Maryam S Mirian

University of British Columbia | UBC · Pacific Parkinson's Research Centre

Motor dysfunction in Parkinson's Disease (PD) patients is typically assessed by clinicians employing the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Such comprehensive clinical assessments are time-consuming, expensive, semi-subjective, and may potentially result in conflicting labels across different raters. T...

The primary treatment for Parkinson’s disease (PD) is supplementation of levodopa (L-dopa). With disease progression, people may experience motor and non-motor fluctuations, whereby the PD symptoms return before the next dose of medication. Paradoxically, in order to prevent wearing-off, one must take the next dose while still feeling well, as the...

Parkinson's disease (PD) is a neurological disorder based on changes in dynamic brain activity, which can be partially ameliorated with invasive Deep Brain Stimulation. Galvanic vestibular stimulation (GVS), a non-invasive method, could potentially improve the motor symptoms of Parkinson’s disease, but the mechanisms are unclear. Biomarkers based o...

Introduction: Sleep disturbances are common in Alzheimer's disease (AD), with estimates of prevalence as high as 65%. Recent work suggests that specific sleep stages, such as slow-wave sleep (SWS) and rapid eye movement (REM), may directly impact AD pathophysiology. A major limitation to sleep staging is the requirement for clinical polysomnograph...

Objective To develop a semi-unsupervised automatic sleep staging method capable of detecting novel sleep patterns beyond standard sleep stages in the EEG, as may be seen in clinical populations. Methods We employed a two-step approach that utilized prior knowledge extracted from labeled data to cluster unlabeled data into standard sleep stages and...

Parkinson’s disease (PD) is characterized by abnormal brain oscillations that can change rapidly. Tracking neural alternations with high temporal resolution electrophysiological monitoring methods such as EEG can lead to valuable information about alterations observed in PD. Concomitantly, there have been advances in the high-accuracy performance o...

Background: Impaired motor vigor (MV) is a critical aspect of Parkinson's disease (PD) pathophysiology. While MV is predominantly encoded in the basal ganglia, deriving (cortical) EEG measures of MV may provide valuable targets for modulation via galvanic vestibular stimulation (GVS). Objective: To find EEG features predictive of MV and examine the...

Introduction: Numerous non-motor symptoms are associated with Parkinson’s disease (PD) including fatigue. The challenge in the clinic is to detect relevant non-motor symptoms while keeping patient-burden of questionnaires low and to take potential subgroups such as sex differences into account. The Fatigue Severity Scale (FSS) effectively detects c...

Sleep disturbances are common in Alzheimer’s disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requirement for overnight polysomnography (PSG) to achieve formal sleep staging. This is not only costly, but...

The expertise of human experts can be formally extracted from their written documents, research projects, and everyday activities. The process whereby experts are recognized according to their activities is called expert finding. In this paper, we propose an approach to identify the experts in a given field according to the content of 3 easily acce...

In this paper, an ontology-driven multi-agent based energy management system (EMS) is proposed for monitoring and optimal control of an integrated homes/buildings and microgrid system with various renewable energy resources (RESs) and controllable loads. Different agents ranging from simple-reflex to complex learning agents are designed and impleme...

Knowledge is the primary asset of today’s organisations; thus, knowledge management has been focused on discovery, representation, modification, transformation, and creation of knowledge within an enterprise. A knowledge map is a knowledge management tool that makes organisational processes more visible, feasible, and practicable. It is a graphical...

Mood, as one of the human affects, plays a vital role in human-human interaction, especially due to its long lasting effects. In this paper, we introduce an approach in which a companion robot, capable of mood detection, is employed to detect and report the mood state of a person to his/her partner to make him/her prepared for upcoming encounters....

With the emerging of small-scale integrated energy systems (IESs), there are significant potentials to increase the functionality of a typical demand-side management (DSM) strategy and typical implementation of building-level distributed energy resources (DERs). By integrating DSM and DERs into a cohesive, networked package that fully utilizes smar...

We propose a joint information approach for automatic analysis of 2D echocardiography (echo) data. The approach combines a priori images, their segmentations and patient diagnostic information within a unified framework to determine various clinical parameters, such as cardiac chamber volumes, and cardiac disease labels. The main idea behind the ap...

In this paper, we propose a markerless based approach to differentiate between two different human walking gaits, specifically, the normal walking versus tip toe walking. The markerless based approaches are suitable in many applications, such as screening of autistic children from normal ones based on their body movement patterns or detecting walki...

A great deal of scientific evidence suggests that there is a close relationship between mood and cognitive processes of human in everyday tasks. In this study, we have investigated the feasibility of determining mood from gaze, which is one of the human cognitive processes that can be recorded during interaction with computers. To do so, we have de...

Without knowing what is happening in an enterprise a manager is hardly able to make an informed rational decision. Fortunately, the current state of an organization has been concealed inside the enterprise operational data. If this data is purposefully processed according to the required decision variables, it can be mapped into a knowledge map. A...

Determining the mood of a person is an important step in the Human-Robot interaction. In this paper, we propose a human-inspired approach in which the changes in emotions, done using emotion induction, can be used to determine the mood of a person. The emotion induction, which can be done through robot actions or through showing video clips, stimul...

The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic applicatio...

Automatic detection of the human emotion is an important area in computer science and psychology to develop adaptive Human-Computer Interaction (HCI). In this paper, a novel approach is proposed to extract a user's pleasure during English comprehension. The approach is based on processing the mouse movement data during the interaction with the syst...

The ability to determine mood is one of fundamental challenges in affective computing. In this paper, we present a novel approach for mood detection via emotional variations. In this approach, the mood is considered as a low magnitude and more stable, i.e. low frequency, emotion that can be detected using emotion detection approaches. A Bayes class...

In a multisensory task, human adults integrate information from different sensory modalities -behaviorally in an optimal Bayesian fashion- while children mostly rely on a single sensor modality for decision making. The reason behind this change of behavior over age and the process behind learning the required statistics for optimal integration are...

The Treebanks as the sets of syntactically annotated sentences, are the most widely used language resource in the application of Natural Language Processing. The occurrence of errors in the automatically created Treebanks is one of the main obstacles limiting the using of these resources in the real world applications. This paper aims to introduce...

Teleoperation is a mechanism that makes it possible to accomplish a task in an environment where the presence of the master at the slave site is either undesirable or hardly possible The communication delay is a challenge with undesirable effects, jeopardizing the teleoperated systems the most. When the salve agent is performing in a dynamically ch...

Using Bayesian networks (BNs) for classification tasks has received significant attention as BNs can encode and represent domain-experts' knowledge as well as data in their structures and conditional probability tables. While structure learning and constructing the structure by hand according to an ensemble of domain-expert opinions are two common...

One of the substantial concerns of researchers in machine learning area is designing an artificial agent with an autonomous behaviour in a complex environment. In this paper, we considered a learning problem with multiple critics. The importance of each critic for the agent is different, and attention of agent to them is variable during its life. I...

In a research organization, there are many decision making issues (such as employing a new member, finding some one who is expert in a filed and can take a role, defining areas of excellence and so on) that can be facilitated if the organization has a clear perception of what its human knowledge experts really know. Such knowledge can be clearly tr...

Rapid increase in the size and complexity of sensory systems demands for attention control in real world robotic tasks. However, attention control and the task are often highly interlaced which demands for interactive learning. In this paper, a framework called METAL mixture-of-experts task and attention learning is proposed to cope with this compl...

A probabilistic framework for interactive learning in continuous and multimodal perceptual spaces is proposed. In this framework, the agent learns the task along with adaptive partitioning of its multimodal perceptual space. The learning process is formulated in a Bayesian reinforcement learning setting to facilitate the adaptive partitioning. The...

In this letter, we propose a learning system, active decision fusion learning (ADFL), for active fusion of decisions. Each decision maker, referred to as a local decision maker, provides its suggestion in the form of a probability distribution over all possible decisions. The goal of the system is to learn the active sequential selection of the loc...

In this paper a unified architecture is proposed for the new emerging concept of knowledge networks. This architecture is based on a multi-aspect view to the problem of establishing such a network in organizations. By integrating infrastructure, knowledge and business layers mounted on two aspects of human-based and organizational supportive condit...

The proposed approach is based on the classical model of Mixture-of-Experts and tries to concurrently learn two tightly coupled issues. As the main goal, it learns the optimal classification and at the same time, it learns the best sequence of council with previously designed local decision experts to reach the former optimal classification strat-e...

In this chapter, a framework is discussed for creating contents to help significant organizational tasks such as planning, research, innovation, education, development, et cetera be achieved in an efficient way. The proposed framework is based on an interplay between the ontologies of the key segments and the problem context using the linguisticall...

In the knowledge era, learning organizations (LO) are being emerged and knowledge is the key fuel for such organizations where Knowledge Management (KM) plays a critical role in learning more rapidly than the competitors. However, implementing KM requires a number of steps to be taken. These steps usually lead to significant changes in organization...

We present a general mathematical description of the top-down attention control problem. Three important components are identified in the model: context extraction, attention focus and decision making. The context gives a coarse blurry representation of the whole input; the attention module models the focus of attention on a limited part of input,...

An approach is presented to show how contents can be created through an inter-play between the ontology of their key segments and the ontology of problem context using the knowledge on some nominal values which stand for the way linguistically-significant notions are tackled. The approach seems to be suitable for any kind of organizational task for...

Learning attention control is a real need specifically when a robot tries to learn a sequential decision-making-type task. This is even more critical when learning directly in the perceptual space is not feasible mainly due to the high dimensionality thus non-homogeneity. Therefore, two learning problems are raised to be solved at the same time. In...

Research in the area of the semantic web is cur-rently in a state where ontologies have been ap-plied in applications such as information retrieval. In order to simplify the use of ontologies, there is a need for a suitable representational and computa-tional infrastructure for building, storing and ac-cessing ontologies. Based upon this fact, in t...

The first question answered in this paper is whether or not learning attention control in the decision space is feasible and how to develop an online as well as interactive learning approach for such control in this space, in case of feasibility. Here, decision space is formed by the decision vector of the agents each has allowed to dynamically obs...

This paper proposes some methods to improve the fault-tolerance in distributed systems specifically in deterministic situations by distributed decision-making and co-ordination. Providing a distributed system with fault tolerance is a feasible but hard problem due to the intrinsic aspects of such systems such as: independency, unpredictability and...

In this paper, a multi-agent architecture with non-deterministic decision-making for distributed task allocation is proposed. The main goal is providing the system with graceful degradation when a fault occurs. In fact, when one agent becomes faulty and this is informed to the other agents by sending a help request, the helper agents attempt to rec...

This paper focuses on distributed fault recovery in agent-based systems by providing help for faulty members. In the presented method, if one faulty agent requests for help or agents are informed of fault in one of their teammates, they first decide if they are able to help or not. In the case that they are able to help and several help requests ex...

Looking at distributed hardware systems as teams of agents enables the designers to use new techniques developed for multi-agent systems to increase fault tolerance. Multi agent systems like other distributed systems are prone to failures. An important challenge to creating an effective and functional multi-agent system is providing it with suffici...

This paper reports on the implementation and evaluation of a knowledge-based domain-specific question answering system called TeLQAS. This system employs a reasoning engine built based on an extended version of Human Plausible Reasoning theory. The knowledge base of the system has been filled manually with logical statements about Fiber Optics. An...

Distributed hardware systems can be considered as teams of distributed cooperative agents. Thinking this way, will power the designers to develop new agent-based ideas to increase systems' fault tolerance. In this paper, recovering the system from potential faults by helping the faulty agents in performing their tasks is considered. Besides, each a...

Today, automated reasoning is a real need for intelligent systems. Information Retrieval systems in general and specifically a question answering system require a reasoning mechanism as well. In this paper, a heuristic reasoning mechanism, implemented in the online phase of TeLQAS is proposed. TeLQAS is an ontology-based natural language question a...

Reinforcement learning methods are slow and inefficient in large and continuous perceptual spaces. Discretizing such perceptual spaces generates a large number of states which slows down learning process. This motivates us that inspir-ing from concept learning in humans and cognitive science propose a framework for learning the concepts of the agen...