Double beta decay and the proton-neutron residual interaction

The validity of the pn-QRPA and -RQRPA descriptions of double beta decay transition amplitudes is analyzed by using an exactly solvable model. It is shown that the collapse of the QRPA is physically meaningful and that it is associated with the appearance of a state with zero energy in the spectrum. It is shown that in the RQRPA this particular feature is not present and that this approach leads to finite but otherwise spurious results for the double beta decay transition amplitudes near the point of collapse.Comment: LaTeX, 10 pages plus 3 fugures as LaTeX files. Accepted for publication in Physics Letters

The Friedrichs-Model with fermion-boson couplings II

In this work we present a formal solution of the extended version of the Friedrichs Model. The Hamiltonian consists of discrete and continuum bosonic states, which are coupled to fermions. The simultaneous treatment of the couplings of the fermions with the discrete and continuous sectors of the bosonic degrees of freedom leads to a system of coupled equations, whose solutions are found by applying standard methods of representation of bound and resonant states.Comment: 13 page

Single- and double-beta decay Fermi-transitions in an exactly solvable model

An exactly solvable model suitable for the description of single and double-beta decay processes of the Fermi-type is introduced. The model is equivalent to the exact shell-model treatment of protons and neutrons in a single j-shell. Exact eigenvalues and eigenvectors are compared to those corresponding to the hamiltonian in the quasiparticle basis (qp) and with the results of both the standard quasiparticle random phase approximation (QRPA) and the renormalized one (RQRPA). The role of the scattering term of the quasiparticle hamiltonian is analyzed. The presence of an exact eigenstate with zero energy is shown to be related to the collapse of the QRPA. The RQRPA and the qp solutions do not include this zero-energy eigenvalue in their spectra, probably due to spurious correlations. The meaning of this result in terms of symmetries is presented.Comment: 29 pages, 9 figures included in a Postsript file. Submitted to Physcal Review

Giant dipole resonance with exact treatment of thermal fluctuations

The shape fluctuations due to thermal effects in the giant dipole resonance (GDR) observables are calculated using the exact free energies evaluated at fixed spin and temperature. The results obtained are compared with Landau theory calculations done by parameterizing the free energy. The Landau theory is found to be insufficient when the shell effects are dominating.Comment: 5 pages, 2 figure

Towards Active Learning Interfaces for Multi-Inhabitant Activity Recognition

Semi-supervised approaches for activity recognition are a promising way to address the labeled data scarcity problem. Those methods only require a small training set in order to be initialized, and the model is continuously updated and improved over time. Among the several solutions existing in the literature, active learning is emerging as an effective technique to significantly boost the recognition rate: when the model is uncertain about the current activity performed by the user, the system asks her to provide the ground truth. This feedback is then used to update the recognition model. While active learning has been mostly proposed in single-inhabitant settings, several questions arise when such a system has to be implemented in a realistic environment with multiple users. Who to ask a feedback when the system is uncertain about a collaborative activity? In this paper, we investigate this and more questions on this topic, proposing a preliminary study of the requirements of an active learning interface for multi-inhabitant settings. In particular, we formalize the problem and we describe the solutions adopted in our system prototype

Double beta decay to excited states in Nd150

The pseudo SU(3) model is used to study the double beta decay of Nd150 to the ground and excited states of Sm150 . Low lying collective excitations of Sm150 and its BE(2) intensities are well reproduced. Expressions for the two neutrino double beta decay to excited states are developed and used to describe the decay of Nd150 . The existence of selection rules which strongly restrict the decay is discussed.Comment: 22 pages, LaTeX, 1 figure with Postscript file available under request at [email protected]

CAVIAR: Context-driven Active and Incremental Activity Recognition

Activity recognition on mobile device sensor data has been an active research area in mobile and pervasive computing for several years. While the majority of the proposed techniques are based on supervised learning, semi-supervised approaches are being considered to reduce the size of the training set required to initialize the model. These approaches usually apply self-training or active learning to incrementally refine the model, but their effectiveness seems to be limited to a restricted set of physical activities. We claim that the context which surrounds the user (e.g., time, location, proximity to transportation routes) combined with common knowledge about the relationship between context and human activities could be effective in significantly increasing the set of recognized activities including those that are difficult to discriminate only considering inertial sensors, and the highly context-dependent ones. In this paper, we propose CAVIAR, a novel hybrid semi-supervised and knowledge-based system for real-time activity recognition. Our method applies semantic reasoning on context-data to refine the predictions of an incremental classifier. The recognition model is continuously updated using active learning. Results on a real dataset obtained from 26 subjects show the effectiveness of our approach in increasing the recognition rate, extending the number of recognizable activities and, most importantly, reducing the number of queries triggered by active learning. In order to evaluate the impact of context reasoning, we also compare CAVIAR with a purely statistical version, considering features computed on context-data as part of the machine learning process