Statistical approach based iris recognition using local binary pattern

Among biometric features utilized for identity recognition purposes, iris has proven to be the most reliable one in terms of sufficient distinctiveness, which has direct implications and importance towards improving the performance and safety of the security verification process through which it is decided whether any instance at hand should be granted permission to access preserved locations or sources of information. This paper deals with the main challenge involved in iris recognition, which lies in its comparatively high computational complexity, having remained unresolved heretofore, at least, as far as the existing literature is concerned. The enhancement brought about by the proposed methodology originates from taking advantage of local binary patterns for processing each segment of the original image, having undergone equalization in advance, as well as applying probability distribution functions separately to every layer of the pixel values, whereas being represented with respect to mutually-independent hue-saturation-intensity color channels. Besides, the Kullback-Leibler Distance between the vectors obtained through concatenation of the feature vectors is taken into account as the classification criterion, which has led to an outstanding recognition rate of 98.44 percent when tested on the UPOL database, with 192 iris images

Distilling Information Reliability and Source Trustworthiness from Digital Traces

Online knowledge repositories typically rely on their users or dedicated editors to evaluate the reliability of their content. These evaluations can be viewed as noisy measurements of both information reliability and information source trustworthiness. Can we leverage these noisy evaluations, often biased, to distill a robust, unbiased and interpretable measure of both notions? In this paper, we argue that the temporal traces left by these noisy evaluations give cues on the reliability of the information and the trustworthiness of the sources. Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness. Furthermore, we develop an efficient convex optimization procedure to learn the parameters of the model from historical traces. Experiments on real-world data gathered from Wikipedia and Stack Overflow show that our modeling framework accurately predicts evaluation events, provides an interpretable measure of information reliability and source trustworthiness, and yields interesting insights about real-world events.Comment: Accepted at 26th World Wide Web conference (WWW-17

Decentralized Dictionary Learning Over Time-Varying Digraphs

This paper studies Dictionary Learning problems wherein the learning task is distributed over a multi-agent network, modeled as a time-varying directed graph. This formulation is relevant, for instance, in Big Data scenarios where massive amounts of data are collected/stored in different locations (e.g., sensors, clouds) and aggregating and/or processing all data in a fusion center might be inefficient or unfeasible, due to resource limitations, communication overheads or privacy issues. We develop a unified decentralized algorithmic framework for this class of nonconvex problems, which is proved to converge to stationary solutions at a sublinear rate. The new method hinges on Successive Convex Approximation techniques, coupled with a decentralized tracking mechanism aiming at locally estimating the gradient of the smooth part of the sum-utility. To the best of our knowledge, this is the first provably convergent decentralized algorithm for Dictionary Learning and, more generally, bi-convex problems over (time-varying) (di)graphs

Medical image illumination enhancement and sharpening by using stationary wavelet transform

Medical images captured by various devices have different illumination states based on chemicals used by patient prior to scanning. Consider a MRI image which has low contrast or is too bright, hence the experts cannot analysis that image due to poor representation of data in the image. In this paper we are proposing new medical image illumination enhancement and sharpening technique based on stationary wavelet transform which is addressing the aforementioned problem. The technique decomposes the input medical image into the four frequency subbands by using stationary wavelet transformation and enhances the illumination of the low-low subband image, and then it enhanced edges of image by adding the high frequency subbands to the image. The technique is compared with the conventional and state-of-art image illumination enhancement techniques such as histogram equalisation, local histogram equalisation, singular value equalisation, and discrete wavelet transform followed by singular value decomposition contrast enhancement techniques. The experimental results are showing the superiority of the proposed method over the conventional and the state-of-art techniques

Probability distribution function based iris recognition boosted by the mean rule

In this work, a new iris recognition algorithm based on tonal distribution of iris images is introduced. During the process of identification probability distribution functions of colored irises are generated in HSI and YCbCr color spaces. The discrimination between classes is obtained by using Kullback-Leibler divergence. In order to obtain the final decision on recognition, the multi decision on various color channels has been combined by employing mean rule. The decisions of H, S, Y, Cb and Cr color channels have been combined. The proposed technique overcome the conventional principle component analysis technique and achieved a recognition rate of 100% using the UPOL database. The major advantage is the fact that it is computationally less complex than the Daugman\u27s algorithm and it is suitable for using visible light camera as opposed to the one proposed by Daugman where NIR cameras are used for obtaining the irises

Natural history and clinical effect of aortic valve regurgitation after left ventricular assist device implantation

ObjectivesAortic valve regurgitation reduces left ventricular assist device mechanical efficiency. Evidence has also suggested that left ventricular assist device implantation can induce or exacerbate aortic valve regurgitation. However, this has not been compared with aortic valve regurgitation progression in a nonsurgical end-stage heart failure population. Furthermore, its clinical effect is unclear. We sought to characterize the development and progression of aortic valve regurgitation in left ventricular assist device recipients and to identify its clinical effect.MethodsA review of all consecutive patients who received an intracorporeal left ventricular assist device at Duke University Medical Center from January 2004 to January 2011 was conducted. Cases of previous or concomitant aortic valve surgery were excluded. Data from the remaining implants (n = 184) and a control group of contemporaneous nonsurgical patients with end-stage heart failure (n = 132) were analyzed. Serial transthoracic echocardiography was used to characterize aortic valve regurgitation as a function of time.ResultsLeft ventricular assist device implantation was associated with worsening aortic valve regurgitation, defined as an increase in aortic valve regurgitation grade, relative to the nonsurgical patients with end-stage heart failure (P < .0001). The recipients of continuous flow left ventricular assist devices were more likely than recipients of pulsatile left ventricular assist devices to develop worsening aortic valve regurgitation (P = .0348). Moderate or severe aortic valve regurgitation developed in 21 left ventricular assist device recipients; this was unrelated to the type of device implanted (continuous vs pulsatile; P = .754) or aortic valve regurgitation grade before left ventricular assist device implantation (P = .42). Five patients developed severe aortic valve regurgitation; all of whom underwent aortic valve procedures.ConclusionsNative aortic valve regurgitation developed and/or progressed after left ventricular assist device implantation, with this effect being more pronounced in continuous flow left ventricular assist device recipients. However, the preoperative aortic valve regurgitation grade failed to correlate with the development of substantial aortic valve regurgitation after left ventricular assist device implantation. After left ventricular assist device implantation, aortic valve regurgitation had a small, but discernible, clinical effect, with some patients developing severe aortic valve regurgitation and requiring aortic valve procedures. These data have implications for the long-term management of left ventricular assist device recipients, in particular as the durability of implantable continuous flow left ventricular assist device therapy improves

An Environmental Science and Engineering Framework for Combating Antimicrobial Resistance

On June 20, 2017, members of the environmental engineering and science (EES) community convened at the Association of Environmental Engineering and Science Professors (AEESP) Biennial Conference for a workshop on antimicrobial resistance. With over 80 registered participants, discussion groups focused on the following topics: risk assessment, monitoring, wastewater treatment, agricultural systems, and synergies. In this study, we summarize the consensus among the workshop participants regarding the role of the EES community in understanding and mitigating the spread of antibiotic resistance via environmental pathways. Environmental scientists and engineers offer a unique and interdisciplinary perspective and expertise needed for engaging with other disciplines such as medicine, agriculture, and public health to effectively address important knowledge gaps with respect to the linkages between human activities, impacts to the environment, and human health risks. Recommendations that propose priorities for research within the EES community, as well as areas where interdisciplinary perspectives are needed, are highlighted. In particular, risk modeling and assessment, monitoring, and mass balance modeling can aid in the identification of “hot spots” for antibiotic resistance evolution and dissemination, and can help identify effective targets for mitigation. Such information will be essential for the development of an informed and effective policy aimed at preserving and protecting the efficacy of antibiotics for future generations

Integrated Expression of Circulating miR375 and miR371 to Identify Teratoma and Active Germ Cell Malignancy Components in Malignant Germ Cell Tumors

Active germ cell malignancies express high levels of specific circulating micro-RNAs (miRNAs), including miR-371a-3p (miR371), which is undetectable in teratoma. Teratoma markers are urgently needed for theselection of patients and treatments because of the risk of malignant transformation and growing teratoma syndrome. To assess the accuracy of plasma miR375 alone or in combination with miR371 in detecting teratoma, 100 germ cell tumor patients, divided into two cohorts, were enrolled in a prospective multi-institutional study. In the discovery cohort, patients with pure teratoma and with no/low risk of harboring teratoma were compared; the validation cohort included patients with confirmed teratoma, active germ cell malignancy, or complete response after chemotherapy. The area under the receiver operating characteristic curve values for miR375, miR371, and miR371-miR375 were, respectively, 0.93 (95% confidence interval [CI]: 0.87-0.99), 0.59 (95% CI: 0.44-0.73), and 0.95 (95% CI: 0.90-0.99) in the discovery cohort and 0.55 (95% CI: 0.36-0.74), 0.74 (95% CI: 0.58-0.91), and 0.77 (95% CI: 0.62-0.93) in the validation cohort. Our study demonstrated that the plasma miR371-miR375 integrated evaluation is highly accurate to detect teratoma. PATIENT SUMMARY: The evaluation of two micro-RNAs (miR375-miR371) in the blood of patients with germ cell tumors is promising to predict teratoma. This test could be particularly relevant to the identification of teratoma in patients with postchemotherapy residual disease.info:eu-repo/semantics/publishedVersio

Case report – ancient schwannoma of the scrotum

BACKGROUND: Scrotal schwannoma is a rare neoplasm and poses a diagnostic challenge to urologists. This article describes a rare case of ancient scrotal schwannoma and reviews the current modality of investigation and treatment of this tumour. CASE REPORT: A 28 year old man presented with a 3-month history of an asymptomatic scrotal swelling. Ultrasonography and computer topography revealed an intra-scrotal and extra-testicular mass without local invasion. Surgical excision was undertaken and histology was an ancient schwannoma of the scrotum. CONCLUSION: Schwannoma is a benign encapsulating neoplasm with an overall low incidence, occurring mostly in the head and neck region and seldom in the scrotum. Histology shows two distinctive patterns, Antoni type A and B areas. Variations of schwannoma such as cellular, ancient, glandular and epithelioid are observed based on the appearances. Ancient schwannoma exhibits pleomorphism without mitosis as the result of cellular degeneration, which can lead to an erroneous diagnosis of malignancy. Imaging modalities are non-specific for schwannomas, but can define tumour size, site and extension. The mainstay treatment is complete excision, although local recurrence may occur in large and incompletely excised lesions. Malignant change is exceedingly rare