Prototyping Across the Disciplines

This article pursues the idea that within interdisciplinary teams in which researchers might find themselves participating, there are very different notions of research outcomes, as well as languages in which they are expressed. We explore the notion of the software prototype within the discussion of making and building in digital humanities. The backdrop for our discussion is a collaboration between project team members from computer science and literature that resulted in a tool named TopoText that was built to geocode locations within an unstructured text and to perform some basic Natural Language Processing (NLP) tasks about the context of those locations. In the interest of collaborating more effectively with increasingly larger and more multidisciplinary research communities, we move outward from that specific collaboration to explore one of the ways that such research is characterized in the domain of software engineering—the ISO/IEC 25010:2011 standard. Although not a perfect fit with discourses of value in the humanities, it provides a possible starting point for forging shared vocabularies within the research collaboratory. In particular, we focus on a subset of characteristics outlined by the standard and attempt to translate them into terms generative of further discussion in the digital humanities community

TopoText: Interactive Digital Mapping of Literary Text

We demonstrate TopoText, an interactive tool for digital mapping of literary text. TopoText takes as input a literary piece of text such as a novel or a biography article and automatically extracts all place names in the text. The identified places are then geoparsed and displayed on an interactive map. TopoText calculates the number of times a place was mentioned in the text, which is then reflected on the map allowing the end-user to grasp the importance of the different places within the text. It also displays the most frequent words mentioned within a specified proximity of a place name in context or across the entire text. This can also be faceted according to part of speech tags. Finally, TopoText keeps the human in the loop by allowing the end-user to disambiguate places and to provide specific place annotations. All extracted information such as geolocations, place frequencies, as well as all user-provided annotations can be automatically exported as a CSV file that can be imported later by the same user or other users

An optimized parallel implementation of non-iteratively trained recurrent neural networks

Recurrent neural networks (RNN) have been successfully applied to various sequential decision-making tasks, natural language processing applications, and time-series predictions. Such networks are usually trained through back-propagation through time (BPTT) which is prohibitively expensive, especially when the length of the time dependencies and the number of hidden neurons increase. To reduce the training time, extreme learning machines (ELMs) have been recently applied to RNN training, reaching a 99% speedup on some applications. Due to its non-iterative nature, ELM training, when parallelized, has the potential to reach higher speedups than BPTT. In this work, we present Opt-PR-ELM, an optimized parallel RNN training algorithm based on ELM that takes advantage of the GPU shared memory and of parallel QR factorization algorithms to efficiently reach optimal solutions. The theoretical analysis of the proposed algorithm is presented on six RNN architectures, including LSTM and GRU, and its performance is empirically tested on ten time-series prediction applications. Opt- PR-ELM is shown to reach up to 461 times speedup over its sequential counterpart and to require up to 20x less time to train than parallel BPTT. Such high speedups over new generation CPUs are extremely crucial in real-time applications and IoT environments

BAR — A Reinforcement Learning Agent for Bounding-Box Automated Refinement

Research has shown that deep neural networks are able to help and assist human workers throughout the industrial sector via different computer vision applications. However, such data-driven learning approaches require a very large number of labeled training images in order to generalize well and achieve high accuracies that meet industry standards. Gathering and labeling large amounts of images is both expensive and time consuming, specifically for industrial use-cases. In this work, we introduce BAR (Bounding-box Automated Refinement), a reinforcement learning agent that learns to correct inaccurate bounding-boxes that are weakly generated by certain detection methods, or wrongly annotated by a human, using either an offline training method with Deep Reinforcement Learning (BAR-DRL), or an online one using Contextual Bandits (BAR-CB). Our agent limits the human intervention to correcting or verifying a subset of bounding-boxes instead of re-drawing new ones. Results on a car industry-related dataset and on the PASCAL VOC dataset show a consistent increase of up to 0.28 in the Intersection-over-Union of bounding-boxes with their desired ground-truths, while saving 30%-82% of human intervention time in either correcting or re-drawing inaccurate proposals

A progressive and cross-domain deep transfer learning framework for wrist fracture detection

There has been an amplified focus on and benefit from the adoption of artificial intelligence (AI) in medical imaging applications. However, deep learning approaches involve training with massive amounts of annotated data in order to guarantee generalization and achieve high accuracies. Gathering and annotating large sets of training images require expertise which is both expensive and time-consuming, especially in the medical field. Furthermore, in health care systems where mistakes can have catastrophic consequences, there is a general mistrust in the black-box aspect of AI models. In this work, we focus on improving the performance of medical imaging applications when limited data is available while focusing on the interpretability aspect of the proposed AI model. This is achieved by employing a novel transfer learning framework, progressive transfer learning, an automated annotation technique and a correlation analysis experiment on the learned representations. Progressive transfer learning helps jump-start the training of deep neural networks while improving the performance by gradually transferring knowledge from two source tasks into the target task. It is empirically tested on the wrist fracture detection application by first training a general radiology network RadiNet and using its weights to initialize RadiNetwrist, that is trained on wrist images to detect fractures. Experiments show that RadiNetwrist achieves an accuracy of 87% and an AUC ROC of 94% as opposed to 83% and 92% when it is pre-trained on the ImageNet dataset. This improvement in performance is investigated within an explainable AI framework. More concretely, the learned deep representations of RadiNetwrist are compared to those learned by the baseline model by conducting a correlation analysis experiment. The results show that, when transfer learning is gradually applied, some features are learned earlier in the network. Moreover, the deep layers in the progressive transfer learning framework are shown to encode features that are not encountered when traditional transfer learning techniques are applied. In addition to the empirical results, a clinical study is conducted and the performance of RadiNetwrist is compared to that of an expert radiologist. We found that RadiNetwrist exhibited similar performance to that of radiologists with more than 20 years of experience. This motivates follow-up research to train on more data to feasibly surpass radiologists’ performance, and investigate the interpretability of AI models in the healthcare domain where the decision-making process needs to be credible and transparent

Safety and Outcome of Revascularization Treatment in Patients With Acute Ischemic Stroke and COVID-19: The Global COVID-19 Stroke Registry.

BACKGROUND AND OBJECTIVES COVID-19 related inflammation, endothelial dysfunction and coagulopathy may increase the bleeding risk and lower efficacy of revascularization treatments in patients with acute ischemic stroke. We aimed to evaluate the safety and outcomes of revascularization treatments in patients with acute ischemic stroke and COVID-19. METHODS Retrospective multicenter cohort study of consecutive patients with acute ischemic stroke receiving intravenous thrombolysis (IVT) and/or endovascular treatment (EVT) between March 2020 and June 2021, tested for SARS-CoV-2 infection. With a doubly-robust model combining propensity score weighting and multivariate regression, we studied the association of COVID-19 with intracranial bleeding complications and clinical outcomes. Subgroup analyses were performed according to treatment groups (IVT-only and EVT). RESULTS Of a total of 15128 included patients from 105 centers, 853 (5.6%) were diagnosed with COVID-19. 5848 (38.7%) patients received IVT-only, and 9280 (61.3%) EVT (with or without IVT). Patients with COVID-19 had a higher rate of symptomatic intracerebral hemorrhage (SICH) (adjusted odds ratio [OR] 1.53; 95% CI 1.16-2.01), symptomatic subarachnoid hemorrhage (SSAH) (OR 1.80; 95% CI 1.20-2.69), SICH and/or SSAH combined (OR 1.56; 95% CI 1.23-1.99), 24-hour (OR 2.47; 95% CI 1.58-3.86) and 3-month mortality (OR 1.88; 95% CI 1.52-2.33).COVID-19 patients also had an unfavorable shift in the distribution of the modified Rankin score at 3 months (OR 1.42; 95% CI 1.26-1.60). DISCUSSION Patients with acute ischemic stroke and COVID-19 showed higher rates of intracranial bleeding complications and worse clinical outcomes after revascularization treatments than contemporaneous non-COVID-19 treated patients. Current available data does not allow direct conclusions to be drawn on the effectiveness of revascularization treatments in COVID-19 patients, or to establish different treatment recommendations in this subgroup of patients with ischemic stroke. Our findings can be taken into consideration for treatment decisions, patient monitoring and establishing prognosis

Safety and Outcome of Revascularization Treatment in Patients With Acute Ischemic Stroke and COVID-19: The Global COVID-19 Stroke Registry

BACKGROUND AND OBJECTIVES: COVID-19 related inflammation, endothelial dysfunction and coagulopathy may increase the bleeding risk and lower efficacy of revascularization treatments in patients with acute ischemic stroke. We aimed to evaluate the safety and outcomes of revascularization treatments in patients with acute ischemic stroke and COVID-19. METHODS: Retrospective multicenter cohort study of consecutive patients with acute ischemic stroke receiving intravenous thrombolysis (IVT) and/or endovascular treatment (EVT) between March 2020 and June 2021, tested for SARS-CoV-2 infection. With a doubly-robust model combining propensity score weighting and multivariate regression, we studied the association of COVID-19 with intracranial bleeding complications and clinical outcomes. Subgroup analyses were performed according to treatment groups (IVT-only and EVT). RESULTS: Of a total of 15128 included patients from 105 centers, 853 (5.6%) were diagnosed with COVID-19. 5848 (38.7%) patients received IVT-only, and 9280 (61.3%) EVT (with or without IVT). Patients with COVID-19 had a higher rate of symptomatic intracerebral hemorrhage (SICH) (adjusted odds ratio [OR] 1.53; 95% CI 1.16-2.01), symptomatic subarachnoid hemorrhage (SSAH) (OR 1.80; 95% CI 1.20-2.69), SICH and/or SSAH combined (OR 1.56; 95% CI 1.23-1.99), 24-hour (OR 2.47; 95% CI 1.58-3.86) and 3-month mortality (OR 1.88; 95% CI 1.52-2.33).COVID-19 patients also had an unfavorable shift in the distribution of the modified Rankin score at 3 months (OR 1.42; 95% CI 1.26-1.60). DISCUSSION: Patients with acute ischemic stroke and COVID-19 showed higher rates of intracranial bleeding complications and worse clinical outcomes after revascularization treatments than contemporaneous non-COVID-19 treated patients. Current available data does not allow direct conclusions to be drawn on the effectiveness of revascularization treatments in COVID-19 patients, or to establish different treatment recommendations in this subgroup of patients with ischemic stroke. Our findings can be taken into consideration for treatment decisions, patient monitoring and establishing prognosis