Sensitive and Makeable Computational Materials for the Creation of Smart Everyday Objects

The vision of computational materials is to create smart everyday objects using the materi- als that have sensing and computational capabilities embedded into them. However, today’s development of computational materials is limited because its interfaces (i.e. sensors) are unable to support wide ranges of human interactions , and withstand the fabrication meth- ods of everyday objects (e.g. cutting and assembling). These barriers hinder citizens from creating smart every day objects using computational materials on a large scale. To overcome the barriers, this dissertation presents the approaches to develop compu- tational materials to be 1) sensitive to a wide variety of user interactions, including explicit interactions (e.g. user inputs) and implicit interactions (e.g. user contexts), and 2) makeable against a wide range of fabrication operations, such cutting and assembling. I exemplify the approaches through five research projects on two common materials, textile and wood. For each project, I explore how a material interface can be made to sense user inputs or activities, and how it can be optimized to balance sensitivity and fabrication complexity. I discuss the sensing algorithms and machine learning model to interpret the sensor data as high-level abstraction and interaction. I show the practical applications of developed computational materials. I demonstrate the evaluation study to validate their performance and robustness. In the end of this dissertation, I summarize the contributions of my thesis and discuss future directions for the vision of computational materials

Activity Recognition and Prediction in Real Homes

In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its mean time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low-resolution depth video data from seven apartments, and classify four activities - no movement, standing up, sitting down, and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.Comment: 12 pages, Symposium of the Norwegian AI Society NAIS 201

Solving kk-means on High-dimensional Big Data

In recent years, there have been major efforts to develop data stream algorithms that process inputs in one pass over the data with little memory requirement. For the $k$-means problem, this has led to the development of several $(1+\varepsilon)$-approximations (under the assumption that $k$ is a constant), but also to the design of algorithms that are extremely fast in practice and compute solutions of high accuracy. However, when not only the length of the stream is high but also the dimensionality of the input points, then current methods reach their limits. We propose two algorithms, piecy and piecy-mr that are based on the recently developed data stream algorithm BICO that can process high dimensional data in one pass and output a solution of high quality. While piecy is suited for high dimensional data with a medium number of points, piecy-mr is meant for high dimensional data that comes in a very long stream. We provide an extensive experimental study to evaluate piecy and piecy-mr that shows the strength of the new algorithms.Comment: 23 pages, 9 figures, published at the 14th International Symposium on Experimental Algorithms - SEA 201

Sorafenib dose escalation is not uniformly associated with blood pressure elevations in normotensive patients with advanced malignancies.

Hypertension after treatment with vascular endothelial growth factor (VEGF) receptor inhibitors is associated with superior treatment outcomes for advanced cancer patients. To determine whether increased sorafenib doses cause incremental increases in blood pressure (BP), we measured 12-h ambulatory BP in 41 normotensive advanced solid tumor patients in a randomized dose-escalation study. After 7 days' treatment (400 mg b.i.d.), mean diastolic BP (DBP) increased in both study groups. After dose escalation, group A (400 mg t.i.d.) had marginally significant further increase in 12-h mean DBP (P = 0.053), but group B (600 mg b.i.d.) did not achieve statistically significant increases (P = 0.25). Within groups, individuals varied in BP response to sorafenib dose escalation, but these differences did not correlate with changes in steady-state plasma sorafenib concentrations. These findings in normotensive patients suggest BP is a complex pharmacodynamic biomarker of VEGF inhibition. Patients have intrinsic differences in sensitivity to sorafenib's BP-elevating effects

The absolute position of a resonance peak

It is common practice in scattering theory to correlate between the position of a resonance peak in the cross section and the real part of a complex energy of a pole of the scattering amplitude. In this work we show that the resonance peak position appears at the absolute value of the pole's complex energy rather than its real part. We further demonstrate that a local theory of resonances can still be used even in cases previously thought impossible

Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems

Simulating complex physical systems often involves solving partial differential equations (PDEs) with some closures due to the presence of multi-scale physics that cannot be fully resolved. Although the advancement of high performance computing has made resolving small-scale physics possible, such simulations are still very expensive. Therefore, reliable and accurate closure models for the unresolved physics remains an important requirement for many computational physics problems, e.g., turbulence simulation. Recently, several researchers have adopted generative adversarial networks (GANs), a novel paradigm of training machine learning models, to generate solutions of PDEs-governed complex systems without having to numerically solve these PDEs. However, GANs are known to be difficult in training and likely to converge to local minima, where the generated samples do not capture the true statistics of the training data. In this work, we present a statistical constrained generative adversarial network by enforcing constraints of covariance from the training data, which results in an improved machine-learning-based emulator to capture the statistics of the training data generated by solving fully resolved PDEs. We show that such a statistical regularization leads to better performance compared to standard GANs, measured by (1) the constrained model's ability to more faithfully emulate certain physical properties of the system and (2) the significantly reduced (by up to 80%) training time to reach the solution. We exemplify this approach on the Rayleigh-Bénard convection, a turbulent flow system that is an idealized model of the Earth's atmosphere. With the growth of high-fidelity simulation databases of physical systems, this work suggests great potential for being an alternative to the explicit modeling of closures or parameterizations for unresolved physics, which are known to be a major source of uncertainty in simulating multi-scale physical systems, e.g., turbulence or Earth's climate

The example of CaPSURE: lessons learned from a national disease registry

IntroductionAlthough randomized controlled trials (RCTs) remain the gold standard for determining evidence-based clinical practices, large disease registries that enroll large numbers of patients have become paramount as a relatively cost-effective additional tool.MethodsWe highlight the advantages of disease registries focusing on the example of prostate cancer and the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE™) registry.ResultsCaPSURE collects approximately 1,000 clinical and patient-reported variables, in over 13,000 men that are enrolled. Thus far, CaPSURE has yielded over 130 peer-reviewed publications, with several others in press, in key areas of risk migration, practice patterns, outcome prediction, and quality of life outcomes.ConclusionsDisease registries, like CaPSURE complement RCTs and CaPSURE, have provided a means to better understand many aspects of prostate cancer epidemiology, practice patterns, oncologic and HRQOL outcomes, and costs of care across populations. Specialized observational disease registries such as CaPSURE provide insight and have broad implications for disease management and policy

Stability analysis and quasinormal modes of Reissner Nordstr{\o}m Space-time via Lyapunov exponent

We explicitly derive the proper time $(\tau)$ principal Lyapunov exponent ($\lambda_{p}$) and coordinate time ($t$) principal Lyapunov exponent ($\lambda_{c}$) for Reissner Nordstr{\o}m (RN) black hole (BH) . We also compute their ratio. For RN space-time, it is shown that the ratio is $\frac{\lambda_{p}}{\lambda_{c}}=\frac{r_{0}}{\sqrt{r_{0}^2-3Mr_{0}+2Q^2}}$ for time-like circular geodesics and for Schwarzschild BH it is $\frac{\lambda_{p}}{\lambda_{c}}=\frac{\sqrt{r_{0}}}{\sqrt{r_{0}-3M}}$. We further show that their ratio $\frac{\lambda_{p}}{\lambda_{c}}$ may vary from orbit to orbit. For instance, Schwarzschild BH at innermost stable circular orbit(ISCO), the ratio is $\frac{\lambda_{p}}{\lambda_{c}}\mid_{r_{ISCO}=6M}=\sqrt{2}$ and at marginally bound circular orbit (MBCO) the ratio is calculated to be $\frac{\lambda_{p}}{\lambda_{c}}\mid_{r_{mb}=4M}=2$. Similarly, for extremal RN BH the ratio at ISCO is $\frac{\lambda_{p}}{\lambda_{c}}\mid_{r_{ISCO}=4M}=\frac{2\sqrt{2}}{\sqrt{3}}$. We also further analyse the geodesic stability via this exponent. By evaluating the Lyapunov exponent, it is shown that in the eikonal limit , the real and imaginary parts of the quasi-normal modes of RN BH is given by the frequency and instability time scale of the unstable null circular geodesics.Comment: Accepted in Pramana, 07/09/201