Structural and geomorphological framework of the upper Maira Valley (Western Alps, Italy): the case study of the Gollone Landslide

An interdisciplinary study has been adopted to investigate the upper Maira Valley (Western Alps, Italy). A geological map of an unmapped area, of about 12 km2, at scale 1:10.000, has been realized. The combination of field surveys, GIS database creation, aerial photo observation, local archival data consultation, geo-structural analysis and drillholes re-interpretation outlined a relationship between structures and landforms. A ductile and brittle deformation history with the definition of four discontinuity systems (F1-F4) has been detected. Where the fracturation is intense, rock-falls and topplings are triggered. In area associated with a homogeneous presence of weathered cover, debris flows were identified. The geo-structural pattern obtained from the surveys in the upper Maira Valley allowed characterizing detachment zones of the slope overlooking Acceglio town. The Gollone Landslide is an excellent case study to unravel the structural-morphological interaction and the kinematic evolution due to its framework

Cracking KD-Tree: The first multidimensional adaptive indexing

Workload-aware physical data access structures are crucial to achieve short response time with (exploratory) data analysis tasks as commonly required for Big Data and Data Science applications. Recently proposed techniques such as automatic index advisers (for a priori known static workloads) and query-driven adaptive incremental indexing (for a priori unknown dynamic workloads) form the state-of-the-art to build single-dimensional indexes for single-attribute query predicates. However, similar techniques for more demanding multi-attribute query predicates, which are vital for any data analysis task, have not been proposed, yet. In this paper, we present our on-going work on a new set of workload-adaptive indexing techniques that focus on creating multidimensional indexes. We present our proof-of-concept, the Cracking KD-Tree, an adaptive indexing approach that generates a KD-Tree based on multidimensional range query predicates. It works by incrementally creating partial multidimensional indexes as a by-product of query processing. The indexes are produced only on those parts of the data that are accessed, and their creation cost is effectively distributed across a stream of queries. Experimental results show that the Cracking KD-Tree is three times faster than creating a full KD-Tree, one order of magnitude faster than executing full scans and two orders of magnitude faster than using uni-dimensional full or adaptive indexes on multiple columns

Whole-body composition features by computed tomography in ovarian cancer: pilot data on survival correlations

Background: The primary objective of this study was to assess the associations of computed tomography (CT)-based whole-body composition values with overall survival (OS) and progression-free survival (PFS) in epithelial ovarian cancer (EOC) patients. The secondary objective was the association of body composition with chemotherapy-related toxicity. Methods: Thirty-four patients (median age 64.9 years; interquartile range 55.4–75.4) with EOC and thorax and abdomen CT scans were included. Clinical data recorded: age; weight; height; stage; chemotherapy-related toxicity; and date of last contact, progression and death. Automatic extraction of body composition values was performed by dedicated software. Sarcopenia was defined according to predefined cutoffs. Statistical analysis included univariate tests to investigate associations of sarcopenia and body composition with chemotoxicity. Association of body composition parameters and OS/PFS was evaluated by log-rank test and Cox proportional hazard model. Multivariate models were adjusted for FIGO stage and/or age at diagnosis. Results: We found significant associations of skeletal muscle volume with OS (p = 0.04) and PFS (p = 0.04); intramuscular fat volume with PFS (p = 0.03); and visceral adipose tissue, epicardial and paracardial fat with PFS (p = 0.04, 0.01 and 0.02, respectively). We found no significant associations between body composition parameters and chemotherapy-related toxicity. Conclusions: In this exploratory study, we found significant associations of whole-body composition parameters with OS and PFS. These results open a window to the possibility to perform body composition profiling without approximate estimations

Multidimensional adaptive & progressive indexes

Exploratory data analysis is the primary technique used by data scientists to extract knowledge from new data sets. This type of workload is composed of trial-and-error hypothesis-driven queries with a human in the loop. To keep up with the data scientist's productivity, the system must be capable of answering queries in interactive times. Given that these queries are highly selective multidimensional queries, multidimensional indexes are necessary to ensure low latency. However, creating the appropriate indexes is not a given due to the highly exploratory and interactive nature of such human-in-the-loop scenarios.In this paper, we identify four main objectives that are desirable for exploratory data analysis workloads: (1) low overhead over the initial queries, (2) low query variance (i.e., high robustness), (3) predictable index convergence, and (4) low total workload time. Given that not all of them can be achieved at the same time, we present three novel incremental multidimensional indexing techniques that represent three sample points on a Pareto front for this multi-objective optimization problem. (a) The Adaptive KD-Tree is designed to achieve the lowest total workload time at the expense of a higher indexing penalty for the initial queries, lack of robustness, and unpredictable convergence. (b) The Progressive KD-Tree has predictable convergence and a user-defined indexing cost for the initial queries. However, total workload time can be higher than with Adaptive KD-Trees, and per-query time still varies. (c) The Greedy Progressive KD-Tree aims at full robustness at the expense of only improving the per-query cost after full index convergence.Our extensive experimental evaluation using both synthetic and real-life data sets and workloads shows that (a) the Adaptive KD-Tree reduc

Granular flow down a rough inclined plane: transition between thin and thick piles

The rheology of granular particles in an inclined plane geometry is studied using molecular dynamics simulations. The flow--no-flow boundary is determined for piles of varying heights over a range of inclination angles $\theta$. Three angles determine the phase diagram: $\theta_{r}$, the angle of repose, is the angle at which a flowing system comes to rest; $\theta_{m}$, the maximum angle of stability, is the inclination required to induce flow in a static system; and $\theta_{max}$ is the maximum angle for which stable, steady state flow is observed. In the stable flow region $\theta_{r}<\theta<\theta_{max}$, three flow regimes can be distinguished that depend on how close $\theta$ is to $\theta_{r}$: i) $\theta>>\theta_{r}$: Bagnold rheology, characterized by a mean particle velocity $v_{x}$ in the direction of flow that scales as $v_{x}\propto h^{3/2}$, for a pile of height $h$, ii) $\theta\gtrsim\theta_{r}$: the slow flow regime, characterized by a linear velocity profile with depth, and iii) $\theta\approx\theta_{r}$: avalanche flow characterized by a slow underlying creep motion combined with occasional free surface events and large energy fluctuations. We also probe the physics of the initiation and cessation of flow. The results are compared to several recent experimental studies on chute flows and suggest that differences between measured velocity profiles in these experiments may simply be a consequence of how far the system is from jamming.Comment: 19 pages, 14 figs, submitted to Physics of Fluid