UNITOR @ DANKMEMES: Combining convolutional models and transformer-based architectures for accurate MEME management

This paper describes the UNITOR system that participated to the “multimoDal Artefacts recogNition Knowledge for MEMES” (DANKMEMES) task within the context of EVALITA 2020. UNITOR implements a neural model which combines a Deep Convolutional Neural Network to encode visual information of input images and a Transformer-based architecture to encode the meaning of the attached texts. UNITOR ranked first in all subtasks, clearly confirming the robustness of the investigated neural architectures and suggesting the beneficial impact of the proposed combination strategy

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

Multi-task and Generative Adversarial Learning for Robust and Sustainable Text Classification

Modern neural networks are quite demanding regarding the size and coverage of adequate training evidences, as far as complex inferences are involved. This is the case of offensive language detection that focuses on a phenomenon, the recognition of offensive uses of language, that is elusive and multifaceted. In this scenarios gathering training data can be prohibitively expensive and the dynamics and multidimensional nature of the abusive language phenomena are also demanding of timely and evolving evidence for training in a continuous fashion. The MT-GAN-BERT approach proposed here aims to reduce the requirements of neural approaches both in terms of the amount of annotated data and the computational cost required at classification time. It focuses corresponds to a general BERT-based architecture for multi faceted text classification tasks. On the one side, MT-GAN-BERT enables semi-supervised learning for Transformers based on the Generative Adversarial Learning paradigm. It also implements a Multi-task Learning approach able to train over and solve multiple tasks, simultaneously. A single BERT-based model is used to encode the input examples, while multiple linear layers are used to implement the classification steps, with a significant reduction of the computational costs. In the experimental evaluations we studied six classification tasks related to the detection of abusive uses of language in Italian. Outcomes suggest that MT-GAN-BERT is sustainable and generally improves the raw adoption of multiple BERT-based models, with much lighter requirements in terms of annotated data and computational costs

MT-GAN-BERT: Multi-Task and Generative Adversarial Learning for sustainable Language Processing

In this paper, we present MT-GAN-BERT, i.e., a BERT-based architecture for faceted classification tasks. It aims to reduce the requirements of Transformers both in terms of the amount of annotated data and the computational cost required at classification time. First, MT-GAN-BERT enables semi-supervised learning in BERT-based architectures based on Generative Adversarial Learning. Second, it implements a Multi-task Learning approach to solve multiple tasks simultaneously. A single BERTbased model is used to encode the input examples, while multiple linear layers are used to implement the classification steps, with a significant reduction of the computational costs. Experimental evaluations against six classification tasks involved in detecting abusive languages in Italian suggest that MT-GAN-BERT represents a sustainable solution that generally improves the raw adoption of multiple BERT-based models with lighter requirements in terms of annotated data and computational costs

OCT and OCT Angiography Update: Clinical Application to Age-Related Macular Degeneration, Central Serous Chorioretinopathy, Macular Telangiectasia, and Diabetic Retinopathy

Similar to ultrasound adapting soundwaves to depict the inner structures and tissues, optical coherence tomography (OCT) utilizes low coherence light waves to assess characteristics in the eye. Compared to the previous gold standard diagnostic imaging fluorescein angiography, OCT is a noninvasive imaging modality that generates images of ocular tissues at a rapid speed. Two commonly used iterations of OCT include spectral-domain (SD) and swept-source (SS). Each comes with different wavelengths and tissue penetration capacities. OCT angiography (OCTA) is a functional extension of the OCT. It generates a large number of pixels to capture the tissue and underlying blood flow. This allows OCTA to measure ischemia and demarcation of the vasculature in a wide range of conditions. This review focused on the study of four commonly encountered diseases involving the retina including age-related macular degeneration (AMD), diabetic retinopathy (DR), central serous chorioretinopathy (CSC), and macular telangiectasia (MacTel). Modern imaging techniques including SD-OCT, TD-OCT, SS-OCT, and OCTA assist with understanding the disease pathogenesis and natural history of disease progression, in addition to routine diagnosis and management in the clinical setting. Finally, this review compares each imaging technique’s limitations and potential refinements

The fate and prognostic implications of hyperreflective crystalline deposits in nonneovascular age-related macular degeneration

© 2019 The Authors. All rights reserved. PURPOSE. To explore patterns of disease progression in nonneovascular age-related macular degeneration (AMD) associated with hyperreflective crystalline deposits (HCDs) in the subretinal pigment epithelium–basal laminar space. METHODS. Retrospective review of medical records, multimodal imaging, and longitudinal eyetracked near-infrared reflectance (NIR) and optical coherence tomography (OCT) spanning years. NIR/OCT images were analyzed with ImageJ software to identify HCD morphology and location. Associated macular complications were reviewed from the time of HCD detection to the most recent follow-up, using NIR/OCT. RESULTS. Thirty-three eyes with HCDs from 33 patients (mean age: 72 ± 7.5 years) had 46.7 months (95% confidence limits: 33.7, 59.6) of serial eye-tracked NIR/OCT follow-up. Baseline best-corrected visual acuity (BCVA) was 0.44 logMAR (Snellen equivalent 20/55). At a mean of 11.3 months (3.1, 19.6) after HCD detection, 31/33 (93.9%) eyes had developed macular complications including de novo areas of complete retinal pigment epithelium and outer retinal atrophy (cRORA) in 21/33 (64%) eyes, enlargement of preexisting cRORA in 4/33 (12%) eyes, and incident macular neovascularization in 3/33 (9%) eyes. Movement and clearance of HCDs in 9/33 (27%) eyes was associated with enlargement of preexisting cRORA (r = 0.44, P = 0.02). BCVA at the last follow-up visit had decreased to 0.72 logMAR (20/105). CONCLUSIONS. Eyes with nonneovascular AMD demonstrating HCDs are at risk for vision loss due to macular com lications, particularly when movement and clearance of these structures appear on multimodal imaging. HCD reflectivity and dynamism may be amenable to automated recognition and analysis to assess cellular activity related to drusen end-stages

The fate and prognostic implications of hyperreflective crystalline deposits in nonneovascular age-related macular degeneration

PURPOSE. To explore patterns of disease progression in nonneovascular age-related macular degeneration (AMD) associated with hyperreflective crystalline deposits (HCDs) in the subretinal pigment epithelium–basal laminar space. METHODS. Retrospective review of medical records, multimodal imaging, and longitudinal eyetracked near-infrared reflectance (NIR) and optical coherence tomography (OCT) spanning years. NIR/OCT images were analyzed with ImageJ software to identify HCD morphology and location. Associated macular complications were reviewed from the time of HCD detection to the most recent follow-up, using NIR/OCT. RESULTS. Thirty-three eyes with HCDs from 33 patients (mean age: 72 ± 7.5 years) had 46.7 months (95% confidence limits: 33.7, 59.6) of serial eye-tracked NIR/OCT follow-up. Baseline best-corrected visual acuity (BCVA) was 0.44 logMAR (Snellen equivalent 20/55). At a mean of 11.3 months (3.1, 19.6) after HCD detection, 31/33 (93.9%) eyes had developed macular complications including de novo areas of complete retinal pigment epithelium and outer retinal atrophy (cRORA) in 21/33 (64%) eyes, enlargement of preexisting cRORA in 4/33 (12%) eyes, and incident macular neovascularization in 3/33 (9%) eyes. Movement and clearance of HCDs in 9/33 (27%) eyes was associated with enlargement of preexisting cRORA (r = 0.44, P = 0.02). BCVA at the last follow-up visit had decreased to 0.72 logMAR (20/105). CONCLUSIONS. Eyes with nonneovascular AMD demonstrating HCDs are at risk for vision loss due to macular com lications, particularly when movement and clearance of these structures appear on multimodal imaging. HCD reflectivity and dynamism may be amenable to automated recognition and analysis to assess cellular activity related to drusen end-stages

New York City COVID-19 resident physician exposure during exponential phase of pandemic

© 2020, American Society for Clinical Investigation. BACKGROUND. From March 2, 2020, to April 12, 2020, New York City (NYC) experienced exponential growth of the COVID-19 pandemic due to novel coronavirus (SARS-CoV-2). Little is known regarding how physicians have been affected. We aimed to characterize the COVID-19 impact on NYC resident physicians. METHODS. IRB-exempt and expedited cross-sectional analysis through survey to NYC residency program directors April 3-12, 2020, encompassing events from March 2, 2020, to April 12, 2020. RESULTS. From an estimated 340 residency programs around NYC, recruitment yielded 91 responses, representing 24 specialties and 2306 residents. In 45.1% of programs, at least 1 resident with confirmed COVID-19 was reported. One hundred one resident physicians were confirmed COVID-19-positive, with an additional 163 residents presumed positive for COVID-19 based on symptoms but awaiting or unable to obtain testing. Two COVID-19-positive residents were hospitalized, with 1 in intensive care. Among specialties with more than 100 residents represented, negative binomial regression indicated that infection risk differed by specialty (P = 0.039). In 80% of programs, quarantining a resident was reported. Ninety of 91 programs reported reuse or extended mask use, and 43 programs reported that personal protective equipment (PPE) was suboptimal. Sixty-five programs (74.7%) redeployed residents elsewhere to support COVID-19 efforts. CONCLUSION. Many resident physicians around NYC have been affected by COVID-19 through direct infection, quarantine, or redeployment. Lack of access to testing and concern regarding suboptimal PPE are common among residency programs. Infection risk may differ by specialty. Copyright