Unsupervised Shift-invariant Feature Learning from Time-series Data

Unsupervised feature learning is one of the key components of machine learningand articial intelligence. Learning features from high dimensional streaming data isan important and dicult problem which is incorporated with number of challenges.Moreover, feature learning algorithms need to be evaluated and generalized for timeseries with dierent patterns and components. A detailed study is needed to clarifywhen simple algorithms fail to learn features and whether we need more complicatedmethods.In this thesis, we show that the systematic way to learn meaningful featuresfrom time-series is by using convolutional or shift-invariant versions of unsupervisedfeature learning. We experimentally compare the shift-invariant versions of clustering,sparse coding and non-negative matrix factorization algorithms for: reconstruction,noise separation, prediction, classication and simulating auditory lters from acousticsignals. The results show that the most ecient and highly scalable clustering algorithmwith a simple modication in inference and learning phase is able to produce meaningfulresults. Clustering features are also comparable with sparse coding and non-negativematrix factorization in most of the tasks (e.g. classication) and even more successful insome (e.g. prediction). Shift invariant sparse coding is also used on a novel application,inferring hearing loss from speech signal and produced promising results.Performance of algorithms with regard to some important factors such as: timeseries components, number of features and size of receptive eld is also analyzed. Theresults show that there is a signicant positive correlation between performance of clusteringwith degree of trend, frequency skewness, frequency kurtosis and serial correlationof data, whereas, the correlation is negative in the case of dataset average bandwidth.Performance of shift invariant sparse coding is aected by frequency skewness, frequencykurtosis and serial correlation of data. Non-Negative matrix factorization is influenced by data characteristics same as clustering

Active Perception by Interaction with Other Agents in a Predictive Coding Framework: Application to Internet of Things Environment

Predicting the state of an agent\u27s partially-observable environment is a problem of interest in many domains. Typically in the real world, the environment consists of multiple agents, not necessarily working towards a common goal. Though the goal and sensory observation for each agent is unique, one agent might have acquired some knowledge that may benefit the other. In essence, the knowledge base regarding the environment is distributed among the agents. An agent can sample this distributed knowledge base by communicating with other agents. Since an agent is not storing the entire knowledge base, its model can be small and its inference can be efficient and fault-tolerant. However, the agent needs to learn -- when, with whom and what -- to communicate (in general interact) under different situations.This dissertation presents an agent model that actively and selectively communicates with other agents to predict the state of its environment efficiently. Communication is a challenge when the internal models of other agents is unknown and unobservable. The proposed agent learns communication policies as mappings from its belief state to when, with whom and what to communicate. The policies are learned using predictive coding in an online manner, without any reinforcement. The proposed agent model is evaluated on widely-studied applications, such as human activity recognition from multimodal, multisource and heterogeneous sensor data, and transferring knowledge across sensor networks. In the applications, either each sensor or each sensor network is assumed to be monitored by an agent. The recognition accuracy on benchmark datasets is comparable to the state-of-the-art, even though our model has significantly fewer parameters and infers the state in a localized manner. The learned policy reduces number of communications. The agent is tolerant to communication failures and can recognize the reliability of each agent from its communication messages. To the best of our knowledge, this is the first work on learning communication policies by an agent for predicting the state of its environment

DEVELOPING A PROTOTYPE OF A SMART-LIGHTING SYSTEM FOR ISOLATED RURAL INTERSECTIONS

Rural intersections are high-risk locations for road users. Particularly, during the nighttime, lower traffic volumes make it difficult for drivers to discern an intersection despite traffic signs. The lack of alertness may lead to severe crashes. An effective way to reduce the likelihood of crashes at isolated intersections is to warn road users of the intersection in advance. A smart-lighting system can detect approaching vehicles using sensors and transmit this information to a receiver to illuminate the intersection. By deploying a demand-responsive light, it is expected that the system will provide adequate warning to road users, both motorized and non-motorized. This report documents the development and deployment of a smart-lighting system at the University of Alaska Anchorage (UAA)

Synthesizing Skeletal Motion and Physiological Signals as a Function of a Virtual Human's Actions and Emotions

Round-the-clock monitoring of human behavior and emotions is required in many healthcare applications which is very expensive but can be automated using machine learning (ML) and sensor technologies. Unfortunately, the lack of infrastructure for collection and sharing of such data is a bottleneck for ML research applied to healthcare. Our goal is to circumvent this bottleneck by simulating a human body in virtual environment. This will allow generation of potentially infinite amounts of shareable data from an individual as a function of his actions, interactions and emotions in a care facility or at home, with no risk of confidentiality breach or privacy invasion. In this paper, we develop for the first time a system consisting of computational models for synchronously synthesizing skeletal motion, electrocardiogram, blood pressure, respiration, and skin conductance signals as a function of an open-ended set of actions and emotions. Our experimental evaluations, involving user studies, benchmark datasets and comparison to findings in the literature, show that our models can generate skeletal motion and physiological signals with high fidelity. The proposed framework is modular and allows the flexibility to experiment with different models. In addition to facilitating ML research for round-the-clock monitoring at a reduced cost, the proposed framework will allow reusability of code and data, and may be used as a training tool for ML practitioners and healthcare professionals

Electric Power Grids Under High-Absenteeism Pandemics: History, Context, Response, and Opportunities.

Widespread outbreaks of infectious disease, i.e., the so-called pandemics that may travel quickly and silently beyond boundaries, can significantly upsurge the morbidity and mortality over large-scale geographical areas. They commonly result in enormous economic losses, political disruptions, social unrest, and quickly evolve to a national security concern. Societies have been shaped by pandemics and outbreaks for as long as we have had societies. While differing in nature and in realizations, they all place the normal life of modern societies on hold. Common interruptions include job loss, infrastructure failure, and political ramifications. The electric power systems, upon which our modern society relies, is driving a myriad of interdependent services, such as water systems, communication networks, transportation systems, health services, etc. With the sudden shifts in electric power generation and demand portfolios and the need to sustain quality electricity supply to end customers (particularly mission-critical services) during pandemics, safeguarding the nation's electric power grid in the face of such rapidly evolving outbreaks is among the top priorities. This paper explores the various mechanisms through which the electric power grids around the globe are influenced by pandemics in general and COVID-19 in particular, shares the lessons learned and best practices taken in different sectors of the electric industry in responding to the dramatic shifts enforced by such threats, and provides visions for a pandemic-resilient electric grid of the future. [Abstract copyright: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

Palm oil: features and applications

Palm fruits are the source of two distinct but related vegetable oils, namely palm oil and palm kernel oil. Palm oil has many food and industrial applications. Ever-increasing demands for palm oil have substantially impacted its industry, creating some environmental concerns. Both types of oil are high in saturated fatty acids with potential cardiovascular risks. Several attempts have been made to reduce undesirable health and environmental impacts. However, additional research and development activities are needed to meet the concerns of the medical professionals and environmental activists

Environmental and ecological studies in northern Alborz aimed at developing the fisheries resources

Caspian Sea with an area of 400 thousand square kilometers is the largest lake in the world. The Caspian Sea about 1200 km from north to south on the longest section and an average width of 320 km. Along the coastline around the Caspian Sea is about 6500 kilometers. Caspian Sea is about 78,000 cubic kilometers of water volume that is 44% of stocks of blue lakes around the world. Caspian Sea basin, which is composed of seven major basins of the main watershed from west to east are: juniper, Talsh- Anzali, large Sefidrood between Haraz Sefid and, Hraz- Gharehsou, Nagorno Sv- Gorgan and Atrak in the basin of Aras no limits to the beach. Aras sub-basin is located in the North West and Iran, the second largest sub-basin of the Caspian Sea. Talysh-Anzali on the Caspian Sea basin West and the seventh largest sub-basin of the Caspian Sea. White basin is located in the South East of the Caspian Sea and the extent of the sub-basin of the Caspian Sea. Haraz located in the south Caspian Sea basin and the ninth largest sub-basin of the Caspian Sea. Gorgan is located in the South East of the Caspian Sea basin and the fourth largest sub-basin of the Caspian Sea. In these areas, about 28 percent of the total fish production in the northern waters of aquatic allocated

Environmental and ecological studies in northern Alborz (Guilan Province)

Caspian Sea with an area of 400 thousand square kilometers is the largest lake in the world. The Caspian Sea about 1200 km from north to south on the longest section and an average width of 320 km. along the coastline around the Caspian Sea is about 6500 kilometers. Caspian Sea is about 78,000 cubic kilometers of water volume that is 44% of stocks of blue lakes around the world. Caspian Sea basin, which is composed of seven major basins of the main watershed from west to east are: juniper, Talsh- Anzali, large Sefidrood between Haraz Sefid and, Hraz- Gharehsou, Nagorno Sv- Gorgan and Atrak in the basin of Aras no limits to the beach. Aras sub-basin is located in the North West and Iran, the second largest sub-basin of the Caspian Sea. Talysh-Anzali on the Caspian Sea basin West and the seventh largest sub-basin of the Caspian Sea. White basin is located in the South East of the Caspian Sea and the extent of the sub-basin of the Caspian Sea. Haraz located in the south Caspian Sea basin and the ninth largest sub-basin of the Caspian Sea. Gorgan is located in the South East of the Caspian Sea basin and the fourth largest sub-basin of the Caspian Sea. In these areas, about 28 percent of the total fish production in the northern waters of aquatic allocated

Large animal models of cardiovascular disease

The human cardiovascular system is a complex arrangement of specialized structures with distinct functions. The molecular landscape, including the genome, transcriptome and proteome, is pivotal to the biological complexity of both normal and abnormal mammalian processes. Despite our advancing knowledge and understanding of cardiovascular disease (CVD) through the principal use of rodent models, this continues to be an increasing issue in today's world. For instance, as the ageing population increases, so does the incidence of heart valve dysfunction. This may be because of changes in molecular composition and structure of the extracellular matrix, or from the pathological process of vascular calcification in which bone-formation related factors cause ectopic mineralization. However, significant differences between mice and men exist in terms of cardiovascular anatomy, physiology and pathology. In contrast, large animal models can show considerably greater similarity to humans. Furthermore, precise and efficient genome editing techniques enable the generation of tailored models for translational research. These novel systems provide a huge potential for large animal models to investigate the regulatory factors and molecular pathways that contribute to CVD in vivo. In turn, this will help bridge the gap between basic science and clinical applications by facilitating the refinement of therapies for cardiovascular disease. Copyright (c) 2016 John Wiley & Sons, Ltd

Longitudinal Imaging of the Ageing Mouse

Several non-invasive imaging techniques are used to investigate the effect of pathologies and treatments over time in mouse models. Each preclinical in vivo technique provides longitudinal and quantitative measurements of changes in tissues and organs, which are fundamental for the evaluation of alterations in phenotype due to pathologies, interventions and treatments. However, it is still unclear how these imaging modalities can be used to study ageing with mice models. Almost all age related pathologies in mice such as osteoporosis, arthritis, diabetes, cancer, thrombi, dementia, to name a few, can be imaged in vivo by at least one longitudinal imaging modality. These measurements are the basis for quantification of treatment effects in the development phase of a novel treatment prior to its clinical testing. Furthermore, the non-invasive nature of such investigations allows the assessment of different tissue and organ phenotypes in the same animal and over time, providing the opportunity to study the dysfunction of multiple tissues associated with the ageing process. This review paper aims to provide an overview of the applications of the most commonly used in vivo imaging modalities used in mouse studies: micro-computed-tomography, preclinical magnetic-resonance-imaging, preclinical positron-emission-tomography, preclinical single photon emission computed tomography, ultrasound, intravital microscopy, and whole body optical imaging