Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices

Federated Learning enables training of a general model through edge devices without sending raw data to the cloud. Hence, this approach is attractive for digital health applications, where data is sourced through edge devices and users care about privacy. Here, we report on the feasibility to train deep neural networks on the Raspberry Pi4s as edge devices. A CNN, a LSTM and a MLP were successfully trained on the MNIST data-set. Further, federated learning is demonstrated experimentally on IID and non-IID samples in a parametric study, to benchmark the model convergence. The weight updates from the workers are shared with the cloud to train the general model through federated learning. With the CNN and the non-IID samples a test-accuracy of up to 85% could be achieved within a training time of 2 minutes, while exchanging less than $10$ MB data per device. In addition, we discuss federated learning from an use-case standpoint, elaborating on privacy risks and labeling requirements for the application of emotion detection from sound. Based on the experimental findings, we discuss possible research directions to improve model and system performance. Finally, we provide best practices for a practitioner, considering the implementation of federated learning.Comment: Accepted in ACM AIChallengeIoT 2019, New York, US

Bevacizumab-induced tumor calcifications can be elicited in glioblastoma microspheroid culture and represent massive calcium accumulation death (MCAD) of tumor endothelial cells

Bähr and colleagues reported that 22 of 36 glioblastoma patients treated with bevacizumab showed tumor calcifications on 8 week post therapy follow up with MRI. Early tumor calcification strongly predicted for response, time to progression, and overall survival. The authors didn’t understand the mechanism, but speculated that it was vascular in nature. At the 13th International Anti-Angiogenic Symposium (2011), we presented our discovery of the phenomenon of massive calcium accumulation death, wherein MCAD occurred in endothelial cells (tumor, circulating, and HUVEC), in response to VEGF depletion by bevacizumab and other putative anti-angiogenic agents, but not in response to non-specific cytotoxins. In subsequent work, we have documented marked MCAD to occur in primary microcluster cultures from 6 fresh human glioblastoma biopsies, following 96 hours of VEGF depletion in vitro by bevacizumab. The presence and degree of MCAD is strikingly dependent on the type of serum in the culture medium (RPMI-1640 + 25% serum) -- typically most striking in (very low VEGF) fetal calf serum, but inhibited (often) or enhanced (rarely) by 25% human serum from different patients or normal donors containing variable quantities of VEGF. There was not a linear relationship between VEGF concentration and MCAD inhibition (or enhancement), suggesting that other pro-angiogenic (or anti-angiogenic) serum factors may play a role. In epithelial metastatic tumors, circulating peripheral blood endothelial cells may be easily tested, using our methods, and the serum inhibition (or, rarely, enhancement) is faithfully reproduced on circulating endothelial cells, in comparison with the tumor cluster-associated endothelial cells. We propose MCAD as the mechanism of glioblastoma calcification following bevacizumab and further propose that testing tumor microclusters and/or circulating endothelial cells, in the presence of autologous serum, could be a useful predictive biomarker and research tool

Dispersal and mixing of stormwater run-off plumes in the Port of Tauranga, New Zealand

Buoyancy differences between two water bodies can often dominate flows such as stormwater discharge or river plumes in the coastal environment. The buoyancy difference usually arises due to differences in salinity, temperature and suspended solids. These flows form plumes or ‘gravity currents’, which can also transport pollutants and nutrients around in the receiving water body. The plume consists of a bulbous head, a mixing region on the tail, and billows behind the head. The form of the head and the plume water properties dictate what kinds of instabilities develop, which in turn influence the degree and manner of mixing that occurs. Additionally, the mixing depends on the local hydrodynamics of the receiving water body. I report observations of the dynamics of a stormwater run-off plume in a strongly tidal estuary, with particular emphasis on investigation of dispersion and dilution processes. The field site for my thesis research is the barrier-enclosed basin of Tauranga Harbour adjacent to the Port of Tauranga wharf in Mount Maunganui. The Port of Tauranga is the largest timber export port in New Zealand. The area has about 20 storm water runoff pipes that discharge into the main tidal channel of the estuary. The log handling produces bark leachates and resin acids, which get discharged during and after rain events. The leachate is responsible for a serious discolouration of the water. Several surveys were undertaken during rain events to measure plume characteristics and these are compared with a similar undertaken during dry conditions. Based on these measurements and visual observations, the plume was estimated to disperse within around four hours as the freshwater was dispersed into a relatively strong tidal flow (maximum speeds of 0.7 m/s). Acoustic Doppler Current Profiler data and conductivity-temperature-depth data indicated that the maximum across-channel extent of the plume was around 120 m and the maximum along channel extent was around 200 m (for the conditions observed). The plume stability decreases with distance from the source. The plume can be classified as a free buoyant jet or upstream intruding plume. This study will provide inputs into the toxicity assessment of the storm runoff, which will be investigated in a separate project using caged arrays of filter-feeding bivalves to determine the cumulative effects of resin acids and leachates on mussels

AB2CD: AI for Building Climate Damage Classification and Detection

We explore the implementation of deep learning techniques for precise building damage assessment in the context of natural hazards, utilizing remote sensing data. The xBD dataset, comprising diverse disaster events from across the globe, serves as the primary focus, facilitating the evaluation of deep learning models. We tackle the challenges of generalization to novel disasters and regions while accounting for the influence of low-quality and noisy labels inherent in natural hazard data. Furthermore, our investigation quantitatively establishes that the minimum satellite imagery resolution essential for effective building damage detection is 3 meters and below 1 meter for classification using symmetric and asymmetric resolution perturbation analyses. To achieve robust and accurate evaluations of building damage detection and classification, we evaluated different deep learning models with residual, squeeze and excitation, and dual path network backbones, as well as ensemble techniques. Overall, the U-Net Siamese network ensemble with F-1 score of 0.812 performed the best against the xView2 challenge benchmark. Additionally, we evaluate a Universal model trained on all hazards against a flood expert model and investigate generalization gaps across events, and out of distribution from field data in the Ahr Valley. Our research findings showcase the potential and limitations of advanced AI solutions in enhancing the impact assessment of climate change-induced extreme weather events, such as floods and hurricanes. These insights have implications for disaster impact assessment in the face of escalating climate challenges.Comment: 9 pages, 4 figure

Experimental investigation into vortex structure and pressure drop across microcavities in 3D integrated electronics

Hydrodynamics in microcavities with cylindrical micropin fin arrays simulating a single layer of a water-cooled electronic chip stack is investigated experimentally. Both inline and staggered pin arrangements are investigated using pressure drop and microparticle image velocimetry (μPIV) measurements. The pressure drop across the cavity shows a flow transition at pin diameter-based Reynolds numbers (Re d) ~200. Instantaneous μPIV, performed using a pH-controlled high seeding density of tracer microspheres, helps visualize vortex structure unreported till date in microscale geometries. The post-transition flow field shows vortex shedding and flow impingement onto the pins explaining the pressure drop increase. The flow fluctuations start at the chip outlet and shift upstream with increasing Re d. No fluctuations are observed for a cavity with pin height-to-diameter ratio h/d=1 up to Re d ~330; however, its pressure drop was higher than for a cavity with h/d=2 due to pronounced influence of cavity wall

Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation

Climate change results in an increased probability of extreme weather events that put societies and businesses at risk on a global scale. Therefore, near real-time mapping of natural hazards is an emerging priority for the support of natural disaster relief, risk management, and informing governmental policy decisions. Recent methods to achieve near real-time mapping increasingly leverage deep learning (DL). However, DL-based approaches are designed for one specific task in a single geographic region based on specific frequency bands of satellite data. Therefore, DL models used to map specific natural hazards struggle with their generalization to other types of natural hazards in unseen regions. In this work, we propose a methodology to significantly improve the generalizability of DL natural hazards mappers based on pre-training on a suitable pre-task. Without access to any data from the target domain, we demonstrate this improved generalizability across four U-Net architectures for the segmentation of unseen natural hazards. Importantly, our method is invariant to geographic differences and differences in the type of frequency bands of satellite data. By leveraging characteristics of unlabeled images from the target domain that are publicly available, our approach is able to further improve the generalization behavior without fine-tuning. Thereby, our approach supports the development of foundation models for earth monitoring with the objective of directly segmenting unseen natural hazards across novel geographic regions given different sources of satellite imagery.Comment: Accepted at IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023

Automatisierte Modellierung der mikrobiologischen Sicherheit von Rezepturen

Die mikrobiologische Sicherheit steht bei der Entwicklung neuer Produkte im Vordergrund. Neue datengetriebene Berechnungsmodelle ermöglichen es, dies bereits während des Designprozesses zu gewährleisten. Das vorgestellte Praxisbeispiel basiert auf der von Co-Autor Marco Brunschwiler erstellten Bachelor-Diplomarbeit

Multisensory Home-Monitoring in Individuals With Stable Chronic Obstructive Pulmonary Disease and Asthma: Usability Study of the CAir-Desk

Background: Research integrating multisensory home-monitoring in respiratory disease is scarce. Therefore, we created a novel multisensory home-monitoring device tailored for long-term respiratory disease management (named the CAir-Desk). We hypothesize that recent technological accomplishments can be integrated into a multisensory participant-driven platform. We also believe that this platform could improve chronic disease management and be accessible to large groups at an acceptable cost. Objective: This study aimed to report on user adherence and acceptance as well as system functionality of the CAir-Desk in a sample of participants with stable chronic obstructive pulmonary disease (COPD) or asthma. Methods: We conducted an observational usability study. Participants took part in 4 weeks of home-monitoring with the CAir-Desk. The CAir-Desk recorded data from all participants on symptom burden, physical activity, spirometry, and environmental air quality; data on sputum production, and nocturnal cough were only recorded for participants who experienced symptoms. After the study period, participants reported on their perceptions of the usability of the monitoring device through a purpose-designed questionnaire. We used descriptive statistics and visualizations to display results. Results: Ten participants, 5 with COPD and 5 with asthma took part in this study. They completed symptom burden questionnaires on a median of 96% (25th percentile 14%, 75th percentile 96%), spirometry recordings on 55% (20%, 94%), wrist-worn physical activity recordings on 100% (97%, 100%), arm-worn physical activity recordings on 45% (13%, 63%), nocturnal cough recordings on 34% (9%, 54%), sputum recordings on 5% (3%, 12%), and environmental air quality recordings on 100% (99%, 100%) of the study days. The participants indicated that the measurements consumed a median of 13 (10, 15) min daily, and that they preferred the wrist-worn physical activity monitor to the arm-worn physical activity monitor. Conclusions: The CAir-Desk showed favorable technical performance and was well-accepted by our sample of participants with stable COPD and asthma. The obtained insights were used in a redesign of the CAir-Desk, which is currently applied in a randomized controlled trial including an interventional program