Statistical and Computational Trade-Offs in Kernel K-Means

We investigate the efficiency of k-means in terms of both statistical and computational requirements. More precisely, we study a Nystrom approach to kernel k-means. We analyze the statistical properties of the proposed method and show that it achieves the same accuracy of exact kernel k-means with only a fraction of computations. Indeed, we prove under basic assumptions that sampling oot pn Nystrom landmarks allows to greatly reduce computational costs without incurring in any loss of accuracy. To the best of our knowledge this is the first result of this kind for unsupervised learning

On Fast Leverage Score Sampling and Optimal Learning

Leverage score sampling provides an appealing way to perform approximate computations for large matrices. Indeed, it allows to derive faithful approximations with a complexity adapted to the problem at hand. Yet, performing leverage scores sampling is a challenge in its own right requiring further approximations. In this paper, we study the problem of leverage score sampling for positive definite matrices defined by a kernel. Our contribution is twofold. First we provide a novel algorithm for leverage score sampling and second, we exploit the proposed method in statistical learning by deriving a novel solver for kernel ridge regression. Our main technical contribution is showing that the proposed algorithms are currently the most efficient and accurate for these problems

Drug Usage Analysis and Health Care Resources Consumption in Patients With Rheumatoid Arthritis

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Secure Vehicular Communication Systems: Implementation, Performance, and Research Challenges

Vehicular Communication (VC) systems are on the verge of practical deployment. Nonetheless, their security and privacy protection is one of the problems that have been addressed only recently. In order to show the feasibility of secure VC, certain implementations are required. In [1] we discuss the design of a VC security system that has emerged as a result of the European SeVeCom project. In this second paper, we discuss various issues related to the implementation and deployment aspects of secure VC systems. Moreover, we provide an outlook on open security research issues that will arise as VC systems develop from today's simple prototypes to full-fledged systems

Constrained DMPs for Feasible Skill Learning on Humanoid Robots

In the context of humanoid skill learning, movement primitives have gained much attention because of their compact representation and convenient combination with a myriad of optimization approaches. Among them, a well-known scheme is to use Dynamic Movement Primitives (DMPs) with reinforcement learning (RL) algorithms. While various remarkable results have been reported, skill learning with physical constraints has not been sufficiently investigated. For example, when RL is employed to optimize the robot joint trajectories, the exploration noise could drive the resulting trajectory out of the joint limits. In this paper, we focus on robot skill learning characterized by joint limit avoidance, by introducing the novel Constrained Dynamic Movement Primitives (CDMPs). By controlling a set of transformed states (called exogenous states) instead of the original DMPs states, CDMPs are capable of maintaining the joint trajectories within the safety limits. We validate CDMPs on the humanoid robot iCub, showing the applicability of our approach

Learning to Sequence Multiple Tasks with Competing Constraints

Imitation learning offers a general framework where robots can efficiently acquire novel motor skills from demonstrations of a human teacher. While many promising achievements have been shown, the majority of them are only focused on single-stroke movements, without taking into account the problem of multi-tasks sequencing. Conceivably, sequencing different atomic tasks can further augment the robot's capabilities as well as avoid repetitive demonstrations. In this paper, we propose to address the issue of multi-tasks sequencing with emphasis on handling the so-called competing constraints, which emerge due to the existence of the concurrent constraints from Cartesian and joint trajectories. Specifically, we explore the null space of the robot from an information-theoretic perspective in order to maintain imitation fidelity during transition between consecutive tasks. The effectiveness of the proposed method is validated through simulated and real experiments on the iCub humanoid robot

Learning to Avoid Obstacles With Minimal Intervention Control

Programming by demonstration has received much attention as it offers a general framework which allows robots to efficiently acquire novel motor skills from a human teacher. While traditional imitation learning that only focuses on either Cartesian or joint space might become inappropriate in situations where both spaces are equally important (e.g., writing or striking task), hybrid imitation learning of skills in both Cartesian and joint spaces simultaneously has been studied recently. However, an important issue which often arises in dynamical or unstructured environments is overlooked, namely how can a robot avoid obstacles? In this paper, we aim to address the problem of avoiding obstacles in the context of hybrid imitation learning. Specifically, we propose to tackle three subproblems: (i) designing a proper potential field so as to bypass obstacles, (ii) guaranteeing joint limits are respected when adjusting trajectories in the process of avoiding obstacles, and (iii) determining proper control commands for robots such that potential human-robot interaction is safe. By solving the aforementioned subproblems, the robot is capable of generalizing observed skills to new situations featuring obstacles in a feasible and safe manner. The effectiveness of the proposed method is validated through a toy example as well as a real transportation experiment on the iCub humanoid robot

Pattern electroretinogram detects localized glaucoma defects

Purpose: We evaluated the clinical ability of pattern electroretinogram (PERG) to detect functional losses in the affected hemifield of open-angle glaucoma patients with localized perimetric defects. Methods: Hemifield (horizontally-defined) steady-state PERGs (h-PERGs) were recorded in response to 1.7 c/deg alternating gratings from 32 eyes of 29 glaucomatous patients with a perimetric, focal one-hemifield defect, 10 eyes of 10 glaucomatous patients with a diffuse perimetric defect, and 18 eyes of 18 age-matched normal subjects. Standard automated perimetry (SAP) and spectral-domain optical coherence tomography (SD-OCT) for retinal nerve fiber layer (RNFL) thickness also were performed. h-PERG amplitudes and ratios, calculated corresponding hemifield perimetric deviations, as well as hemiretina RNFL thicknesses were analyzed. Results: h-PERG amplitudes, perimetric deviations, and RNFL thicknesses showed losses (P < 0.001) when comparing affected with unaffected hemifields of localized glaucomatous eyes. No differences were found in h-PERG amplitudes between hemifields of normal or diffuse glaucomatous eyes. h-PERG amplitude ratios (affected/ unaffected hemifield) in localized glaucoma were lower (P < 0.001) than the ratios from normal or diffuse glaucomatous eyes. The areas under the receiver operating characteristic curves for h-PERG amplitude ratios, comparing localized-defect glaucomatous eyes with normal or diffuse glaucomatous eyes, were 0.93 and 0.91, respectively. Conclusions: h-PERG assessment showed good diagnostic accuracy to confirm localized glaucomatous defects detected perimetrically. This test may be particularly useful in cognitively impaired patients or young/nonverbal patients unable to provide reliable visual fields. Translational Relevance: h-PERG provides a sensitive objective measure to confirm

MicroRNA expression analysis identifies a subset of downregulated miRNAs in ALS motor neuron progenitors

Amyotrophic lateral sclerosis (ALS) is a fatal neurological disorder that is characterized by a progressive degeneration of motor neurons (MNs). The pathomechanism underlying the disease is largely unknown, even though increasing evidence suggests that RNA metabolism, including microRNAs (miRNAs) may play an important role. In this study, human ALS induced pluripotent stem cells were differentiated into MN progenitors and their miRNA expression profiles were compared to those of healthy control cells. We identified 15 downregulated miRNAs in patients' cells. Gene ontology and molecular pathway enrichment analysis indicated that the predicted target genes of the differentially expressed miRNAs were involved in neurodegeneration-related pathways. Among the 15 examined miRNAs, miR-34a and miR504 appeared particularly relevant due to their involvement in the p53 pathway, synaptic vesicle regulation and general involvement in neurodegenerative diseases. Taken together our results demonstrate that the neurodegenerative phenotype in ALS can be associated with a dysregulation of miRNAs involved in the control of disease-relevant genetic pathways, suggesting that targeting entire gene networks can be a potential strategy to treat complex diseases such as ALS