A fast and flexible machine learning approach to data quality monitoring

We present a machine learning based approach for real-time monitoring of particle detectors. The proposed strategy evaluates the compatibility between incoming batches of experimental data and a reference sample representing the data behavior in normal conditions by implementing a likelihood-ratio hypothesis test. The core model is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. The resulting algorithm is fast, efficient and agnostic about the type of potential anomaly in the data. We show the performance of the model on multivariate data from a drift tube chambers muon detector

Learning new physics efficiently with nonparametric methods

We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original proposal by D'Agnolo and Wulzer (arXiv:1806.02350), the model evaluates the compatibility between experimental data and a reference model, by implementing a hypothesis testing procedure based on the likelihood ratio. Model-independence is enforced by avoiding any prior assumption about the presence or shape of new physics components in the measurements. We show that our approach has dramatic advantages compared to neural network implementations in terms of training times and computational resources, while maintaining comparable performances. In particular, we conduct our tests on higher dimensional datasets, a step forward with respect to previous studies.Comment: 22 pages, 13 figure

Efficient Unsupervised Learning for Plankton Images

Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that can reflect into consequent morphological and dynamical modifications. Nowadays, the availability of advanced automatic or semi-automatic acquisition systems has been allowing the production of an increasingly large amount of plankton image data. The adoption of machine learning algorithms to classify such data may be affected by the significant cost of manual annotation, due to both the huge quantity of acquired data and the numerosity of plankton species. To address these challenges, we propose an efficient unsupervised learning pipeline to provide accurate classification of plankton microorganisms. We build a set of image descriptors exploiting a two-step procedure. First, a Variational Autoencoder (VAE) is trained on features extracted by a pre-trained neural network. We then use the learnt latent space as image descriptor for clustering. We compare our method with state-of-the-art unsupervised approaches, where a set of pre-defined hand-crafted features is used for clustering of plankton images. The proposed pipeline outperforms the benchmark algorithms for all the plankton datasets included in our analysis, providing better image embedding properties

A comparison study of co-simulation frameworks for multi-energy systems: the scalability problem

The transition to a low-carbon society will completely change the structure of energy systems from a standalone hierarchical centralised vision to cooperative and dis- tributed Multi-Energy Systems. The analysis of these complex systems requires the collaboration of researchers from different disciplines in the energy, ICT, social, economic, and political sectors. Combining such disparate disciplines into a single tool for modeling and analyzing such a complex environment as a Multi-Energy System requires tremendous effort. Researchers have overcome this effort by using co-simulation techniques that give the possibility of integrating existing domain-specific simulators in a single environment. Co-simulation frameworks, such as Mosaik and HELICS, have been developed to ease such integration. In this context, an additional challenge is the different temporal and spatial scales that are involved in the real world and that must be addressed during co-simulation. In particular, the huge number of heterogeneous actors populating the system makes it difficult to represent the system as a whole. In this paper, we propose a comparison of the scalability performance of two major co-simulation frameworks (i.e. HELICS and Mosaik) and a particular implementation of a well-known multi-agent systems library (i.e. AIOMAS). After describing a generic co-simulation framework infrastructure and its related challenges in managing a distributed co-simulation environment, the three selected frameworks are introduced and compared with each other to highlight their principal structure. Then, the scalability problem of co-simulation frameworks is introduced presenting four benchmark configurations to test their ability to scale in terms of a number of running instances. To carry out this comparison, a simplified multi-model energy scenario was used as a common testing environment. This work helps to understand which of the three frameworks and four configurations to select depending on the scenario to analyse. Experimental results show that a Multi-processing configuration of HELICS reaches the best performance in terms of KPIs defined to assess the scalability among the co-simu- lation frameworks

A novel sensor for ion electron emission microscopy

Abstract An ion electron emission microscope (IEEM) to be installed at the SIRAD heavy ion irradiation facility at the 15 MV tandem accelerator of the INFN Legnaro laboratory (Italy) will be used to characterize the sensitivity of electronic devices to single event effects (SEE) to ion impacts with micrometric lateral resolutions. The secondary electrons emitted by ion impacts from the target surface are transported and focused by an electron microscope onto a micro-channel plate (MCP) detector coupled to a fast phosphor. The luminous signal is then detected by a position sensitive photon detector located outside the vacuum chamber. The high repetition rates and high spatial resolution, required to temporally distinguish ion impacts for SEE studies and avoid degrading of the initial resolution of the IEEM and MCP are met by the system, presented here for the first time, based on two orthogonal linear CCDs

Tuberculous Arthritis of the Ankle

Tuberculosis (TB) is an infectious disease caused by the Mycobacterium tuberculosis complex (MTBC). Pulmonary TB is the most common form of presentation, but extrapulmonary tuberculosis (EPTB) contributes significantly to morbidity and mortality. Rarely, patients with EPTB develop a form of ankle or foot arthritis. The diagnosis of TB arthritis is often overlooked because of the insidious onset and the non-specific clinical symptoms. Prognosis is generally poor; early diagnosis and delivery of the most appropriate treatment is critical to avoid functional disability

Bioengineered constructs combined with exercise enhance stem cell-mediated treatment of volumetric muscle loss

Volumetric muscle loss (VML) is associated with loss of skeletal muscle function, and current treatments show limited efficacy. Here we show that bioconstructs suffused with genetically-labelled muscle stem cells (MuSCs) and other muscle resident cells (MRCs) are effective to treat VML injuries in mice. Imaging of bioconstructs implanted in damaged muscles indicates MuSCs survival and growth, and ex vivo analyses show force restoration of treated muscles. Histological analysis highlights myofibre formation, neovascularisation, but insufficient innervation. Both innervation and in vivo force production are enhanced when implantation of bioconstructs is followed by an exercise regimen. Significant improvements are also observed when bioconstructs are used to treat chronic VML injury models. Finally, we demonstrate that bioconstructs made with human MuSCs and MRCs can generate functional muscle tissue in our VML model. These data suggest that stem cell-based therapies aimed to engineer tissue in vivo may be effective to treat acute and chronic VML

Dark Matter Annihilation around Intermediate Mass Black Holes: an update

The formation and evolution of Black Holes inevitably affects the distribution of dark and baryonic matter in the neighborhood of the Black Hole. These effects may be particularly relevant around Supermassive and Intermediate Mass Black Holes (IMBHs), the formation of which can lead to large Dark Matter overdensities, called {\em spikes} and {\em mini-spikes} respectively. Despite being larger and more dense, spikes evolve at the very centers of galactic halos, in regions where numerous dynamical effects tend to destroy them. Mini-spikes may be more likely to survive, and they have been proposed as worthwhile targets for indirect Dark Matter searches. We review here the formation scenarios and the prospects for detection of mini-spikes, and we present new estimates for the abundances of mini-spikes to illustrate the sensitivity of such predictions to cosmological parameters and uncertainties regarding the astrophysics of Black Hole formation at high redshift. We also connect the IMBHs scenario to the recent measurements of cosmic-ray electron and positron spectra by the PAMELA, ATIC, H.E.S.S., and Fermi collaborations.Comment: 12 pages, 7 figures. Invited contribution to NJP Focus Issue on "Dark Matter and Particle Physics

A population of gamma-ray emitting globular clusters seen with the Fermi Large Area Telescope

Globular clusters with their large populations of millisecond pulsars (MSPs) are believed to be potential emitters of high-energy gamma-ray emission. Our goal is to constrain the millisecond pulsar populations in globular clusters from analysis of gamma-ray observations. We use 546 days of continuous sky-survey observations obtained with the Large Area Telescope aboard the Fermi Gamma-ray Space Telescope to study the gamma-ray emission towards 13 globular clusters. Steady point-like high-energy gamma-ray emission has been significantly detected towards 8 globular clusters. Five of them (47 Tucanae, Omega Cen, NGC 6388, Terzan 5, and M 28) show hard spectral power indices $(0.7 < \Gamma <1.4)$ and clear evidence for an exponential cut-off in the range 1.0-2.6 GeV, which is the characteristic signature of magnetospheric emission from MSPs. Three of them (M 62, NGC 6440 and NGC 6652) also show hard spectral indices $(1.0 < \Gamma < 1.7)$, however the presence of an exponential cut-off can not be unambiguously established. Three of them (Omega Cen, NGC 6388, NGC 6652) have no known radio or X-ray MSPs yet still exhibit MSP spectral properties. From the observed gamma-ray luminosities, we estimate the total number of MSPs that is expected to be present in these globular clusters. We show that our estimates of the MSP population correlate with the stellar encounter rate and we estimate 2600-4700 MSPs in Galactic globular clusters, commensurate with previous estimates. The observation of high-energy gamma-ray emission from a globular cluster thus provides a reliable independent method to assess their millisecond pulsar populations that can be used to make constraints on the original neutron star X-ray binary population, essential for understanding the importance of binary systems in slowing the inevitable core collapse of globular clusters.Comment: Accepted for publication in A&A. Corresponding authors: J. Kn\"odlseder, N. Webb, B. Pancraz