Outlier Mining Methods Based on Graph Structure Analysis

Outlier detection in high-dimensional datasets is a fundamental and challenging problem across disciplines that has also practical implications, as removing outliers from the training set improves the performance of machine learning algorithms. While many outlier mining algorithms have been proposed in the literature, they tend to be valid or efficient for specific types of datasets (time series, images, videos, etc.). Here we propose two methods that can be applied to generic datasets, as long as there is a meaningful measure of distance between pairs of elements of the dataset. Both methods start by defining a graph, where the nodes are the elements of the dataset, and the links have associated weights that are the distances between the nodes. Then, the first method assigns an outlier score based on the percolation (i.e., the fragmentation) of the graph. The second method uses the popular IsoMap non-linear dimensionality reduction algorithm, and assigns an outlier score by comparing the geodesic distances with the distances in the reduced space. We test these algorithms on real and synthetic datasets and show that they either outperform, or perform on par with other popular outlier detection methods. A main advantage of the percolation method is that is parameter free and therefore, it does not require any training; on the other hand, the IsoMap method has two integer number parameters, and when they are appropriately selected, the method performs similar to or better than all the other methods tested.Peer ReviewedPostprint (published version

Nonlinear optical thresholding in a 4-Channel OCDMA system via two-photon absorption

We demonstrate the use of a Two-Photon Absorption based detector in an OCDMA system. This detector provides a significant performance improvement over standard linear detection

Exploiting relocation to reduce network dimensions of resilient optical grids

Optical grids are widely deployed to solve complex problems we are facing today. An important aspect of the supporting network is resiliency i.e. the ability to overcome network failures. In contrast to classical network protection schemes, we will not necessarily provide a back-up path between the source and the original destination. Instead, we will try to relocate the job to another server location if this means that we can provide a backup path which comprises less wavelengths than the one the traditional scheme would suggest. This relocation can be backed up by the grid specific anycast principle: a user generally does not care where his job is executed and is only interested in its results. We present ILP formulations for both resilience schemes and we evaluate them in a case study on an European network topology

Origin and neurochemical properties of bulbospinal neurons projecting to the rat lumbar spinal cord via the medial longitudinal fasciculus and caudal ventrolateral medulla

Bulbospinal systems (BS) originate from various regions of the brainstem and influence spinal neurons by classical synaptic and modulatory mechanisms. Our aim was to determine the brainstem locations of cells of origin of BS pathways passing through the medial longitudinal fasciculus (MLF) and the caudal ventrolateral medulla (CVLM). We also examined the transmitter content of spinal terminations of the CVLM pathway. Six adult rats received Fluorogold (FG) injections to the right intermediate gray matter of the lumbar cord (L1–L2) and the b-subunit of cholera toxin (CTb) was injected either into the MLF or the right CVLM (3 animals each). Double-labeled cells were identified within brainstem structures with confocal microscopy and mapped onto brainstem diagrams. An additional 3 rats were injected with CTb in the CVLM to label axon terminals in the lumbar spinal cord. Double-labeled cells projecting via the MLF or CVLM were found principally in reticular regions of the medulla and pons but small numbers of cells were also located within the midbrain. CVLM projections to the lumbar cord were almost exclusively ipsilateral and concentrated within the intermediate gray matter. Most (62%) of terminals were immunoreactive for the vesicular glutamate transporter 2 while 23% contained the vesicular GABA transporter. The inhibitory subpopulation was glycinergic, GABAergic or contained both transmitters. The proportions of excitatory and inhibitory axons projecting via the CVLM to the lumbar cord are similar to those projecting via the MLF. Unlike the MLF pathway, CVLM projections are predominantly ipsilateral and concentrated within intermediate gray but do not extend into motor nuclei or laminia VIII. Terminations of the CVLM pathway are located in a region of the gray matter that is rich in premotor interneurons; thus its primary function may be to coordinate activity of premotor networks

Morphological properties of mass-spring networks for optimal locomotion learning

Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength. On the one hand, the use of flexible materials can lead to higher power efficiency and more fluent and robust motions. On the other hand, using embodiment in a closed-loop controller, part of the control task itself can be outsourced to the body dynamics. This can significantly simplify the additional resources required for locomotion control. To this goal, a first step consists in an exploration of the trade-offs between morphology, efficiency of locomotion, and the ability of a mechanical body to serve as a computational resource. In this work, we use a detailed dynamical model of a Mass–Spring–Damper (MSD) network to study these trade-offs. We first investigate the influence of the network size and compliance on locomotion quality and energy efficiency by optimizing an external open-loop controller using evolutionary algorithms. We find that larger networks can lead to more stable gaits and that the system’s optimal compliance to maximize the traveled distance is directly linked to the desired frequency of locomotion. In the last set of experiments, the suitability of MSD bodies for being used in a closed loop is also investigated. Since maximally efficient actuator signals are clearly related to the natural body dynamics, in a sense, the body is tailored for the task of contributing to its own control. Using the same simulation platform, we therefore study how the network states can be successfully used to create a feedback signal and how its accuracy is linked to the body size