A co-occurrence framework conceptualized for bridging the gap between basic science, clinical research and clinical practices

The intellectual impulsiveness of man to understand the unknown and the continual need of the society to improve healthcare have encouraged extensive investigation on numerous and diverse cause-and-effect relationships. The nature of this endeavor, however, renders the inability of investigator at all levels to escape beyond the narrow conceptual boundary described by an early French philosopher as the vicious cycle. To enjoy the theoretically plausible benefits of refined labor division, data-driven healthcare management, and real-time evidence-based practices, it must first be acknowledged that co-occurrence is better than cause-and-effect in explaining how an observation takes place at a particular time. This paper details a co-occurrence framework, and discusses its implications for the global healthcare system

Particles Separation and Tracks in a Hydrocyclone

100學年度研究獎補助論文[[abstract]]Hydrocyclone separation technique recently has been used in an increasing number of applications. Reynolds Stress Turbulence Model (RSM) and Discrete Phase Model (DPM) were employed in Computational Fluid Dynamics (CFD) 3D simulation to draw the motion trace of single particle of different particle size and density in hydrocyclone separator. It is known that, smaller size particles flow out from overflow, larger size particles flow out from underflow, and there is a characteristic size of particles having longer residence time in hydrocyclone separator. Particle size influences separation efficiency more significantly than particle density. Simulation of particle cluster separation efficiency in hydrocyclone separator has some discrepancy from experimental result. It is because air core influence is not considered in this study.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

Enhanced Differential Evolution Based on Adaptive Mutation and Wrapper Local Search Strategies for Global Optimization Problems

AbstractDifferential evolution (DE) is a simple, powerful optimization algorithm, which has been widely used in many areas. However, the choices of the best mutation and search strategies are difficult for the specific issues. To alleviate these drawbacks and enhance the performance of DE, in this paper, the hybrid framework based on the adaptive mutation and Wrapper Local Search (WLS) schemes, is proposed to improve searching ability to efficiently guide the evolution of the population toward the global optimum. Furthermore, the effective particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA) that we previously published is applied to always produce feasible candidate solutions for solving the Flexible Job-shop Scheduling Problem (FJSP). Experiments were conducted on comprehensive set of complex benchmarks including the unimodal, multimodal and hybrid composition function, to validate performance of the proposed method and to compare with other state-of-the art DE variants such as jDE, JADE, MDE_pBX etc. Meanwhile, the hybrid DE model incorporating PSOMA is used to solve different representative instances based on practical data for multi-objective FJSP verifications. Simulation results indicate that the proposed method performs better for the majority of the single-objective scalable benchmark functions in terms of the solution accuracy and convergence rate. In addition, the wide range of Pareto-optimal solutions and more Gantt chart decision-makings can be provided for the multi-objective FJSP combinatorial optimizations

Robust Design of Biological Circuits: Evolutionary Systems Biology Approach

Artificial gene circuits have been proposed to be embedded into microbial cells that function as switches, timers, oscillators, and the Boolean logic gates. Building more complex systems from these basic gene circuit components is one key advance for biologic circuit design and synthetic biology. However, the behavior of bioengineered gene circuits remains unstable and uncertain. In this study, a nonlinear stochastic system is proposed to model the biological systems with intrinsic parameter fluctuations and environmental molecular noise from the cellular context in the host cell. Based on evolutionary systems biology algorithm, the design parameters of target gene circuits can evolve to specific values in order to robustly track a desired biologic function in spite of intrinsic and environmental noise. The fitness function is selected to be inversely proportional to the tracking error so that the evolutionary biological circuit can achieve the optimal tracking mimicking the evolutionary process of a gene circuit. Finally, several design examples are given in silico with the Monte Carlo simulation to illustrate the design procedure and to confirm the robust performance of the proposed design method. The result shows that the designed gene circuits can robustly track desired behaviors with minimal errors even with nontrivial intrinsic and external noise

Data Compression Strategies for Use in Advanced Metering Infrastructure Networks

Internet of Things technology has advanced rapidly. For example, numerous sensors can be deployed in a city to collect a variety of data, and such data can be used to monitor the city’s situation. A possible application of such data is smart metering implemented by power suppliers for their consumers; smart metering involves installing a multiplicity of smart meters that, in conjunction with data centers, form a smart grid. Because a smart gird must collect and send data automatically, the establishment of advanced metering infrastructure (AMI) constitutes the primary step to establishing a smart grid. However, problems remain in smart metering: data traffic from smart meters flows rapidly at a huge volume, resulting in bandwidth bottlenecks. Thus, this chapter proposes some data compression technologies as well as a novel scheme for reducing the communication data load in AMI architectures

A Mobile Robot Generating Video Summaries of Seniors' Indoor Activities

We develop a system which generates summaries from seniors' indoor-activity videos captured by a social robot to help remote family members know their seniors' daily activities at home. Unlike the traditional video summarization datasets, indoor videos captured from a moving robot poses additional challenges, namely, (i) the video sequences are very long (ii) a significant number of video-frames contain no-subject or with subjects at ill-posed locations and scales (iii) most of the well-posed frames contain highly redundant information. To address this problem, we propose to \hl{exploit} pose estimation \hl{for detecting} people in frames\hl{. This guides the robot} to follow the user and capture effective videos. We use person identification to distinguish a target senior from other people. We \hl{also make use of} action recognition to analyze seniors' major activities at different moments, and develop a video summarization method to select diverse and representative keyframes as summaries.Comment: accepted by MobileHCI'1

Knowledge-Enriched Visual Storytelling

Stories are diverse and highly personalized, resulting in a large possible output space for story generation. Existing end-to-end approaches produce monotonous stories because they are limited to the vocabulary and knowledge in a single training dataset. This paper introduces KG-Story, a three-stage framework that allows the story generation model to take advantage of external Knowledge Graphs to produce interesting stories. KG-Story distills a set of representative words from the input prompts, enriches the word set by using external knowledge graphs, and finally generates stories based on the enriched word set. This distill-enrich-generate framework allows the use of external resources not only for the enrichment phase, but also for the distillation and generation phases. In this paper, we show the superiority of KG-Story for visual storytelling, where the input prompt is a sequence of five photos and the output is a short story. Per the human ranking evaluation, stories generated by KG-Story are on average ranked better than that of the state-of-the-art systems. Our code and output stories are available at https://github.com/zychen423/KE-VIST.Comment: AAAI 202

Toward Transparent Sequence Models with Model-Based Tree Markov Model

In this study, we address the interpretability issue in complex, black-box Machine Learning models applied to sequence data. We introduce the Model-Based tree Hidden Semi-Markov Model (MOB-HSMM), an inherently interpretable model aimed at detecting high mortality risk events and discovering hidden patterns associated with the mortality risk in Intensive Care Units (ICU). This model leverages knowledge distilled from Deep Neural Networks (DNN) to enhance predictive performance while offering clear explanations. Our experimental results indicate the improved performance of Model-Based trees (MOB trees) via employing LSTM for learning sequential patterns, which are then transferred to MOB trees. Integrating MOB trees with the Hidden Semi-Markov Model (HSMM) in the MOB-HSMM enables uncovering potential and explainable sequences using available information