DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout

The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently trains a neural model as a committee machine of subnetworks, each capable of predicting with a subset of the original input features. We discuss the application of the DropIn methodology in the context of Reservoir Computing models and targeting applications characterized by input sources that are unreliable or prone to be disconnected, such as in pervasive wireless sensor networks and ambient intelligence. We provide an experimental assessment using real-world data from such application domains, showing how the Dropin methodology allows to maintain predictive performances comparable to those of a model without missing features, even when 20\%-50\% of the inputs are not available

Competing coherent and dissipative dynamics close to quantum criticality

We investigate the competition of coherent and dissipative dynamics in many-body systems at continuous quantum transitions. We consider dissipative mechanisms that can be effectively described by Lindblad equations for the density matrix of the system. The interplay between the critical coherent dynamics and dissipation is addressed within a dynamic finite-size scaling framework, which allows us to identify the regime where they develop a nontrivial competition. We analyze protocols that start from critical many-body ground states and put forward general dynamic scaling behaviors involving the Hamiltonian parameters and the coupling associated with the dissipation. This scaling scenario is supported by a numerical study of the dynamic behavior of a one-dimensional lattice fermion gas undergoing a quantum Ising transition in the presence of dissipative mechanisms such as local pumping, decaying, and dephasing.Comment: 9 pages, 4 figure

Shape-from-intrinsic operator

Shape-from-X is an important class of problems in the fields of geometry processing, computer graphics, and vision, attempting to recover the structure of a shape from some observations. In this paper, we formulate the problem of shape-from-operator (SfO), recovering an embedding of a mesh from intrinsic differential operators defined on the mesh. Particularly interesting instances of our SfO problem include synthesis of shape analogies, shape-from-Laplacian reconstruction, and shape exaggeration. Numerically, we approach the SfO problem by splitting it into two optimization sub-problems that are applied in an alternating scheme: metric-from-operator (reconstruction of the discrete metric from the intrinsic operator) and embedding-from-metric (finding a shape embedding that would realize a given metric, a setting of the multidimensional scaling problem)

Ratiometric control for differentiation of cell populations endowed with synthetic toggle switches

We consider the problem of regulating by means of external control inputs the ratio of two cell populations. Specifically, we assume that these two cellular populations are composed of cells belonging to the same strain which embeds some bistable memory mechanism, e.g. a genetic toggle switch, allowing them to switch role from one population to another in response to some inputs. We present three control strategies to regulate the populations' ratio to arbitrary desired values which take also into account realistic physical and technological constraints occurring in experimental microfluidic platforms. The designed controllers are then validated in-silico using stochastic agent-based simulations.Comment: Accepted to CDC'201

PAMPC: Perception-Aware Model Predictive Control for Quadrotors

We present the first perception-aware model predictive control framework for quadrotors that unifies control and planning with respect to action and perception objectives. Our framework leverages numerical optimization to compute trajectories that satisfy the system dynamics and require control inputs within the limits of the platform. Simultaneously, it optimizes perception objectives for robust and reliable sens- ing by maximizing the visibility of a point of interest and minimizing its velocity in the image plane. Considering both perception and action objectives for motion planning and control is challenging due to the possible conflicts arising from their respective requirements. For example, for a quadrotor to track a reference trajectory, it needs to rotate to align its thrust with the direction of the desired acceleration. However, the perception objective might require to minimize such rotation to maximize the visibility of a point of interest. A model-based optimization framework, able to consider both perception and action objectives and couple them through the system dynamics, is therefore necessary. Our perception-aware model predictive control framework works in a receding-horizon fashion by iteratively solving a non-linear optimization problem. It is capable of running in real-time, fully onboard our lightweight, small-scale quadrotor using a low-power ARM computer, to- gether with a visual-inertial odometry pipeline. We validate our approach in experiments demonstrating (I) the contradiction between perception and action objectives, and (II) improved behavior in extremely challenging lighting conditions

Aggressive Quadrotor Flight through Narrow Gaps with Onboard Sensing and Computing using Active Vision

We address one of the main challenges towards autonomous quadrotor flight in complex environments, which is flight through narrow gaps. While previous works relied on off-board localization systems or on accurate prior knowledge of the gap position and orientation, we rely solely on onboard sensing and computing and estimate the full state by fusing gap detection from a single onboard camera with an IMU. This problem is challenging for two reasons: (i) the quadrotor pose uncertainty with respect to the gap increases quadratically with the distance from the gap; (ii) the quadrotor has to actively control its orientation towards the gap to enable state estimation (i.e., active vision). We solve this problem by generating a trajectory that considers geometric, dynamic, and perception constraints: during the approach maneuver, the quadrotor always faces the gap to allow state estimation, while respecting the vehicle dynamics; during the traverse through the gap, the distance of the quadrotor to the edges of the gap is maximized. Furthermore, we replan the trajectory during its execution to cope with the varying uncertainty of the state estimate. We successfully evaluate and demonstrate the proposed approach in many real experiments. To the best of our knowledge, this is the first work that addresses and achieves autonomous, aggressive flight through narrow gaps using only onboard sensing and computing and without prior knowledge of the pose of the gap

Generalized Riemann hypotheses: sufficient and equivalent criteria

This paper presents new sufficient and equivalent conditions for the generalized version of the Riemann Hypothesis. The paper derives also statements and remarks concerning zero-free regions, modified Hadamard-product formulas and the behaviour of $|\frac{L(\bar{\chi},s)}{L(\chi,1-s)}|$.Comment: Few changes. Accepted for publication in JP Journal of Algebra, Number Theory and Application

Knowledge and the Importance of Being Right

Some philosophers have recently argued that whether a true belief amounts to knowledge in a specific circumstance depends on features of the subject’s practical situation that are unrelated to the truth of the subject’s belief, such as the costs for the subject of being wrong about whether the believed proposition is true. One of the best-known arguments used to support this view is that it best explains a number of paradigmatic cases, such as the well-known Bank Case, in which a difference in knowledge occurs in subjects differing exclusively with respect to their practical situation. I suggest an alternative explanation of such cases. My explanation has a disjunctive character: on the one hand, it accounts for cases in which the subject is aware of the costs of being wrong in a given situation in terms of the influence of psychological factors on her mechanisms of belief-formation and revision. On the other hand, it accounts for cases in which the subject is ignorant of the costs of being wrong in her situation by imposing a new condition on knowledge. This condition is that one knows that p only if one does not underestimate the importance of being right about whether p. I argue that my explanation has a number of advantages over other invariantist explanations: it accounts for all the relevant cases preserving the semantic significance of our ordinary intuitions, it is compatible with an intellectualist account of knowledge and it escapes several problems affecting competing views