A model of the user's proximity for bayesian inference

Embodied nonverbal cues are fundamental for regulating human-human social iteractions. The physical embodiment of robots makes it likely that they will have to exhibit appropriate nonverbal interactive behaviors. In this paper we propose a model of the user's proximity based on a superposition of quasi-Gaussian probability distributions which allows to express findings from HRI trials regarding distances and direction of approach in a human-robot interaction scenario. The way the model is formulated is suitable for well-established Bayesian filtering techniques, and thus the inference of the preferred distance and direction of approach in a human robot interaction scenario can be regarded as a state estimation problem. Results derived from simulations show the effectiveness of the inference process

Quantum Annealing for Neural Network optimization problems: a new approach via Tensor Network simulations

Quantum Annealing (QA) is one of the most promising frameworks for quantum optimization. Here, we focus on the problem of minimizing complex classical cost functions associated with prototypical discrete neural networks, specifically the paradigmatic Hopfield model and binary perceptron. We show that the adiabatic time evolution of QA can be efficiently represented as a suitable Tensor Network. This representation allows for simple classical simulations, well-beyond small sizes amenable to exact diagonalization techniques. We show that the optimized state, expressed as a Matrix Product State (MPS), can be recast into a Quantum Circuit, whose depth scales only linearly with the system size and quadratically with the MPS bond dimension. This may represent a valuable starting point allowing for further circuit optimization on near-term quantum devices

Trichoderma harzianum Strain T22 Modulates Direct Defense of Tomato Plants in Response to Nezara viridula Feeding Activity

Plant growth-promoting fungi belonging to genus Trichoderma are known to help plants when dealing with biotic stressors by enhancing plant defenses. While beneficial effects of Trichoderma spp. against plant pathogens have long been documented, fewer studies have investigated their effect on insect pests. Here, we studied the impact of Trichoderma root colonization on the plant defense responses against stink bug feeding attack. For this purpose, a model system consisting of tomato plant, Solanum lycopersicum cv Dwarf San Marzano, Trichoderma harzianum strain T22 and the southern green stink bug, Nezara viridula, was used. We firstly determined stink bug performance in terms of relative growth rate and survival on tomato plants inoculated by T. harzianum T22. Then, we evaluated relative expression of plant defense-related genes on inoculated plants induced by stink bug feeding. We found evidence that T. harzianum T22 affects tomato defense responses against N. viridula nymphs leading to reduction of growth rate. Our results also showed that T. harzianum T22 enhances plant direct defenses by an early increase of transcript levels of jasmonic acid marker genes. Yet this effect was time-dependent and only detected 8 h after herbivore induction. Taken together, our findings provide better understanding on the mechanisms underlying tomato induced resistance against herbivorous stink bugs

Beyond the “Pain Matrix,” inter-run synchronization during mechanical nociceptive stimulation

Pain is a complex experience that is thought to emerge from the activity of multiple brain areas, some of which are inconsistently detected using traditional fMRI analysis. One hypothesis is that the traditional analysis of pain-related cerebral responses, by relying on the correlation of a predictor and the canonical hemodynamic response function (HRF)- the general linear model (GLM)- may under-detect the activity of those areas involved in stimulus processing that do not present a canonical HRF. In this study, we employed an innovative data-driven processing approach- an inter-run synchronization (IRS) analysis- that has the advantage of not establishing any pre-determined predictor definition. With this method we were able to evidence the involvement of several brain regions that are not usually found when using predictor-based analysis. These areas are synchronized during the administration of mechanical punctate stimuli and are characterized by a BOLD response different from the canonical HRF. This finding opens to new approaches in the study of pain imaging

Avoiding barren plateaus via transferability of smooth solutions in a Hamiltonian variational ansatz

A large ongoing research effort focuses on variational quantum algorithms (VQAs), representing leading candidates to achieve computational speed-ups on current quantum devices. The scalability of VQAs to a large number of qubits, beyond the simulation capabilities of classical computers, is still debated. Two major hurdles are the proliferation of low-quality variational local minima, and the exponential vanishing of gradients in the cost-function landscape, a phenomenon referred to as barren plateaus. In this work, we show that by employing iterative search schemes, one can effectively prepare the ground state of paradigmatic quantum many-body models, also circumventing the barren plateau phenomenon. This is accomplished by leveraging the transferability to larger system sizes of a class of iterative solutions, displaying an intrinsic smoothness of the variational parameters, a result that does not extend to other solutions found via random-start local optimization. Our scheme could be directly tested on near-term quantum devices, running a refinement optimization in a favorable local landscape with nonvanishing gradients

Quantum approximate optimization algorithm applied to the binary perceptron

We apply digitized Quantum Annealing (QA) and Quantum Approximate Optimization Algorithm (QAOA) to a paradigmatic task of supervised learning in artificial neural networks: the optimization of synaptic weights for the binary perceptron. At variance with the usual QAOA applications to MaxCut, or to quantum spin-chains ground state preparation, the classical is characterized by highly non-local multi-spin interactions. Yet, we provide evidence for the existence of optimal solutions for the QAOA parameters, which are among typical instances of the same problem, and we prove numerically an enhanced performance of QAOA over traditional QA. We also investigate on the role of the landscape geometry in this problem. \revision{By artificially breaking this geometrical structure, we show that the detrimental effect of a gap-closing transition, encountered in QA, is also negatively affecting the performance of our QAOA implementation

Automatic Configuration of Multi-Agent Model Predictive Controllers based on Semantic Graph World Models

We propose a shared semantic map architecture to construct and configure Model Predictive Controllers (MPC) dynamically, that solve navigation problems for multiple robotic agents sharing parts of the same environment. The navigation task is represented as a sequence of semantically labeled areas in the map, that must be traversed sequentially, i.e. a route. Each semantic label represents one or more constraints on the robots' motion behaviour in that area. The advantages of this approach are: (i) an MPC-based motion controller in each individual robot can be (re-)configured, at runtime, with the locally and temporally relevant parameters; (ii) the application can influence, also at runtime, the navigation behaviour of the robots, just by adapting the semantic labels; and (iii) the robots can reason about their need for coordination, through analyzing over which horizon in time and space their routes overlap. The paper provides simulations of various representative situations, showing that the approach of runtime configuration of the MPC drastically decreases computation time, while retaining task execution performance similar to an approach in which each robot always includes all other robots in its MPC computations

Insect oviposition in herbaceous plants attracts egg parasitoids despite fungal phytopathogen infection

Egg parasitoids are important natural enemies of several insect pests. The ability to kill the pest before it can inflict damage to the plant makes egg parasitoids ideal candidates for biological control. Several studies have shown that egg parasitoids exploit oviposition-induced plant volatiles (OIPVs) to locate host eggs laid on plant organs. Yet such studies have often overlooked that, in nature, plants frequently suffer concurrent attack by insect herbivores and phytopathogens. These dual attacks can modify the emission of induced plant volatiles, which may potentially interfere with the host location abilities of egg parasitoids. We investigated this research question using the following study organisms: the broad bean Vicia faba, the plant pathogen Stemphylium sp., the southern green stink bug Nezara viridula and its associated egg parasitoid Trissolcus basalis. We showed that T. basalis is able to exploit OPIVs in order to locate N. viridula egg masses even when V. faba plants were previously infected by Stemphylium sp. Chemical analyses indicate that the egg parasitoid ability to exploit OIPVs persists despite significant alterations of the volatile blends emitted by plants suffering multiple biotic stresses. This study highlights the importance of incorporating the complexity of multiple biotic stresses when studying parasitoid foraging behavior, in order to comprehend how to enhance the effectiveness of natural enemies in crop protection

Alexithymia in Fibromyalgia Syndrome: is it a discriminant factor?

9nonemixedGHIGGIA, ADA; TESIO, VALENTINA; ROMEO, ANNUNZIATA; Monoli, F; COLONNA, FABRIZIO; LEOMBRUNI, Paolo; Fusaro, E; TORTA, Riccardo; CASTELLI, LorysGhiggia, Ada; Tesio, Valentina; Romeo, Annunziata; Monoli, F; Colonna, Fabrizio; Leombruni, Paolo; Fusaro, E; Torta, Riccardo; Castelli, Lory