It’s not about the mass

Electrocaloric cooling devices are traditionally based on sub-millimetre-thick ceramic working bodies. Using instead a flexible polymer that is one order-of-magnitude thinner yields lightweight devices that have now been stacked to pump heat across a relatively wide temperature span

Engagement-aware computing: Modelling user engagement from mobile contexts

In this paper, we examine the potential of using mobile context to model user engagement. Taking an experimental approach, we systematically explore the dynamics of user engagement with a smartphone through three different studies. Specifically, to understand the feasibility of detecting user engagement from mobile context, we first assess an EEG artifact with 10 users and observe a strong correlation between automatically detected engagement scores and user's subjective perception of engagement. Grounded on this result, we model a set of application level features derived from smartphone usage of 10 users to detect engagement of a usage session using a Random Forest classifier. Finally, we apply this model to train a variety of contextual factors acquired from smartphone usage logs of 130 users to predict user engagement using an SVM classifier with a F1-Score of 0.82. Our experimental results highlight the potential of mobile contexts in designing engagement-aware applications and provide guidance to future explorations

Unsupervised domain adaptation under label space mismatch for speech classification

Unsupervised domain adaptation using adversarial learning has shown promise in adapting speech models from a labeled source domain to an unlabeled target domain. However, prior works make a strong assumption that the label spaces of source and target domains are identical, which can be easily violated in real-world conditions. We present AMLS, an end-to-end architecture that performs Adaptation under Mismatched Label Spaces using two weighting schemes to separate shared and private classes in each domain. An evaluation on three speech adaptation tasks, namely gender, microphone, and emotion adaptation, shows that AMLS provides significant accuracy gains over baselines used in speech and vision adaptation tasks. Our contribution paves the way for applying UDA to speech models in unconstrained settings with no assumptions on the source and target label spaces

Large electrocaloric effects in single-crystal ammonium sulfate.

Electrocaloric (EC) effects are typically studied near phase transitions in ceramic and polymer materials. Here, we investigate EC effects in an inorganic salt, namely ammonium sulfate (NH4)2SO4, with an order-disorder transition whose onset occurs at 223 K on cooling. For a single crystal thinned to 50 μm, we use a Maxwell relation to find a large isothermal entropy change of 30 J K(-1) kg(-1) in response to a field change of 400 kV cm(-1) The Clausius-Clapeyron equation implies a corresponding adiabatic temperature change of 4.5 K.This article is part of the themed issue 'Taking the temperature of phase transitions in cool materials'.Royal SocietyThis is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Royal Society Publishing

Electrocaloric effects in multilayer capacitors for cooling applications

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Electrocaloric Cooling Cycles in Lead Scandium Tantalate with True Regeneration via Field Variation

There is growing interest in heat pumps based on materials that show thermal changes when phase transitions are driven by changes of electric, magnetic or stress field. Importantly, regeneration permits sinks and loads to be thermally separated by many times the changes of temperature that can arise in the materials themselves. However, performance and parameterization are compromised by net heat transfer between caloric working bodies and heat transfer fluids. Here we show that this net transfer can be avoided-resulting in true, balanced regeneration-if one varies the applied electric field while an electrocaloric (EC) working body dumps heat on traversing a passive fluid regenerator. Our EC working body is represented by bulk PbSc0.5Ta0.5O3 (PST) near its first-order ferroelectric phase transition, where we record directly measured adiabatic temperature changes of up to 2.2 K. Indirectly measured adiabatic temperature changes of similar magnitude were identified, unlike normal, from adiabatic measurements of polarization, at nearby starting temperatures, without assuming a constant heat capacity. The resulting high-resolution field-temperature-entropy maps of our material, and a small clamped companion sample, were used to construct cooling cycles that assume the use of an ideal passive regenerator in order to span $\leq$20 K. These cooling cycles possess well defined coefficients of performance that are bounded by well defined Carnot limits, resulting in large ($>$50%) well defined efficiencies that are not unduly compromised by a small field hysteresis. Our approach permits the limiting performance of any caloric material in a passive regenerator to be established, optimized and compared; provides a recipe for true regeneration in prototype cooling devices; and could be extended to balance active regeneration.Gates Cambridge, the Winton Programme for the Physics of Sustainabilit

Unsupervised domain adaptation for robust sensory systems

Despite significant advances in the performance of sensory inference models, their poor robustness to changing environmental conditions and hardware remains a major hurdle for widespread adoption. In this paper, we introduce the concept of unsupervised domain adaptation which is a technique to adapt sensory inference models to new domains only using unlabeled data from the target domain. We present two case-studies to motivate the problem and highlight some of our recent work in this space. Finally, we discuss the core challenges in this space that can trigger further ubicomp research on this topic

FlexAdapt: Flexible cycle-consistent adversarial domain adaptation

Unsupervised domain adaptation is emerging as a powerful technique to improve the generalizability of deep learning models to new image domains without using any labeled data in the target domain. In the literature, solutions which perform cross-domain feature-matching (e.g., ADDA), pixel-matching (CycleGAN), and combination of the two (e.g., CyCADA) have been proposed for unsupervised domain adaptation. Many of these approaches make a strong assumption that the source and target label spaces are the same, however in the real-world, this assumption does not hold true. In this paper, we propose a novel solution, FlexAdapt, which extends the state-of-the-art unsupervised domain adaptation approach of CyCADA to scenarios where the label spaces in source and target domains are only partially overlapped. Our solution beats a number of state-of-the-art baseline approaches by as much as 29% in some scenarios, and represent a way forward for applying domain adaptation techniques in the real world