Investigation of intrinsic channel characteristics of hydrogenated amorphous silicon thin-film transistors by gated-four-probe structure

We use a new hydrogenated amorphous silicon (a-Si:Ha-Si:H) device structure, the gated-four-probe a-Si:Ha-Si:H thin-film transistor (TFT), to investigate the intrinsic channel characteristics of inverted-staggered a-Si:Ha-Si:H TFTs without the influence of source/drain series resistances. The experimental results have shown that, for the conventional a-Si:Ha-Si:H TFT structure, the field-effect mobility, threshold voltage, and field-effect channel conductance activation energy have a strong dependence on a-Si:Ha-Si:H thickness and TFT channel length. On the other hand, for the gated-four-probe a-Si:Ha-Si:H TFT structure, these values are a-Si:Ha-Si:H thickness and TFT channel length independent, clearly indicating that this new a-Si:Ha-Si:H TFT structure can be effectively used to measure the channel intrinsic properties of a-Si:Ha-Si:H TFTs. © 1998 American Institute of Physics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/70688/2/APPLAB-72-22-2874-1.pd

Metabolic Pathways Enhancement Confers Poor Prognosis in p53 Exon Mutant Hepatocellular Carcinoma.

RNA-Sequencing (RNA-Seq), the most commonly used sequencing application tool, is not only a method for measuring gene expression but also an excellent media to detect important structural variants such as single nucleotide variants (SNVs), insertion/deletion (Indels), or fusion transcripts. The Cancer Genome Atlas (TCGA) contains genomic data from a variety of cancer types and also provides the raw data generated by TCGA consortium. p53 is among the top 10 somatic mutations associated with hepatocellular carcinoma (HCC). The aim of the present study was to analyze concordant different gene profiles and the priori defined set of genes based on p53 mutation status in HCC using RNA-Seq data. In the study, expression profile of 11 799 genes on 42 paired tumor and adjacent normal tissues was collected, processed, and further stratified by the mutated versus normal p53 expression. Furthermore, we used a knowledge-based approach Gene Set Enrichment Analysis (GSEA) to compare between normal and p53 mutation gene expression profiles. The statistical significance (nominal P value) of the enrichment score (ES) genes was calculated. The ranked gene list that reflects differential expression between p53 wild-type and mutant genotypes was then mapped to metabolic process by KEGG, an encyclopedia of genes and genomes to assign functional meanings. These approaches enable us to identify pathways and potential target gene/pathways that are highly expressed in p53 mutated HCC. Our analysis revealed 2 genes, the hexokinase 2 (HK2) and Enolase 1 (ENO1), were conspicuous of red pixel in the heatmap. To further explore the role of these genes in HCC, the overall survival plots by Kaplan-Meier method were performed for HK2 and ENO1 that revealed high HK2 and ENO1 expression in patients with HCC have poor prognosis. These results suggested that these glycolysis genes are associated with mutated-p53 in HCC that may contribute to poor prognosis. In this proof-of-concept study, we proposed an approach for identifying novel potential therapeutic targets in human HCC with mutated p53. These approaches can take advantage of the massive next-generation sequencing (NGS) data generated worldwide and make more out of it by exploring new potential therapeutic targets

Combing customer profiles for members' return visit rate predictions

[[abstract]]The major profit of companies in Taiwan is generated by online advertising and e-commerce. Effective advertising requires predicting how a user responds to an advertisement and then targeting (presenting the advertisement) to reflect users’ favor. As customers’ preferences may change over time, we take the different types of past behavior patterns of the registered members to capture concept drifts. Then, we combine the click preference index (CPI) and the preference drifts to propose a Behavioral Preference (BP) model, and to predict the members’ return visit rates in the specific category of the portal site. The marketers of the portal site can target the registered members with high return visit rates and design corresponding marketing strategies. The experimental results with a real dataset show that our model can effectively predict the registered members’ return visit rates.[[notice]]補正完畢[[journaltype]]國外[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]JP

Catastrophic Emission of Charges from Near-Extremal Nariai Black Holes

Using the in-out formalism and also the monodromy method, we study the emission of charges from near-extremal charged Nariai black holes with the black hole and cosmological horizons close to each other. The emission becomes catastrophic for a charge with energy greater than its chemical potential, whose leading exponential factor increases inversely proportional to the separation of two horizons. This implies that near-extremal Nariai black holes quickly evaporate through the charge emission and end in the de Sitter space, in contrast to near-extremal RN-dS black holes that have the Breitenlohner-Friedman bound below which they become stable against Hawking radiation and Schwinger effect of charge emission. We illuminate the origin of the catastrophic emission in the phase-integral formulation by comparing near-extremal charged Nariai black holes with near-extremal RN-dS black holes.Comment: 15 page

Quantum Critical Spin-2 Chain with Emergent SU(3) Symmetry

We study the quantum critical phase of a SU(2) symmetric spin-2 chain obtained from spin-2 bosons in a one-dimensional lattice. We obtain the scaling of the entanglement entropy and finite-size energies by exact diagonalization and density-matrix renormalization group methods. From the numerical results of the energy spectrum, central charge, and scaling dimension we identify the conformal field theory describing the whole critical phase to be the SU(3)$_1$ Wess-Zumino-Witten model. We find that while in the whole critical phase the Hamiltonian is only SU(2) invariant, there is an emergent SU(3) symmetry in the thermodynamic limit

Modality-Independent Teachers Meet Weakly-Supervised Audio-Visual Event Parser

Audio-visual learning has been a major pillar of multi-modal machine learning, where the community mostly focused on its modality-aligned setting, i.e., the audio and visual modality are both assumed to signal the prediction target. With the Look, Listen, and Parse dataset (LLP), we investigate the under-explored unaligned setting, where the goal is to recognize audio and visual events in a video with only weak labels observed. Such weak video-level labels only tell what events happen without knowing the modality they are perceived (audio, visual, or both). To enhance learning in this challenging setting, we incorporate large-scale contrastively pre-trained models as the modality teachers. A simple, effective, and generic method, termed Visual-Audio Label Elaboration (VALOR), is innovated to harvest modality labels for the training events. Empirical studies show that the harvested labels significantly improve an attentional baseline by 8.0 in average F-score (Type@AV). Surprisingly, we found that modality-independent teachers outperform their modality-fused counterparts since they are noise-proof from the other potentially unaligned modality. Moreover, our best model achieves the new state-of-the-art on all metrics of LLP by a substantial margin (+5.4 F-score for Type@AV). VALOR is further generalized to Audio-Visual Event Localization and achieves the new state-of-the-art as well. Code is available at: https://github.com/Franklin905/VALOR

Semantic Segmentation Using Super Resolution Technique as Pre-Processing

Combining high-level and low-level visual tasks is a common technique in the field of computer vision. This work integrates the technique of image super resolution to semantic segmentation for document image binarization. It demonstrates that using image super-resolution as a preprocessing step can effectively enhance the results and performance of semantic segmentation

IT Enabled Service Innovation In E-Government: The Case Of Taiwan Drug Abuse Reduction Service

Drug abuse problem is one of the toughest issues faced by governments in the world. The typical solution is every time when the drug abuse offenders are under arrest, they are jailed for a while. There is high probability that they will repeat the offense after leaving the prison. Thus, such a solution wastes lots of administrative resources from the government, yet still cannot reduce the recidivism of drug abuse. Nowadays, most countries treat drug abuse offenders as patients, and offer them substitute treatment in order to reduce the dependence on drug and also reduce the risk of infecting AIDS. The patients will go to work as a normal person, live as a normal person, and keep their human dignity. In this study, we introduce the care of Taiwan drug abuse reduction service by service blueprinting method. The service integrates several ministries of Taiwan government in signal information system, and will be triggered automatically when the drug abuse offender is leaving the prison. Subsequently, we analyze the case by the framework of Service Open System View and then provide some suggestions for improvement of the existing service. This study share the case of Taiwan drug abuse reduction service and provide the best practice of improving existing service by the view point of service science to academics

Learning Resolution-Invariant Deep Representations for Person Re-Identification

Person re-identification (re-ID) solves the task of matching images across cameras and is among the research topics in vision community. Since query images in real-world scenarios might suffer from resolution loss, how to solve the resolution mismatch problem during person re-ID becomes a practical problem. Instead of applying separate image super-resolution models, we propose a novel network architecture of Resolution Adaptation and re-Identification Network (RAIN) to solve cross-resolution person re-ID. Advancing the strategy of adversarial learning, we aim at extracting resolution-invariant representations for re-ID, while the proposed model is learned in an end-to-end training fashion. Our experiments confirm that the use of our model can recognize low-resolution query images, even if the resolution is not seen during training. Moreover, the extension of our model for semi-supervised re-ID further confirms the scalability of our proposed method for real-world scenarios and applications.Comment: Accepted to AAAI 2019 (Oral