Report on the quality of the LHCb-Muon four-gap MWPC produced at LNF

The LNF-LHCb team produced 185 four-gap Multi-Wire Proportional Chambers (MWPC). In this note we report the summary of the results of the panel quality controls and of the measurements performed on the assembled detector

MONDO: A tracker for the characterization of secondary fast and ultrafast neutrons emitted in particle therapy

n/

On the non-symplectic involutions of the Hilbert square of a K3 surface

We investigate the interplay between the moduli spaces of ample (2)-polarized IHS manifolds of type K3[2] and of IHS manifolds of type K3[2] with a non-symplectic involution with invariant lattice of rank one. In particular, we describe geometrically some new involutions of the Hilbert square of a K3 surface whose existence was proven in a previous paper of Boissi\ue8re, Cattaneo, Nieper-Wisskirchen, and Sarti

Searching for dominant high-level features for music information retrieval

Music Information Retrieval systems are often based on the analysis of a large number of low-level audio features. When dealing with problems of musical genre description and visualization, however, it would be desirable to work with a very limited number of highly informative and discriminant macro-descriptors. In this paper we focus on a specific class of training-based descriptors, which are obtained as the loglikelihood of a Gaussian Mixture Model trained with short musical excerpts that selectively exhibit a certain semantic homogeneity. As these descriptors are critically dependent on the training sets, we approach the problem of how to automatically generate suitable training sets and optimize the associated macro-features in terms of discriminant power and informative impact. We then show the application of a set of three identified macro-features to genre visualization, tracking and classification

Motion estimation and signaling techniques for 2D+t scalable video coding

We describe a fully scalable wavelet-based 2D+t (in-band) video coding architecture. We propose new coding tools specifically designed for this framework aimed at two goals: reduce the computational complexity at the encoder without sacrificing compression; improve the coding efficiency, especially at low bitrates. To this end, we focus our attention on motion estimation and motion vector encoding. We propose a fast motion estimation algorithm that works in the wavelet domain and exploits the geometrical properties of the wavelet subbands. We show that the computational complexity grows linearly with the size of the search window, yet approaching the performance of a full search strategy. We extend the proposed motion estimation algorithm to work with blocks of variable sizes, in order to better capture local motion characteristics, thus improving in terms of rate-distortion behavior. Given this motion field representation, we propose a motion vector coding algorithm that allows to adaptively scale the motion bit budget according to the target bitrate, improving the coding efficiency at low bitrates. Finally, we show how to optimally scale the motion field when the sequence is decoded at reduced spatial resolution. Experimental results illustrate the advantages of each individual coding tool presented in this paper. Based on these simulations, we define the best configuration of coding parameters and we compare the proposed codec with MC-EZBC, a widely used reference codec implementing the t+2D framework

Variational Autoencoders for chord sequence generation conditioned on Western harmonic music complexity

In recent years, the adoption of deep learning techniques has allowed to obtain major breakthroughs in the automatic music generation research field, sparking a renewed interest in generative music. A great deal of work has focused on the possibility of conditioning the generation process in order to be able to create music according to human-understandable parameters. In this paper, we propose a technique for generating chord progressions conditioned on harmonic complexity, as grounded in the Western music theory. More specifically, we consider a pre-existing dataset annotated with the related complexity values and we train two variations of Variational Autoencoders (VAE), namely a Conditional-VAE (CVAE) and a Regressor-based VAE (RVAE), in order to condition the latent space depending on the complexity. Through a listening test, we analyze the effectiveness of the proposed techniques

Invariant mass line shape of B -> PP decays at LHCb

The family of B meson decays into pairs of charmless charged pseudo-scalar mesons comprises many different channels. In order to disentagle the overlapped mass peaks of the various decay modes, an accurate description of the invariant mass distribution of each mode is required. In particular, the invariant mass parameterization must take into account the effect of QED final state radiation, which leads to the presence of a long tail on the lower side of the mass peak. In this document we propose a new parameterization based on a complete QED calculation of the photon emission rate and we compare it to a simpler one based on phenomenological arguments. Furthermore, we show how the shape of the invariant mass distributions under the pi+pi- mass hypothesis, for every decay mode of interest, can be described very precisely by means of analytical calculations

Experimental evaluation of a localization algorithm for multiple acoustic sources in reverberating environments

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200

A neural network-based method for spruce tonewood characterization

The acoustical properties of wood are primarily a function of its elastic properties. Numerical and analytical methods for wood material characterization are available, although they are either computationally demanding or not always valid. Therefore, an affordable and practical method with sufficient accuracy is missing. In this article, we present a neural network-based method to estimate the elastic properties of spruce thin plates. The method works by encoding information of both the eigenfrequencies and eigenmodes of the system and using a neural network to find the best possible material parameters that reproduce the frequency response function. Our results show that data-driven techniques can speed up classic finite element model updating by several orders of magnitude and work as a proof of concept for a general neural network-based tool for the workshop. © 2023 Acoustical Society of America

A new method based on noise counting to monitor the frontend electronics of the LHCb muon detector

A new method has been developed to check the correct behaviour of the frontend electronics of the LHCb muon detector. This method is based on the measurement of the electronic noise rate at different thresholds of the frontend discriminator. The method was used to choose the optimal discriminator thresholds. A procedure based on this method was implemented in the detector control system and allowed the detection of a small percentage of frontend channels which had deteriorated. A Monte Carlo simulation has been performed to check the validity of the method