Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data

Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the robust \emph{time series cluster kernel} (TCK). The approach taken leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with informative prior distributions. An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel. We evaluate the TCK on synthetic and real data and compare to other state-of-the-art techniques. The experimental results demonstrate that the TCK is robust to parameter choices, provides competitive results for MTS without missing data and outstanding results for missing data.Comment: 23 pages, 6 figure

Field Effect Transistor Nanosensor for Breast Cancer Diagnostics

Silicon nanochannel field effect transistor (FET) biosensors are one of the most promising technologies in the development of highly sensitive and label-free analyte detection for cancer diagnostics. With their exceptional electrical properties and small dimensions, silicon nanochannels are ideally suited for extraordinarily high sensitivity. In fact, the high surface-to-volume ratios of these systems make single molecule detection possible. Further, FET biosensors offer the benefits of high speed, low cost, and high yield manufacturing, without sacrificing the sensitivity typical for traditional optical methods in diagnostics. Top down manufacturing methods leverage advantages in Complementary Metal Oxide Semiconductor (CMOS) technologies, making richly multiplexed sensor arrays a reality. Here, we discuss the fabrication and use of silicon nanochannel FET devices as biosensors for breast cancer diagnosis and monitoring

On Recursive Edit Distance Kernels with Application to Time Series Classification

This paper proposes some extensions to the work on kernels dedicated to string or time series global alignment based on the aggregation of scores obtained by local alignments. The extensions we propose allow to construct, from classical recursive definition of elastic distances, recursive edit distance (or time-warp) kernels that are positive definite if some sufficient conditions are satisfied. The sufficient conditions we end-up with are original and weaker than those proposed in earlier works, although a recursive regularizing term is required to get the proof of the positive definiteness as a direct consequence of the Haussler's convolution theorem. The classification experiment we conducted on three classical time warp distances (two of which being metrics), using Support Vector Machine classifier, leads to conclude that, when the pairwise distance matrix obtained from the training data is \textit{far} from definiteness, the positive definite recursive elastic kernels outperform in general the distance substituting kernels for the classical elastic distances we have tested.Comment: 14 page

Hydrophobic and hydrophilic au and ag nanoparticles. Breakthroughs and perspectives

This review provides a broad look on the recent investigations on the synthesis, characterization and physico-chemical properties of noble metal nanoparticles, mainly gold and silver nanoparticles, stabilized with ligands of different chemical nature. A comprehensive review of the available literature in this field may be far too large and only some selected representative examples will be reported here, together with some recent achievements from our group, that will be discussed in more detail. Many efforts in finding synthetic routes have been performed so far to achieve metal nanoparticles with well-defined size, morphology and stability in different environments, to match the large variety of applications that can be foreseen for these materials. In particular, the synthesis and stabilization of gold and silver nanoparticles together with their properties in different emerging fields of nanomedicine, optics and sensors are reviewed and briefly commented

Geometric and Energetic Properties of Defects at Complementary Soft Material Interfaces

Surface architecture can influence mechanical properties, such as adhesion and friction, in many natural systems. The careful study of these systems elucidates understanding of the many biological roles these properties serve and the mechanisms by which they occur. One means of controlling surface mechanical properties is through shape complementarity. Predicated on some natural systems’ surface architectural design, shape complementarity can be used to enhance selectively between synthetic elastomeric surfaces. Complementary arrays of surface structures, such as 1D ridges or fibrils arranged in a 2D lattice, can inter-digitate to achieve adhesion enhancement controlled by shape recognition. It has been shown that relative misorientation (twist) is accommodated by defects that are mesoscale screw dislocations. The arrangement of such dislocations plays a critical role in determining the mechanical properties of the interface. The objective of our work is to increase the understanding of adhesive and frictional enhancement mechanisms through the study of complementary surface pattern interactions of precisely designed soft elastomeric materials. Here we study the geometric properties of one-dimensional (ridge/channel) and two-dimensional (arrays of pillars) shape-complementary interfaces in the presence of relative misorientation and difference in lattice spacing. Relative misorientation without difference in lattice period spacing is accommodated by arrays of screw dislocations. Differences in lattice spacing without misorientation is accommodated by arrays of edge dislocations. In general, we observe arrays of dislocations with mixed screw and edge character. The spacing, orientation, and potential mechanical properties of these arrays can be predicted using the geometry of Moiré patterns. More broadly, we show that soft materials with shape-complementary patterns can be used to generate dislocations of arbitrary edge and screw character at the mesolength scale of tens of microns. Because these dislocations are easily observed and occur periodically, Moiré pattern information is used to study relationships between dislocations, parameter selection, and surface mechanical properties.We extend the studies further by examining and taking advantage of the translational symmetry of fibrillar lattice structures for our experiments. We use typical 2D Bravais lattice structures patterned by microfibrils on the surfaces of a soft material and attempt to understand the roles of periodicity, fibril size, and density on surface mechanical properties. We develop a means of interpreting these results systematically based on pattern misorientation that is applicable to soft materials, thereby bridging crystallography with 2D soft material interactions. The increased understanding and interdisciplinary impact of these surface interfacial interactions can be used in many fields of study including soft material substrate tissue and graft engineering, mechanical engineering and mechanics scale up studies, and various industrial applications

Application of Convolutional Neural Network to TSOM Images for Classification of 6 nm Node Patterned Defects

With the rapid growth in the semiconductor industry, it is becoming critical to detect and classify increasingly smaller patterned defects. Recently machine learning, including deep learning, has come to aid in this endeavor in a big way. However, the literature shows that it is challenging to successfully classify defect types at the 6 nm node with 100% accuracy using low-cost and high-volume-manufacturing compatible optical imaging methods. Here we combine a convolutional neural network (CNN) with that of an optical imaging method called through-focus scanning optical microscopy (TSOM) to successfully classify patterned defects for the 6 nm node targets using simulated optical images at the 193 nm illumination wavelength. We demonstrate the successful classification of eight variations of the defects, including the 3 nm difference in the defect size in one dimension, which is over 50 times smaller than the illumination wavelength used.Comment: 9 pages, 6 figure