6,741 research outputs found
Assessment of intervention measures for the 2003 SARS epidemic in Taiwan by use of a back-projection method
OBJECTIVES. To reconstruct the infection curve for the 2003 severe acute respiratory syndrome (SARS) epidemic in Taiwan and to ascertain the temporal changes in the daily number of infections that occurred during the course of the outbreak. METHOD. Back-projection method. RESULTS. The peaks of the epidemic correspond well with the occurrence of major infection clusters in the hospitals. The overall downward trend of the infection curve after early May corresponds well to the date (May 10) when changes in the review and classification procedure were implemented by the SARS Prevention and Extrication Committee. CONCLUSION. The major infection control measures taken by the Taiwanese government over the course of the SARS epidemic, particularly those regarding infection control in hospitals, played a crucial role in containing the outbreak. © 2007 by The Society for Healthcare Epidemiology of America. All rights reserved.published_or_final_versio
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features
One-class support vector machine (OC-SVM) for a long time has been one of the
most effective anomaly detection methods and extensively adopted in both
research as well as industrial applications. The biggest issue for OC-SVM is
yet the capability to operate with large and high-dimensional datasets due to
optimization complexity. Those problems might be mitigated via dimensionality
reduction techniques such as manifold learning or autoencoder. However,
previous work often treats representation learning and anomaly prediction
separately. In this paper, we propose autoencoder based one-class support
vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier
features to approximate the radial basis kernel, into deep learning context by
combining it with a representation learning architecture and jointly exploit
stochastic gradient descent to obtain end-to-end training. Interestingly, this
also opens up the possible use of gradient-based attribution methods to explain
the decision making for anomaly detection, which has ever been challenging as a
result of the implicit mappings between the input space and the kernel space.
To the best of our knowledge, this is the first work to study the
interpretability of deep learning in anomaly detection. We evaluate our method
on a wide range of unsupervised anomaly detection tasks in which our end-to-end
training architecture achieves a performance significantly better than the
previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201
Deep level defect in Si-implanted GaN n +-p junction
The results of deep level transient spectroscopy (DLTS) experiments on GaN junctions, fabricated by silicon implantation, were discussed. An unusual appearance of a minority peak in the majority carrier DLTS spectra within the interfacial region of the junctions was observed. The presence of this minority peak suggested a high concentration of a deep level defect within the interfacial region.published_or_final_versio
Luminescent zinc(ii) and copper( i) complexes for high-performance solution-processed monochromic and white organic light-emitting devices
published_or_final_versio
Non-Redundant Spectral Dimensionality Reduction
Spectral dimensionality reduction algorithms are widely used in numerous
domains, including for recognition, segmentation, tracking and visualization.
However, despite their popularity, these algorithms suffer from a major
limitation known as the "repeated Eigen-directions" phenomenon. That is, many
of the embedding coordinates they produce typically capture the same direction
along the data manifold. This leads to redundant and inefficient
representations that do not reveal the true intrinsic dimensionality of the
data. In this paper, we propose a general method for avoiding redundancy in
spectral algorithms. Our approach relies on replacing the orthogonality
constraints underlying those methods by unpredictability constraints.
Specifically, we require that each embedding coordinate be unpredictable (in
the statistical sense) from all previous ones. We prove that these constraints
necessarily prevent redundancy, and provide a simple technique to incorporate
them into existing methods. As we illustrate on challenging high-dimensional
scenarios, our approach produces significantly more informative and compact
representations, which improve visualization and classification tasks
Recent translational research: stem cells as the roots of breast cancer
Common phenotypes of cancer and stem cells suggest that breast cancers arise from stem cells. Breast epithelial cells with stem cell phenotypes have been shown to be more susceptible to immortalization and neoplastic transformation. Breast tumor stem cells with CD44(+)/CD24(-/low)Lineage(- )markers have been isolated. The role of these cells in tumor progression and clinical outcome is not clear. The relationship between breast stem cell and tumor stem cell may be elucidated by further studies of carcinogenesis of nonadherent mammosphere cells with stem cell features and by derivation of CD44(+)/CD24(-/low )cells from an adherent breast epithelial stem cell type
Clinical significance of serological biomarkers and neuropsychological performances in patients with temporal lobe epilepsy
<p>Abstract</p> <p>Background</p> <p>Temporal lobe epilepsy (TLE) is a common form of focal epilepsy. Serum biomarkers to predict cognitive performance in TLE patients without psychiatric comorbidities and the link with gray matter (GM) atrophy have not been fully explored.</p> <p>Methods</p> <p>Thirty-four patients with TLE and 34 sex - and age-matched controls were enrolled for standardized cognitive tests, neuroimaging studies as well as measurements of serum levels of heat shock protein 70 (HSP70), S100ß protein (S100ßP), neuronal specific enolase (NSE), plasma nuclear and mitochondrial DNA levels.</p> <p>Results</p> <p>Compared with the controls, the patients with TLE had poorer cognitive performances and higher HSP70 and S100ßP levels (<it>p </it>< 0.01). The patients with higher frequencies of seizures had higher levels of HSP70, NSE and S100ßP (<it>p </it>< 0.01). Serum HSP70 level correlated positively with duration of epilepsy (σ = 0.413, <it>p </it>< 0.01), and inversely with memory scores in the late registration (σ = −0.276, <it>p </it>= 0.01) and early recall score (σ = −0.304, <it>p </it>= 0.007). Compared with the controls, gray matter atrophy in the hippocampal and parahippocampal areas, putamen, thalamus and supplementary motor areas were found in the patient group. The HSP70 levels showed an inverse correlation with hippocampal volume (R square = 0.22, <it>p </it>= 0.007) after controlling for the effect of age.</p> <p>Conclusions</p> <p>Our results suggest that serum biomarkers were predictive of higher frequencies of seizures in the TLE group. HSP70 may be considered to be a stress biomarker in patients with TLE in that it correlated inversely with memory scores and hippocampal volume. In addition, the symmetric extratemporal atrophic patterns may be related to damage of neuronal networks and epileptogenesis in TLE.</p
Logics of Finite Hankel Rank
We discuss the Feferman-Vaught Theorem in the setting of abstract model
theory for finite structures. We look at sum-like and product-like binary
operations on finite structures and their Hankel matrices. We show the
connection between Hankel matrices and the Feferman-Vaught Theorem. The largest
logic known to satisfy a Feferman-Vaught Theorem for product-like operations is
CFOL, first order logic with modular counting quantifiers. For sum-like
operations it is CMSOL, the corresponding monadic second order logic. We
discuss whether there are maximal logics satisfying Feferman-Vaught Theorems
for finite structures.Comment: Appeared in YuriFest 2015, held in honor of Yuri Gurevich's 75th
birthday. The final publication is available at Springer via
http://dx.doi.org/10.1007/978-3-319-23534-9_1
"Detachment of the carinal hook following endobronchial intubation with a double lumen tube"
<p>Abstract</p> <p>Background</p> <p>Carinal hooks increases difficulty at endotracheal intubation. Amputation of the carinal hook during passage and malpositioning of the tube to the hook are some of the potential problems related with left-sided Carlens double lumen tube (DLT). This article reports an amputation of the hook during a difficult selective intubation and aimed at calling the attention to complications associated with DLTs and the importance of fiberoptic bronchoscopy.</p> <p>Case presentation</p> <p>A 68 year-old woman was scheduled for right-sided thoracotomy in whom blind DLT insertion was performed. Narrowed trachea causes difficulty in rotating the DLT 90° counter-clockwise. After carinal hook was noticed upon visual inspection of the DLT, fiberoptic bronchoscopy was used to remove the missing part (with the use of forceps) from the right mainstem bronchus.</p> <p>Conclusion</p> <p>Insertion of DLTs with carinal hook is associated with technical problems and potentially life-threatening hazards have discouraged their use. Fiberoptic evaluation and repositioning solves most of the problems. Although amputation of the carinal hook has not been previously reported, clinicians should be alert. This case report emphasizes the utility of the fiberoptic bronchoscopy in the operating theatre for placement, positioning and inspection of the carinal hook DLT.</p
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