Improvement of mammographic mass characterization using spiculation measures and morphological features

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135119/1/mp1548.pd

Türkiye’de fizyoloji lisansüstü eğitimine genel bakış

A questionnaire was prepared to determine the current situation, characteristics, main problems and solution proposals of post-graduate physiology education in Turkey. The questionnaire was answered by 40% of the physiology departments with post-graduate programs. The results of the questionnaire demonstrate that 31% of master students and 45% of PhD students have academic positions. Most of the post-graduate physiology students are employed in the "academic staff training program" (62% of master and 59% of PhD students). Post-graduate physiology students were mainly composed of biologists (25%) and medical doctors (21%). All or the majority (81%) of postgraduate students have completed their education within the legal periods. We have observed that post-graduate physiology students do not sufficiently benefit from the national and international student exchange programs, scholarships and do not participate in academic activities. Publication rates of the post-graduate thesis in national and international journals are also below the anticipated level. The general problems faced in providing post-graduate physiology education are insufficiency of available academic positions, scholarships, number of academic staff, inadequate financial support in producing qualified research as well as lack of infrastructure. The results of the questionnaire demonstrate that comprehensive studies with broad participation are necessary in order to improve post-graduate education in our country

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134952/1/mp8389.pd

Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods

The present paper examines the relative out-of-sample predictive ability of GARCH, GARCH-M, EGARCH, TGARCH and PGARCH models for ten Asian markets by using three different time frames and two different methods, considering the features of volatility clustering, leverage effect and volatility persistence phenomena, for which the evidence of existence is found in the data. Five measures of comparison are employed in this research, and a further dimension is investigated based on the classification of the selected models, in order to identify the existence or lack of any differences between the recursive and rolling window methods. The empirical results reveal that asymmetric models, led by the EGARCH model, provide better forecasts compared to symmetric models in higher time frames. However, when it comes to lower time frames, symmetric GARCH models tend to outperform their asymmetric counterparts. Furthermore, linear GARCH models are penalized more by the rolling window method, while recursive method places them amongst the best performers, highlighting the importance of choosing a proper approach. In addition, this study reveals an important controversy: that one error statistic may suggest a particular model is the best, while another suggests the same model to be the worst, indicating that the performance of the model heavily depends on which loss function is used. Finally, it is proved that GARCH-type models can appropriately adapt to the volatility of Asian stock indices and provide a satisfactory degree of forecast accuracy in all selected time frames. These results are also supported by the Diebold-Mariano (DM) pairwise comparison test

A new automated method for the segmentation and characterization of breast masses on ultrasound images

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134986/1/mp0069.pd

Pulse Frequency Fluctuations of Magnetars

Using \emph{RXTE}, \emph{Chandra}, \emph{XMM-Newton} and \emph{Swift} observations, we for the first time construct the power spectra and torque noise strengths of magnetars. For some of the sources, we measure strong red noise on timescales months to years which might be a consequence of their outbursts. We compare noise strengths of magnetars with those of radio pulsars by investigating possible correlations of noise strengths with spin-down rate, magnetic field and age. Using these correlations, we find that magnetar noise strengths are obeying similar trends with radio pulsars. On the contrary, we do not find any correlation between noise strength and X-ray luminosity which was seen in accretion powered pulsars. Our findings suggest that the noise behaviour of magnetars resembles that of radio pulsars but they possess higher noise levels likely due to their stronger magnetic fields.Comment: 18 pages, 1 table, 4 figures, accepted for publication in MNRA

Do artificial neural networks provide improved volatility forecasts: evidence from Asian markets

This paper enters the ongoing volatility forecasting debate by examining the ability of a wide range of Machine Learning methods (ML), and specifically Artificial Neural Network (ANN) models. The ANN models are compared against traditional econometric models for ten Asian markets using daily data for the time period from 12 September 1994 to 05 March 2018. The empirical results indicate that ML algorithms, across the range of countries, can better approximate dependencies compared to traditional benchmark models. Notably, the predictive performance of such deep learning models is superior perhaps due to its ability in capturing long-range dependencies. For example, the Neuro Fuzzy models of ANFIS and CANFIS, which outperform the EGARCH model, are more flexible in modelling both asymmetry and long memory properties. This offers new insights for Asian markets. In addition to standard statistics forecast metrics, we also consider risk management measures including the value-at-risk (VaR) average failure rate, the Kupiec LR test, the Christoffersen independence test, the expected shortfall (ES) and the dynamic quantile test. The study concludes that ML algorithms provide improving volatility forecasts in the stock markets of Asia and suggest that this may be a fruitful approach for risk management

The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohorts

Randomized clinical trials, while often viewed as the highest evidentiary bar by which to judge the quality of a medical intervention, are far from perfect. In silico imaging trials are computational studies that seek to ascertain the performance of a medical device by collecting this information entirely via computer simulations. The benefits of in silico trials for evaluating new technology include significant resource and time savings, minimization of subject risk, the ability to study devices that are not achievable in the physical world, allow for the rapid and effective investigation of new technologies and ensure representation from all relevant subgroups. To conduct in silico trials, digital representations of humans are needed. We review the latest developments in methods and tools for obtaining digital humans for in silico imaging studies. First, we introduce terminology and a classification of digital human models. Second, we survey available methodologies for generating digital humans with healthy and diseased status and examine briefly the role of augmentation methods. Finally, we discuss the trade-offs of four approaches for sampling digital cohorts and the associated potential for study bias with selecting specific patient distributions