Robust Subspace System Identification via Weighted Nuclear Norm Optimization

Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The proposed framework takes the form of a convex optimization problem with an objective that trades off fit, rank and sparsity. As in robust PCA, it can be problematic to find a suitable regularization parameter. We show how the space in which a suitable parameter should be sought can be limited to a bounded open set of the two dimensional parameter space. In practice, this is very useful since it restricts the parameter space that is needed to be surveyed.Comment: Submitted to the IFAC World Congress 201

A Learning Based Approach to Control Synthesis of Markov Decision Processes for Linear Temporal Logic Specifications

We propose to synthesize a control policy for a Markov decision process (MDP) such that the resulting traces of the MDP satisfy a linear temporal logic (LTL) property. We construct a product MDP that incorporates a deterministic Rabin automaton generated from the desired LTL property. The reward function of the product MDP is defined from the acceptance condition of the Rabin automaton. This construction allows us to apply techniques from learning theory to the problem of synthesis for LTL specifications even when the transition probabilities are not known a priori. We prove that our method is guaranteed to find a controller that satisfies the LTL property with probability one if such a policy exists, and we suggest empirically with a case study in traffic control that our method produces reasonable control strategies even when the LTL property cannot be satisfied with probability one

Intranasal insulin treatment improves memory and learning in a rat amyloid-beta model of Alzheimer’s disease

Recently, insulin has been used as a pro-cognitive agent for the potential treatment of Alzheimer’s disease (AD), because of its ability to cross the brain–blood barrier (BBB) by a saturable transport system. This study has been designed to evaluate the effects of intranasal insulin regimen, as a bypass system of BBB, on spatial memory in amyloid-beta (Aβ) model of AD in rat. Unilateral infusion of Aβ25–35 (10 nmol/2 µl/rat) into the lateral ventricular region of brain was used to produce a rat model of AD. After a 24-h recovery period, rats received insulin or vehicle via intraperitoneal or intranasal route (0.1, 0.2, and 0.3 IU) for 14 days. Memory function in rats was assessed by Morris water maze test, with 5 days of training and consequent probe test protocol. Different doses of intraperitoneal insulin did not have a significant effect on learning and memory in AD rats. However, intranasal insulin at doses of 0.2 and 0.3 IU improved the learning and memory in Aβ-received rats. In conclusion, intranasal insulin as a non-invasive strategy improves spatial learning and memory in AD model

Comparative survey on anesthetizing effects of medicinal herbs Valerian officinalis, Melissa officinalis, Papaver somniferum, and Papaver bracteatum on gold fish (Carassius auratus)

Anesthetic drugs are widely used aquaculture farms and can create consumption problems for humans, so there is a great need to safer drugs. With regard to long record of herbal drugs consumption in Iran and their benefits, we survey the possibility of using Valerian officinalis, Melissa officinalis, Papaver somniferum, and P. bracteatum as fish anesthetic. We provided, processed, and made consistent extractions of V. officinalis (2, 3 4g/lit), M. officinalis (5, 10, 15g/lit), P. somniferum (0.85, 1.6, 3.2g/lit) and P. bracteatum (0.3, 0.6, 0.9g/lit). We selected 60 goldfish Carassius auratus in the weight range of 7.41 plus or minus 0/2g, and the length range of 8.4 plus or minus 0/11cm and kept them in laboratory conditions under the same oxygen and temperature. The fish were divided into four groups each containing 15 fish and further into three subgroups of five fish each. During the experiments, two herbs P. somniferum and P. bracteaturn were eliminated from statistical analysis because of biased results

Diagnosis and Repair for Synthesis from Signal Temporal Logic Specifications

We address the problem of diagnosing and repairing specifications for hybrid systems formalized in signal temporal logic (STL). Our focus is on the setting of automatic synthesis of controllers in a model predictive control (MPC) framework. We build on recent approaches that reduce the controller synthesis problem to solving one or more mixed integer linear programs (MILPs), where infeasibility of a MILP usually indicates unrealizability of the controller synthesis problem. Given an infeasible STL synthesis problem, we present algorithms that provide feedback on the reasons for unrealizability, and suggestions for making it realizable. Our algorithms are sound and complete, i.e., they provide a correct diagnosis, and always terminate with a non-trivial specification that is feasible using the chosen synthesis method, when such a solution exists. We demonstrate the effectiveness of our approach on the synthesis of controllers for various cyber-physical systems, including an autonomous driving application and an aircraft electric power system

From Uncertainty Data to Robust Policies for Temporal Logic Planning

We consider the problem of synthesizing robust disturbance feedback policies for systems performing complex tasks. We formulate the tasks as linear temporal logic specifications and encode them into an optimization framework via mixed-integer constraints. Both the system dynamics and the specifications are known but affected by uncertainty. The distribution of the uncertainty is unknown, however realizations can be obtained. We introduce a data-driven approach where the constraints are fulfilled for a set of realizations and provide probabilistic generalization guarantees as a function of the number of considered realizations. We use separate chance constraints for the satisfaction of the specification and operational constraints. This allows us to quantify their violation probabilities independently. We compute disturbance feedback policies as solutions of mixed-integer linear or quadratic optimization problems. By using feedback we can exploit information of past realizations and provide feasibility for a wider range of situations compared to static input sequences. We demonstrate the proposed method on two robust motion-planning case studies for autonomous driving