1,822 research outputs found
2015 Summer Research Symposium Abstract Book
2015 Summer volume of abstracts for science research projects conducted by students at Trinity College
Improving the Generalisability of Brain Computer Interface Applications via Machine Learning and Search-Based Heuristics
Brain Computer Interfaces (BCI) are a domain of hardware/software in which a user can interact with a machine without the need for motor activity, communicating instead via signals generated by the nervous system. These interfaces provide life-altering benefits to users, and refinement will both allow their application
to a much wider variety of disabilities, and increase their practicality. The primary method of acquiring these signals is Electroencephalography (EEG). This technique is susceptible to a variety of different sources of noise, which compounds the inherent problems in BCI training data: large dimensionality, low numbers of samples, and non-stationarity between users and recording sessions. Feature Selection and Transfer Learning have been used to overcome these problems, but they fail to account for several characteristics of BCI. This
thesis extends both of these approaches by the use of Search-based algorithms. Feature Selection techniques, known as Wrappers use âblack boxâ evaluation of feature subsets, leading to higher classification accuracies than ranking methods known as Filters. However, Wrappers are more computationally expensive, and are prone to over-fitting to training data. In this thesis, we applied Iterated Local Search (ILS) to the BCI field for the first time in literature, and demonstrated competitive results with state-of-the-art methods such as Least Absolute Shrinkage and Selection Operator and Genetic Algorithms. We then developed ILS variants with guided perturbation operators. Linkage was used to develop a multivariate metric, Intrasolution Linkage. This takes into account pair-wise dependencies of features with the label, in the context of the solution. Intrasolution Linkage was then integrated into two ILS variants. The Intrasolution Linkage Score was discovered to have a stronger correlation with the solutions predictive accuracy on unseen data than Cross Validation Error (CVE) on the training set, the typical approach to feature subset evaluation. Mutual Information was used to create Minimum Redundancy Maximum Relevance Iterated Local Search (MRMR-ILS). In this algorithm, the perturbation operator was guided using an existing Mutual Information measure, and compared with current Filter and Wrapper methods. It was found to achieve generally lower CVE rates and higher predictive accuracy on unseen data than existing algorithms. It was also noted that solutions found by the MRMR-ILS provided CVE rates that had a stronger correlation with the accuracy on unseen data than solutions found by other algorithms. We suggest that this may be due to the guided perturbation leading to solutions that are richer in Mutual Information. Feature Selection reduces computational demands and can increase the accuracy of our desired models, as evidenced in this thesis. However, limited quantities of training samples restricts these models, and greatly reduces their generalisability. For this reason, utilisation of data from a wide range of users is an ideal solution. Due to the differences in neural structures between users, creating adequate models is difficult. We adopted an existing state-of-the-art ensemble technique Ensemble Learning Generic Information (ELGI), and developed an initial optimisation phase. This involved using search to
transplant instances between user subsets to increase the generalisability of each subset, before combination in the ELGI. We termed this Evolved Ensemble Learning Generic Information (eELGI). The eELGI achieved higher accuracy than user-specific BCI models, across all eight users. Optimisation of the training dataset allowed smaller training sets to be used, offered protection against neural drift, and created models that performed similarly across participants, regardless of neural impairment. Through the introduction and hybridisation of search based algorithms to several problems in BCI we have been able to show improvements in modelling accuracy and efficiency. Ultimately, this represents a step towards more practical BCI systems that will provide life altering benefits to users
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Automatic sound synthesizer programming: techniques and applications
The aim of this thesis is to investigate techniques for, and applications of automatic sound synthesizer programming. An automatic sound synthesizer programmer is a system which removes the requirement to explicitly specify parameter settings for a sound synthesis algorithm from the user. Two forms of these systems are discussed in this thesis:
tone matching programmers and synthesis space explorers. A tone matching programmer takes at its input a sound synthesis algorithm and a desired target sound. At its output it produces a configuration for the sound synthesis algorithm which causes it to emit a
similar sound to the target. The techniques for achieving this that are investigated are
genetic algorithms, neural networks, hill climbers and data driven approaches. A synthesis
space explorer provides a user with a representation of the space of possible sounds
that a synthesizer can produce and allows them to interactively explore this space. The
applications of automatic sound synthesizer programming that are investigated include
studio tools, an autonomous musical agent and a self-reprogramming drum machine. The
research employs several methodologies: the development of novel software frameworks
and tools, the examination of existing software at the source code and performance levels
and user trials of the tools and software. The main contributions made are: a method
for visualisation of sound synthesis space and low dimensional control of sound synthesizers; a general purpose framework for the deployment and testing of sound synthesis and optimisation algorithms in the SuperCollider language sclang; a comparison of a variety of optimisation techniques for sound synthesizer programming; an analysis of sound synthesizer error surfaces; a general purpose sound synthesizer programmer compatible with industry standard tools; an automatic improviser which passes a loose equivalent of the Turing test for Jazz musicians, i.e. being half of a man-machine duet which was rated as one of the best sessions of 2009 on the BBC's 'Jazz on 3' programme
Smart Sensors for Healthcare and Medical Applications
This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue âSmart Sensors for Healthcare and Medical Applicationsâ. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare
Women in Science 2015
Women in Science 2015 summarizes research done by Smith Collegeâs Summer Research Fellowship (SURF) Program participants. Ever since its 1967 start, SURF has been a cornerstone of Smithâs science education. In 2015, 162 students participated in SURF (153 hosted on campus and nearby eld sites), supervised by 60 faculty mentor-advisors drawn from the Clark Science Center and connected to its eighteen science, mathematics, and engineering departments and programs and associated centers and units. At summerâs end, SURF participants were asked to summarize their research experiences for this publication.https://scholarworks.smith.edu/clark_womeninscience/1002/thumbnail.jp
Time-slice analysis of dyadic human activity
La reconnaissance dâactivitĂ©s humaines Ă partir de donnĂ©es vidĂ©o est utilisĂ©e pour la surveillance ainsi que pour des applications dâinteraction homme-machine. Le principal objectif est de classer les vidĂ©os dans lâune des k classes dâactions Ă partir de vidĂ©os entiĂšrement observĂ©es. Cependant, de tout temps, les systĂšmes intelligents sont amĂ©liorĂ©s afin de prendre des dĂ©cisions basĂ©es sur des incertitudes et ou des informations incomplĂštes. Ce besoin nous motive Ă introduire le problĂšme de lâanalyse de lâincertitude associĂ©e aux activitĂ©s humaines et de pouvoir passer Ă un nouveau niveau de gĂ©nĂ©ralitĂ© liĂ© aux problĂšmes dâanalyse dâactions. Nous allons Ă©galement prĂ©senter le problĂšme de reconnaissance dâactivitĂ©s par intervalle de temps, qui vise Ă explorer lâactivitĂ© humaine dans un intervalle de temps court. Il a Ă©tĂ© dĂ©montrĂ© que lâanalyse par intervalle de temps est utile pour la caractĂ©risation des mouvements et en gĂ©nĂ©ral pour lâanalyse de contenus vidĂ©o. Ces Ă©tudes nous encouragent Ă utiliser ces intervalles de temps afin dâanalyser lâincertitude associĂ©e aux activitĂ©s humaines. Nous allons dĂ©tailler Ă quel degrĂ© de certitude chaque activitĂ© se produit au cours de la vidĂ©o. Dans cette thĂšse, lâanalyse par intervalle de temps dâactivitĂ©s humaines avec incertitudes sera structurĂ©e en 3 parties. i) Nous prĂ©sentons une nouvelle famille de descripteurs spatiotemporels optimisĂ©s pour la prĂ©diction prĂ©coce avec annotations dâintervalle de temps. Notre reprĂ©sentation prĂ©dictive du point dâintĂ©rĂȘt spatiotemporel (Predict-STIP) est basĂ©e sur lâidĂ©e de la contingence entre intervalles de temps. ii) Nous exploitons des techniques de pointe pour extraire des points dâintĂ©rĂȘts afin de reprĂ©senter ces intervalles de temps. iii) Nous utilisons des relations (uniformes et par paires) basĂ©es sur les rĂ©seaux neuronaux convolutionnels entre les diffĂ©rentes parties du corps de lâindividu dans chaque intervalle de temps. Les relations uniformes enregistrent lâapparence locale de la partie du corps tandis que les relations par paires captent les relations contextuelles locales entre les parties du corps. Nous extrayons les spĂ©cificitĂ©s de chaque image dans lâintervalle de temps et examinons diffĂ©rentes façons de les agrĂ©ger temporellement afin de gĂ©nĂ©rer un descripteur pour tout lâintervalle de temps. En outre, nous crĂ©ons une nouvelle base de donnĂ©es qui est annotĂ©e Ă de multiples intervalles de temps courts, permettant la modĂ©lisation de lâincertitude inhĂ©rente Ă la reconnaissance dâactivitĂ©s par intervalle de temps. Les rĂ©sultats expĂ©rimentaux montrent lâefficience de notre stratĂ©gie dans lâanalyse des mouvements humains avec incertitude.Recognizing human activities from video data is routinely leveraged for surveillance and human-computer interaction applications. The main focus has been classifying videos into one of k action classes from fully observed videos. However, intelligent systems must to make decisions under uncertainty, and based on incomplete information. This need motivates us to introduce the problem of analysing the uncertainty associated with human activities and move to a new level of generality in the action analysis problem. We also present the problem of time-slice activity recognition which aims to explore human activity at a small temporal granularity. Time-slice recognition is able to infer human behaviours from a short temporal window. It has been shown that temporal slice analysis is helpful for motion characterization and for video content representation in general. These studies motivate us to consider timeslices for analysing the uncertainty associated with human activities. We report to what degree of certainty each activity is occurring throughout the video from definitely not occurring to definitely occurring. In this research, we propose three frameworks for time-slice analysis of dyadic human activity under uncertainty. i) We present a new family of spatio-temporal descriptors which are optimized for early prediction with time-slice action annotations. Our predictive spatiotemporal interest point (Predict-STIP) representation is based on the intuition of temporal contingency between time-slices. ii) we exploit state-of-the art techniques to extract interest points in order to represent time-slices. We also present an accumulative uncertainty to depict the uncertainty associated with partially observed videos for the task of early activity recognition. iii) we use Convolutional Neural Networks-based unary and pairwise relations between human body joints in each time-slice. The unary term captures the local appearance of the joints while the pairwise term captures the local contextual relations between the parts. We extract these features from each frame in a time-slice and examine different temporal aggregations to generate a descriptor for the whole time-slice. Furthermore, we create a novel dataset which is annotated at multiple short temporal windows, allowing the modelling of the inherent uncertainty in time-slice activity recognition. All the three methods have been evaluated on TAP dataset. Experimental results demonstrate the effectiveness of our framework in the analysis of dyadic activities under uncertaint
Neurobiological markers for remission and persistence of childhood attention-deficit/hyperactivity disorder
Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in children. Symptoms of childhood ADHD persist into adulthood in around 65% of patients, which elevates the risk for a number of adverse outcomes, resulting in substantial individual and societal burden. A neurodevelopmental double dissociation model is proposed based on existing studies in which the early onset of childhood ADHD is suggested to associate with dysfunctional subcortical structures that remain static throughout the lifetime; while diminution of symptoms over development could link to optimal development of prefrontal cortex. Current existing studies only assess basic measures including regional brain activation and connectivity, which have limited capacity to characterize the functional brain as a high performance parallel information processing system, the field lacks systems-level investigations of the structural and functional patterns that significantly contribute to the symptom remission and persistence in adults with childhood ADHD. Furthermore, traditional statistical methods estimate group differences only within a voxel or region of interest (ROI) at a time without having the capacity to explore how ROIs interact in linear and/or non-linear ways, as they quickly become overburdened when attempting to combine predictors and their interactions from high-dimensional imaging data set.
This dissertation is the first study to apply ensemble learning techniques (ELT) in multimodal neuroimaging features from a sample of adults with childhood ADHD and controls, who have been clinically followed up since childhood. A total of 36 adult probands who were diagnosed with ADHD combined-type during childhood and 36 matched normal controls (NCs) are involved in this dissertation research. Thirty-six adult probands are further split into 18 remitters (ADHD-R) and 18 persisters (ADHD-P) based on the symptoms in their adulthood from DSM-IV ADHD criteria. Cued attention task-based fMRI, structural MRI, and diffusion tensor imaging data from each individual are analyzed. The high-dimensional neuroimaging features, including pair-wise regional connectivity and global/nodal topological properties of the functional brain network for cue-evoked attention process, regional cortical thickness and surface area, subcortical volume, volume and fractional anisotropy of major white matter fiber tract for each subject are calculated. In addition, all the currently available optimization strategies for ensemble learning techniques (i.e., voting, bagging, boosting and stacking techniques) are tested in a pool of semi-final classification results generated by seven basic classifiers, including K-Nearest Neighbors, support vector machine (SVM), logistic regression, NaĂŻve Bayes, linear discriminant analysis, random forest, and multilayer perceptron.
As hypothesized, results indicate that the features of nodal efficiency in right inferior frontal gyrus, right middle frontal (MFG)-inferior parietal (IPL) functional connectivity, and right amygdala volume significantly contributed to accurate discrimination between ADHD probands and controls; higher nodal efficiency of right MFG greatly contributed to inattentive and hyperactive/impulsive symptom remission, while higher right MFG-IPL functional connectivity strongly linked to symptom persistence in adults with childhood ADHD. The utilization of ELTs indicates that the bagging-based ELT with the base model of SVM achieves the best results, with the most significant improvement of the area under the receiver of operating characteristic curve (0.89 for ADHD probands vs. NCs, and 0.9 for ADHD-P vs. ADHD-R). The outcomes of this dissertation research have considerable value for the development of novel interventions that target mechanisms associated with recovery
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