Digital Transformations in Taiwanese TV Industry

In the past, TV was always regarded as an indispensable member of every family. Watching TV programs with the whole family was once one of the key consumer behaviors. However, with the development of technology, the digital wave and the invasion of Over-The-Top (OTT)platforms, consumer behavior has begun to undergo drastic changes. Mobile phones and tablets occupy most of our time. Multi-screens have long become the norm. According to the Digital Whirlpool report published by IMD in 2019: Due to the impact of digital convergence, digital disruption has already occurred in the media, entertainment, and telecommunications industries. If digital transformation is not carried out in time, the next five may be replaced by other new services . Observe that the number of cable TV subscribers in Taiwan has dropped from 5.23 million in 2017. With the influence of online platforms and online pirated content, it has fallen all the way to the current low of 4.83 million in 2021.Facing the changes in viewers’ viewing behaviors and the shift in TV advertising budgets in recent years, various TV stations have also provided solutions and actively transformed from internal thinking to external environments. TV stations such as TVBS, Eastern Broadcasting Company (EBC), Sanli TV and Ctitv have begun their digital transformation

Using Webpage Comparison Method for Automated Web Application Testing with Reinforcement Learning

Web application testing often uses crawlers to explore the application under test (AUT) and identify potential vulnerabilities. For dynamically generated pages, crawlers must provide test inputs for web forms. A previous tool combines a web crawler with a reinforcement learning agent, which uses code coverage to guide the crawler in filling web forms. This paper aims to improve the applicability of web application testing by using webpage comparison techniques instead of code coverage and source code access, thereby enhancing the handling of multiple web forms on a single page. Experimental results show that this approach explores more pages, reaches greater crawling depths, and achieves better code coverage than the original method. It also interacts more efficiently with multiple web forms and outperforms a random-action Monkey on new, untrained web applications. Therefore, this approach is promising for automated web application testing

Inflammatory cytokines and biofilm production sustain Staphylococcus aureus outgrowth and persistence: A pivotal interplay in the pathogenesis of Atopic Dermatitis

Individuals with Atopic dermatitis (AD) are highly susceptible to Staphylococcus aureus colonization. However, the mechanisms driving this process as well as the impact of S. aureus in AD pathogenesis are still incompletely understood. In this study, we analysed the role of biofilm in sustaining S. aureus chronic persistence and its impact on AD severity. Further we explored whether key inflammatory cytokines overexpressed in AD might provide a selective advantage to S. aureus. Results show that the strength of biofilm production by S. aureus correlated with the severity of the skin lesion, being significantly higher (P < 0.01) in patients with a more severe form of the disease as compared to those individuals with mild AD. Additionally, interleukin (IL)-β and interferon γ (IFN-γ), but not interleukin (IL)-6, induced a concentration-dependent increase of S. aureus growth. This effect was not observed with coagulase-negative staphylococci isolated from the skin of AD patients. These findings indicate that inflammatory cytokines such as IL1-β and IFN-γ, can selectively promote S. aureus outgrowth, thus subverting the composition of the healthy skin microbiome. Moreover, biofilm production by S. aureus plays a relevant role in further supporting chronic colonization and disease severity, while providing an increased tolerance to antimicrobials

High Mortality of Pneumonia in Cirrhotic Patients with Ascites

[[abstract]]Background Cirrhotic patients with ascites are prone to develop various infectious diseases. This study aimed to evaluate the occurrence and effect of major infectious diseases on the mortality of cirrhotic patients with ascites. Methods We reviewed de-identified patient data from the National Health Insurance Database, derived from the Taiwan National Health Insurance Program, to enroll 4,576 cirrhotic patients with ascites, who were discharged from Taiwan hospitals between January 1, 2004 and June 30, 2004. We collected patients’ demographic and clinical data, and reviewed diagnostic codes to determine infectious diseases and comorbid disorders of their hospitalizations. Patients were divided into an infection group and non-infection group and hazard ratios (HR) were determined for specific infectious diseases. Results Of the total 4,576 cirrhotic patients with ascites, 1,294 (28.2%) were diagnosed with infectious diseases during hospitalization. The major infectious diseases were spontaneous bacterial peritonitis (SBP) (645, 49.8%), urinary tract infection (151, 11.7%), and pneumonia (100, 7.7%). After adjusting for patients’ age, gender, and other comorbid disorders, the HRs of infectious diseases for 30-day and 90-day mortality of cirrhotic patients with ascites were 1.81 (1.54-2.11) and 1.60 (1.43-1.80) respectively, compared to those in the non-infection group. The adjusted HRs of pneumonia, urinary tract infection (UTI), spontaneous bacterial peritonitis (SBP), and sepsis without specific focus (SWSF) were 2.95 (2.05-4.25), 1.32 (0.86-2.05), 1.77 (1.45-2.17), and 2.19 (1.62-2.96) for 30-day mortality, and 2.57 (1.93-3.42), 1.36 (1.01-1.82), 1.51 (1.29-1.75), and 2.13 (1.70-2.66) for 90-day mortality, compared to those in the non-infection group. Conclusion Infectious diseases increased 30-day and 90-day mortality of cirrhotic patients with ascites. Among all infectious diseases identified, pneumonia carried the highest risk for mortality.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]電子

Inferring Genetic Interactions via a Data-Driven Second Order Model

Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R3) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm

SemPLeS: Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation

Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation models using image data with only image-level supervision. Since precise pixel-level annotations are not accessible, existing methods typically focus on producing pseudo masks for training segmentation models by refining CAM-like heatmaps. However, the produced heatmaps may capture only the discriminative image regions of object categories or the associated co-occurring backgrounds. To address the issues, we propose a Semantic Prompt Learning for WSSS (SemPLeS) framework, which learns to effectively prompt the CLIP latent space to enhance the semantic alignment between the segmented regions and the target object categories. More specifically, we propose Contrastive Prompt Learning and Prompt-guided Semantic Refinement to learn the prompts that adequately describe and suppress the co-occurring backgrounds associated with each target object category. In this way, SemPLeS can perform better semantic alignment between object regions and the associated class labels, resulting in desired pseudo masks for training the segmentation model. The proposed SemPLeS framework achieves SOTA performance on the standard WSSS benchmarks, PASCAL VOC and MS COCO, and shows compatibility with other WSSS methods. The source codes are provided in the supplementary

Significance testing in ridge regression for genetic data.

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