COMIC: Towards A Compact Image Captioning Model with Attention

Recent works in image captioning have shown very promising raw performance. However, we realize that most of these encoder-decoder style networks with attention do not scale naturally to large vocabulary size, making them difficult to be deployed on embedded system with limited hardware resources. This is because the size of word and output embedding matrices grow proportionally with the size of vocabulary, adversely affecting the compactness of these networks. To address this limitation, this paper introduces a brand new idea in the domain of image captioning. That is, we tackle the problem of compactness of image captioning models which is hitherto unexplored. We showed that, our proposed model, named COMIC for COMpact Image Captioning, achieves comparable results in five common evaluation metrics with state-of-the-art approaches on both MS-COCO and InstaPIC-1.1M datasets despite having an embedding vocabulary size that is 39x - 99x smaller. The source code and models are available at: https://github.com/jiahuei/COMIC-Compact-Image-Captioning-with-AttentionComment: Added source code link and new results in Table

Design of Pixellated CMOS Photon Detector for Secondary Electron Detection in the Scanning Electron Microscope

This paper presents a novel method of detecting secondary electrons generated in the scanning electron microscope (SEM). The method suggests that the photomultiplier tube (PMT), traditionally used in the Everhart-Thornley (ET) detector, is to be replaced with a configurable multipixel solid-state photon detector offering the advantages of smaller dimension, lower supply voltage and power requirements, and potentially cheaper product cost. The design of the proposed detector has been implemented using a standard 0.35 μm CMOS technology with optical enhancement. This microchip comprises main circuit constituents of an array of photodiodes connecting to respective noise-optimised transimpedance amplifiers (TIAs), a selector-combiner (SC) circuit, and a postamplifier (PA). The design possesses the capability of detecting photons with low input optical power in the range of 1 nW with 100 μm × 100 μm sized photodiodes and achieves a total amplification of 180 dBΩ at the output.Peer Reviewe

Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm

Fed-batch fermentation has gained attention in recent years due to its beneficial impact in the economy and productivity of bioprocesses. However, the complexity of these processes requires an expert system that involves swarm intelligence-based metaheuristics such as Artificial Algae Algorithm (AAA), Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Differential Evolution (DE) for simulation and optimization of the feeding trajectories. DE traditionally performs better than other evolutionary algorithms and swarm intelligence techniques in optimization of fed-batch fermentation. In this work, an improved version of DE namely Backtracking Search Algorithm (BSA) has edged DE and other recent metaheuristics to emerge as superior optimization method. This is shown by the results obtained by comparing the performance of BSA, DE, CMAES, AAA and ABC in solving six fed batch fermentation case studies. BSA gave the best overall performance by showing improved solutions and more robust convergence in comparison with various metaheuristics used in this work. Also, there is a gap in the study of fed-batch application of wastewater and sewage sludge treatment. Thus, the fed batch fermentation problems in winery wastewater treatment and biogas generation from sewage sludge are investigated and reformulated for optimization

As an emerging economy, should Malaysia adopt carbon taxation?

On 1 July 2014, the Australian Government announced the abolition of its new carbon tax policy barely two years into implementation. The Australia’s policy U-turn raises a very important question: Should an emerging economy such as Malaysia adopt carbon and climate change policy as part of a larger tax reform? In order to answer this, the key issues, main driving forces and barriers in the use of carbon tax as an incentive-based instrument for economic and environmental policies purposes are examined. With the recent global climate challenges and the fiscal needs of the national budget, it is submitted that the implementation of a carbon tax framework in Malaysia should be regarded not as an ultimate goal in itself but as a starting point to develop the right behavioural response for a better and more comprehensive national fiscal and climate policy reform in the future. © The Author(s) 2018

Assessing clinical usefulness of readmission risk prediction model

Readmission manifests signs of degraded quality of care and increased healthcare cost. Such adverse event may be attributed to premature discharge, unsuccessful treatments, or worsening comorbidities. Predictive modeling provides useful information to identify patients at a higher risk for readmission for targeted interventions. Though many studies have proposed readmission risk predictive models and validated their discriminative ability with performance metrics, few examined the net benefit realized by a predictive model. We compared traditional logistic regression against modern neural network to predict unplanned readmission. An added value of 7% on discriminative ability is observed for modern machine learning model compared to regression. A cost analysis is provided to assist physicians and hospital management for translating the theoretical value into real cost and resource allocation after model implementation. The neural network model is projected to contribute 15× more savings by reducing readmissions. Aside from constructing better performing models, the results of our study demonstrate the potential of a clinically helpful prediction tool in terms of strategies to reduce cost associated with readmission

Prediction of spine decompression post-surgery outcome through transcranial motor evoked potential using linear discriminant analysis algorithm

Transcranial motor evoked potential (TcMEP) is one of the modalities in intraoperative neuromonitoring (IONM) which has been used in spine surgeries to prevent motor function injuries. Studies have shown that improvement to TcMEP could be a potential prognostic information on the actual improvement to the patient after surgery. There is no objective way currently to identify which TcMEP signal is significant to indicate actual positive relief of symptoms. The proposed method utilized linear discriminant analysis (LDA) machine learning algorithm to predict the TcMEP response that correlates to relieve of symptoms post-surgery. TcMEP data were obtained from four patients that had pre surgery symptoms with post-surgery actual relief of symptoms, and six patients that had no pre surgery and post-surgery symptoms which were divided into training and prediction test. The result of the proposed method produced 87.5% of accuracy in prediction capabilities

Technical data-driven tool condition monitoring challenges for CNC milling: a review

CNC milling is a highly complex machining process highly valued in various industries, including the automotive and aerospace industries. With the increasing competition, manufacturers are aiming to keep maintenance costs low while ensuring high levels of manufacturing equipment reliability. It is also highly important for them to maximize the service life of each cutting tool by avoiding premature replacements while minimizing the risks of scrap due to tool breakage. This calls for the need for a condition-based maintenance approach and more powerful, flexible and robust tool condition monitoring (TCM) techniques with minimal reliance on subjective diagnosis based on the expert knowledge. This paper discusses the technical aspects of recent developments in state-of-the-art TCM techniques and current challenges which limit the viability of TCM in real-life industrial applications. The technical challenges in modern TCM were split into two major groups of problems: (1) challenges in data processing and (2) issues regarding tool wear model performance. Current methodologies to overcome issues in each of the sections in this paper are discussed and, where possible, compared to highlight their respective advantages and disadvantages. Finally, this paper concludes with a discussion on possible trends in TCM development and interesting avenues for future research. © 2020, Springer-Verlag London Ltd., part of Springer Nature