Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project \emph{Gravity Spy} has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program

Advances on the automatic estimation of the P-wave onset time.

This work describes the automatic picking of the P-phase arrivals of the 3*10^6 seismic registers originated during the TOMO-ETNA experiment. Air-gun shots produced by the vessel “Sarmiento de Gamboa” and contemporary passive seismicity occurring in the island are recorded by a dense network of stations deployed for the experiment. In such scenario, automatic processing is needed given: (i) the enormous amount of data, (ii) the low signal-to-noise ratio of many of the available registers and, (iii) the accuracy needed for the velocity tomography resulting from the experiment. A preliminary processing is performed with the records obtained from all stations. Raw data formats from the different types of stations are unified, eliminating defective records and reducing noise through filtering in the band of interest for the phase picking. The advanced multiband picking algorithm (AMPA) is then used to process the big database obtained and determine the travel times of the seismic phases. The approach of AMPA, based on frequency multiband denoising and enhancement of expected arrivals through optimum detectors, is detailed together with its calibration and quality assessment procedure. Examples of its usage for active and passive seismic events are presented.PublishedS04342V. Dinamiche di unrest e scenari pre-eruttiviJCR Journalope

Dynamics of a Quantum Phase Transition and Relaxation to a Steady State

We review recent theoretical work on two closely related issues: excitation of an isolated quantum condensed matter system driven adiabatically across a continuous quantum phase transition or a gapless phase, and apparent relaxation of an excited system after a sudden quench of a parameter in its Hamiltonian. Accordingly the review is divided into two parts. The first part revolves around a quantum version of the Kibble-Zurek mechanism including also phenomena that go beyond this simple paradigm. What they have in common is that excitation of a gapless many-body system scales with a power of the driving rate. The second part attempts a systematic presentation of recent results and conjectures on apparent relaxation of a pure state of an isolated quantum many-body system after its excitation by a sudden quench. This research is motivated in part by recent experimental developments in the physics of ultracold atoms with potential applications in the adiabatic quantum state preparation and quantum computation.Comment: 117 pages; review accepted in Advances in Physic

Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications

Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time–frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of Advanced LIGO. We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory

Evaluation of ground-level and space-borne sensor as tools in monitoring nitrogen nutrition status in immature and mature oil palm

Monitoring nitrogen (N) in oil palm is crucial for the production sustainability. The objective of this study is to examine the capability of visible (Vis), near infrared (NIR) and a combination of Vis and NIR (Vis + NIR) spectral indices acquired from different sensors for predicting foliar N content of different palm age groups. The N treatments varied from 0 to 2 kg per palm, subjected according to immature, young mature and prime mature classes. The Vis + NIR indices from the ground level-sensor that is green + red + NIR (G + R + NIR) was the best index for predicting N for immature palms (R2 = 0.91), while Vis indices blue + red (B + R) and Green Red Index from the space-borne sensor were significantly useful for N assessment of young and prime mature palms (R2 = 0.70 and 0.50), respectively. The application of vegetation indices for monitoring N status of oil palm is beneficial to examine extensive plantation areas

Critical properties of Sudden Quench Dynamics in the anisotropic XY Model

We study the zero temperature quantum dynamical critical behavior of the anisotropic XY chain under a sudden quench in a transverse field. We demonstrate theoretically that both quench magnetic susceptibility and two-particle quench correlation can be used to describe the dynamical quantum phase transition (QPT) properties. Either the quench magnetic susceptibility or the derivative of correlation functions as a function of initial magnetic field $a$ exhibits a divergence at the critical points when final magnetic field $b$ is fixed. A special case that final magnetic field $b$ is just at the critical point is discussed separately. Some of the critical exponents of the dynamical QPT are obtained and the long-range correlation of the quench system is analyzed. We also compare our result with that of the static QPT.Comment: published on EPJ

Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project Gravity Spy has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO’s second observing run, we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program

Enhancing quantum efficiency of thin-film silicon solar cells by Pareto optimality

We present a composite design methodology for the simulation and optimization of the solar cell performance. Our method is based on the synergy of different computational techniques and it is especially designed for the thin-film cell technology. In particular, we aim to efficiently simulate light trapping and plasmonic effects to enhance the light harvesting of the cell. The methodology is based on the sequential application of a hierarchy of approaches: (a) full Maxwell simulations are applied to derive the photon’s scattering probability in systems presenting textured interfaces; (b) calibrated Photonic Monte Carlo is used in junction with the scattering matrices method to evaluate coherent and scattered photon absorption in the full cell architectures; (c) the results of these advanced optical simulations are used as the pair generation terms in model implemented in an effective Technology Computer Aided Design tool for the derivation of the cell performance; (d) the models are investigated by qualitative and quantitative sensitivity analysis algorithms, to evaluate the importance of the design parameters considered on the models output and to get a first order descriptions of the objective space; (e) sensitivity analysis results are used to guide and simplify the optimization of the model achieved through both Single Objective Optimization (in order to fully maximize devices efficiency) and Multi Objective Optimization (in order to balance efficiency and cost); (f) Local, Global and “Glocal” robustness of optimal solutions found by the optimization algorithms are statistically evaluated; (g) data-based Identifiability Analysis is used to study the relationship between parameters. The results obtained show a noteworthy improvement with respect to the quantum efficiency of the reference cell demonstrating that the methodology presented is suitable for effective optimization of solar cell devices

Fluorescence‐based bowel anastomosis perfusion evaluation: results from the IHU‐IRCAD‐EAES EURO‐FIGS registry

Background: Anastomotic leakage (AL) is one of the dreaded complications following surgery in the digestive tract. Near-infrared fluorescence (NIRF) imaging is a means to intraoperatively visualize anastomotic perfusion, facilitating fluorescence image-guided surgery (FIGS) with the purpose to reduce the incidence of AL. The aim of this study was to analyze the current practices and results of NIRF imaging of the anastomosis in digestive tract surgery through the EURO-FIGS registry. Methods: Analysis of data prospectively collected by the registry members provided patient and procedural data along with the ICG dose, timing, and consequences of NIRF imaging. Among the included upper-GI, colorectal, and bariatric surgeries, subgroup analysis was performed to identify risk factors associated with complications. Results: A total of 1240 patients were included in the study. The included patients, 74.8% of whom were operated on for cancer, originated from 8 European countries and 30 hospitals. A total of 54 surgeons performed the procedures. In 83.8% of cases, a pre-anastomotic ICG dose was administered, and in 60.1% of cases, a post-anastomotic ICG dose was administered. A significant difference (p < 0.001) was found in the ICG dose given in the four pathology groups registered (range: 0.013–0.89 mg/kg) and a significant (p < 0.001) negative correlation was found between the ICG dose and BMI. In 27.3% of the procedures, the choice of the anastomotic level was guided by means of NIRF imaging which means that in these cases NIRF imaging changed the level of anastomosis which was first decided based on visual findings in conventional white light imaging. In 98.7% of the procedures, the use of ICG partly or strongly provided a sense of confidence about the anastomosis. A total of 133 complications occurred, without any statistical significance in the incidence of complications in the anastomoses, whether they were ICG-guided or not. Conclusion: The EURO-FIGS registry provides an insight into the current clinical practice across Europe with respect to NIRF imaging of anastomotic perfusion during digestive tract surgery

A Mouse Model of Pulmonary Metastasis from Spontaneous Osteosarcoma Monitored In Vivo by Luciferase Imaging

BACKGROUND: Osteosarcoma (OSA) is lethal when metastatic after chemotherapy and/or surgical treatment. Thus animal models are necessary to study the OSA metastatic spread and to validate novel therapies able to control the systemic disease. We report the development of a syngeneic (Balb/c) murine OSA model, using a cell line derived from a spontaneous murine tumor. METHODOLOGY: The tumorigenic and metastatic ability of OSA cell lines were assayed after orthotopic injection in mice distal femur. Expression profiling was carried out to characterize the parental and metastatic cell lines. Cells from metastases were propagated and engineered to express Luciferase, in order to follow metastases in vivo. PRINCIPAL FINDINGS: Luciferase bioluminescence allowed to monitor the primary tumor growth and revealed the appearance of spontaneous pulmonary metastases. In vivo assays showed that metastasis is a stable property of metastatic OSA cell lines after both propagation in culture and luciferase trasduction. When compared to parental cell line, both unmodified and genetically marked metastatic cells, showed comparable and stable differential expression of the enpp4, pfn2 and prkcd genes, already associated to the metastatic phenotype in human cancer. CONCLUSIONS: This OSA animal model faithfully recapitulates some of the most important features of the human malignancy, such as lung metastatization. Moreover, the non-invasive imaging allows monitoring the tumor progression in living mice. A great asset of this model is the metastatic phenotype, which is a stable property, not modifiable after genetic manipulation