A procedure used for a ground truth study of a land use map of North Alabama generated from LANDSAT data

A land use map of a five county area in North Alabama was generated from LANDSAT data using a supervised classification algorithm. There was good overall agreement between the land use designated and known conditions, but there were also obvious discrepancies. In ground checking the map, two types of errors were encountered - shift and misclassification - and a method was developed to eliminate or greatly reduce the errors. Randomly selected study areas containing 2,525 pixels were analyzed. Overall, 76.3 percent of the pixels were correctly classified. A contingency coefficient of correlation was calculated to be 0.7 which is significant at the alpha = 0.01 level. The land use maps generated by computers from LANDSAT data are useful for overall land use by regional agencies. However, care must be used when making detailed analysis of small areas. The procedure used for conducting the ground truth study together with data from representative study areas is presented

Determination and Correlation of Anticardiolipin Antibody with High Sensitivity C- reactive Proteins and its Role in Predicting Short Term Outcome in Patients with Acute Coronary Syndrome

Anticardiolipin antibody (aCL) is considered to be an independent risk factor while high sensitivity C reactive protein (hsCRP) is an established marker for coronary artery disease. This study was conducted to determine levels of aCL antibodies and hsCRP, their correlation and role in predicting recurrence of events in patients presenting with Acute Coronary Syndrome (ACS). Sixty patients admitted with Acute Coronary Syndrome were followed up for 7 days or until discharge. Patients were classified into two groups as those having experienced an ischemic event needing intervention within 7 days (Group I) and other having an event free recovery (Group II). aCL antibody and hsCRP levels were estimated and compared in these two groups. Twenty age and sex matched disease free persons served as controls. The levels of aCL were significantly higher in patients with ACS as compared to the controls (p=0.020). However the levels of aCL in Group I (13.39±9.46 GPL-U/ml) and Group II (13.51±9.93 GPL-U/ml) were not significantly different (p =0.838). The mean hsCRP levels were higher in cases with an event (23.30±10.68 mg/dl) than in cases without an event (20.60±11.45mg/dl) though it was not significant statistically (p=0.389). aCL and CRP were not found to be significantly correlated in causing the recurrence of events(p=0.178). Therefore anticardiolipin antibody is an independent risk factor which could be implicated in the pathogenesis of ACS. However it is not significantly associated with recurrence of short-term events in patients with ACS. Also, aCL antibody does not have significant correlation with hSCRP in causing recurrence of events in the patients of acute coronary syndrome

Learning a Static Analyzer from Data

To be practically useful, modern static analyzers must precisely model the effect of both, statements in the programming language as well as frameworks used by the program under analysis. While important, manually addressing these challenges is difficult for at least two reasons: (i) the effects on the overall analysis can be non-trivial, and (ii) as the size and complexity of modern libraries increase, so is the number of cases the analysis must handle. In this paper we present a new, automated approach for creating static analyzers: instead of manually providing the various inference rules of the analyzer, the key idea is to learn these rules from a dataset of programs. Our method consists of two ingredients: (i) a synthesis algorithm capable of learning a candidate analyzer from a given dataset, and (ii) a counter-example guided learning procedure which generates new programs beyond those in the initial dataset, critical for discovering corner cases and ensuring the learned analysis generalizes to unseen programs. We implemented and instantiated our approach to the task of learning JavaScript static analysis rules for a subset of points-to analysis and for allocation sites analysis. These are challenging yet important problems that have received significant research attention. We show that our approach is effective: our system automatically discovered practical and useful inference rules for many cases that are tricky to manually identify and are missed by state-of-the-art, manually tuned analyzers

Loss Guided Activation for Action Recognition in Still Images

One significant problem of deep-learning based human action recognition is that it can be easily misled by the presence of irrelevant objects or backgrounds. Existing methods commonly address this problem by employing bounding boxes on the target humans as part of the input, in both training and testing stages. This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only human features. However, we consider this solution is inefficient, since the bounding boxes might not be available. Hence, instead of using a person bounding box as an input, we introduce a human-mask loss to automatically guide the activations of the feature maps to the target human who is performing the action, and hence suppress the activations of misleading contexts. We propose a multi-task deep learning method that jointly predicts the human action class and human location heatmap. Extensive experiments demonstrate our approach is more robust compared to the baseline methods under the presence of irrelevant misleading contexts. Our method achieves 94.06\% and 40.65\% (in terms of mAP) on Stanford40 and MPII dataset respectively, which are 3.14\% and 12.6\% relative improvements over the best results reported in the literature, and thus set new state-of-the-art results. Additionally, unlike some existing methods, we eliminate the requirement of using a person bounding box as an input during testing.Comment: Accepted to appear in ACCV 201

Reply to Itin, Obukhov and Hehl paper "An Electric Charge has no Screw Sense - A Comment on the Twist-Free Formulation of Electrodynamics by da Rocha & Rodrigues"

In this note we briefly comment a paper by Itin, Obukhov and Hehl criticising our previous paper. We show that all remarks by our critics are ill conceived or irrelevant to our approach and moreover we provide some pertinent new comments to their critical paper, with the aim to clarify even more our view on the subject.Comment: This paper is a reply to arXiv:0911.5175 [physics.class-ph] which made some criticisms on our paper "Pair and Impar, Even and Odd Form Fields and Electromagnetism" arXiv:0811.1713 [math-ph] to appear in Annalen der Physik. A short version of our reply will also appear in Annalen de Physi

Exploring Diabetes and Users\u27 Lifestyle Choices in Digital Spaces to Improve Health Outcomes

The information derived from social media analytic studies provides valuable sources of information for healthcare stakeholders. However, there is still a lack of research with using social media to identify the lifestyle choices of those dealing with diabetes in order to better understand and design impactful health interventions before an extremity like death occurs due to diabetes. This exploratory study aims to demonstrate how social media can be leveraged as a data source to help us understand the lifestyle choices of those dealing with diabetes. Using two text mining approaches - sentiment analysis and unsupervised topic modeling - food and physiology were topics expressed in both sentiments. Overall, lifestyle related topics accounted for nearly 25% of the topics identified in the corpus of data. There is a pressing need for incorporating predictive modelling approaches to this study in order to quantify our findings and how this knowledge can improve health outcomes from a population perspective

Biobleaching of wheat straw-rich-soda pulp by the application of alkalophilic and thermophilic mannanase from Streptomyces sp. PG-08-3

An alkalophilic and thermophilic mannanase from Streptomyces sp. PG-08-3 was applied to wheat straw-rich-soda pulp to check its bleaching potential. Optimum conditions for bio-bleaching of pulp were as follows: Mannanase 5 Ug-1 of pulp at pH 8.5 with temperature 55ºC that enhanced the brightness by 7.3% and reduced the kappa number by 24.6% within 4 h of incubation. Tear index (20%) and burst index (11.2%) were also improved by mannanase-treated pulp as compared to the untreated pulp. Treatment of chemically (CEH1H2) bleached pulp with enzyme showed significant effect on release of chromophores, hydrophobic and reducing compounds. Mannanase-prebleaching of raw pulp reduced the use of hypochlorite by 16% to achieve brightness of resultant hand sheets similar to the fully chemically bleached pulp. Scanning electron microscopy of wheat straw rich soda-pulp after treatment with denatured and active mannanase was performed. There was appearance of micro-fibers on the surface of pulp that was treated with active mannanase.Key words: Biobleaching, mannanase, wheat straw-rich-soda pulp