Evidence of distributed subpial T2* signal changes at 7T in multiple sclerosis : an histogram based approach

Subpial lesions are the most frequent type of cortical lesion in multiple sclerosis (MS), and are thought to be closely associated with poor clinical outcome. Neuropathological studies report that subpial lesions may come in two major types: they may appear as circumscribed, focal lesions, or extend across multiple adjacent gyri leading to a phenomenon termed “general subpial demyelination” [1]. The in vivo evaluation of diffuse subpial disease is challenging – signal changes may be subtle, and extend across large regions where signal inhomogeneities due to B1 and RF receive coil non-uniformities become more pronounced. Here, we investigate whether a histogram-based analysis of T2* signal intensity in the cortex, at 7T MRI, can show evidence of distributed subpial cortical changes in patients with MS, as described histopathologically. We hypothesized that this phenomenon would be associated with significantly increased T2* signal intensity in patients compared to age-matched controls.Center Algoritm

Recommendatory system for supermarket online shopping

With the recent rise of online purchasing, many companies have focused on developing recommendation systems, where customers are suggested different options for complementing their purchases. This thesis will introduce and test four different approaches for a recommendation system for online shopping at supermarkets, based on the historical of previous customers. The four proposed algorithms are based on successful recommendation systems, which include a histogram-based approach, a graph theory-based approach, an embedding-based approach and finally a support-vector machine-based approach

A histogram-based approach for object-based query-by-shape-and-color in image and video databases

Cataloged from PDF version of article.Considering the fact that querying by low-level object features is essential in image and video data, an efficient approach for querying and retrieval by shape and color is proposed. The approach employs three specialized histograms, (i.e. distance, angle, and color histograms) to store feature-based information that is extracted from objects. The objects can be extracted from images or video frames. The proposed histogram-based approach is used as a component in the query-by-feature subsystem of a video database management system. The color and shape information is handled together to enrich the querying capabilities for content-based retrieval. The evaluation of the retrieval effectiveness and the robustness of the proposed approach is presented via performance experiments. (C) 2005 Elsevier Ltd All rights reserved

A generalised framework for saliency-based point feature detection

Here we present a novel, histogram-based salient point feature detector that may naturally be applied to both images and 3D data. Existing point feature detectors are often modality specific, with 2D and 3D feature detectors typically constructed in separate ways. As such, their applicability in a 2D-3D context is very limited, particularly where the 3D data is obtained by a LiDAR scanner. By contrast, our histogram-based approach is highly generalisable and as such, may be meaningfully applied between 2D and 3D data. Using the generalised approach, we propose salient point detectors for images, and both untextured and textured 3D data. The approach naturally allows for the detection of salient 3D points based jointly on both the geometry and texture of the scene, allowing for broader applicability. The repeatability of the feature detectors is evaluated using a range of datasets including image and LiDAR input from indoor and outdoor scenes. Experimental results demonstrate a significant improvement in terms of 2D-2D and 2D-3D repeatability compared to existing multi-modal feature detectors

SENATUS: An Approach to Joint Traffic Anomaly Detection and Root Cause Analysis

In this paper, we propose a novel approach, called SENATUS, for joint traffic anomaly detection and root-cause analysis. Inspired from the concept of a senate, the key idea of the proposed approach is divided into three stages: election, voting and decision. At the election stage, a small number of \nop{traffic flow sets (termed as senator flows)}senator flows are chosen\nop{, which are used} to represent approximately the total (usually huge) set of traffic flows. In the voting stage, anomaly detection is applied on the senator flows and the detected anomalies are correlated to identify the most possible anomalous time bins. Finally in the decision stage, a machine learning technique is applied to the senator flows of each anomalous time bin to find the root cause of the anomalies. We evaluate SENATUS using traffic traces collected from the Pan European network, GEANT, and compare against another approach which detects anomalies using lossless compression of traffic histograms. We show the effectiveness of SENATUS in diagnosing anomaly types: network scans and DoS/DDoS attacks

TopicFlow: Disentangling quark and gluon jets with normalizing flows

The isolation of pure samples of quark and gluon jets is of key interest at hadron colliders. Recent work has employed topic modeling to disentangle the underlying distributions in mixed samples obtained from experiments. However, current implementations do not scale to high-dimensional observables as they rely on binning the data. In this work we introduce TopicFlow, a method based on normalizing flows to learn quark and gluon jet topic distributions from mixed datasets. These networks are as performant as the histogram-based approach, but since they are unbinned, they are efficient even in high dimension. The models can also be oversampled to alleviate the statistical limitations of histograms. As an example use case, we demonstrate how our models can improve the calibration accuracy of a classifier. Finally, we discuss how the flow likelihoods can be used to perform outlier-robust quark/gluon classification.Comment: 10 pages, 5 figures. v2: Added footnote in Section III B. Added baseline and related discussion to Section III C. v3: Match published versio

A generalised framework for saliency-based point feature detection

Here we present a novel, histogram-based salient point feature detector that may naturally be applied to both images and 3D data. Existing point feature detectors are often modality specific, with 2D and 3D feature detectors typically constructed in separate ways. As such, their applicability in a 2D-3D context is very limited, particularly where the 3D data is obtained by a LiDAR scanner. By contrast, our histogram-based approach is highly generalisable and as such, may be meaningfully applied between 2D and 3D data. Using the generalised approach, we propose salient point detectors for images, and both untextured and textured 3D data. The approach naturally allows for the detection of salient 3D points based jointly on both the geometry and texture of the scene, allowing for broader applicability. The repeatability of the feature detectors is evaluated using a range of datasets including image and LiDAR input from indoor and outdoor scenes. Experimental results demonstrate a significant improvement in terms of 2D-2D and 2D-3D repeatability compared to existing multi-modal feature detectors

Internal combustion engine sensor network analysis using graph modeling

In recent years there has been a rapid development in technologies for smart monitoring applied to many different areas (e.g. building automation, photovoltaic systems, etc.). An intelligent monitoring system employs multiple sensors distributed within a network to extract useful information for decision-making. The management and the analysis of the raw data derived from the sensor network includes a number of specific challenges still unresolved, related to the different communication standards, the heterogeneous structure and the huge volume of data. In this paper we propose to apply a method based on complex network theory, to evaluate the performance of an Internal Combustion Engine. Data are gathered from the OBD sensor subset and from the emission analyzer. The method provides for the graph modeling of the sensor network, where the nodes are represented by the sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs. The resulting functional graph is then analyzed with the topological metrics of the network, to define characteristic proprieties representing useful indicator for the maintenance and diagnosis