Stroke is a medical condition that can affect motor function, particularly dynamic balance. Biofeedback can aid in rehabilitation procedures which help patients to regain lost motor activity and recover functionality. In this work, we are presenting a robotic smart-vest device that can analyze Inertial Measurement Unit (IMU) data and assist in rehabilitation procedures by providing timed feedback in the form of vibrotactile stimulation. Information provided by principal caregivers and patients in the form of surveys and interviews, is used to hypothesize potential clinical causes and to derive alternative three alternative clinical modalities: Artificial Vestibular Feedback, Gait Pacemaker and Risk-Predictor.

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy over 85% and the CASTC model achieved an overall accuracy of 76%. We found that the cashew area in Benin almost doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 55%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.

There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. A widespread Deep Neural Networks do not provide interpretable solutions. Meanwhile, symbolic expressions give us a clear relation between observations and the target variable. However, at the moment, there is no dominant solution for the symbolic regression task, and we aim to reduce this gap with our algorithm. In this work, we propose a novel deep learning framework for symbolic expression generation via variational autoencoder (VAE). In a nutshell, we suggest using a VAE to generate mathematical expressions, and our training strategy forces generated formulas to fit a given dataset. Our framework allows encoding apriori knowledge of the formulas into fast-check predicates that speed up the optimization process. We compare our method to modern symbolic regression benchmarks and show that our method outperforms the competitors under noisy conditions. The recovery rate of SEGVAE is 65% on the Ngyuen dataset with a noise level of 10%, which is better than the previously reported SOTA by 20%. We demonstrate that this value depends on the dataset and can be even higher.

Classifying the behavior of humans or animals from videos is important in biomedical fields for understanding brain function and response to stimuli. Action recognition, classifying activities performed by one or more subjects in a trimmed video, forms the basis of many of these techniques. Deep learning models for human action recognition have progressed significantly over the last decade. Recently, there is an increased interest in research that incorporates deep learning-based action recognition for animal behavior classification. However, human action recognition methods are more developed. This survey presents an overview of human action recognition and pose estimation methods that are based on convolutional neural network (CNN) architectures and have been adapted for animal behavior classification in neuroscience. Pose estimation, estimating joint positions from an image frame, is included because it is often applied before classifying animal behavior. First, we provide foundational information on algorithms that learn spatiotemporal features through 2D, two-stream, and 3D CNNs. We explore motivating factors that determine optimizers, loss functions and training procedures, and compare their performance on benchmark datasets. Next, we review animal behavior frameworks that use or build upon these methods, organized by the level of supervision they require. Our discussion is uniquely focused on the technical evolution of the underlying CNN models and their architectural adaptations (which we illustrate), rather than their usability in a neuroscience lab. We conclude by discussing open research problems, and possible research directions. Our survey is designed to be a resource for researchers developing fully unsupervised animal behavior classification systems of which there are only a few examples in the literature.

This thesis focuses on data that has complex spatio-temporal structure and on probabilistic graphical models that learn the structure in an interpretable and scalable manner. We target two research areas of interest: Gaussian graphical models for tensor-variate data and summarization of complex time-varying texts using topic models. This work advances the state-of-the-art in several directions. First, it introduces a new class of tensor-variate Gaussian graphical models via the Sylvester tensor equation. Second, it develops an optimization technique based on a fast-converging proximal alternating linearized minimization method, which scales tensor-variate Gaussian graphical model estimations to modern big-data settings. Third, it connects Kronecker-structured (inverse) covariance models with spatio-temporal partial differential equations (PDEs) and introduces a new framework for ensemble Kalman filtering that is capable of tracking chaotic physical systems. Fourth, it proposes a modular and interpretable framework for unsupervised and weakly-supervised probabilistic topic modeling of time-varying data that combines generative statistical models with computational geometric methods. Throughout, practical applications of the methodology are considered using real datasets. This includes brain-connectivity analysis using EEG data, space weather forecasting using solar imaging data, longitudinal analysis of public opinions using Twitter data, and mining of mental health related issues using TalkLife data. We show in each case that the graphical modeling framework introduced here leads to improved interpretability, accuracy, and scalability.

Machine learning enables the development of new, supplemental, and empowering tools that can either expand existing technologies or invent new ones. In education, space exists for a tool that supports generic student course review formats to organize and recapitulate students' views on the pedagogical practices to which they are exposed. Often, student opinions are gathered with a general comment section that solicits their feelings towards their courses without polling specifics about course contents. Herein, we show a novel approach to summarizing and organizing students' opinions via analyzing their sentiment towards a course as a function of the language/vocabulary used to convey their opinions about a class and its contents. This analysis is derived from their responses to a general comment section encountered at the end of post-course review surveys. This analysis, accomplished with Python, LaTeX, and Google's Natural Language API, allows for the conversion of unstructured text data into both general and topic-specific sub-reports that convey students' views in a unique, novel way.

Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges & Opportunities of the Artificial Intelligence in Aviation Chapter 4: Presenting the Artificial Intelligence in Aviation Market Factor Analysis, Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.

To this end, we present a comprehensive comparative analysis of more than 500 models derived from ten different state-of-the-art architectures and compare them to a variety of multi-class and multi-label classification tasks from 22 datasets with different sizes and properties. In addition to models trained entirely on these datasets, we benchmark models trained in the context of transfer learning, leveraging pre-trained model variants, as it is typically performed in practice. All presented approaches are general and can be easily extended to many other remote sensing image classification tasks not considered in this study.

Visual sensor networks are used for monitoring traffic in large cities and are promised to support automated driving in complex road segments. The pose of these sensors, i.e. position and orientation, directly determines the coverage of the driving environment, and the ability to detect and track objects navigating therein. Existing sensor pose optimisation methods either maximise the coverage of ground surfaces, or consider the visibility of target objects (e.g. cars) as binary variables, which fails to represent their degree of visibility. For example, such formulations fail in cluttered environments where multiple objects occlude each other. This paper proposes two novel sensor pose optimisation methods, one based on gradient-ascent and one using integer programming techniques, which maximise the visibility of multiple target objects. Both methods are based on a rendering engine that provides pixel-level visibility information about the target objects, and thus, can cope with occlusions in cluttered environments. The methods are evaluated in a complex driving environment and show improved visibility of target objects when compared to existing methods. Such methods can be used to guide the cost effective deployment of sensor networks in smart cities to improve the safety and efficiency of traffic monitoring systems.

Team competition in multi-agent Markov games is an increasingly important setting for multi-agent reinforcement learning, due to its general applicability in modeling many real-life situations. Multi-agent actor-critic methods are the most suitable class of techniques for learning optimal policies in the team competition setting, due to their flexibility in learning agent-specific critic functions, which can also learn from other agents. In many real-world team competitive scenarios, the roles of the agents naturally emerge, in order to aid in coordination and collaboration within members of the teams. However, existing methods for learning emergent roles rely heavily on the Q-learning setup which does not allow learning of agent-specific Q-functions. In this paper, we propose RAC, a novel technique for learning the emergent roles of agents within a team that are diverse and dynamic. In the proposed method, agents also benefit from predicting the roles of the agents in the opponent team. RAC uses the actor-critic framework with role encoder and opponent role predictors for learning an optimal policy. Experimentation using 2 games demonstrates that the policies learned by RAC achieve higher rewards than those learned using state-of-the-art baselines. Moreover, experiments suggest that the agents in a team learn diverse and opponent-aware policies.