XLearn : learning activity labels across heterogeneous datasets

Sensor-driven systems often need to map sensed data into meaningfully labelled activities to classify the phenomena being observed. A motivating and challenging example comes from human activity recognition in which smart home and other datasets are used to classify human activities to support applications such as ambient assisted living, health monitoring, and behavioural intervention. Building a robust and meaningful classifier needs annotated ground truth, labelled with what activities are actually being observed—and acquiring high-quality, detailed, continuous annotations remains a challenging, time-consuming, and error-prone task, despite considerable attention in the literature. In this article, we use knowledge-driven ensemble learning to develop a technique that can combine classifiers built from individually labelled datasets, even when the labels are sparse and heterogeneous. The technique both relieves individual users of the burden of annotation and allows activities to be learned individually and then transferred to a general classifier. We evaluate our approach using four third-party, real-world smart home datasets and show that it enhances activity recognition accuracies even when given only a very small amount of training data.PostprintPeer reviewe

Continual learning in sensor-based human activity recognition : an empirical benchmark analysis

Sensor-based human activity recognition (HAR), i.e., the ability to discover human daily activity patterns from wearable or embedded sensors, is a key enabler for many real-world applications in smart homes, personal healthcare, and urban planning. However, with an increasing number of applications being deployed, an important question arises: how can a HAR system autonomously learn new activities over a long period of time without being re-engineered from scratch? This problem is known as continual learning and has been particularly popular in the domain of computer vision, where several techniques to attack it have been developed. This paper aims to assess to what extent such continual learning techniques can be applied to the HAR domain. To this end, we propose a general framework to evaluate the performance of such techniques on various types of commonly used HAR datasets. Then, we present a comprehensive empirical analysis of their computational cost and of their effectiveness of tackling HAR specific challenges (i.e., sensor noise and labels’ scarcity). The presented results uncover useful insights on their applicability and suggest future research directions for HAR systems.PostprintPeer reviewe

ContrasGAN : unsupervised domain adaptation in Human Activity Recognition via adversarial and contrastive learning

Human Activity Recognition (HAR) makes it possible to drive applications directly from embedded and wearable sensors. Machine learning, and especially deep learning, has made significant progress in learning sensor features from raw sensing signals with high recognition accuracy. However, most techniques need to be trained on a large labelled dataset, which is often difficult to acquire. In this paper, we present ContrasGAN, an unsupervised domain adaptation technique that addresses this labelling challenge by transferring an activity model from one labelled domain to other unlabelled domains. ContrasGAN uses bi-directional generative adversarial networks for heterogeneous feature transfer and contrastive learning to capture distinctive features between classes. We evaluate ContrasGAN on three commonly-used HAR datasets under conditions of cross-body, cross-user, and cross-sensor transfer learning. Experimental results show a superior performance of ContrasGAN on all these tasks over a number of state-of-the-art techniques, with relatively low computational cost.PostprintPeer reviewe

Fault-tolerant multiqubit geometric entangling gates using photonic cat-state qubits

We propose a theoretical protocol to implement multiqubit geometric gates (i.e., the M{\o}lmer-S{\o}rensen gate) using photonic cat-state qubits. These cat-state qubits stored in high-$Q$ resonators are promising for hardware-efficient universal quantum computing. Specifically, in the limit of strong two-photon drivings, phase-flip errors of the cat-state qubits are effectively suppressed, leaving only a bit-flip error to be corrected. Because this dominant error commutes with the evolution operator, our protocol preserves the error bias, and, thus, can lower the code-capacity threshold for error correction. A geometric evolution guarantees the robustness of the protocol against stochastic noise along the evolution path. Moreover, by changing detunings of the cavity-cavity couplings at a proper time, the protocol can be robust against parameter imperfections (e.g., the total evolution time) without introducing extra noises into the system. As a result, the gate can produce multi-mode entangled cat states in a short time with high fidelities.Comment: 16 pages, 8 figure

TCAD Analysis of Leakage Current and Breakdown Voltage in Small Pitch 3D Pixel Sensors

Small-pitch 3D pixel sensors have been developed to equip the innermost layers of the ATLAS and CMS tracker upgrades at the High Luminosity LHC. They feature 50 × 50 and 25 × 100 μm2 geometries and are fabricated on p-type Si-Si Direct Wafer Bonded substrates of 150 μm active thickness with a single-sided process. Due to the short inter-electrode distance, charge trapping effects are strongly mitigated, making these sensors extremely radiation hard. Results from beam test measurements of 3D pixel modules irradiated at large fluences (1016neq/cm2) indeed demonstrated high efficiency at maximum bias voltages of the order of 150 V. However, the downscaled sensor structure also lends itself to high electric fields as the bias voltage is increased, meaning that premature electrical breakdown due to impact ionization is a concern. In this study, TCAD simulations incorporating advanced surface and bulk damage models are used to investigate the leakage current and breakdown behavior of these sensors. Simulations are compared with measured characteristics of 3D diodes irradiated with neutrons at fluences up to 1.5 × 1016neq/cm2. The dependence of the breakdown voltage on geometrical parameters (e.g., the n+ column radius and the gap between the n+ column tip and the highly doped p++ handle wafer) is also discussed for optimization purposes

Exiting fieldwork ‘with Grace’: reflections on the unintended consequences of participant observation and researcher-participant relationships

Purpose The purpose of this paper is to illustrate the methodological importance of how researchers exit fieldwork to draw attention to implications for participant and researcher well-being. Design/methodology/approach Reflecting in detail on one researcher’s final six-months exiting fieldwork at a retirement village, this paper critically examines the unintended consequences of participant observation and researcher-participant relationships. Findings The paper illustrates that difficulties to exit fieldwork can be unintended consequences of participant observation activities and developing researcher-participant relationships. The findings also discuss how fieldwork exit can impose upon participant and researcher well-being. Research limitations/implications The findings are built upon fieldwork at a retirement village where the researcher served as a volunteer. Thus, the discussion focusses on participant observation activities that are likely to lead to close researcher-participant relationships. However, this paper aims to serve as a useful resource for researchers when considering how to exit their unique fieldwork contexts “with grace”. Practical implications The paper provides practical suggestions to help marketing researchers such as ethnographers, manage fieldwork exits with participant and researcher well-being concerns in mind. Social implications The practical suggestions provided by this paper aim to enable marketing researchers to exit fieldwork contexts “with grace” through reflection and proactive management of the social impacts of their research activities. Originality/value Even though researchers acknowledge fieldwork is social and personal by nature, little research attention has been paid to the management of researcher-participant relationships and the exit stage of fieldwork. This paper discusses and addresses this blind-spot in marketing research

Reducing climate change impacts and inequality of the global food system through diet shifts

How much and what we eat and where it is produced can create huge differences in greenhouse gas emissions. Bridging food consumption with detailed household-expenditure data, this study estimates dietary emissions from 13 food categories consumed by 201 expenditure groups in 139 countries, and further models the emission mitigation potential of worldwide adoption of the EAT–Lancet planetary health diet. We find that the consumption of groups with higher expenditures generally creates larger dietary emissions due to excessive red meat and dairy intake. As countries develop, the disparities in both emission volumes and patterns among expenditure groups tend to decrease. Global dietary emissions would fall by 17% if all countries adopted the planetary health diet, primarily attributed to decreased red meat and grains, despite a substantial increase in emissions related to increased consumption of legumes and nuts. The wealthiest populations in developed and rapidly developing countries have greater potential to reduce emissions through diet shifts, while the bottom and lower-middle populations from developing countries would cause a considerable emission increase to reach the planetary health diet. Our findings highlight the opportunities and challenges to combat climate change and reduce food inequality through shifting to healthier diets