3,681 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Deep Learning Techniques for Electroencephalography Analysis
In this thesis we design deep learning techniques for training deep neural networks on electroencephalography (EEG) data and in particular on two problems, namely EEG-based motor imagery decoding and EEG-based affect recognition, addressing challenges associated with them. Regarding the problem of motor imagery (MI) decoding, we first consider the various kinds of domain shifts in the EEG signals, caused by inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and impede robust cross-subject generalization. We build a two-stage model ensemble architecture and propose two objectives to train it, combining the strengths of curriculum learning and collaborative training. Our subject-independent experiments on the large datasets of Physionet and OpenBMI, verify the effectiveness of our approach. Next, we explore the utilization of the spatial covariance of EEG signals through alignment techniques, with the goal of learning domain-invariant representations. We introduce a Riemannian framework that concurrently performs covariance-based signal alignment and data augmentation, while training a convolutional neural network (CNN) on EEG time-series. Experiments on the BCI IV-2a dataset show that our method performs superiorly over traditional alignment, by inducing regularization to the weights of the CNN. We also study the problem of EEG-based affect recognition, inspired by works suggesting that emotions can be expressed in relative terms, i.e. through ordinal comparisons between different affective state levels. We propose treating data samples in a pairwise manner to infer the ordinal relation between their corresponding affective state labels, as an auxiliary training objective. We incorporate our objective in a deep network architecture which we jointly train on the tasks of sample-wise classification and pairwise ordinal ranking. We evaluate our method on the affective datasets of DEAP and SEED and obtain performance improvements over deep networks trained without the additional ranking objective
Opioid use disorder: current trends and potential treatments
Opioid use disorder (OUD) is a major public health threat, contributing to morbidity and mortality from addiction, overdose, and related medical conditions. Despite our increasing knowledge about the pathophysiology and existing medical treatments of OUD, it has remained a relapsing and remitting disorder for decades, with rising deaths from overdoses, rather than declining. The COVID-19 pandemic has accelerated the increase in overall substance use and interrupted access to treatment. If increased naloxone access, more buprenorphine prescribers, greater access to treatment, enhanced reimbursement, less stigma and various harm reduction strategies were effective for OUD, overdose deaths would not be at an all-time high. Different prevention and treatment approaches are needed to reverse the concerning trend in OUD. This article will review the recent trends and limitations on existing medications for OUD and briefly review novel approaches to treatment that have the potential to be more durable and effective than existing medications. The focus will be on promising interventional treatments, psychedelics, neuroimmune, neutraceutical, and electromagnetic therapies. At different phases of investigation and FDA approval, these novel approaches have the potential to not just reduce overdoses and deaths, but attenuate OUD, as well as address existing comorbid disorders
A Framework on Setting Strategies for Enhancing the Efficiency of State Power use in Thailand’s Pursuit of a Green Economy
The objectives of this study are to investigate the efficiency of state power use in governing the country towards a green economy and to examine proactive strategies to enhance the efficiency of state power use. This study employs a mixed-methods research approach, including quantitative research involving the construction of a model, SEM-LCM-VECM, to assess the above efficiency. Additionally, the findings from quantitative research are integrated into qualitative research to formulate proactive strategies for exercising state power to foster sustainable development. The findings indicate that the use of state power for the development of a green economy, in accordance with the 20-Year National Strategic Plan and various development strategies of Thailand, has proven to be inefficient. This inefficiency stems from continuous growth in the economic and social sectors, while the environmental sector has consistently deteriorated. The most significant contributing factor directly impacting the environment is the economic sector, followed by the social sector. Moreover, Thailand's adaptability towards sustainability has been notably slow and falls below the established standards. If the government continues to use state power and pursue policies in a manner similar to the past, it is likely to have severe adverse consequences for the environment. This is due to the fact that reactive measures, including civil measures, administrative measures, and criminal measures, cannot effectively facilitate the development of a green economy. Therefore, the guidelines for addressing and formulating proactive strategies are of paramount importance and highly necessary for achieving sustainability. Research findings suggest that the government must establish reactive measures alongside proactive measures in economic aspects. These measures include 1) taxation and revenue collection; 2) subsidies and tax incentives; 3) financial enforcement incentives; 4) deposit systems and refund mechanisms; and 5) ownership and market creation systems. The study also reveals that countries efficiently implementing these economic measures for sustainability include European nations and Asian countries such as South Korea and Japan. Consequently, Thailand should consider applying the research findings to appropriately and efficiently shape the use of state power before the nation causes further irreparable damage. It is imperative that these proactive measures are pursued diligently and continuously to promote green economy policies and ensure sustainability in both the present and future
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies
Developing an fMRI paradigm for studying reinforcement learning with gustatory stimuli
One of the main challenges for global public health in the modern world is the rising prevalence of obesity. Obtaining a better understanding of the dysregulated feeding behaviour that leads to obesity, by investigating the decision making and learning processes underlying it, could advance our capabilities in battling the obesity epidemic. Consequently, our aim in this study is to design an experiment that could evaluate these processes.
We examined ten healthy participants using a modified version of the "probabilistic selection task". We used gustatory stimuli as a replacement for monetary rewards, to assess the effect of nutritional rewards on the learning behaviour. We subsequently analysed the behavioural results with computational modelling and combined this with imaging data simultaneously acquired with a functional magnetic resonance imaging (fMRI) multiband sequence.
All participants in this study succeeded in interpreting and interacting with the gustatory stimuli appropriately. Performance on the task was affected by the subjective valuation of the reward. Participants whose motivation to drink the reward and liking of its taste decreased during the task presented difficulties correctly choosing the more rewarding cues.
Computational modelling of the behaviour found that the so-called asymmetric learning model, in which positive and negative reinforcement are differently weighted, best explained the group. The acquired fMRI data was suboptimal and we did not detect the neurological activity we expected in the reward system, which is central to our scientific question.
Thus, our study shows it is possible to implement the PST with gustatory stimuli. However, to evaluate the corresponding neurological activity, our fMRI configuration requires improvement. An optimised system could be used in further studies to improve our understanding of the neurobiological mechanisms of learning that lead to obesity and elucidate the role of food as a distinctive reinforcer
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