2,632 research outputs found
Fluids mobilization in Arabia Terra, Mars: depth of pressurized reservoir from mounds self-similar clustering
Arabia Terra is a region of Mars where signs of past-water occurrence are
recorded in several landforms. Broad and local scale geomorphological,
compositional and hydrological analyses point towards pervasive fluid
circulation through time. In this work we focus on mound fields located in the
interior of three casters larger than 40 km (Firsoff, Kotido and unnamed crater
20 km to the east) and showing strong morphological and textural resemblance to
terrestrial mud volcanoes and spring-related features. We infer that these
landforms likely testify the presence of a pressurized fluid reservoir at depth
and past fluid upwelling. We have performed morphometric analyses to
characterize the mound morphologies and consequently retrieve an accurate
automated mapping of the mounds within the craters for spatial distribution and
fractal clustering analysis. The outcome of the fractal clustering yields
information about the possible extent of the percolating fracture network at
depth below the craters. We have been able to constrain the depth of the
pressurized fluid reservoir between ~2.5 and 3.2 km of depth and hence, we
propose that mounds and mounds alignments are most likely associated to the
presence of fissure ridges and fluid outflow. Their process of formation is
genetically linked to the formation of large intra-crater bulges previously
interpreted as large scale spring deposits. The overburden removal caused by
the impact crater formation is the inferred triggering mechanism for fluid
pressurization and upwelling, that through time led to the formation of the
intra-crater bulges and, after compaction and sealing, to the widespread mound
fields in their surroundings
A New Estimator of Intrinsic Dimension Based on the Multipoint Morisita Index
The size of datasets has been increasing rapidly both in terms of number of
variables and number of events. As a result, the empty space phenomenon and the
curse of dimensionality complicate the extraction of useful information. But,
in general, data lie on non-linear manifolds of much lower dimension than that
of the spaces in which they are embedded. In many pattern recognition tasks,
learning these manifolds is a key issue and it requires the knowledge of their
true intrinsic dimension. This paper introduces a new estimator of intrinsic
dimension based on the multipoint Morisita index. It is applied to both
synthetic and real datasets of varying complexities and comparisons with other
existing estimators are carried out. The proposed estimator turns out to be
fairly robust to sample size and noise, unaffected by edge effects, able to
handle large datasets and computationally efficient
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach
This paper proposes a probabilistic approach for the detection and the
tracking of particles in fluorescent time-lapse imaging. In the presence of a
very noised and poor-quality data, particles and trajectories can be
characterized by an a contrario model, that estimates the probability of
observing the structures of interest in random data. This approach, first
introduced in the modeling of human visual perception and then successfully
applied in many image processing tasks, leads to algorithms that neither
require a previous learning stage, nor a tedious parameter tuning and are very
robust to noise. Comparative evaluations against a well-established baseline
show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application
From colloidal dispersions to colloidal pastesthrough solid–liquid separation processes
Solid–liquid separation is an operation that starts with a dispersion of solid particles in a liquid and removes some of the liquid from the particles, producing a concentrated
solid paste and a clean liquid phase. It is similar to thermodynamic processes where pressure is applied to a system in order to reduce its volume. In dispersions, the resistance to this osmotic compression depends on interactions between the dispersed particles.
The first part of this work deals with dispersions of repelling particles, which are either silica nanoparticles or synthetic clay platelets, dispersed in aqueous solutions. In these conditions, each particle is surrounded by an ionic layer, which repels other ionic layers. This results in a structure with strong short-range order. At high particle volume fractions, the overlap
of ionic layers generates large osmotic pressures; these pressures may be calculated, through the cell model, as the cost of reducing the volume of each cell. The variation of osmotic pressure with volume fraction is the equation of state of the dispersion.
The second part of this work deals with dispersions of aggregated particles, which are silica nanoparticles, dispersed in water and flocculated by multivalent cations. This produces large bushy aggregates, with fractal structures that are maintained through interparticle surface– surface bonds. As the paste is submitted to osmotic pressures, small relative displacements
of the aggregated particles lead to structural collapse. The final structure is made of a dense skeleton immersed in a nearly homogeneous matrix of aggregated particles. The variation of osmotic resistance with volume fraction is the compression law of the paste; it may be calculated through a numerical model that takes into account the noncentral interparticle forces. According to this model, the response of aggregated pastes to applied stress may be
controlled through the manipulation of interparticle adhesion
Efficient similarity search in high-dimensional data spaces
Similarity search in high-dimensional data spaces is a popular paradigm for many modern database applications, such as content based image retrieval, time series analysis in financial and marketing databases, and data mining. Objects are represented as high-dimensional points or vectors based on their important features. Object similarity is then measured by the distance between feature vectors and similarity search is implemented via range queries or k-Nearest Neighbor (k-NN) queries.
Implementing k-NN queries via a sequential scan of large tables of feature vectors is computationally expensive. Building multi-dimensional indexes on the feature vectors for k-NN search also tends to be unsatisfactory when the dimensionality is high. This is due to the poor index performance caused by the dimensionality curse.
Dimensionality reduction using the Singular Value Decomposition method is the approach adopted in this study to deal with high-dimensional data. Noting that for many real-world datasets, data distribution tends to be heterogeneous, dimensionality reduction on the entire dataset may cause a significant loss of information. More efficient representation is sought by clustering the data into homogeneous subsets of points, and applying dimensionality reduction to each cluster respectively, i.e., utilizing local rather than global dimensionality reduction.
The thesis deals with the improvement of the efficiency of query processing associated with local dimensionality reduction methods, such as the Clustering and Singular Value Decomposition (CSVD) and the Local Dimensionality Reduction (LDR) methods. Variations in the implementation of CSVD are considered and the two methods are compared from the viewpoint of the compression ratio, CPU time, and retrieval efficiency.
An exact k-NN algorithm is presented for local dimensionality reduction methods by extending an existing multi-step k-NN search algorithm, which is designed for global dimensionality reduction. Experimental results show that the new method requires less CPU time than the approximate method proposed original for CSVD at a comparable level of accuracy.
Optimal subspace dimensionality reduction has the intent of minimizing total query cost. The problem is complicated in that each cluster can retain a different number of dimensions. A hybrid method is presented, combining the best features of the CSVD and LDR methods, to find optimal subspace dimensionalities for clusters generated by local dimensionality reduction methods. The experiments show that the proposed method works well for both real-world datasets and synthetic datasets
Octave-spanning broadband absorption of terahertz light using metasurface fractal-cross absorbers
Synthetic fractals inherently carry spatially encoded frequency
information that renders them as an ideal candidate for broadband optical structures.
Nowhere is this more true than in the terahertz (THz) band where there is a lack of
naturally occurring materials with valuable optical properties. One example are perfect
absorbers that are a direct step toward the development of highly sought after detectors
and sensing devices. Metasurface absorbers that can be used to substitute for natural
materials suffer from poor broadband performance, while those with high absorption
and broadband capability typically involve complex fabrication and design and are
multilayered. Here, we demonstrate a polarization-insensitive ultrathin (∼λ/6) planar
metasurface THz absorber composed of supercells of fractal crosses capable of spanning
one optical octave in bandwidth, while still being highly efficient. A sufficiently thick
polyimide interlayer produces a unique absorption mechanism based on Salisbury
screen and antireflection responses, which lends to the broadband operation.
Experimental peak absorption exceeds 93%, while the average absorption is 83% from 2.82 THz to 5.15 THz. This new
ultrathin device architecture, achieving an absorption-bandwidth of one optical octave, demonstrates a major advance toward a
synthetic metasurface blackbody absorber in the THz ban
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