This paper paper develops a theory-based, explainable deep learning convolutional neural network (CNN) classifier to predict the time-varying emotional response to music. We design novel CNN filters that leverage the frequency harmonics structure from acoustic physics known to impact the perception of musical features. Our theory-based model is more parsimonious, but provides comparable predictive performance to atheoretical deep learning models, while performing better than models using handcrafted features. Our model can be complemented with handcrafted features, but the performance improvement is marginal. Importantly, the harmonics-based structure placed on the CNN filters provides better explainability for how the model predicts emotional response (valence and arousal), because emotion is closely related to consonance--a perceptual feature defined by the alignment of harmonics. Finally, we illustrate the utility of our model with an application involving digital advertising. Motivated by YouTube mid-roll ads, we conduct a lab experiment in which we exogenously insert ads at different times within videos. We find that ads placed in emotionally similar contexts increase ad engagement (lower skip rates, higher brand recall rates). Ad insertion based on emotional similarity metrics predicted by our theory-based, explainable model produces comparable or better engagement relative to atheoretical models.

Deep learning (DL) models have enjoyed tremendous success across application domains within the broader umbrella of artificial intelligence (AI) technologies. However, their "black-box" nature, coupled with their extensive use across application sectors--including safety-critical and risk-sensitive ones such as healthcare, finance, aerospace, law enforcement, and governance--has elicited an increasing need for explainability, interpretability, and transparency of decision-making in these models.11,14,18,24 With the recent progression of legal and policy frameworks that mandate explaining decisions made by AI-driven systems (for example, the European Union's GDPR Article 15(1)(h) and the Algorithmic Accountability Act of 2019 in the U.S.), explainability has become a cornerstone of responsible AI use and deployment. In the Indian context, NITI Aayog recently released a two-part strategy document on envisioning and operationalizing Responsible AI in India,15,16 which puts significant emphasis on the explainability and transparency of AI models. Explainability of DL models lies at the human-machine interface, and different users may expect different explanations in different contexts.

Deep neural networks (DNNs) are an indispensable machine learning tool despite the difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real-world domains, from legislation and law enforcement to healthcare, such diagnosis is essential to ensure that DNN decisions are driven by aspects appropriate in the context of its use. The development of methods and studies enabling the explanation of a DNN's decisions has thus blossomed into an active and broad area of research. The field's complexity is exacerbated by competing definitions of what it means "to explain" the actions of a DNN and to evaluate an approach's "ability to explain". This article offers a field guide to explore the space of explainable deep learning for those in the AI/ML field who are uninitiated. The field guide: i) Introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning, ii) discusses the evaluations for model explanations, iii) places explainability in the context of other related deep learning research areas, and iv) discusses user-oriented explanation design and future directions. We hope the guide is seen as a starting point for those embarking on this research field.

The Covid-19 crisis has placed the spotlight on the healthcare systems around the world. It placed an additional strain on systems that in many cases were already under stress to meet demand and led to a growth in digital medicine. A video from the BBC observers that Covid-19 brings remote medicine revolution to the UK "Apps which allow doctors to connect with patients remotely have been available for a while, but the coronavirus pandemic has seen doctors finding new ways to consult with critical patient care, including reviewing scans and X-rays from home." McKinsey in an article relating to the US healthcare situation and entitled "Preparing for the next normal now: How health systems can adopt a growth transformation in the COVID-19 world" state that "Covid-19 unprecedented impact on health, economies, and daily life has created a humanitarian crisis. Health systems have been at the epicenter of the fight against COVID-19, and have had to balance the need to alleviate suffering and save lives with substantial financial pressures."

The Covid-19 tragedy and crisis has placed the spotlight on the healthcare systems around the world and placed an additional strain on systems that in many cases were already under stress to meet demand and led to a growth in digital medicine. A video from the BBC observers that Covid-19 brings remote medicine revolution to the UK "Apps which allow doctors to connect with patients remotely have been available for a while, but the coronavirus pandemic has seen doctors finding new ways to consult with critical patient care, including reviewing scans and X-rays from home." McKinsey in an article relating to the US healthcare situation and entitled "Preparing for the next normal now: How health systems can adopt a growth transformation in the COVID-19 world" state that "Covid-19 unprecedented impact on health, economies, and daily life has created a humanitarian crisis. Health systems have been at the epicenter of the fight against COVID-19, and have had to balance the need to alleviate suffering and save lives with substantial financial pressures." "Health systems' income statements are likely to see negative pressure as a result of the COVID-19 crisis. While health systems have ramped up capacity to handle COVID-19 cases and incurred additional costs to procure personal protective equipment and operationalize surge capacity plans, they also have had declines of up to 70 percent in surgical volume and 60 percent in emergency department traffic. In a recent McKinsey survey of health system CFOs, more than 90 percent of respondents reported that COVID-19 will have a negative financial impact, even after accounting for federal and state funding."

Here are the most tweeted papers that were uploaded onto arXiv during April 2020. Results are powered by Arxiv Sanity Preserver. Abstract: Manga is a world popular comic form originated in Japan, which typically employs black-and-white stroke lines and geometric exaggeration to describe humans' appearances, poses, and actions. In this paper, we propose MangaGAN, the first method based on Generative Adversarial Network (GAN) for unpaired photo-to-manga translation. Inspired by how experienced manga artists draw manga, MangaGAN generates the geometric features of manga face by a designed GAN model and delicately translates each facial region into the manga domain by a tailored multi-GANs architecture.

Deep neural network (DNN) is an indispensable machine learning tool for achieving human-level performance on many learning tasks. Yet, due to its black-box nature, it is inherently difficult to understand which aspects of the input data drive the decisions of the network. There are various real-world scenarios in which humans need to make actionable decisions based on the output DNNs. Such decision support systems can be found in critical domains, such as legislation, law enforcement, etc. It is important that the humans making high-level decisions can be sure that the DNN decisions are driven by combinations of data features that are appropriate in the context of the deployment of the decision support system and that the decisions made are legally or ethically defensible.

Deep neural network (DNN) is an indispensable machine learning tool for achieving human-level performance on many learning tasks. Yet, due to its black-box nature, it is inherently difficult to understand which aspects of the input data drive the decisions of the network. There are various real-world scenarios in which humans need to make actionable decisions based on the output DNNs. Such decision support systems can be found in critical domains, such as legislation, law enforcement, etc. It is important that the humans making high-level decisions can be sure that the DNN decisions are driven by combinations of data features that are appropriate in the context of the deployment of the decision support system and that the decisions made are legally or ethically defensible. Due to the incredible pace at which DNN technology is being developed, the development of new methods and studies on explaining the decision-making process of DNNs has blossomed into an active research field. A practitioner beginning to study explainable deep learning may be intimidated by the plethora of orthogonal directions the field is taking. This complexity is further exacerbated by the general confusion that exists in defining what it means to be able to explain the actions of a deep learning system and to evaluate a system's "ability to explain". To alleviate this problem, this article offers a "field guide" to deep learning explainability for those uninitiated in the field. The field guide: i) Discusses the traits of a deep learning system that researchers enhance in explainability research, ii) places explainability in the context of other related deep learning research areas, and iii) introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning. The guide is designed as an easy-to-digest starting point for those just embarking in the field.

The popularity of Deep Learning for real-world applications is ever-growing. With the introduction of high performance hardware, applications are no longer limited to image recognition. With the introduction of more complex problems comes more and more complex solutions, and the increasing need for explainable AI. Deep Neural Networks for Video tasks are amongst the most complex models, with at least twice the parameters of their Image counterparts. However, explanations for these models are often ill-adapted to the video domain. The current work in explainability for video models is still overshadowed by Image techniques, while Video Deep Learning itself is quickly gaining on methods for still images. This paper seeks to highlight the need for explainability methods designed with video deep learning models, and by association spatio-temporal input in mind, by first illustrating the cutting edge for video deep learning, and then noting the scarcity of research into explanations for these methods.

Health Care systems around the world are facing a fundamental financial crisis. Moreover, there is the issue of misdiagnosis or time taken to correctly diagnose due to information overload across the system. In part these failures are due to over worked medical staff and in part due to handling Big Data with inadequate information management systems. There is a real cost to both misdiagnosis and delays for correct diagnosis. These costs are both economic and also personal as people have to deal with the consequences of shattered lives that may result.