Interpretable Categorization of Heterogeneous Time Series Data

Understanding heterogeneous multivariate time series data is important in many applications ranging from smart homes to aviation. Learning models of heterogeneous multivariate time series that are also human-interpretable is challenging and not adequately addressed by the existing literature. We propose grammar-based decision trees (GBDTs) and an algorithm for learning them. GBDTs extend decision trees with a grammar framework. Logical expressions derived from a context-free grammar are used for branching in place of simple thresholds on attributes. The added expressivity enables support for a wide range of data types while retaining the interpretability of decision trees. In particular, when a grammar based on temporal logic is used, we show that GBDTs can be used for the interpretable classi cation of high-dimensional and heterogeneous time series data. Furthermore, we show how GBDTs can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply GBDTs to analyze the classic Australian Sign Language dataset as well as data on near mid-air collisions (NMACs). The NMAC data comes from aircraft simulations used in the development of the next-generation Airborne Collision Avoidance System (ACAS X).Comment: 9 pages, 5 figures, 2 tables, SIAM International Conference on Data Mining (SDM) 201

Improving Trust in Deep Neural Networks with Nearest Neighbors

Deep neural networks are used increasingly for perception and decision-making in UAVs. For example, they can be used to recognize objects from images and decide what actions the vehicle should take. While deep neural networks can perform very well at complex tasks, their decisions may be unintuitive to a human operator. When a human disagrees with a neural network prediction, due to the black box nature of deep neural networks, it can be unclear whether the system knows something the human does not or whether the system is malfunctioning. This uncertainty is problematic when it comes to ensuring safety. As a result, it is important to develop technologies for explaining neural network decisions for trust and safety. This paper explores a modification to the deep neural network classification layer to produce both a predicted label and an explanation to support its prediction. Specifically, at test time, we replace the final output layer of the neural network classifier by a k-nearest neighbor classifier. The nearest neighbor classifier produces 1) a predicted label through voting and 2) the nearest neighbors involved in the prediction, which represent the most similar examples from the training dataset. Because prediction and explanation are derived from the same underlying process, this approach guarantees that the explanations are always relevant to the predictions. We demonstrate the approach on a convolutional neural network for a UAV image classification task. We perform experiments using a forest trail image dataset and show empirically that the hybrid classifier can produce intuitive explanations without loss of predictive performance compared to the original neural network. We also show how the approach can be used to help identify potential issues in the network and training process

Why do people lose their friends after a stroke?

Background: It is well-known that people lose friends after a stroke; what is less well understood is why this occurs. Aims: This study explored why people lose contact with their friends, and whether there are any protective factors. It also examined how friendship loss and change is perceived by the individual. Methods and Procedures: Participants with a first stroke were recruited from one acute stroke unit in the UK. In-depth qualitative interviews took place between 8 and 15 months post stroke. Outcomes and Results: 29 participants were recruited of whom 10 had aphasia. The main reasons given for losing friends were: loss of shared activities, reduced energy levels, physical disability, aphasia, unhelpful responses of others, environmental barriers, and changing social desires. The subset of participants who experienced the most extensive loss of friends were those who described a sense that they were ‘closing in’ on themselves leading to a withdrawal from social contact and a new preference for meeting only close friends and family. Those with aphasia experienced the most hurtful negative responses from others and found it more difficult to retain their friends unless they had strong supportive friendship patterns prior to the stroke. The factors which helped to protect friendships included: having a shared history, friends who showed concern, who lived locally, where the friendship was not activity-based, and where the participant had a ‘friends-based’ social network prior to the stroke. Conclusions and Implications: Given the link between depression and loss of friends post stroke, supporting an individual in maintaining a social network is likely to be beneficial. For intervention to be effective, however, it may need to take into account not only the impact of new physical and language disabilities, but also changing social desires

Provider Perspectives on the Influence of Family on Nursing Home Resident Transfers to the Emergency Department: Crises at the End of Life.

Background. Nursing home (NH) residents often experience burdensome and unnecessary care transitions, especially towards the end of life. This paper explores provider perspectives on the role that families play in the decision to transfer NH residents to the emergency department (ED). Methods. Multiple stakeholder focus groups (n = 35 participants) were conducted with NH nurses, NH physicians, nurse practitioners, physician assistants, NH administrators, ED nurses, ED physicians, and a hospitalist. Stakeholders described experiences and challenges with NH resident transfers to the ED. Focus group interviews were recorded and transcribed verbatim. Transcripts and field notes were analyzed using a Grounded Theory approach. Findings. Providers perceive that families often play a significant role in ED transfer decisions as they frequently react to a resident change of condition as a crisis. This sense of crisis is driven by 4 main influences: insecurities with NH care; families being unprepared for end of life; absent/inadequate advance care planning; and lack of communication and agreement within families regarding goals of care. Conclusions. Suboptimal communication and lack of access to appropriate and timely palliative care support and expertise in the NH setting may contribute to frequent ED transfers

Using Game Theoretic Models to Predict Pilot Behavior in NextGen Merging and Landing Scenario

In this paper, we present an implementation of the Semi Network-Form Game framework to predict pilot behavior in a merging and landing scenario. In this scenario, two aircraft are approaching to a freeze horizon with approximately equal distance when they become aware of each other via an ADS-B communication link that will be available in NextGen airspace. Both pilots want to gain advantage over the other by entering the freeze horizon earlier and obtain the first place in landing. They re-adjust their speed accordingly. However, they cannot simply increase their speed to the maximum allowable values since they are concerned with safety, separation distance, effort, possibility of being vectored-off from landing and possibility of violating speed constraints. We present how to model these concerns and the rest of the system using semi network-from game framework. Using this framework, based on certain assumptions on pilot utility functions and on system configuration, we provide estimates of pilot behavior and overall system evolution in time. We also discuss the possible employment of this modeling tool for airspace design optimization. To support this discussion, we provide a case where we investigate the effect of increasing the merging point speed limit on the commanded speed distribution and on the percentage of vectored aircraft