A Lightweight Regression Method to Infer Psycholinguistic Properties for Brazilian Portuguese

Psycholinguistic properties of words have been used in various approaches to Natural Language Processing tasks, such as text simplification and readability assessment. Most of these properties are subjective, involving costly and time-consuming surveys to be gathered. Recent approaches use the limited datasets of psycholinguistic properties to extend them automatically to large lexicons. However, some of the resources used by such approaches are not available to most languages. This study presents a method to infer psycholinguistic properties for Brazilian Portuguese (BP) using regressors built with a light set of features usually available for less resourced languages: word length, frequency lists, lexical databases composed of school dictionaries and word embedding models. The correlations between the properties inferred are close to those obtained by related works. The resulting resource contains 26,874 words in BP annotated with concreteness, age of acquisition, imageability and subjective frequency.Comment: Paper accepted for TSD201

A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images

Collateral circulation results from specialized anastomotic channels which are capable of providing oxygenated blood to regions with compromised blood flow caused by ischemic injuries. The quality of collateral circulation has been established as a key factor in determining the likelihood of a favorable clinical outcome and goes a long way to determine the choice of stroke care model - that is the decision to transport or treat eligible patients immediately. Though there exist several imaging methods and grading criteria for quantifying collateral blood flow, the actual grading is mostly done through manual inspection of the acquired images. This approach is associated with a number of challenges. First, it is time-consuming - the clinician needs to scan through several slices of images to ascertain the region of interest before deciding on what severity grade to assign to a patient. Second, there is a high tendency for bias and inconsistency in the final grade assigned to a patient depending on the experience level of the clinician. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data. First, we formulate a region of interest detection task as a reinforcement learning problem and train a deep learning network to automatically detect the occluded region within the 3D MR perfusion volumes. Second, we extract radiomic features from the obtained region of interest through local image descriptors and denoising auto-encoders. Finally, we apply a convolutional neural network and other machine learning classifiers to the extracted radiomic features to automatically predict the collateral flow grading of the given patient volume as one of three severity classes - no flow (0), moderate flow (1), and good flow (2)..

Interpreting the Time-Resolved Photoluminescence of Quasi-2D Perovskites

Optical excitation of quasi-2D perovskites leads to excited-state populations of excitons, free charge carriers, or a mixture of both, depending on the type and amount of 2D spacer used. The fluence dependence of three quantities: 1) the time-resolved photoluminescence decay, 2) the photoluminescence quantum yield (PLQY) after pulsed excitation, and 3) the initial rate of photon emission, allow the mixture of excited states present to be determined. These can be described by a simple model considering noninteracting populations of excitons and charge carriers in separate subvolumes of the film. The model reproduces all unique features of the data, such as the anomalous peak of the PLQY at intermediate fluences, due to bimolecular free carrier emission gaining efficiency before exciton–exciton annihilation reduces the exciton emission efficiency. The excited state population varies from 100% excitons in films made from high concentrations of butylamine spacers to ≈7% excitons and 93% free carriers for low concentrations of 1-naphthylmethylamine spacers. The effective rates of free carrier recombination and exciton–exciton annihilation are high, often on the order of 1 × 10−9 cm3 s−1. The implications for the different excited-state populations and their dynamics in terms of device engineering are discussed

Correlative In Situ Multichannel Imaging for Large-Area Monitoring of Morphology Formation in Solution-Processed Perovskite Layers

To scale up production of perovskite photovoltaics, state-of-the-art laboratory recipes and processes must be transferred to large-area coating and drying systems. The development of in situ monitoring methods that provide real-time feedback for process control is pivotal to overcome this challenge. Herein, correlative in situ multichannel imaging (IMI) obtaining reflectance, photoluminescence intensity, and central photoluminescence emission wavelength images on areas larger than 100 cm2 with subsecond temporal resolution using a simple, cost-effective setup is demonstrated. Installed on top of a drying channel with controllable laminar air flow and substrate temperature, IMI is shown to consistently monitor solution film drying, perovskite nucleation, and perovskite crystallization. If the processing parameters differ, IMI reveals characteristic changes in large-area perovskite formation dynamics already before the final annealing step. Moreover, when IMI is used to study >130 blade-coated devices processed at the same parameters, about 90% of low-performing devices contain coating inhomogeneities detected by IMI. The results demonstrate that IMI should be of value for real-time 2D monitoring and feedback control in industrial-scale, high-throughput fabrication such as roll-to-roll printing