Secretory Carcinoma: A Silent Mass Increasing in the Parotid Gland

BACKGROUND: Secretory carcinoma (SC) of the salivary gland, also known as mammary analog secretory carcinoma, is a rare tumor in the parotid gland. This kind of tumor is characterized by generally indolent clinical behavior and expression of a break in the ETV6 gene. CASE REPORT: We present a unique case of secretory carcinoma and show its favorable prognoses. CONCLUSION: Secretory carcinoma of the salivary gland is a low-grade carcinoma with a favorable prognosis. It has low regional lymph node and distant metastasis potential. Due to the possibility of misdiagnosis, immunohistochemical studies and FISH are suggested. The most effective treatment is complete surgical excision with negative surgical margins

Integrated Degradation Models in R Using iDEMO

Degradation models are widely used to assess the lifetime information for highly reliable products with quality characteristics whose degradation over time can be related to reliability. The performance of a degradation model largely depends on an appropriate model description of the product's degradation path. The cross-platform package iDEMO (integrated degradation models) is developed in R and the interface is built using the Tcl/Tk bindings provided by the tcltk and tcltk2 packages included with R. It is a tool to build a linear degradation model which can simultaneously consider the unit-to-unit variation, time-dependent structure and measurement error in the degradation paths. The package iDEMO provides the maximum likelihood estimates of the unknown parameters, mean-time-to-failure and q-th quantile, and their corresponding confidence intervals based on the different information matrices. In addition, degradation model selection and goodness-of-fit tests are provided to determine and diagnose the degradation model for the user's current data by the commonly used criteria. By only enabling user interface elements when necessary, input errors are minimized

Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.Comment: CVPR 2018 Spotligh

The International Decision-Making and Travel Behavior of Graduates Participating in Working Holiday

After graduation, most graduates find themselves at a significant stage in their life as they have to decide between “further study” and “working.” When faced with this confusion and uncertainty, a “working holiday” combining travel and work has coincidentally becomes a third option. This study employed a qualitative approach through literature review, in-depth interviews, and semi-structured interviews. The research revealed that graduates are influenced by “internal personal thinking” and “external driving forces” when they embark on a working holiday. The former includes negative obstructions and positive stimulus. The latter factor’s stimulus includes attraction of natural landscapes, history and culture, learning foreign languages, safety concerns, difficulties in visa application, and the opportunity to obtain a salaried job. The process of embarking on a working holiday was complex and unpredictable. The traveling behavior of working holiday destinations included short-distance leisure behavior and long-distance traveling behavior. In terms of the influences of short-distance leisure behavior, graduates preferred being employed by service industries that had less working hours, flexible work arrangements and included the purchase of preferential price tickets. Graduates’ long-distance traveling behavior was affected by the work they performed. The travel time was different between various industries

Algorithm of Impact Point Prediction for Intercepting Reentry Vehicles

Intercepting reentry vehicles is difficult because these move nearly at hypersonic speedsthat traditional interceptors cannot match. Counterparallel guidance law was developed fordefending a high speed target that guides the interceptor to intercept the target at a 180° aspectangle. When applying the counterparallel guidance law, it is best to predict the impact pointbefore launch. Estimation and prediction of a reentry vehicle path are the first steps in establishingthe impact point prediction algorithm. Model validation is a major challenge within the overalltrajectory estimation problem. The adaptive Kalman filter, consising of an extended Kalman filterand a recursive input estimator, accurately estimates reentry vehicle trajectory by means of aninput estimator which processes the model validation problem. This investigation presents analgorithm of impact point prediction for a reentry vehicle and an interceptor at an optimal interceptaltitude based on the adaptive Kalman filter. Numerical simulation using a set of data, generatedfrom a complicated model, verifies the accuracy of the proposed algorithm. The algorithm alsoperforms exceptionally well using a set of flight test data. The presented algorithm is effectivein solving the intercept problems

Mining association language patterns using a distributional semantic model for negative life event classification

AbstractPurposeNegative life events, such as the death of a family member, an argument with a spouse or the loss of a job, play an important role in triggering depressive episodes. Therefore, it is worthwhile to develop psychiatric services that can automatically identify such events. This study describes the use of association language patterns, i.e., meaningful combinations of words (e.g., <loss, job>), as features to classify sentences with negative life events into predefined categories (e.g., Family, Love, Work).MethodsThis study proposes a framework that combines a supervised data mining algorithm and an unsupervised distributional semantic model to discover association language patterns. The data mining algorithm, called association rule mining, was used to generate a set of seed patterns by incrementally associating frequently co-occurring words from a small corpus of sentences labeled with negative life events. The distributional semantic model was then used to discover more patterns similar to the seed patterns from a large, unlabeled web corpus.ResultsThe experimental results showed that association language patterns were significant features for negative life event classification. Additionally, the unsupervised distributional semantic model was not only able to improve the level of performance but also to reduce the reliance of the classification process on the availability of a large, labeled corpus