Using level-2 fuzzy sets to combine uncertainty and imprecision in fuzzy regions

In many applications, spatial data need to be considered but are prone to uncertainty or imprecision. A fuzzy region - a fuzzy set over a two dimensional domain - allows the representation of such imperfect spatial data. In the original model, points of the fuzzy region where treated independently, making it impossible to model regions where groups of points should be considered as one basic element or subregion. A first extension overcame this, but required points within a group to have the same membership grade. In this contribution, we will extend this further, allowing a fuzzy region to contain subregions in which not all points have the same membership grades. The concept can be used as an underlying model in spatial applications, e.g. websites showing maps and requiring representation of imprecise features or websites with routing functions needing to handle concepts as walking distance or closeby

Effect of random disorder and spin frustration on the reentrant spin glass phase and ferromagnetic phase in stage-2 Cu_{0.93}Co_{0.07}Cl_{2} graphite intercalation compound near the multicritical point

Stage-2 Cu$_{0.93}$Co$_{0.07}$Cl$_{2}$ graphite intercalation compound magnetically behaves like a reentrant ferromagnet near the multicritical point ($c_{MCP} \approx 0.96$). It undergoes two magnetic phase transitions at $T_{RSG}$ ($= 6.64 \pm 0.05$ K) and $T_{c}$ ($= 8.62 \pm 0.05$ K). The static and dynamic nature of the ferromagnetic and reentrant spin glass phase has been studied using DC and AC magnetic susceptibility. Characteristic memory phenomena of the DC susceptibility are observed at $T_{RSG}$ and $T_{c}$. The nonlinear AC susceptibility $\chi_{3}^{\prime}$ has a positive local maximum at $T_{RSG}$, and a negative local minimum at $T_{c}$. The relaxation time $\tau$ between $T_{RSG}$ and $T_{c}$ shows a critical slowing down: $\tau$ with $x = 13.1 \pm 0.4$ and $\tau_{0}^{*} = (2.5 \pm 0.5) \times 10^{-13}$ sec. The influence of the random disorder on the critical behavior above $T_{c}$ is clearly observed: $\alpha = -0.66$, $\beta = 0.63$, and $\gamma = 1.40$. The exponent of $\alpha$ is far from that of 3D Heisenberg model.Comment: 15 pages, 16 figures, submitted to Phys. Rev.

Fuzzy regions: adding subregions and the impact on surface and distance calculation

In the concept of fuzzy regions we introduced before, a region was considered to be a fuzzy set of points, each having its own membership grade. While this allows the modelling of regions in which points only partly belong to the region, it has the downside that all the points are considered independently, which is too loose a restriction for some situations. The model is not able to support the fact that some points may be linked together. In this contribution, we propose an extension to the model, so that points can be made related to one another. It will permit the user to, for instance, specify points or even (sub)regions within the fuzzy region that are linked together: they all belong to the region to the same extent at the same time. By letting the user specify such subregions, the accuracy Of the model can be increased: the model can match the real situation better; while at the same time decreasing the fuzziness: if points are known to be related, there is no need to consider them independently. As an example, the use of such a fuzzy region to represent a lake with a variable water level can be considered: as the water level rises, a set of points will become flooded; it is interesting to represent this set of points as a. subset of the region, as these points are somewhat related (the same can be done for different water levels). The impact of this extension to the model on both surface area calculation an distance measurement are considered, and new appropriate definitions are introduced

Phylogenetic relationships among NE Atlantic <i>Plocamionida</i> Topsent (1927) (Porifera, Poecilosclerida): under-estimated diversity in reef ecosystems

Background: Small and cryptic sponges associated with cold-water coral reefs are particularly numerous and challenging to identify, but their ecological and biochemical importance is likely to compete with megabenthic specimens.Methodology/Principal Findings: Here we use a combination of the standard M1M6 and I3M11 partitions of the COI fragment, partial rDNA 28S sequences and morphology to delineate small encrusting Plocamionida species. In total, 46 specimens were retrieved from seven shallow to deep-water coral locations, crossing 3,000 km along the European margins. Our work provides evidence that the Plocamionida ambigua f. tylotata and f. grandichelata can be considered valid species, whereas Plocamionida ambigua f. tornata corresponds to the species P. ambigua. Within the monophyletic group of Plocamionida, P. microcionides is shown as really divergent from the other taxa, and four putative new Plocamionida species are suggested.Conclusions/Significance: This study shows that the use of molecular and morphological information in an integrative approach is a powerful tool for the identification of sponge species, and suggests that an under-estimated biodiversity of sponges occurs in cold-water coral reefs

The K Group Nearest-Neighbor Query on Non-indexed RAM-Resident Data

Data sets that are used for answering a single query only once (or just a few times) before they are replaced by new data sets appear frequently in practical applications. The cost of buiding indexes to accelerate query processing would not be repaid for such data sets. We consider an extension of the popular (K) Nearest-Neighbor Query, called the (K) Group Nearest Neighbor Query (GNNQ). This query discovers the (K) nearest neighbor(s) to a group of query points (considering the sum of distances to all the members of the query group) and has been studied during recent years, considering data sets indexed by efficient spatial data structures. We study (K) GNNQs, considering non-indexed RAM-resident data sets and present an existing algorithm adapted to such data sets and two Plane-Sweep algorithms, that apply optimizations emerging from the geometric properties of the problem. By extensive experimentation, using real and synthetic data sets, we highlight the most efficient algorithm

Supervised Domain Adaptation using Graph Embedding

Getting deep convolutional neural networks to perform well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transfer learning to leverage a related larger dataset (source) in order to improve the performance on the small dataset (target). Among the transfer learning approaches, domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. In this paper, we consider the domain adaptation problem from the perspective of dimensionality reduction and propose a generic framework based on graph embedding. Instead of solving the generalised eigenvalue problem, we formulate the graph-preserving criterion as a loss in the neural network and learn a domain-invariant feature transformation in an end-to-end fashion. We show that the proposed approach leads to a powerful Domain Adaptation framework; a simple LDA-inspired instantiation of the framework leads to state-of-the-art performance on two of the most widely used Domain Adaptation benchmarks, Office31 and MNIST to USPS datasets.Comment: 7 pages, 3 figures, 3 table

EuCd2_2As2_2: a magnetic semiconductor

EuCd$_2$As$_2$ is now widely accepted as a topological semimetal in which a Weyl phase is induced by an external magnetic field. We challenge this view through firm experimental evidence using a combination of electronic transport, optical spectroscopy and excited-state photoemission spectroscopy. We show that the EuCd$_2$As$_2$ is in fact a semiconductor with a gap of 0.77 eV. We show that the externally applied magnetic field has a profound impact on the electronic band structure of this system. This is manifested by a huge decrease of the observed band gap, as large as 125~meV at 2~T, and consequently, by a giant redshift of the interband absorption edge. However, the semiconductor nature of the material remains preserved. EuCd$_2$As$_2$ is therefore a magnetic semiconductor rather than a Dirac or Weyl semimetal, as suggested by {\em ab initio} computations carried out within the local spin-density approximation.Comment: Accepted for publication in Physical Review Letter