The project title is the Global Special Interest Group (SIG) on Early Identification and Intervention in Autism. Formed in 2012, the aim of this project is to enhance research impact in diverse communities through the iterative and dynamic process of knowledge translation: the synthesis, dissemination, exchange, and application of knowledge to improve quality of life for people affected by autism. The SIG is a knowledge translation network which brings together over 100 researchers, clinicians, and advocates from over 20 countries.
Obesity and diabetes are significant risk factors for mood disorders. Diabetes doubles the incidence of depression and as many as 1 in 3 people with diabetes has depressed mood at a level that diminishes functioning, glycemic control, adherence to treatment and increases the risk of diabetes complications. Over consumption of energy-dense foods significantly contribute to the development of obesity and type 2 diabetes (T2D). Palatable high-fat foods are rewarding and can provide short-term relief from stress and negative emotional states.
A hallmark of linguistic communication is that 'what is said' often underdetermines 'what is intended' by a given utterance. Sentences such as 'The author began the book' or 'The boy finished the game' are semantically indeterminate because it is not clear what the author or the boy were really doing. When the activity is not specified, interpreting these common types of sentences might rely on different linguistic and cognitive processes.
We will vary the strength of a feature in one stream and measure perception (discrimination task), processing time, and action (eye movements) of a feature in the other stream.
Prior relevant research:
Tchernikov & Fallah (2010)
Perry & Fallah (2012) Awaiting more information from the professor. Please check back soon. Do not contact Globalink Research Internships.
Autism is a growing public health challenge in Canada and internationally. Despite major scientific advances in autism research and improvements in practice, families still experience serious delays and complications in diagnosis and access to care. Moreover, community capacity, e.g., treatment and support programs, remains very limited relative to the needs of those affected. In turn, this situation increases the burden of suffering on families and ultimately the long-term costs to health systems and society.
Energy networks are often very complex which results in highly unpredictable congestion patterns in the physical constraints of the network. Most efforts to model congestion in energy networks are made on toy problems. Here, the objective is to model and predict congestion in a real physical energy network using automated machine learning systems, in particular deep neural networks. Physical properties of the network will also be properly modelled. The proposed method is expected to predict congestion with higher accuracy than the existing methods used by the partner organization.
Deep neural networks (DNNs) for automatic speech recognition (ASR) require large amounts of labelled data, which can be difficult and expensive to collect. However, recent research has shown that some features learned by DNNs are highly transferable to other tasks and datasets. Here we propose to design a multi-lingual training procedure to leverage large amounts of off-task data based on the transferability of acoustic features learned by DNNs. Our primary goal is to improve ASR for low-resource languages.
Proper brain development is required for normal cognitive functions in mammals. Recent studies have shown that both protein-coding genes and noncoding RNAs, especially microRNAs (miRNAs), play crucial roles in brain development. We have identified several miRNAs that are expressed in specific regions in the embryonic and adult mouse cerebral cortex, and demonstrated their important roles in cortical development and functions.
The overall objective of this work is to gain a better understanding of the experience of patients living with acne and how to support their decision making process in terms of acne treatment options. This research will investigate issues surrounding how patients understand how to accurately determine the severity of their acne and the treatment options available to them. From this, an effective patient aid will be developed to inform and support patients decision-making process when seeking treatment.
The cleanliness of restroom drives peoples preferences. In places such as hotels, restaurants and hospitals, a dirty bathroom will drive away potential customers as it lead them to question the cleanliness of the whole facility. Visionstate Inc. developed WANDA (Washroom Attendant Notification Digital Aid), a LCD touch screen that display the time of most recent restroom service and a interactive interface to request restroom maintenance. The proposed study is to investigate whether features of WANDA enhance peoples perception of bathroom cleanliness.