Stochastic coarse-grained simulations of polyelectrolytes

Stochastic coarse-grained simulations are implemented to investigate the behavior of both strong and weak polyelectrolytes in acqueous solution. A primitive electrolyte model is used to represent polyeletrolytes and mobile ions, whereas the solvent is implcitly represented by a dielectric continuum. The polyelectrolytes dissociation equilibria are taken into account by the constant-pH method where necessary. Several different chemico-physical systems have been investigated: 1. linear and star weak polyelectrolytes (both in solutions or confined in semi-permeable spherical cavities) able to interact via charged hydrogen bonds; 2. linear and star weak polyelectrolytes interacting with an oppositely charged macroion, the latter represented either via the usual charge-centered model or via monovalent charges tethered to (but free to move and rearrange on) its surface: 3. linear and star strong polyelectrolytes interacting with a primitive model of a zwitterionic micelle; 4. mixtures of oppositely charged star-shaped strong polyelectrolytes that self-assemble to form gel-like phases at the free swelling equilibrium; 5. weak knotted ring polyelectrolytes, the latter showing a non monotonic behavior of their size versus their ionization degree, an evidence that was not predicted by mean-filed approaches. Overall, our simulations demonstrated that the polyelectrolytes behavior often deviates from the one expected for "canonical" polyelectrolytes in diluted aqueous solutions when chemically specific interactions (such as charged hydrogen bonds) have to be taken into account, or when charge correlation play a fiundamental role

Sensation seeking, non-contextual decision making, and driving abilities as measured through a moped simulator.

The general aim of the present study was to explore the relations between driving style (assessed through a moped riding simulator) and psychological variables such as sensation seeking and decision making. Because the influences of sensation seeking and decision making on driving styles have been studied separately in the literature, we have tried to investigate their mutual relations so as to include them in a more integrated framework. Participants rode the Honda Riding Trainer (HRT) simulator, filled in the Sensation Seeking Scale V (SSS V), and performed the Iowa Gambling Task (IGT). A cluster analysis of the HRT riding indexes identified three groups: Prudent, Imprudent, and Insecure riders. First, the results showed that Insecure males seek thrills and adventure less than both Prudent males and Insecure females, whereas Prudent females are less disinhibited than both Prudent males and Insecure females. Moreover, concerning the relations among SSS, decision making as measured by the IGT, and riding performance, high thrill and adventure seekers performed worse in the simulator only if they were also bad decision makers, indicating that these two traits jointly contribute to the quality of riding performance. From an applied perspective, these results also provide useful information for the development of protocols for assessing driving abilities among novice road users. Indeed, the relation between risk proneness and riding style may allow for the identification of road-user populations who require specific training

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

Modulation of DNA repair genes induced by TLR9 agonists: A strategy to eliminate “altered” cells?

We provided evidence that the TLR9 engagement of innate immune cells present in the tumor microenvironment by CpG-oligodeoxynucleotide (CpG-ODN) induces down-modulation of DNA repair gene expression in tumor cells, sensitizing cancer cells to DNA-damaging chemotherapy. These findings expand the benefits of CpG-ODN therapy beyond induction of a strong immune response

Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor

Disturbance estimation for Micro Aerial Vehicles (MAVs) is crucial for robustness and safety. In this paper, we use novel, bio-inspired airflow sensors to measure the airflow acting on a MAV, and we fuse this information in an Unscented Kalman Filter (UKF) to simultaneously estimate the three-dimensional wind vector, the drag force, and other interaction forces (e.g. due to collisions, interaction with a human) acting on the robot. To this end, we present and compare a fully model-based and a deep learning-based strategy. The model-based approach considers the MAV and airflow sensor dynamics and its interaction with the wind, while the deep learning-based strategy uses a Long Short-Term Memory (LSTM) neural network to obtain an estimate of the relative airflow, which is then fused in the proposed filter. We validate our methods in hardware experiments, showing that we can accurately estimate relative airflow of up to 4 m/s, and we can differentiate drag and interaction force.Comment: The first two authors contributed equall

An innovative 8 channels system for time-resolved diffuse optical tomography based on SiPMs

We present the design of a novel 8 channels system for time resolved optical tomography based on Silicon Photomultipliers (SiPMs), therefore knocking down cost and complexity of this technique and paving the way to a widespread diffusion. We validated the system performances on phantoms