A scale-based approach to finding effective dimensionality in manifold learning

The discovering of low-dimensional manifolds in high-dimensional data is one of the main goals in manifold learning. We propose a new approach to identify the effective dimension (intrinsic dimension) of low-dimensional manifolds. The scale space viewpoint is the key to our approach enabling us to meet the challenge of noisy data. Our approach finds the effective dimensionality of the data over all scale without any prior knowledge. It has better performance compared with other methods especially in the presence of relatively large noise and is computationally efficient.Comment: Published in at http://dx.doi.org/10.1214/07-EJS137 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

The color-singlet contribution to e^+ e^- ->J/psi + X at the endpoint

Recent observations of the J/psi spectrum produced in e^+e^- collisions at the Upsilon(4S) resonance are in conflict with fixed-order calculations using Non-Relativsitic QCD effective theory (NRQCD). One problem is an enhancement in the cross section when the J/psi has maximal energy, due to large perturbative corrections (Sudakov logarithms). In a recent paper, the Sudakov logarithms in the color-octet contribution were summed by combining NRQCD with the Soft-Collinear Effective Theory. However to be consistent, the color-singlet contributions must also be summed in the endpoint region which was not done in that paper. In this paper, we sum the leading and next-to-leading logarithms in the color-singlet contribution to the J/psi production cross section. We find that the color-singlet cross section is suppressed near endpoint compared to the fixed order NRQCD prediction.Comment: 17 pages, 7 figure

Understanding new products’ market performance using Google Trends

This paper seeks to empirically examine diffusion models and Google Trends’ ability to explain and nowcast the new product growth phenomenon. In addition to the selected diffusion models and Google Trends, this study proposes a new model that incorporates the two. The empirical analysis is based on the cases of the iPhone and the iPad. The results show that the new model exhibits a better curve fit among all the studied ones. In terms of nowcasting, although the performance of the new model differs from that of Google Trends in the two cases, they both produce more accurate results than the selected diffusion models

Stratified Transfer Learning for Cross-domain Activity Recognition

In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.Comment: 10 pages; accepted by IEEE PerCom 2018; full paper. (camera-ready version

Anaplastic Thyroid Carcinoma Histologically Mimicking a Plasmacytoma.

Anaplastic (undifferentiated) thyroid carcinoma (ATC) is a rare malignancy which may arise from transformation of a pre-existing differentiated carcinoma. We report the unique case where a lesion of thyroid origin presented with the histological features of mature plasma cells. Immunohistochemistry confirmed the lesion to be an anaplastic thyroid carcinoma arising from papillary thyroid carcinoma. A tumor mimicking a malignancy of a different cellular origin can lead clinicians to incorrect treatment approaches. Careful correlation with clinical details and knowledge of these unique presentations is important for reaching the correct diagnosis

Gene Co-expression Network and Copy Number Variation Analyses Identify Transcription Factors Associated With Multiple Myeloma Progression

Multiple myeloma (MM) has two clinical precursor stages of disease: monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). However, the mechanism of progression is not well understood. Because gene co-expression network analysis is a well-known method for discovering new gene functions and regulatory relationships, we utilized this framework to conduct differential co-expression analysis to identify interesting transcription factors (TFs) in two publicly available datasets. We then used copy number variation (CNV) data from a third public dataset to validate these TFs. First, we identified co-expressed gene modules in two publicly available datasets each containing three conditions: normal, MGUS, and SMM. These modules were assessed for condition-specific gene expression, and then enrichment analysis was conducted on condition-specific modules to identify their biological function and upstream TFs. TFs were assessed for differential gene expression between normal and MM precursors, then validated with CNV analysis to identify candidate genes. Functional enrichment analysis reaffirmed known functional categories in MM pathology, the main one relating to immune function. Enrichment analysis revealed a handful of differentially expressed TFs between normal and either MGUS or SMM in gene expression and/or CNV. Overall, we identified four genes of interest (MAX, TCF4, ZNF148, and ZNF281) that aid in our understanding of MM initiation and progression

A novel folic acid-conjugated TiO2–SiO2 photosensitizer for cancer targeting in photodynamic therapy

In this paper, a novel folic acid-conjugated silica-coated titanium dioxide (TiO2–SiO2) photosensitizer was synthesized and characterized using various analytical instruments. The photosensitizer was further assessed with regards to its photoreactivity, cellular and hemocompatibility, cell internalization, and phototoxicity. Conjugating folic acid with TiO2–SiO2 has shown a significantly improved compatibility of the nanoparticles with the mouse fibroblast cells (L929) at 24 h. An improved compatibility with the human nasopharyngeal epidermoid cancer (KB) cells was also demonstrated, but to a slightly reduced degree. Enhanced cell internalization was well demonstrated in the TiO2–SiO2 folate nanoparticles. Upon exposure to UV light, TiO2–SiO2 folate nanoparticles maintained a high level photodynamic reactivity and yielded a 38–43% photo-killing of KB cells. The photo-killing effect increased with increasing dosage in the investigated concentration range of 50–100 μg ml−1