Light sterile neutrino sensitivity of 163Ho experiments

We explore the sensitivity of $^{163}$Ho electron capture experiments to neutrino masses in the standard framework of three-neutrino mixing and in the framework of 3+1 neutrino mixing with a sterile neutrino which mixes with the three standard active neutrinos, as indicated by the anomalies found in short-baseline neutrino oscillations experiments. We calculate the sensitivity to neutrino masses and mixing for different values of the energy resolution of the detectors, of the unresolved pileup fraction and of the total statistics of events, considering the expected values of these parameters in the two planned stages of the ECHo project (ECHo-1k and ECHo-1M). We show that an extension of the ECHo-1M experiment with the possibility to collect $10^{16}$ events will be competitive with the KATRIN experiment. This statistics will allow to explore part of the 3+1 mixing parameter space indicated by the global analysis of short-baseline neutrino oscillation experiments. In order to cover all the allowed region, a statistics of about $10^{17}$ events will be needed.Comment: 11 page

A Computationally Light Pruning Strategy for Single Layer Neural Networks based on Threshold Function

Embedded machine learning relies on inference functions that can fit resource-constrained, low-power computing devices. The literature proves that single layer neural networks using threshold functions can provide a suitable trade off between classification accuracy and computational cost. In this regard, the number of neurons directly impacts both on computational complexity and on resources allocation. Thus, the present research aims at designing an efficient pruning technique that can take into account the peculiarities of the threshold function. The paper shows that feature selection criteria based on filter models can effectively be applied to neuron selection. In particular, valuable outcomes can be obtained by designing ad-hoc objective functions for the selection process. An extensive experimental campaign confirms that the proposed objective function compares favourably with state-of-the-art pruning techniques

A survey on deep learning in image polarity detection: Balancing generalization performances and computational costs

Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general exploit a pre-trained CNN as the feature extractor that in turn feeds a classification unit. While the latter unit is trained from scratch, the pre-trained CNN is subject to fine-tuning. As a result, the specific CNN architecture employed as the feature extractor strongly affects the overall performance of the model. This paper analyses state-of-the-art literature on image polarity detection and identifies the most reliable CNN architectures. Moreover, the paper provides an experimental protocol that should allow assessing the role played by the baseline architecture in the polarity detection task. Performance is evaluated in terms of both generalization abilities and computational complexity. The latter attribute becomes critical as polarity predictors, in the era of social networks, might need to be updated within hours or even minutes. In this regard, the paper gives practical hints on the advantages and disadvantages of the examined architectures both in terms of generalization and computational cost

On the keV sterile neutrino search in electron capture

A joint effort of cryogenic microcalorimetry (CM) and high-precision Penning-trap mass spectrometry (PT-MS) in investigating atomic orbital electron capture (EC) can shed light on the possible existence of heavy sterile neutrinos with masses from 0.5 to 100 keV. Sterile neutrinos are expected to perturb the shape of the atomic de-excitation spectrum measured by CM after a capture of the atomic orbital electrons by a nucleus. This effect should be observable in the ratios of the capture probabilities from different orbits. The sensitivity of the ratio values to the contribution of sterile neutrinos strongly depends on how accurately the mass difference between the parent and the daughter nuclides of EC-transitions can be measured by, e.g., PT-MS. A comparison of such probability ratios in different isotopes of a certain chemical element allows one to exclude many systematic uncertainties and thus could make feasible a determination of the contribution of sterile neutrinos on a level below 1%. Several electron capture transitions suitable for such measurements are discussed.Comment: 16 pages, 9 figures, 2 table

CONTAINER LOCALISATION AND MASS ESTIMATION WITH AN RGB-D CAMERA

In the research area of human-robot interactions, the automatic estimation of the mass of a container manipulated by a person leveraging only visual information is a challenging task. The main challenges consist of occlusions, different filling materials and lighting conditions. The mass of an object constitutes key information for the robot to correctly regulate the force required to grasp the container. We propose a single RGB-D camera-based method to locate a manipulated container and estimate its empty mass i.e., independently of the presence of the content. The method first automatically selects a number of candidate containers based on the distance with the fixed frontal view, then averages the mass predictions of a lightweight model to provide the final estimation. Results on the CORSMAL Containers Manipulation dataset show that the proposed method estimates empty container mass obtaining a score of 71.08% under different lighting or filling conditions

Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenanceprogram. Partialdischarges(PDs)phenomenaaffecttheinsulationsystemofanelectrical machine and\u2014in the long term\u2014can lead to a breakdown, with a consequent, signi\ufb01cant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is speci\ufb01cally designed to run on low-cost embedded devices

Sponge-Like Behaviour in Isoreticular Cu(Gly-His-X) Peptide-Based Porous Materials

We report two isoreticular 3D peptide-based porous frameworks formed by coordination of the tripeptides Gly-l-His-Gly and Gly-l-His-l-Lys to Cu(II) which display sponge-like behaviour. These porous materials undergo structural collapse upon evacuation that can be reversed by exposure to water vapour, which permits recovery of the original open channel structure. This is further confirmed by sorption studies that reveal that both solids exhibit selective sorption of H(2)O while CO(2) adsorption does not result in recovery of the original structures. We also show how the pendant aliphatic amine chains, present in the framework from the introduction of the lysine amino acid in the peptidic backbone, can be post-synthetically modified to produce urea-functionalised networks by following methodologies typically used for metal–organic frameworks built from more rigid “classical” linkers