Labor Supply Elasticities: Can Micro Be Misleading for Macro?

In this paper we compare “micro” and “macro” labor supply elasticities in a MaCurdy-type equation. Using PSID data, we obtain the micro elasticity from standard panel techniques, and the macro elasticity from the time series generated by aggregating individuals every year. This procedure relies on the exact aggregation of first-order conditions in a life-cycle model with home production. We find an individual elasticity of about 0.1, a low value in line with mainstream microeconometric studies, and an aggregate elasticity of about 1, a much larger value often assumed in calibration studies. This discrepancy is not due to aggregation bias: it is due to the fact that individual and total hours are different variables, with the extensive margin that empirically dominates. A broader implication of our result is that micro evidence is not always appropriate for calibrating an aggregate model economyelasticity of labor supply, aggregation, calibration

Robot Learning-Based Pipeline for Autonomous Reshaping of a Deformable Linear Object in Cluttered Backgrounds

open2noThis work was supported in part by the European Union’s Horizon 2020 Research and Innovation Program as part of RIA Project Robotic tEchnologies for the Manipulation of cOmplex DeformablE Linear objects (REMODEL) under Grant 870133.In this work, the robotic manipulation of a highly Deformable Linear Object (DLO) is addressed by means of a sequence of pick-and-drop primitives driven by visual data. A decision making process learns the optimal grasping location exploiting deep Q-learning and finds the best releasing point from a path representation of the DLO shape. The system effectively combines a state-of-the-art algorithm for semantic segmentation specifically designed for DLOs with deep reinforcement learning. Experimental results show that our system is capable to manipulate a DLO into a variety of different shapes in few steps. The intermediate steps of deformation that lead the object from its initial configuration to the target one are also provided and analyzed.openZanella R.; Palli G.Zanella R.; Palli G

Quantum simulation of a spin polarization device in an electron microscope

A proposal for an electron-beam device that can act as an efficient spin-polarization filter has been recently put forward [E. Karimi et al., Phys. Rev. Lett. 108, 044801 (2012)]. It is based on combining the recently developed diffraction technology for imposing orbital angular momentum to the beam with a multipolar Wien filter inducing a sort of artificial non-relativistic spin-orbit coupling. Here we reconsider the proposed device with a fully quantum-mechanical simulation of the electron beam propagation, based on the well established multi-slice method, supplemented with a Pauli term for taking into account the spin degree of freedom. Using this upgraded numerical tool, we study the feasibility and practical limitations of the proposed method for spin-polarizing a free electron bea

Limited-memory scaled gradient projection methods for real-time image deconvolution in microscopy

Gradient projection methods have given rise to effective tools for image deconvolution in several relevant areas, such as microscopy, medical imaging and astronomy. Due to the large scale of the optimization problems arising in nowadays imaging applications and to the growing request of real-time reconstructions, an interesting challenge to be faced consists in designing new acceleration techniques for the gradient schemes, able to preserve the simplicity and low computational cost of each iteration. In this work we propose an acceleration strategy for a state of the art scaled gradient projection method for image deconvolution in microscopy. The acceleration idea is derived by adapting a step-length selection rule, recently introduced for limited-memory steepest descent methods in unconstrained optimization, to the special constrained optimization framework arising in image reconstruction. We describe how important issues related to the generalization of the step-length rule to the imaging optimization problem have been faced and we evaluate the improvements due to the acceleration strategy by numerical experiments on large-scale image deconvolution problems

Passivizing learned policies and learning passive policies with virtual energy tanks in robotics

Within a robotic context, we merge the techniques of passivity-based control (PBC) and reinforcement learning (RL) with the goal of eliminating some of their reciprocal weaknesses, as well as inducing novel promising features in the resulting framework. We frame our contribution in a scenario where PBC is implemented by means of virtual energy tanks, a control technique developed to achieve closed-loop passivity for any arbitrary control input. Albeit the latter result is heavily used, we discuss why its practical application at its current stage remains rather limited, which makes contact with the highly debated claim that passivity-based techniques are associated with a loss of performance. The use of RL allows us to learn a control policy that can be passivized using the energy tank architecture, combining the versatility of learning approaches and the system theoretic properties which can be inferred due to the energy tanks. Simulations show the validity of the approach, as well as novel interesting research directions in energy-aware robotics.Comment: 8 pages, 5 figure

RT-DLO: Real-Time Deformable Linear Objects Instance Segmentation

Deformable Linear Objects (DLOs) such as cables, wires, ropes, and elastic tubes are numerously present both in domestic and industrial environments. Unfortunately, robotic systems handling DLOs are rare and have limited capabilities due to the challenging nature of perceiving them. Hence, we propose a novel approach named RT-DLO for real-time instance segmentation of DLOs. First, the DLOs are semantically segmented from the background. Afterward, a novel method to separate the DLO instances is applied. It employs the generation of a graph representation of the scene given the semantic mask where the graph nodes are sampled from the DLOs center-lines whereas the graph edges are selected based on topological reasoning. RT-DLO is experimentally evaluated against both DLO-specific and general-purpose instance segmentation deep learning approaches, achieving overall better performances in terms of accuracy and inference time

A simplified constitutive model for a SEBS gel muscle simulant - Development and experimental validation for finite elements simulations of handgun and rifle ballistic impacts

An original simplified constitutive model is proposed to simulate the effects of ballistic impacts on blocks of synthetic muscle simulant based on mineral oil and styrene ethylene-butylene styrene polymers (SEBS) as a convenient substitute for Fackler ballistic gelatin. The model is based on a quasi-static elastic-plastic model associated with hydrodynamic properties regulated by a polynomial equation of state. The paper illustrates the development and experimental validation of the model to simulate 9x21mm FMJ round-nose, 7.62x39 mm FMJ, and 5.56x45 mm NATO bullets penetrating 145x145x400 mm gel blocks. All material parameters are provided to be implemented in built-in LS-Dyna keywords. The validation confirms the effectiveness of the model and suggests possible further developments. The work also confirms the tested synthetic gel as a valid and convenient substitute for Fackler 10% ballistic gelatin at 4 °C

Proteomic analysis of Parietaria judaica pollen and allergen profiling by an immunoproteomic approach

Parietaria judaica pollen is a common cause of airway allergic disease in the Mediterranean area. Proteome analysis of mature Parietaria judaica pollen by two-dimensional gel electrophoresis (2-DE) and mass spectrometry has established the first reference proteome map of this weed. Proteins involved in a variety of cellular functions as well as the occurrence of allergens were detected. By using 2-DE and immunoblotting with sera from Parietaria judaica allergic patients we obtained a more detailed characterization of Parietaria judaica allergen profil

Structural analysis of sulfate vein networks in Gale crater (Mars)

The Curiosity rover's campaign in the Gale crater on Mars provides a large set of close-up images of sedimentary formations outcrops displaying a variety of diagenetic features such as light-toned veins, nodules and raised ridges. Through 2D and 3D analyses of Mastcam images we herein reconstruct the vein network of a sample area and estimated the stress field. Assessment of the spatial distribution of light-toned veins shows that the basin infillings, after burial and consolidation, experienced a sub-vertical compression and lateral extension coupled with fluid overpressure and cracking. Overall, rock failure and light-toned veins formations could have been generated by an overload produced by infilling material within the basin