Hierarchical classification for Multilingual Language Identification and Named Entity Recognition

ABSTRACT This paper describes the approach for Subtask-1 of the FIRE-2015 Shared Task on Mixed Script Information Retrieval. The subtask involved multilingual language identification (including mixed words and anomalous foreign words), named entity recognition (NER) and subclassification. The proposed methodology starts with cleaning the data and then extracting structural and contextual features from the text for further processing. A subset of these features is selected (based on validation) for training supervised classifiers, separately for language identification and NER. Finally, they are applied hierarchically to annotate the entire text. The detected named entities are further subclassified by a novel unsupervised technique based on query refinement and keyword based scoring. The proposed approach on the testing dataset of the shared task showed promising results with a weighed F-measure of 0.8082. However, it is worth noting that the classifiers have been sub-optimal with respect to discriminating between certain linguistically similar languages (for e.g., Gujarati in Hindi and Gujarati pairs). The proposed approach is flexible and robust enough to handle additional languages for identification as well as anomalous foreign or extraneous words. The implementation of the approach has also been shared for the purpose of future research usage

Machine Learning at Microsoft with ML .NET

Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned

Three-dimensional electro-neural interfaces electroplated on subretinal prostheses

Objective. Retinal prosthetics offer partial restoration of sight to patients blinded by retinal degenerative diseases through electrical stimulation of the remaining neurons. Decreasing the pixel size enables increasing prosthetic visual acuity, as demonstrated in animal models of retinal degeneration. However, scaling down the size of planar pixels is limited by the reduced penetration depth of the electric field in tissue. We investigated 3-dimensional (3d) structures on top of photovoltaic arrays for enhanced penetration of the electric field, permitting higher resolution implants. Approach. 3D COMSOL models of subretinal photovoltaic arrays were developed to accurately quantify the electrodynamics during stimulation and verified through comparison to flat photovoltaic arrays. Models were applied to optimize the design of 3D electrode structures (pillars and honeycombs). Return electrodes on honeycomb walls vertically align the electric field with bipolar cells for optimal stimulation. Pillars elevate the active electrode, thus improving proximity to target neurons. The optimized 3D structures were electroplated onto existing flat subretinal prostheses. Main results. Simulations demonstrate that despite exposed conductive sidewalls, charge mostly flows via high-capacitance sputtered iridium oxide films topping the 3D structures. The 24 μm height of honeycomb structures was optimized for integration with the inner nuclear layer cells in the rat retina, whilst 35 μm tall pillars were optimized for penetrating the debris layer in human patients. Implantation of released 3D arrays demonstrates mechanical robustness, with histology demonstrating successful integration of 3D structures with the rat retina in-vivo. Significance. Electroplated 3D honeycomb structures produce vertically oriented electric fields, providing low stimulation thresholds, high spatial resolution, and high contrast for pixel sizes down to 20 μm. Pillar electrodes offer an alternative for extending past the debris layer. Electroplating of 3D structures is compatible with the fabrication process of flat photovoltaic arrays, enabling much more efficient retinal stimulation

Three-dimensional electro-neural interfaces electroplated on subretinal prostheses

Objective High-resolution retinal prosthetics offer partial restoration of sight to patients blinded by retinal degenerative diseases through electrical stimulation of the remaining neurons. Decreasing the pixel size enables an increase in prosthetic visual acuity, as demonstrated in animal models of retinal degeneration. However, scaling down the size of planar pixels is limited by the reduced penetration depth of the electric field in tissue. We investigate 3-dimensional structures on top of the photovoltaic arrays for enhanced penetration of electric field to permit higher-resolution implants. Approach We developed 3D COMSOL models of subretinal photovoltaic arrays that accurately quantify the device electrodynamics during stimulation and verified it experimentally through comparison with the standard (flat) photovoltaic arrays. The models were then applied to optimise the design of 3D electrode structures (pillars and honeycombs) to efficiently stimulate the inner retinal neurons. The return electrodes elevated on top of the honeycomb walls surrounding each pixel orient the electric field inside the cavities vertically, aligning it with bipolar cells for optimal stimulation. Alternatively, pillars elevate the active electrode into the inner nuclear layer, improving proximity to the target neurons. Modelling results informed a microfabrication process of electroplating the 3D electrode structures on top of the existing flat subretinal prosthesis. Main results Simulations demonstrate that despite the conductive sidewalls of the 3D electrodes being exposed to electrolyte, most of the charge flows via the high-capacitance sputtered Iridium Oxide film that caps the top of the 3D structures. The 24 µm height of the electroplated honeycomb structures was optimised for integration with the inner nuclear layer cells in rat retina, while 35 µm height of the pillars was optimized for penetrating the debris layer in human patients. Release from the wafer and implantation of the 3D arrays demonstrated that they are mechanically robust to withstand the associated forces. Histology demonstrated successful integration of the 3D structures with the rat retina in-vivo. Significance Electroplated 3D honeycomb structures produce a vertically oriented electric field that offers low stimulation threshold, high spatial resolution and high contrast for the retinal implants with pixel sizes down to 20µm in width. Pillar electrodes offer an alternative configuration for extending the stimulation past the debris layers. Electroplating of the 3D structures is compatible with the fabrication process of the flat photovoltaic arrays, thereby enabling much more efficient stimulation than in their original flat configuration