Compressed Sensing based Dynamic PSD Map Construction in Cognitive Radio Networks

In the context of spectrum sensing in cognitive radio networks, collaborative spectrum sensing has been proposed as a way to overcome multipath and shadowing, and hence increasing the reliability of the sensing. Due to the high amount of information to be transmitted, a dynamic compressive sensing approach is proposed to map the PSD estimate to a sparse domain which is then transmitted to the fusion center. In this regard, CRs send a compressed version of their estimated PSD to the fusion center, whose job is to reconstruct the PSD estimates of the CRs, fuse them, and make a global decision on the availability of the spectrum in space and frequency domains at a given time. The proposed compressive sensing based method considers the dynamic nature of the PSD map, and uses this dynamicity in order to decrease the amount of data needed to be transmitted between CR sensors’ and the fusion center. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 20 % of full data transmission between sensors and master node. Also, simulation results show the robustness of the proposed method against the channel variations, diverse compression ratios and processing times in comparison with static methods

A Generative Model for Parts-based Object Segmentation

The Shape Boltzmann Machine (SBM) [1] has recently been introduced as a stateof-the-art model of foreground/background object shape. We extend the SBM to account for the foreground object’s parts. Our new model, the Multinomial SBM (MSBM), can capture both local and global statistics of part shapes accurately. We combine the MSBM with an appearance model to form a fully generative model of images of objects. Parts-based object segmentations are obtained simply by performing probabilistic inference in the model. We apply the model to two challenging datasets which exhibit significant shape and appearance variability, and find that it obtains results that are comparable to the state-of-the-art. There has been significant focus in computer vision on object recognition and detection e.g. [2], but a strong desire remains to obtain richer descriptions of objects than just their bounding boxes. One such description is a parts-based object segmentation, in which an image is partitioned into multiple sets of pixels, each belonging to either a part of the object of interest, or its background. The significance of parts in computer vision has been recognized since the earliest days of th

Consensus Message Passing for Layered Graphical Models

Generative models provide a powerful framework for probabilistic reasoning. However, in many domains their use has been hampered by the practical difficulties of inference. This is particularly the case in computer vision, where models of the imaging process tend to be large, loopy and layered. For this reason bottom-up conditional models have traditionally dominated in such domains. We find that widely-used, general-purpose message passing inference algorithms such as Expectation Propagation (EP) and Variational Message Passing (VMP) fail on the simplest of vision models. With these models in mind, we introduce a modification to message passing that learns to exploit their layered structure by passing 'consensus' messages that guide inference towards good solutions. Experiments on a variety of problems show that the proposed technique leads to significantly more accurate inference results, not only when compared to standard EP and VMP, but also when compared to competitive bottom-up conditional models.Comment: Appearing in Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS) 201

Stretching An Anisotropic DNA

We present a perturbation theory to find the response of an anisotropic DNA to the external tension. It is shown that the anisotropy has a nonzero but small contribution to the force-extension curve of the DNA. Thus an anisotropic DNA behaves like an isotropic one with an effective bending constant equal to the harmonic average of its soft and hard bending constants.Comment: 29 pages and 4 figure. To appear in J. Chem. Phy

Meta-analyses: Does long-term PPI use increase the risk of gastric premalignant lesions?

Background: Proton pump inhibitors (PPIs) are the most effective agents available for reducing acid secretion. They are used for medical treatment of various acid-related disorders. PPIs are used extensively and for extended periods of time in gastroesophageal reflux disease (GERD). A troublesome issue regarding maintenance therapy has been the propensity of PPI-treated patients to develop chronic atrophic gastritis while on therapy that could theoretically lead to an increased incidence of gastric cancer. In addition, animal studies have raised concern for development of enterochromaffin-like cell hyperplasia and carcinoid tumors in the stomachs of mice receiving high dose PPIs. Current literature does not provide a clear-cut conclusion on the subject and the reports are sometimes contradictory. Therefore, this study is a systematic review of the available literature to address the safety of long-term PPI use and its relation to the development of malignant/premalignant gastric lesions. Methods: A literature search of biomedical databases was performed. The reference lists of retrieved articles were reviewed to further identify relevant trials. We hand-searched the abstracts of the American Digestive Disease Week (DDW) and the United European Gastroenterology Week (UEGW) from 1995 to 2013. Only randomized clinical trials (RCTs) that used PPIs as the primary treatment for at least six month versus no treatment, placebo, antacid or anti-reflux surgery (ARS) were included. Two reviewers independently extracted the data. Discrepancies in the interpretation were resolved by consensus. All analyses of outcomes were based on the intention-to-treat principle. We performed statistical analysis using Review Manager software. The effect measure of choice was relative risk (RR) for dichotomous data. Results: Six RCTs with a total of 785 patients met the inclusion criteria. Two multicenter RCTs compared Esomeprazole with placebo. One RCT compared omeprazole with ARS. Two RCTs compared omeprazole with ranitidine and one RCT compared lansoprazole with ranitidine. Four of the included RCTs had moderate risk of bias and two had low risk of bias. The number of patients with increased corporal atrophy score, intestinal metaplasia score and chronic antral inflammation did not statistically differ between the PPI maintenance group and controls. Similar results were found when ECL-cell hyperplasia was assessed between the groups. ConclusionS: Maintenance PPIs did not have an association with increased gastric atrophic changes or ECL-cell hyperplasia for at least three years in RCTs

Extreme bendability of DNA double helix due to bending asymmetry

Experimental data of the DNA cyclization (J-factor) at short length scales, as a way to study the elastic behavior of tightly bent DNA, exceed the theoretical expectation based on the wormlike chain (WLC) model by several orders of magnitude. Here, we propose that asymmetric bending rigidity of the double helix in the groove direction can be responsible for extreme bendability of DNA at short length scales and it also facilitates DNA loop formation at these lengths. To account for the bending asymmetry, we consider the asymmetric elastic rod (AER) model which has been introduced and parametrized in an earlier study (B. Eslami-Mossallam and M. Ejtehadi, Phys. Rev. E 80, 011919 (2009)). Exploiting a coarse grained representation of DNA molecule at base pair (bp) level, and using the Monte Carlo simulation method in combination with the umbrella sampling technique, we calculate the loop formation probability of DNA in the AER model. We show that, for DNA molecule has a larger J-factor compared to the WLC model which is in excellent agreement with recent experimental data.Comment: 8 pages, 9 figure

Detection of human papillomavirus DNA sequences in oral lesions using polymerase chain reaction

The purpose of the present study was to estimate the frequency of HPV DNA in four groups of oral lesions, including oral squamous cell carcinoma. Sixty paraffin-embedded oral tissue samples were examined for the presence of HPV DNAs using the PCR technique. These specimens were obtained from patients with oral squamous cell carcinoma (OSCC), leukoplakia, oral lichen planus (OLP), and pyogenic granuloma (PG). Consensus primers for L1 region (MY09 and MY11) and specific primers were used for detection of HPV DNA sequences in this study. we detected HPV DNA in 60% (9 out of 15) of OSCCs, 26.7% (4 out of 15) of leukoplakia, 13.3% (2 out of 15) of OLPs, and 6.7% (1 out of 15) of PGs. Statistical analysis showed that the prevalence of HPV in OSCC was significantly higher than other groups (P < 0.05). The frequency of HPV-16 and 18 detection in OSCC samples were 40% and 20%, respectively. The prevalence of these high risk HPVs was significantly higher in OSCC group (P < 0.05). The results of the present study show a successive increase of detection rate of HPV-16 and 18 DNAs from low level in samples of pyogenic granuloma and non-premalignant or questionably premalignant lesions of OLP to premalignant leukoplakia and to OSCC. © 2007 Tehran University of Medical Sciences. All rights reserved

Search Bias Quantification: Investigating Political Bias in Social Media and Web Search

Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.publishe