Research Seminar Series: Multiple, Object-oriented Segmentation Methods of Mammalian Cell Tomograms
1000 until 1100
Meeting Room 7th Floor
Dr. Nur Intan Raihana Ruhaiyem
Electron tomography (ET) is a powerful tool for quantitatively mapping the complex 3D sub-cellular structures of cells. High accuracy segmentation results are great value to cell biologists. They facilitate the comparison processes across statistically significant datasets of properties or structure information like number of granules, mitochondrial size/volume and the size/number of cisternae of the Golgi apparatus. The value of this data is significantly improved if the cellular compartments (e.g. organelles, particles) are accurately segmented and annotated. Manual segmentation is reasonably accurate but the process might be too slow – since the accuracy is highly dependent on the training of the person conducting the task. Automated segmentation therefore opens a number of opportunities. But these automated methods must be fast and capable of accurately delineating all contours of interest, ideally at organelle and molecular level – where many of which were reportedly not successful on ET datasets. Semi-automated approaches however have substantially allowed wider scope in resulting maintained cellular membrane tracing quality and accuracy and providing improved segmentation time. These reasons have motivated the development of a pipeline – semi-automated cellular tomogram segmentation (CTS) workflow – that will find the best settings of chosen combination methods for high resolution tomogram segmentation specific to the intrinsic properties of the image volume being processed. The study also introduced a set of tools – that allow for the first time the segmentation of organelles of interest with similar image properties done in sub-groups manner – the image categorisation technique and sets of scoring objectives for different organelle sub-groups. These enable timely segmentation of sub-cellular compartments and expedite the process of optimising method settings not currently afforded by any other technique.
Research Review: Enhanced Reinforcement Learning Models and Their Application in Brain Fiber Tracking Problem
0900 until 1030
Meeting Room 7th Floor
Prof. Dr. Mandava Rajeswari
Incremental temporal difference (TD) learning models offer powerful techniques for “value estimation” in sequential selections tasks in the areas of machine learning, adaptive control, decision support systems and industrial/autonomous robotics. Since, these models operate based-on the Gradient-descent (GD) learning, they are presented with some limitations especially on their step-size settings may cause of the models get more sensitive on “type of observations” and “parameter settings”. These limitations are more pronounced in on-line applications where the models are expected to be “adaptive” under non-stationary observations. It means that, inaccurate setting of the sensitive parameters as well as changing their observations characteristic may degrade the “convergence quality” of these Gradient TD algorithms. These issues indicate a gap; the existing TD models are not adaptive. It means that, there is the “parameter dependency” problem in the incremental TD algorithms in reinforcement learning (RL). Consequently, presenting a set of enhanced models that eliminate or minimize this parameter dependency is desirable.
For this purpose, a new class including some TD learning models in RL is presented. These models are governed by steepest descent (SD) optimization approach. The major focus is on the optimal computation of the step-size in incremental TD learning. Experimental results indicate that, proposed models “converge faster” than the existing similar models. This improvement, according to each model, is about 40-70%, while these new models maintain the “original linear complexity”. Besides, they “do not depend on” their step-size and initial parameter settings. These indicating the presented models are adaptive and may fill the gap.
Presenting RL-based brain white matter fiber tracking model is the second purpose of this study. The problem is a “sequential selection task” under “uncertainty” condition which is the target of the RL approach. Tractography processing plays an important role in pre/post brain surgery studies and traumatic brain injury assessments. This process still suffers from “long processing time” while streamline models, with good response time, fail to precisely reveal the brain fiber profiles in uncertainty areas. These were reasons which motivated us to apply RL approaches into the brain fiber tracking problem. Experimental results both on artificial and real dataset indicated considerable improvement in fiber tracking processing, especially in tissues that were challenging to other fiber tracking techniques.
Research Seminar Series: I Feel You: State-of-the-art in Emotion Modelling
1000 until 1100
Meeting Room 7th Floor
Dr. Syaheerah Lebai Lutfi
It is easy to get frustrated at machines, perhaps because they seem to be callous. By and large, the quality of human-computer interaction is affected due to the inability of the computerized agents to recognise and adapt to user emotional state (or sometimes, even personality). Now with the mass appeal of artificially-mediated communication, there has been an increasing need for machines to be socially and emotionally intelligent, that is, to infer
and adapt to their human interlocutors' emotions on the fly, in order to ascertain an affective, empathetic and naturalistic interaction. Recent studies has shown synthetic agents (be it a robot or a virtual agent) that are affect-sensitive have offer great advantages, from reducing user frustration to increasing learner’s engagement. These reasons have motivated the development of artificial agents towards including socio-emotional elements, turning them into affective and socially-sensitive interfaces.
This talk will discuss the state-of-art in Affective Computing including the effort towards culture-sensitive affective interfaces. To facililate listeners’ understanding, a sample of a recent study will be presented.
For more information, please visit www.syaheerah.com
Proposal Review: Optimizing Crowd Evacuation Plan in the Emergency Route Planning Problems
1000 until 1130
Viva Room 7th Floor
Mohd Nor Akmal bin Khalid
Dr. Umi Kalsom Yusof
Disastrous situations, either occurred naturally (such as fires, earthquake, floods, hurricane) or man-made (such as terrorist bombings, chemical spills, etc.), have claimed the lives of thousands in the last millennium. Emergency evacuation is an essential strategy in managing these disastrous situations. From top-down perspective of emergency evacuation, several stages are involves which aggregated as strategic, tactical, and operational. In addition, to mitigate risks and reduce potential fatalities, the operational perspectives of emergency evacuation involves operations such as mitigation, preparedness, response, and recovery. Typically, factors involved in the emergency evacuation include the operational condition, routing complexity, crowd characteristics, environments, and the disaster itself. As such, optimizing the evacuation operations during emergency situation would require an effective crowd evacuation plan, which is acknowledged as one of the vital study of the societal research as well as emergency route planning (ERP) community. This study attempts to contribute to the body of knowledge and the general community by optimizing the ERP problems, supporting the betterment of evacuation routine while reducing the cost of life. Exhaustive prior works have been studied and presented, while the principal research aims had also been made. Various classifications of priory developed approaches for emergency evacuation that encompassed the needs of variety of public community as well as fulfilling the complexity of the situation, are summarized and discussed. Three components of interest is concentrated, which are demand (crowd), resource (roadways/networks), and event (disaster). The direction and trend of the work had been identified which leads to the realization of the potential crowd evacuation approach. The methodology used for the study had also been discussed and elaborated in details. Lastly, the expected deliverable are discussed for the upcoming stage of this work.