Volumetric Lissajous confocal microscopy with tunable spatiotemporal resolution

Dynamic biological systems present challenges to existing three-dimensional (3D) optical microscopes because of their continuous temporal and spatial changes. Most techniques are rigid in adapting the acquisition parameters over time, as in confocal microscopy, where a laser beam is sequentially scanned at a predefined spatial sampling rate and pixel dwell time. Such lack of tunability forces a user to provide scan parameters, which may not be optimal, based on the best assumption before an acquisition starts. Here, we developed volumetric Lissajous confocal microscopy to achieve unsurpassed 3D scanning speed with a tunable sampling rate. The system combines an acoustic liquid lens for continuous axial focus translation with a resonant scanning mirror. Accordingly, the excitation beam follows a dynamic Lissajous trajectory enabling sub-millisecond acquisitions of image series containing 3D information at a sub-Nyquist sampling rate. By temporal accumulation and/or advanced interpolation algorithms, the volumetric imaging rate is selectable using a post-processing step at the desired spatiotemporal resolution for events of interest. We demonstrate multicolor and calcium imaging over volumes of tens of cubic microns with 3D acquisition speeds of 30 Hz and frame rates up to 5 kHz

Measuring the expressivity of graph kernels through the rademacher complexity

Graph kernels are widely adopted in real-world applications that involve learning on graph data. Different graph kernels have been proposed in literature, but no theoretical comparison among them is present. In this paper we provide a formal definition for the expressiveness of a graph kernel by means of the Rademacher Complexity, and analyze the differences among some state-of-the-art graph kernels. Results on real world datasets confirm some known properties of graph kernels, showing that the Rademacher Complexity is indeed a suitable measure for this analysis

Advances in learning with kernels: Theory and practice in a world of growing constraints

Kernel methods consistently outperformed previous generations of learning techniques. They provide a flexible and expressive learning framework that has been successfully applied to a wide range of real world problems but, recently, novel algorithms, such as Deep Neural Networks and Ensemble Methods, have increased their competitiveness against them. Due to the current data growth in size, heterogeneity and structure, the new generation of algorithms are expected to solve increasingly challenging problems. This must be done under growing constraints such as computational resources, memory budget and energy consumption. For these reasons, new ideas have to come up in the field of kernel learning, such as deeper kernels and novel algorithms, to fill the gap that now exists with the most recent learning paradigms. The purpose of this special session is to highlight recent advances in learning with kernels. In particular, this session welcomes contributions toward the solution of the weaknesses (e.g. scalability, computational efficiency and too shallow kernels) and the improvement of the strengths (e.g. the ability of dealing with structural data) of the state of the art kernel methods. We also encourage the submission of new theoretical results in the Statistical Learning Theory framework and innovative solutions to real world problems

Measuring the expressivity of graph kernels through the rademacher complexity

Graph kernels are widely adopted in real-world applications that involve learning on graph data. Different graph kernels have been proposed in literature, but no theoretical comparison among them is present. In this paper we provide a formal definition for the expressiveness of a graph kernel by means of the Rademacher Complexity, and analyze the differences among some state-of-the-art graph kernels. Results on real world datasets confirm some known properties of graph kernels, showing that the Rademacher Complexity is indeed a suitable measure for this analysis