Investigation of electrodynamic stabilization and control of long orbiting tethers

The possibility of using electrodynamic forces to control pendular oscillations during the retrieval of a subsatellite is investigated. The use of the tether for transferring payloads between orbits is studied

Left Imaginal Neglect in Heminattention: Experimental Study with the O'Clock Test

Both sensory and imaginal defects have been reported in unilateral neglect, but their assessment based on the description of famous squares can be difficult in a clinical setting. The O'clock Test is an alternative tool for revealing imaginal defects. Our aim was to demonstrate imaginal neglect in patients with left heminattention. Ten patients were studied and a mild unilateral defect in imaginal processes was found with an increase in the defect when the patients were fatigued

The quantum path kernel: A generalized neural tangent kernel for deep quantum machine learning

Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. A key issue is how to address the inherent non-linearity of classical deep learning, a problem in the quantum domain due to the fact that the composition of an arbitrary number of quantum gates, consisting of a series of sequential unitary transformations, is intrinsically linear. This problem has been variously approached in the literature, principally via the introduction of measurements between layers of unitary transformations. In this paper, we introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning typically associated with superior generalization performance in the classical domain, specifically, hierarchical feature learning. Our approach generalizes the notion of Quantum Neural Tangent Kernel, which has been used to study the dynamics of classical and quantum machine learning models. The Quantum Path Kernel exploits the parameter trajectory, i.e. the curve delineated by model parameters as they evolve during training, enabling the representation of differential layer-wise convergence behaviors, or the formation of hierarchical parametric dependencies, in terms of their manifestation in the gradient space of the predictor function.We evaluate our approach with respect to variants of the classification of Gaussian XOR mixtures - an artificial but emblematic problem that intrinsically requires multilevel learning in order to achieve optimal class separation

Truly scalable K-Truss and max-truss algorithms for community detection in graphs

The notion of k-truss has been introduced a decade ago in social network analysis and security for community detection, as a form of cohesive subgraphs less stringent than a clique (set of pairwise linked nodes), and more selective than a k-core (induced subgraph with minimum degree k). A k-truss is an inclusion-maximal subgraph Hin which each edge belongs to at least k-2triangles inside H. The truss decomposition establishes, for each edge e, the maximum kfor which ebelongs to a k-truss. Analogously to the largest clique and to the maximum k-core, the strongest community for k-truss is the max-truss, which corresponds to the k-truss having the maximum k. Even though the computation of truss decomposition and of the max-truss takes polynomial time, on a large scale, it suffers from handling a potentially cubic number of wedges. In this paper, we provide a new algorithm FMT, which advances the state of the art on different sides: lower execution time, lower memory usage, and no need for expensive hardware. We compare FMT experimentally with the most recent state-of-the-art algorithms on a set of large real-world and synthetic networks with over a billion edges. The massive improvement allows FMT to compute the max-truss of networks of tens of billions of edges on a single standard server machine

The quantum path kernel: A generalized quantum neural tangent kernel for deep quantum machine learning

Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. A key issue is how to address the inherent non-linearity of classical deep learning, a problem in the quantum domain due to the fact that the composition of an arbitrary number of quantum gates, consisting of a series of sequential unitary transformations, is intrinsically linear. This problem has been variously approached in the literature, principally via the introduction of measurements between layers of unitary transformations. In this paper, we introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning typically associated with superior generalization performance in the classical domain, specifically, hierarchical feature learning. Our approach generalizes the notion of Quantum Neural Tangent Kernel, which has been used to study the dynamics of classical and quantum machine learning models. The Quantum Path Kernel exploits the parameter trajectory, i.e. the curve delineated by model parameters as they evolve during training, enabling the representation of differential layer-wise convergence behaviors, or the formation of hierarchical parametric dependencies, in terms of their manifestation in the gradient space of the predictor function. We evaluate our approach with respect to variants of the classification of Gaussian XOR mixtures - an artificial but emblematic problem that intrinsically requires multilevel learning in order to achieve optimal class separation

Analytical investigation of the dynamics of tethered constellations in Earth orbit (phase 2)

The deployment maneuver of three axis vertical constellations with elastic tethers is analyzed. The deployment strategy devised previously was improved. Dampers were added to the system. Effective algorithms for damping out the fundamental vibrational modes of the system were implemented. Simulations of a complete deployment and a subsequent station keeping phase of a three mass constellation is shown