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Jacek, N., M. - C. Chiu, B. Marlin, and E. J. B. Moss, "Assessing the Limits of Program-Specific Garbage Collection Performance", Programming Language Design and Implementation, 2016. Abstract

We consider the ultimate limits of program-specific garbage collector performance for real programs. We first characterize the GC schedule optimization problem using Markov Decision Processes (MDPs). Based on this characterization, we develop a method of determining, for a given program run and heap size, an optimal schedule of collections for a non-generational collector. We further explore the limits of performance of a generational collector, where it is not feasible to search the space of schedules to prove optimality. Still, we show significant improvements with Least Squares Policy Iteration, a reinforcement learning technique for solving MDPs. We demonstrate that there is considerable promise to reduce garbage collection costs by developing program-specific collection policies.

Sadasivam, R. S., S. L. Cutrona, R. L. Kinney, B. M. Marlin, K. M. Mazor, S. C. Lemon, and T. K. Houston, "Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century", Journal of Medical Internet Research, vol. 18, 2016. AbstractWebsite

What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC.

Natarajan, A., K. S. Xu, and B. Eriksson, "Detecting Divisions of the Autonomic Nervous System Using Wearables", 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Florida, USA, 2016. natarajan_embc_16.pdf
Natarajan, A., G. Angarita, E. Gaiser, R. Malison, D. Ganesan, and B. M. Marlin, "Domain Adaptation Methods for Improving Lab-to-field Generalization of Cocaine Detection using Wearable ECG", Proceedings of the 2016 ACM international joint conference on Pervasive and ubiquitous computing (UbiComp 2016), Heidelberg, Germany, 2016. natarajan_ubicomp_2016.pdf
Adams, R., N. Saleheen, E. Thomaz, A. Parate, S. Kumar, and B. Marlin, "Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams", International Conference on Machine Learning, 2016. Abstract

The field of mobile health (mHealth) has the potential to yield new insights into health and behavior through the analysis of continuously recorded data from wearable health and activity sensors. In this paper, we present a hierarchical span-based conditional random field model for the key problem of jointly detecting discrete events in such sensor data streams and segmenting these events into high-level activity sessions. Our model includes higher-order cardinality factors and inter-event duration factors to capture domain-specific structure in the label space. We show that our model supports exact MAP inference in quadratic time via dynamic programming, which we leverage to perform learning in the structured support vector machine framework. We apply the model to the problems of smoking and eating detection using four real data sets. Our results show statistically significant improvements in segmentation performance at the p=0.005 level relative to a hierarchical pairwise CRF.

Dadkhahi, H., N. Saleheen, S. Kumar, and B. Marlin, "Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications", ICML On Device Intelligence Workshop, 2016. Abstract

The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions. However, existing work has largely focused on analyzing collected data in the off-line setting. In this paper, we propose a novel approach to learning shallow detection cascades developed explicitly for use in a real-time wearable-phone or wearable-phone-cloud systems. We apply our approach to the problem of cigarette smoking detection from a combination of wrist-worn actigraphy data and respiration chest band data using two and three stage cascades.

Nguyen, T., R. J. Adams, A. Natarajan, and B. M. Marlin, "Parsing Wireless Electrocardiogram Signals with the CRF-CFG Model", Conference on Uncertainty in Artificial Intelligence Machine Learning for Health Workshop, 2016. Abstract

Recent advances in wearable sensor technology have made it possible to simultaneously collect multiple streams of physiological and context data from individuals as they go about their daily activities in natural environments. However, extracting reliable higher-level inferences from these raw data streams remains a key data analysis challenge. In this paper, we focus on the specific case of the analysis of data from wireless electrocardiogram (ECG) sensors. We present a new robust probabilistic approach to ECG morphology extraction using conditional random field context free grammar models, which have traditionally been applied to parsing problems in natural language processing. We introduce a robust context free grammar for parsing noisy ECG data, and show significantly improved performance on the ECG morphological labeling task.

Winner, K., and D. Sheldon, "Probabilistic Inference with Generating Functions for Poisson Latent Variable Models", Advances in Neural Information Processing Systems, Barcelona, Spain, 2016.
Bernstein, G., and D. R. Sheldon, "Consistently Estimating Markov Chains with Noisy Aggregate Data.", AISTATS, Cadiz, Spain, March 2016.
Soha, R., M. Addison, G. Deepak, M. Benjamin, and G. Jeremy, "iLid: Low-power Sensing of Fatigue and Drowsiness Measures on a Computational Eyeglass", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 1, no. 2: ACM, pp. 23, 2017. Abstractubicomp17-ilid.pdf

The ability to monitor eye closures and blink patterns has long been known to enable accurate assessment of fatigue and drowsiness in individuals. Many measures of the eye are known to be correlated with fatigue including coarse-grained measures like the rate of blinks as well as fine-grained measures like the duration of blinks and the extent of eye closures. Despite a plethora of research validating these measures, we lack wearable devices that can continually and reliably monitor them in the natural environment. In this work, we present a low-power system, iLid, that can continually sense fine-grained measures such as blink duration and Percentage of Eye Closures (PERCLOS) at high frame rates of 100fps. We present a complete solution including design of the sensing, signal processing, and machine learning pipeline; implementation on a prototype computational eyeglass platform; and extensive evaluation under many conditions including illumination changes, eyeglass shifts, and mobility. Our results are very encouraging, showing that we can detect blinks, blink duration, eyelid location, and fatigue-related metrics such as PERCLOS with less than a few percent error.

Adams, R. J., and B. M. Marlin, "Learning Time Series Detection Models from Temporally Imprecise Labels", The 20th International Conference on Artificial Intelligence and Statistics, 2017. Abstractadams17a.pdf

In this paper, we consider a new low-quality label learning problem: learning time series detection models from temporally imprecise labels. In this problem, the data consist of a set of input time series, and supervision is provided by a sequence of noisy time stamps corresponding to the occurrence of positive class events. Such temporally imprecise labels commonly occur in areas like mobile health research where human annotators are tasked with labeling the occurrence of very short duration events. We propose a general learning framework for this problem that can accommodate different base classifiers and noise models. We present results on real mobile health data showing that the proposed framework significantly outperforms a number of alternatives including assuming that the label time stamps are noise-free, transforming the problem into the multiple instance learning framework, and learning on labels that were manually re-aligned.

Dadkhahi, H., and B. Marlin, "Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices", 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017. Abstractfp0911-dadkhahia.pdf

In this paper, we present a new approach to learning cascaded classifiers for use in computing environments that involve networks of heterogeneous and resource-constrained, low-power embedded compute and sensing nodes. We present a generalization of the classical linear detection cascade to the case of tree-structured cascades where different branches of the tree execute on different physical compute nodes in the network. Different nodes have access to different features, as well as access to potentially different computation and energy resources. We concentrate on the problem of jointly learning the parameters for all of the classifiers in the cascade given a fixed cascade architecture and a known set of costs required to carry out the computation at each node. To accomplish the objective of joint learning of all detectors, we propose a novel approach to combining classifier outputs during training that better matches the hard cascade setting in which the learned system will be deployed. This work is motivated by research in the area of mobile health where energy efficient real time detectors integrating information from multiple wireless on-body sensors and a smart phone are needed for real-time monitoring and the delivery of just-in-time adaptive interventions. We evaluate our framework on mobile sensor-based human activity recognition and mobile health detector learning problems.

Dadkhahi, H., M. F. Duarte, and B. M. Marlin, "Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series", IEEE Transactions on Image Processing, vol. 26, no. 11: IEEE, pp. 5435–5446, 2017. Abstract1606.08282.pdf

This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts. Given a set of noise-free training data and its embedding, the proposed framework extends the embedding for a noisy time series. This is achieved by adding a spatio-temporal compactness term to the optimization objective of the embedding. To the best of our knowledge, this is the first method for out-of-sample extension of manifold embeddings that leverages timing information available for the extension set. Experimental results demonstrate that our out-of-sample extension algorithm renders a more robust and accurate embedding of sequentially ordered image data in the presence of various noise and artifacts when compared with other timing-aware embeddings. Additionally, we show that an out-of-sample extension framework based on the proposed algorithm outperforms the state of the art in eye-gaze estimation.