Wearable smart devices are capable of capturing a variety of information from their users using a multitude of noninvasive sensing modalities. Using features from the raw measurements of wearable devices, sensor fusion enables us to obtain a holistic picture of the users’ context and monitor their activity state with increased accuracy. Human activity recognition using noninvasive sensors allows us to capture the natural behavior of users in their day-to-day lives. This in-the-wild activity recognition, however, poses several key challenges that must be addressed to create effective classification models. The main challenges are class imbalance, uncertainty in classifier decisions, and large feature spaces. To address them, this study further explores a probabilistic sensor fusion method called Naive Adaptive Probabilistic Sensor (NAPS) Fusion. In doing so, we establish the viability of NAPS Fusion for natural human activity recognition using noninvasive sensing modalities. NAPS Fusion handles dimensionality reduction by creating reduced feature sets and mitigates the class imbalance issue through the use of Synthetic Minority Oversampling Technique (SMOTE). Moreover, NAPS Fusion addresses uncertainty in the decisions of classifiers using a Dempster-Shafer theoretic late fusion framework. Our empirical evaluation demonstrates that NAPS Fusion has broad applications beyond its original design for cognitive state detection. It outperforms similar decision level sensor fusion methods (late fusion using averaging, LFA, and late fusion using learned weights, LFL) in the detection of exercise and sedentary activities such as walking, running, lying down, and sitting. We observe improvements of up to 56% in F1 score and up to 59% in precision with NAPS Fusion over the compared methods.

The outburst of COVID-19 in late 2019 was the start of a health crisis that shook the world and took millions of lives in the ensuing years. Many governments and health officials failed to arrest the rapid circulation of infection in their communities. The long incubation period and the large proportion of asymptomatic cases made COVID-19 particularly elusive to track. However, wastewater surveillance soon became a promising data source in addition to conventional indicators such as confirmed daily cases, hospitalizations, and deaths. Despite the consensus on the effectiveness of wastewater viral load, there is a lack of methodological approaches that leverage viral load to improve COVID-19 forecasting. This paper proposes a deep learning framework to automatically discover the relationship between daily cases and viral load data. We trained a Deep Temporal Convolutional Network (DeepTCN) and a Temporal Fusion Transformer (TFT) model to obtain a global forecasting model. We supplement the daily confirmed cases with viral loads and other socio-economic factors as covariates to the models. Our results suggest that TFT outperforms DeepTCN and learns a better association between viral load and daily cases. We demonstrate that equipping the models with the viral load improves forecasting accuracy and reduces uncertainty. Moreover, viral load is shown to be the second most predictive input, following the containment and health index. Our results reveal the feasibility of training a location-agnostic deep-learning model to capture the dynamics of infection diffusion when wastewater viral load data is available.

This paper will study the pricing problem of two competitive products in a market characterized by local externalities. For this purpose, a stochastic model of sales propagation among consumers is developed. This model utilizes a compartmentalized schema denoted as a Markov Chain where the local network ef-fects impact transition rates. A key aspect of the proposed model is its multilayer structure, where prod-ucts' information streams through different layers. The equilibrium conditions and the optimal pricing strategies in a competitive market are examined. The pricing problem is investigated in two settings of homogeneous and heterogeneous (i.e., differential). It is shown that the existence and coexistence of in-dividual products in the equilibrium point depends on an epidemic parameter, called reproduction num-ber, that quantifies the speed by which a product's sales spread over the network. Moreover, it is found that the correlation between the network's layers impacts the equilibrium point. Specifically, a negative correlation between the network's layers allows a wider coexistence region than a positive correlation. Additionally, it is found that a negative correlation between the network's layers provides more flexibil-ity to firms for their pricing practices and yields a higher profit. Finally, different pricing strategies are characterized with respect to model parameters and the centrality measures of different networks. It is observed that while centrality measures and optimal prices are highly correlated, node centralities alone are not enough to determine optimal prices. & COPY; 2023 Elsevier B.V. All rights reserved.

The outburst of COVID-19 in late 2019 was the start of a health crisis that shook the world and took millions of lives in the ensuing years. Many governments and health officials failed to arrest the rapid circulation of infection in their communities. The long incubation period and the large proportion of asymptomatic cases made COVID-19 particularly elusive to track. However, wastewater monitoring soon became a promising data source in addition to conventional indicators such as confirmed daily cases, hospitalizations, and deaths. Despite the consensus on the effectiveness of wastewater viral load data, there is a lack of methodological approaches that leverage viral load to improve COVID-19 forecasting. This paper proposes using deep learning to automatically discover the relationship between daily confirmed cases and viral load data. We trained one Deep Temporal Convolutional Networks (DeepTCN) and one Temporal Fusion Transformer (TFT) model to build a global forecasting model. We supplement the daily confirmed cases with viral loads and other socio-economic factors as covariates to the models. Our results suggest that TFT outperforms DeepTCN and learns a better association between viral load and daily cases. We demonstrated that equipping the models with the viral load improves their forecasting performance significantly. Moreover, viral load is shown to be the second most predictive input, following the containment and health index. Our results reveal the feasibility of training a location-agnostic deep-learning model to capture the dynamics of infection diffusion when wastewater viral load data is provided.

Activity recognition using built-in sensors in smart and wearable devices provides great opportunities to understand and detect human behavior in the wild and gives a more holistic view of individuals' health and well being. Numerous computational methods have been applied to sensor streams to recognize different daily activities. However, most methods are unable to capture different layers of activities concealed in human behavior. Also, the performance of the models starts to decrease with increasing the number of activities. This research aims at building a hierarchical classification with Neural Networks to recognize human activities based on different levels of abstraction. We evaluate our model on the Extrasensory dataset; a dataset collected in the wild and containing data from smartphones and smartwatches. We use a two-level hierarchy with a total of six mutually exclusive labels namely, "lying down", "sitting", "standing in place", "walking", "running", and "bicycling" divided into "stationary" and "non-stationary". The results show that our model can recognize low-level activities (stationary/non-stationary) with 95.8% accuracy and overall accuracy of 92.8% over six labels. This is 3% above our best performing baseline (HHAR-net is shared as an open source tool at https://github.com/mehrdadfazli/HHAR-Net ).

Using wastewater surveillance as a continuous pooled sampling technique has been in place in many countries since the early stages of the outbreak of COVID-19. Since the beginning of the outbreak, many research works have emerged, studying different aspects of viral SARS-CoV-2 DNA concentrations (viral load) in wastewater and its potential as an early warning method. However, one of the questions that has remained unanswered is the quantitative relation between viral load and clinical indicators such as daily cases, deaths, and hospitalizations. Few studies have tried to couple viral load data with an epidemiological model to relate the number of infections in the community to the viral burden. This paper proposes a stochastic wastewater-based SEIR model to showcase the importance of viral load in the early detection and prediction of an outbreak in a community. We built three models based on whether or not they use the case count and viral load data and compared their simulations and forecasting quality. Our results demonstrate that a simple SEIR model based on viral load data can reliably predict the number of infections in the future. Therefore, wastewater-based surveillance is a promising way of monitoring the spread of COVID19 and can provide city officials with timely information about the circulation of COVID-19 in the community.

Fundamental theories predict that reductions in thermal conductivity from point and extended defects can arise due to phonon scattering with localized strain fields. To experimentally determine how these strain fields impact phonon scattering mechanisms, we employ ion irradiation as a controlled means of introducing strain and assorted defects into the lattice. In particular, we observe the reduction in thermal conductivity of intrinsic natural silicon after self-irradiation with two different silicon isotopes, $^{28}\mathrm{Si}^{+}$ and $^{29}\mathrm{Si}^{+}$. Irradiating with an isotope with a nearly identical atomic mass as the majority of the host lattice produces a damage profile lacking mass impurities and allows us to assess the role of phonon scattering with local strain fields on the thermal conductivity. Our results demonstrate that point defects will decrease the thermal conductivity more so than spatially extended defect structures assuming the same volumetric defect concentrations due to the larger strain per defect that arises in spatially separated point defects. With thermal conductivity models using density functional theory, we show that for a given defect concentration, the type of defect (i.e., point vs extended) plays a negligible role in reducing the thermal conductivity compared to the strain per defect in a given volume.