An Empirical Analysis about Population, Technological Progress, and Economic Growth in Taiwan

This paper empirically analyzed the relationship between population, technological progress, and economic growth in Taiwan from 1954 to 2005, using the LA-VAR (lag-augmented vector autoregression) model. The empirical results reveal that a major conformational change in the economic development of Taiwan after 2000.

Hospitalized Pediatric Parainfluenza Virus Infections in a Medical Center

Background/PurposeParainfluenza viruses (PIVs) are common pathogens in respiratory tract infections. The aims of this study were to determine the clinical presentation of PIV infections in hospitalized children and to identify particular clinical indications that may effectively distinguish between different PIV serotypes.MethodsA retrospective review of data from children hospitalized with PIV infections at the Mackay Memory Hospital in Taipei between January 2005 and December 2007 was undertaken. Symptoms, signs, laboratory findings and seasonal variations between different types of PIV (serotypes 1, 2 and 3) were compared.ResultsA total of 206 patients [119 (57.8%) boys and 87 (42.2%) girls] were enrolled in the study. Seventy-four (35.9%) patients were infected with PIV serotype 1, 25 (12.1%) with serotype 2 and 107 (51.9%) with serotype 3. The most common clinical presentations were fever (81.1%), cough (66.0%), rhinorrhea (44.2%) and hoarseness (22.3%); 4.9% of the infected children also had skin rashes. No significant differences were found in average white blood cell counts and C-reactive protein levels between the three serotypes. PIV serotype 1 infections were discernible throughout the year; serotype 2 tended to cluster in the late summer and autumn of 2005 and 2007; and serotype 3 was more common in the spring and early summer.ConclusionThe clinical presentation of PIV infection in hospitalized children ranges from upper respiratory tract infection to croup, bronchiolitis and viral bronchopneumonia, with the different types of PIV infections giving rise to similar symptoms. The seasonal distribution of the different serotypes is, nevertheless, quite distinct

MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild

Detecting small scene text instances in the wild is particularly challenging, where the influence of irregular positions and nonideal lighting often leads to detection errors. We present MixNet, a hybrid architecture that combines the strengths of CNNs and Transformers, capable of accurately detecting small text from challenging natural scenes, regardless of the orientations, styles, and lighting conditions. MixNet incorporates two key modules: (1) the Feature Shuffle Network (FSNet) to serve as the backbone and (2) the Central Transformer Block (CTBlock) to exploit the 1D manifold constraint of the scene text. We first introduce a novel feature shuffling strategy in FSNet to facilitate the exchange of features across multiple scales, generating high-resolution features superior to popular ResNet and HRNet. The FSNet backbone has achieved significant improvements over many existing text detection methods, including PAN, DB, and FAST. Then we design a complementary CTBlock to leverage center line based features similar to the medial axis of text regions and show that it can outperform contour-based approaches in challenging cases when small scene texts appear closely. Extensive experimental results show that MixNet, which mixes FSNet with CTBlock, achieves state-of-the-art results on multiple scene text detection datasets

Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits

Variational quantum circuit (VQC) is a promising approach for implementing quantum neural networks on noisy intermediate-scale quantum (NISQ) devices. Recent studies have shown that a tensor-train network (TTN) for VQC, namely TTN-VQC, can improve the representation and generalization powers of VQC. However, the Barren Plateau problem leads to the gradients of the cost function vanishing exponentially small as the number of qubits increases, making it difficult to find the optimal parameters for the VQC. To address this issue, we put forth a new learning approach called Pre+TTN-VQC that builds upon the TTN-VQC architecture by incorporating a pre-trained TTN to alleviate the Barren Plateau problem. The pre-trained TTN allows for efficient fine-tuning of target data, which reduces the depth of the VQC required to achieve good empirical performance and potentially alleviates the training obstacles posed by the Barren Plateau landscape. Furthermore, we highlight the advantages of Pre+TTN-VQC in terms of representation and generalization powers by exploiting the error performance analysis. Moreover, we characterize the optimization performance of Pre+TTN-VQC without the need for the Polyak-Lojasiewicz condition, thereby enhancing the practicality of implementing quantum neural networks on NISQ devices. We conduct experiments on a handwritten digit classification dataset to corroborate our proposed methods and theorems.Comment: 17 pages, 6 figures. In submissio

The Effectiveness of Traditional Chinese Medicine in Treating Patients with Leukemia

Leukemia is the most common malignancy among all childhood cancers and is associated with a low survival rate in adult patients. Since 1995, the National Health Insurance (NHI) program in Taiwan has been offering insurance coverage for Traditional Chinese Medicine (TCM), along with conventional Western medicine (WM). This study analyzes the status of TCM utilization in Taiwan, in both pediatric and adult patients with leukemia. A retrospective cohort study was conducted using population-based National Health Insurance Research Database of Registry of Catastrophic Illness, involving patient data from 2001 to 2010 and follow-up data through 2011. The effectiveness of TCM use was evaluated. Relevant sociodemographic data showed that both pediatric and adult patients who were TCM users one year prior to leukemia diagnosis were more likely to utilize TCM services for cancer therapy. A greater part of medical expenditure of TCM users was lower than that of TCM nonusers, except little discrepancy in drug fee of adult patients. The survival rate is also higher in TCM users. Altogether, these data show that TCM has the potential to serve as an adjuvant therapy when combined with conventional WM in the treatment of patients with leukemia

Emotion and Concentration Integrated System: Applied to the Detection and Analysis of Consumer Preference

With the expansion of consumer market, the appearance becomes an important issue when consumers make decisions under the situation of similar qualities and contents. Accordingly, to attract consumers, companies cost and take much attention on product appearance. Compared to using questionnaires individually, obtaining humans’ thoughts directly from their brains can accurately grasp the actual preference of consumers, which can provide effective and precious decisions for companies. \ In this study, consumers’ brainwaves which are related to concentration and emotion are extracted by wearing a portable and wireless Electroencephalography (EEG) device. The extracted EEG data are then trained by using perceptron learning algorithm (PLA) to make the judgments of concentration and emotion work well with each subject. They are then applied to the detection and analysis of consumer preference. Finally, the questionnaires are also performed and used as the reference on training process. They are integrated with brainwaves data to create one prediction model which can improve the accuracy significantly. The Partial Least Squares is used to compare the correlation between different factors in the model, to ensure the test can accurately meet consumers’ thoughts

Osteopontin mediates tumorigenic transformation of a preneoplastic murine cell line by suppressing anoikis: An Arg‐Gly‐Asp‐dependent‐focal adhesion kinase‐caspase‐8 axis

Osteopontin (OPN), an adhesive, matricellular glycoprotein, is a rate‐limiting factor in tumor promotion of skin carcinogenesis. With a tumor promotion model, the JB6 Cl41.5a cell line, we have shown that suppressing 12‐O‐tetradecanoylphorbol‐13‐acetate (TPA)‐induced OPN expression markedly inhibits TPA‐induced colony formation in soft agar, an assay indicative of tumorigenic transformation. Further, the addition of exogenous OPN promotes colony formation of these cells. These findings support a function of OPN in mediating TPA‐induced neoplastic transformation of JB6 cells. In regard to the mechanism of action by OPN, we hypothesized that, for JB6 cells grown in soft‐agar, secreted OPN induced by TPA stimulates cell proliferation and/or prevents anoikis to facilitate TPA‐induced colony formation. Analyses of cell cycle and cyclin D1 expression, and direct cell counting of JB6 cells treated with OPN indicate that OPN does not stimulate cell proliferation relative to non‐treated controls. Instead, at 24 h, OPN decreases anoikis by 41%, as assessed by annexin V assays. Further, in suspended cells OPN suppresses caspase‐8 activation, which is mediated specifically through its RGD‐cell binding motif that transduces signals through integrin receptors. Transfection studies with wild‐type and mutant focal adhesion kinases (FAK) and Western blot analyses suggest that OPN suppression of caspase‐8 activation is mediated through phosphorylation of FAK at Tyr861. In summary, these studies indicate that induced OPN is a microenvironment modulator that facilitates tumorigenic transformation of JB6 cells by inhibiting anoikis through its RGD‐dependent suppression of caspase‐8 activity, which is mediated in part through the activation of FAK at Tyr861. © 2013 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111135/1/mc22108.pd

SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking

Despite recent progress in Multiple Object Tracking (MOT), several obstacles such as occlusions, similar objects, and complex scenes remain an open challenge. Meanwhile, a systematic study of the cost-performance tradeoff for the popular tracking-by-detection paradigm is still lacking. This paper introduces SMILEtrack, an innovative object tracker that effectively addresses these challenges by integrating an efficient object detector with a Siamese network-based Similarity Learning Module (SLM). The technical contributions of SMILETrack are twofold. First, we propose an SLM that calculates the appearance similarity between two objects, overcoming the limitations of feature descriptors in Separate Detection and Embedding (SDE) models. The SLM incorporates a Patch Self-Attention (PSA) block inspired by the vision Transformer, which generates reliable features for accurate similarity matching. Second, we develop a Similarity Matching Cascade (SMC) module with a novel GATE function for robust object matching across consecutive video frames, further enhancing MOT performance. Together, these innovations help SMILETrack achieve an improved trade-off between the cost ({\em e.g.}, running speed) and performance (e.g., tracking accuracy) over several existing state-of-the-art benchmarks, including the popular BYTETrack method. SMILETrack outperforms BYTETrack by 0.4-0.8 MOTA and 2.1-2.2 HOTA points on MOT17 and MOT20 datasets. Code is available at https://github.com/pingyang1117/SMILEtrack_Officia

Sparse motion bases selection for human motion denoising

Human motion denoising is an indispensable step of data preprocessing for many motion data based applications. In this paper, we propose a data-driven based human motion denoising method that sparsely selects the most correlated subset of motion bases for clean motion reconstruction. Meanwhile, it takes the statistic property of two common noises, i.e., Gaussian noise and outliers, into account in deriving the objective functions. In particular, our method firstly divides each human pose into five partitions termed as poselets to gain a much fine-grained pose representation. Then, these poselets are reorganized into multiple overlapped poselet groups using a lagged window moving across the entire motion sequence to preserve the embedded spatial 13temporal motion patterns. Afterward, five compacted and representative motion dictionaries are constructed in parallel by means of fast K-SVD in the training phase; they are used to remove the noise and outliers from noisy motion sequences in the testing phase by solving !131-minimization problems. Extensive experiments show that our method outperforms its competitors. More importantly, compared with other data-driven based method, our method does not need to specifically choose the training data, it can be more easily applied to real-world applications