Spin physics at COMPASS

COMPASS is a fixed-target experiment at the CERN SPS. The COMPASS physics program is dedicated to the study of the nucleon spin structure and of hadron spectroscopy. The nucleon spin structure is investigated by means of Deep Inelastic Scattering reactions, using a 160GeV muon beam, impinging on a solid-state target. The recent results on the gluon contribution to the nucleon spin, on the helicity distributions for different quark flavors and on the transverse spin effects are presented

SIDIS measurements of leading-twist spin azimuthal asymmetries

In this paper an experimental overview of the leading-twist transverse- and longitudinal-spin azimuthal asymmetries measured by SIDIS experiments is given. The recent results from HERMES, COMPASS and JLab experiments are discussed

New directions in myocardial stress imaging

Non-invasive stress imaging techniques such as echocardiography and myocardial perfusion imaging are widely used for the diagnosis and functional evaluation of coronary artery disease and for the assessment of myocardial viability.1·8 The aim of this thesis was to analyse methods that may expand the clinical utility of stress echocardiographic and perfusion imaging, for the diagnosis of myocardial ischemia and viability in patients with suspected or known coronary artery disease

Form factor in K+ --> pi+ pi0 gamma: interference versus direct emission

We analyze the effect of a form factor in the magnetic contribution to K+ --> pi+ pi0 gamma. We emphasize how this can show up experimentally: in particular we try to explore the difference between a possible interference contribution and a form factor in the magnetic part. The form factor used for K+ --> pi+ pi0 gamma is analogous to the one for KL --> pi+ pi- gamma, experimentally well established.Comment: 9 pages revtex, 10 eps figures; improved presentation of theoretical and experimental status; refs. adde

Monitoring within-field variability of corn yield using sentinel-2 and machine learning techniques

Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4-R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4-R6)

wGrapeUNIPD-DL: An open dataset for white grape bunch detection

National and international Vitis variety catalogues can be used as image datasets for computer vision in viticulture. These databases archive ampelographic features and phenology of several grape varieties and plant structures images (e.g. leaf, bunch, shoots). Although these archives represent a potential database for computer vision in viticulture, plant structure images are acquired singularly and mostly not directly in the vineyard. Localization computer vision models would take advantage of multiple objects in the same image, allowing more efficient training. The present images and labels dataset was designed to overcome such limitations and provide suitable images for multiple cluster identification in white grape varieties. A group of 373 images were acquired from later view in vertical shoot position vineyards in six different Italian locations at different phenological stages. Images were then labelled in YOLO labelling format. The dataset was made available both in terms of images and labels. The real number of bunches counted in the field, and the number of bunches visible in the image (not covered by other vine structures) was recorded for a group of images in this dataset

NaNet: a Low-Latency, Real-Time, Multi-Standard Network Interface Card with GPUDirect Features

While the GPGPU paradigm is widely recognized as an effective approach to high performance computing, its adoption in low-latency, real-time systems is still in its early stages. Although GPUs typically show deterministic behaviour in terms of latency in executing computational kernels as soon as data is available in their internal memories, assessment of real-time features of a standard GPGPU system needs careful characterization of all subsystems along data stream path. The networking subsystem results in being the most critical one in terms of absolute value and fluctuations of its response latency. Our envisioned solution to this issue is NaNet, a FPGA-based PCIe Network Interface Card (NIC) design featuring a configurable and extensible set of network channels with direct access through GPUDirect to NVIDIA Fermi/Kepler GPU memories. NaNet design currently supports both standard - GbE (1000BASE-T) and 10GbE (10Base-R) - and custom - 34~Gbps APElink and 2.5~Gbps deterministic latency KM3link - channels, but its modularity allows for a straightforward inclusion of other link technologies. To avoid host OS intervention on data stream and remove a possible source of jitter, the design includes a network/transport layer offload module with cycle-accurate, upper-bound latency, supporting UDP, KM3link Time Division Multiplexing and APElink protocols. After NaNet architecture description and its latency/bandwidth characterization for all supported links, two real world use cases will be presented: the GPU-based low level trigger for the RICH detector in the NA62 experiment at CERN and the on-/off-shore data link for KM3 underwater neutrino telescope

An evaluation of |Vus| and precise tests of the Standard Model from world data on leptonic and semileptonic kaon decays

We present a global analysis of leptonic and semileptonic kaon decay data, including all recent results published by the BNL-E865, KLOE, KTeV, ISTRA+ and NA48 experiments. This analysis, in conjunction with precise lattice calculations of the hadronic matrix elements now available, leads to a very precise determination of |Vus| and allows us to perform several stringent tests of the Standard Model.Comment: LaTeX, 25 pages, 12 figures, 16 tables. Submitted to EPJC. v2: Minor changes for accepted version. No numerical results change