Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing

This paper introduces TRACE-GPT, which stands for Time-seRies Anomaly-detection with Convolutional Embedding and Generative Pre-trained Transformers. TRACE-GPT is designed to pre-train univariate time-series sensor data and detect faults on unlabeled datasets in semiconductor manufacturing. In semiconductor industry, classifying abnormal time-series sensor data from normal data is important because it is directly related to wafer defect. However, small, unlabeled, and even mixed training data without enough anomalies make classification tasks difficult. In this research, we capture features of time-series data with temporal convolutional embedding and Generative Pre-trained Transformer (GPT) to classify abnormal sequences from normal sequences using cross entropy loss. We prove that our model shows better performance than previous unsupervised models with both an open dataset, the University of California Riverside (UCR) time-series classification archive, and the process log of our Chemical Vapor Deposition (CVD) equipment. Our model has the highest F1 score at Equal Error Rate (EER) across all datasets and is only 0.026 below the supervised state-of-the-art baseline on the open dataset

Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis

We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis. © 2016 Optical Society of America.1

Smartphone-based multispectral imaging and machine-learning based analysis for discrimination between seborrheic dermatitis and psoriasis on the scalp

For appropriate treatment, accurate discrimination between seborrheic dermatitis and psoriasis in a timely manner is crucial to avoid complications. However, when they occur on the scalp, differential diagnosis can be challenging using conventional dermascopes. Thus, we employed smartphone-based multispectral imaging and analysis to discriminate between them with high accuracy. A smartphone-based multispectral imaging system, suited for scalp disease diagnosis, was redesigned. We compared the outcomes obtained using machine learning-based and conventional spectral classification methods to achieve better discrimination. The results demonstrated that smartphone-based multispectral imaging and analysis has great potential for discriminating between these diseases. © 2019 Optical Society of America.1

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Real-Time Pedestrian Flow Analysis Using Networked Sensors for a Smart Subway System

The application of smart city technologies requires new data analysis methods to interpret the voluminous data collected. In this study, we first analyzed the transfer behavior of subway pedestrians using the fingerprinting technique using data collected by more than 100 MAC (Media Access Control) ID sensors installed in a congested subway station serving two subway lines. We then developed a model that employs an AI (Artificial Intelligence)-based methodology, the cumulative visibility of moving objects (CVMO), to present the data in such a manner that it could be used to address pedestrian flow issues in this real-world implementation of smart city technology. The MAC ID location data collected during a three-month monitoring period were mapped using the fingerprinting wireless location sensing method to display the congestion situation in real time. Furthermore we developed a model that can inform immediate response to identified conditions. In addition, we formulated several schemes for disbursing congestion and improving pedestrian flow using behavioral economics, and then confirmed their effectiveness in a follow-up monitoring period. The proposed pedestrian flow analysis method cannot only solve pedestrian congestion, but can also help to prevent accidents and maintain public order

Role of pelitinib in the regulation of migration and invasion of hepatocellular carcinoma cells via inhibition of Twist1

Abstract Background Overexpression of Twist1, one of the epithelial-mesenchymal transition-transcription factors (EMT-TFs), is associated with hepatocellular carcinoma (HCC) metastasis. Pelitinib is known to be an irreversible epidermal growth factor receptor tyrosine kinase inhibitor that is used in clinical trials for colorectal and lung cancers, but the role of pelitinib in cancer metastasis has not been studied. This study aimed to investigate the anti-migration and anti-invasion activities of pelitinib in HCC cell lines. Methods Using three HCC cell lines (Huh7, Hep3B, and SNU449 cells), the effects of pelitinib on cell cytotoxicity, invasion, and migration were determined by cell viability, wound healing, transwell invasion, and spheroid invasion assays. The activities of MMP-2 and -9 were examined through gelatin zymography. Through immunoblotting analyses, the expression levels of EMT-TFs (Snail1, Twist1, and ZEB1) and EMT-related signaling pathways such as mitogen-activated protein kinases (MAPKs) and Akt signaling pathways were measured. The activity and expression levels of target genes were analyzed by reporter assay, RT-PCR, quantitative RT-PCR, and immunoblotting analysis. Statistical analysis was performed using one-way ANOVA with Dunnett's Multiple comparison tests in Prism 3.0 to assess differences between experimental conditions. Results In this study, pelitinib treatment significantly inhibited wound closure in various HCC cell lines, including Huh7, Hep3B, and SNU449. Additionally, pelitinib was found to inhibit multicellular cancer spheroid invasion and metalloprotease activities in Huh7 cells. Further investigation revealed that pelitinib treatment inhibited the migration and invasion of Huh7 cells by inducing Twist1 degradation through the inhibition of MAPK and Akt signaling pathways. We also confirmed that the inhibition of cell motility by Twist1 siRNA was similar to that observed in pelitinib-treated group. Furthermore, pelitinib treatment regulated the expression of target genes associated with EMT, as demonstrated by the upregulation of E-cadherin and downregulation of N-cadherin. Conclusion Based on our novel finding of pelitinib from the perspective of EMT, pelitinib has the ability to inhibit EMT activity of HCC cells via inhibition of Twist1, and this may be the potential mechanism of pelitinib on the suppression of migration and invasion of HCC cells. Therefore, pelitinib could be developed as a potential anti-cancer drug for HCC

Toward Smart Communication Components: Recent Advances in Human and AI Speaker Interaction

This study aims to investigate how humans and artificial intelligence (AI) speakers interact and to examine the interactions based on three types of communication failures: system, semantic, and effectiveness. We divided service failures using AI speaker user data provided by the top telecommunication service providers in South Korea and investigated the means to increase the continuity of product use for each type. We proved the occurrence of failure due to system error (H1) and negative results on sustainable use of the AI speaker due to not understanding the meaning (H2). It was observed that the number of users increases as the effectiveness failure rate increases. For single-person households constituted by persons in their 30s and 70s or older, the continued use of AI speakers was significant. We found that it alleviated loneliness and that human-machine interaction using AI speaker could reach a high level through a high degree of meaning transfer. We also expect AI speakers to play a positive role in single-person households, especially in cases of the elderly, which has become a tough challenge in the recent times