triple-negative (15.6%). The multivariable analysis showed the probability of receiving NT is higher in stage III (odds ratio [OR] 3.83; 95% confidence interval [CI] 2.83–5.18), luminal B (OR 1.87; 95% CI 1.27–2.76), triple-negatives (OR 1.88; 95% CI 1.15–3.08), and in symptomatic cancers (OR 1.98; 95% CI 1.13–3.48). Use of NT varied among geographic areas: Reggio Emilia had the highest rates (OR 2.29; 95% CI 1.37–3.82) while Palermo had the lowest (OR 0.41; 95% CI 0.24–0.68). Conclusions: The use of NT in Italy is limited and variable. There are no signs of greater use in hospitals with more advanced care

SCAN-TO-BIM EFFICIENT APPROACH TO EXTRACT BIM MODELS FROM HIGH PRODUCTIVE INDOOR MOBILE MAPPING SURVEY

Building Information Modeling represents one of the most interesting developments in construction fields in the last 20 years. BIM process supports the creation of intelligent data that can be used throughout the life cycle of a construction project. Where a project involves a pre-existing structure, reality capture can provide the most critical information. The purpose of this paper is to describe an efficient approach to extract 3D models using high productive indoor Mobile Mapping Systems (iMMS) and an optimized scan-to-BIM workflow. The scan-to-BIM procedure allows reconstructing several elements within a digital environment preserving the features and reusing them in the development of the BIM project. The elaboration of the raw data acquired from the iMMS starts with the software HERON® Desktop where a SLAM algorithm runs and a 3D point cloud model is produced. The model is translated in the Gexcel Reconstructor® point cloud post processing software where a number of deliverables as orthophotos, blueprints and a filtered and optimized point cloud are obtained. In the proposed processing workflow, the data are introduced to Autodesk ReCap®, where the model can be edited and the final texturized point cloud model extracted. The identification and modeling of the 3D objects that compose the BIM model is realized in ClearEdge3D EdgeWiseTM and optimized in Autodesk Revit®. The data elaboration workflow implemented shows how an optimized data processing workflow allows making the scan-to-BIM procedure automatic and economically sustainable

Dynamic facial expressions of emotions are discriminated at birth

The ability to discriminate between different facial expressions is fundamental since the first stages of postnatal life. The aim of this study is to investigate whether 2-days-old newborns are capable to discriminate facial expressions of emotions as they naturally take place in everyday interactions, that is in motion. When two dynamic displays depicting a happy and a disgusted facial expression were simultaneously presented (i.e., visual preference paradigm), newborns did not manifest any visual preference (Experiment 1). Nonetheless, after being habituated to a happy or disgusted dynamic emotional expression (i.e., habituation paradigm), newborns successfully discriminated between the two (Experiment 2). These results indicate that at birth newborns are sensitive to dynamic faces expressing emotions

Real-time prediction of breast lesions displacement during Ultrasound scanning using a position-based dynamics approach.

Although ultrasound (US) images represent the most popular modality for guiding breast biopsy, they are sometimes unable to render malignant regions, thus preventing accurate lesion localization which is essential for a successful procedure. Biomechanical models can support the localization of suspicious areas identified on a pre-operative image during US scanning since they are able to account for anatomical deformations resulting from US probe pressure. We propose a deformation model which relies on position-based dynamics (PBD) approach to predict the displacement of internal targets induced by probe interaction during US acquisition. The PBD implementation available in NVIDIA FleX is exploited to create an anatomical model capable of deforming in real-time. In order to account for each patient\u2019s specificities, model parameters are selected as those minimizing the localization error of a US-visible landmark of the anatomy of interest (in our case, a realistic breast phantom). The updated model is used to estimate the displacement of other internal lesions due to probe-tissue interaction. The proposed approach is compared to a finite element model (FEM), generally used in breast biomechanics, and a rigid one. Localization error obtained when applying the PBD model remains below 11 mm for all the tumors even for input displacements in the order of 30 mm. The proposed method obtains results aligned with FE models with faster computational performance, suitable for real-time applications. In addition, it outperforms rigid model used to track lesion position in US-guided breast biopsies, at least halving the localization error for all the displacement ranges considered. Position-based dynamics approach has proved to be successful in modeling breast tissue deformations during US acquisition. Its stability, accuracy and real-time performance make such model suitable for tracking lesions displacement during US-guided breast biopsy

A position-based framework for the prediction of probe-induced lesion displacement in Ultrasound-guided breast biopsy

Although ultrasound (US) images represent the most popular modality for guiding breast biopsy, they are sometimes unable to render malignant regions, thus preventing accurate lesion localization which is essential for a successful procedure. Biomechanical models can support the localization of suspicious areas identified on a pre-operative image during US scanning since they are able to account for anatomical deformations resulting from US probe pressure. We propose a deformation model which relies on position-based dynamics (PBD) approach to predict the displacement of internal targets induced by probe interaction during US acquisition. The PBD implementation available in NVIDIA FleX is exploited to create an anatomical model capable of deforming online. Simulation parameters are initialized on a calibration phantom under different levels of probe-induced deformations, then they are fine-tuned by minimizing the localization error of a US-visible landmark of a realistic breast phantom. The updated model is used to estimate the displacement of other internal lesions due to probe-tissue interaction. The localization error obtained when applying the PBD model remains below 11 mm for all the tumors even for input displacements in the order of 30 mm. This approach outperforms rigid model used to track lesion position in US-guided breast biopsies, at least halving the localization error for all the displacement ranges considered. Position-based dynamics approach has proved to be successful in modeling breast tissue deformations during US acquisition. Its stability, accuracy and real-time performance make such model suitable for tracking lesions displacement during US-guided breast biopsy

Heregulin β1 induces the down regulation and the ubiquitin-proteasome degradation pathway of p185HER2 oncoprotein

AbstractAnalysis of the fate of the p185HER2 oncoprotein following activation by heregulin β1 revealed the induction of the tyrosine-phosphorylation, down-modulation, and polyubiquitination of p185HER2. Receptor ubiquitination was suppressed in cells treated with heregulin β1 in the presence of sodium azide, an inhibitor of ATP-dependent reactions, or genistein, a tyrosine kinase protein inhibitor, indicating the requirement for kinase activity and ATP in p185HER2 polyubiquitination. Ubiquitinated p185HER2 was degradated by the 26S proteasome proteolytic pathway. Kinetics and inhibition experiments indicated that endocytosis of the receptor occurs downstream of the initiation of the degradation process

Henoch-Schönlein Purpura in children: Not only kidney but also lung

Background: Henoch-Sch\uf6nlein Purpura (HSP) is the most common vasculitis of childhood and affects the small blood vessels. Pulmonary involvement is a rare complication of HSP and diffuse alveolar hemorrhage (DAH) is the most frequent clinical presentation. Little is known about the real incidence of lung involvement during HSP in the pediatric age and about its diagnosis, management and outcome. Methods: In order to discuss the main clinical findings and the diagnosis and management of lung involvement in children with HSP, we performed a review of the literature of the last 40 years. Results: We identified 23 pediatric cases of HSP with lung involvement. DAH was the most frequent clinical presentation of the disease. Although it can be identified by chest x-ray (CXR), bronchoalveolar lavage (BAL) is the gold standard for diagnosis. Pulse methylprednisolone is the first-line of therapy in children with DAH. An immunosuppressive regimen consisting of cyclophosphamide or azathioprine plus corticosteroids is required when respiratory failure occurs. Four of the twenty-three patients died, while 18 children had a resolution of the pulmonary involvement. Conclusions: DAH is a life-threatening complication of HSP. Prompt diagnosis and adequate treatment are essential in order to achieve the best outcome