Modeling of Catastrophes and Estimation of National Catastrophe Fund

Arts and Sciences Research SchoalrshipBill 'H.R. 2555: Homeowners' Defense Act of 2010' was considered by the United States of America (US) Congress to form National Catastrophe Risk Consortium. The bill was introduced on April 27, 2010, but was not enacted. One of the functions of the consortium was to fund a National Catastrophe Fund to help public and insurance companies meet the liability claims from hurricane, fire, and blizzard. The bill has to pass through House Financial Services Committee which takes into account the projected costs, disbursements, and the amount required to be appropriated for the task and its source. In this project the following are accomplished: Part I: Modeling • Employed the method of Maximum Likelihood Estimation (MLE) to estimate the parameters such as mean of count of fires, hurricanes and blizzards. • Modeled the number of acres burnt by fires by Weibull distribution. • Modeled the economic damages caused by blizzards by Poisson Distribution. • Employed Poisson distribution to model the number of hurricanes occurring in each hurricane category. Part II: Estimating • Estimated 2013 claims for hurricane, fire, and blizzard in United States of America using data for three catastrophes for the last 100 years. • Carried out Linear regression analysis to compare the results of the new model for acres burnt from fires and economic damage from blizzards. • Predicted the economic damages from the three disasters in 2013 to be 46.7 billion dollars. • The National Catastrophe Fund for the liabilities of fire, blizzard, and hurricane needs to be funded by 10% of the estimated economic damages which for 2013 amounts to 4.67 billion dollars.No embargoAcademic Major: Actuarial Scienc

Using Cross-Lingual Explicit Semantic Analysis for Improving Ontology Translation

Semantic Web aims to allow machines to make inferences using the explicit conceptualisations contained in ontologies. By pointing to ontologies, Semantic Web-based applications are able to inter-operate and share common information easily. Nevertheless, multilingual semantic applications are still rare, owing to the fact that most online ontologies are monolingual in English. In order to solve this issue, techniques for ontology localisation and translation are needed. However, traditional machine translation is difficult to apply to ontologies, owing to the fact that ontology labels tend to be quite short in length and linguistically different from the free text paradigm. In this paper, we propose an approach to enhance machine translation of ontologies based on exploiting the well-structured concept descriptions contained in the ontology. In particular, our approach leverages the semantics contained in the ontology by using Cross Lingual Explicit Semantic Analysis (CLESA) for context-based disambiguation in phrase-based Statistical Machine Translation (SMT). The presented work is novel in the sense that application of CLESA in SMT has not been performed earlier to the best of our knowledge

ESTIMATION OF SERUM COPPER AND ZINC LEVEL IN PATIENTS WITH ORAL SUB MUCOUS FIBROSIS AND ORAL SQUAMOUS CELL CARCINOMA

Trace elements are receiving too much attention as they are found to be significantly altered in head and neck, lung and breast carcinomas, and there is need to develop sensitive, specific and faster tests as an aid in the early diagnosis of the primary tumor and its recurrence or malignant transformation in premalignant states.Aim: To estimate serum copper and zinc levels in Oral Submucous Fibrosisand oral squamous cell carcinoma patients. Methodology: Sera of OSCC (n = 10) and OSMF (n = 10) patients and of healthy controls was analysed for the estimation of Cu and Zn using atomic absorption spectrophotometry. Results: There was an increase in sera levels of Cu while those of Zn were decreased in both Oral Submucous fibrosis and Oral Squamous Cell Carcinoma patients as compared to the healthy controls. Conclusion: It could be concluded that there is an alteration of sera levels of these trace elements which can be helpful in early detection and management in OSMF and OSCC patients

Cross-Lingual Linking of News Stories using ESA

In this paper, we describe our approach for Cross-Lingual linking of Indian news stories, submitted for Cross-Lingual Indian News Story Search (CL!NSS) task at FIRE 2012. Our approach consists of two major steps, the reduction of search space by using di�erent features and ranking of the news stories according to their relatedness scores. Our approach uses Wikipedia-based Cross-Lingual Explicit Semantic Analysis (CLESA) to calculate the semantic similarity and relatedness score between two news stories in di�erent languages. We evaluate our approach on CL!NSS dataset, which consists of 50 news stories in English and 50K news stories in Hindi

Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph

In this paper, we describe an embedding-based entity recommendation framework for Wikipedia that organizes Wikipedia into a collection of graphs layered on top of each other, learns complementary entity representations from their topology and content, and combines them with a lightweight learning-to-rank approach to recommend related entities on Wikipedia. Through offline and online evaluations, we show that the resulting embeddings and recommendations perform well in terms of quality and user engagement. Balancing simplicity and quality, this framework provides default entity recommendations for English and other languages in the Yahoo! Knowledge Graph, which Wikipedia is a core subset of.Comment: 8 pages, 4 figures, 8 tables. To be appeared in Wiki Workshop 2020, Companion Proceedings of the Web Conference 2020(WWW 20 Companion), Taipei, Taiwa

Ensemble Fractional Sensitivity: A Quantitative Approach to Neuron Selection for Decoding Motor Tasks

A robust method to help identify the population of neurons used for decoding motor tasks is developed. We use sensitivity analysis to develop a new metric for quantifying the relative contribution of a neuron towards the decoded output, called “fractional sensitivity.” Previous model-based approaches for neuron ranking have been shown to largely depend on the collection of training data. We suggest the use of an ensemble of models that are trained on random subsets of trials to rank neurons. For this work, we tested a decoding algorithm on neuronal data recorded from two male rhesus monkeys while they performed a reach to grasp a bar at three orientations (45°, 90°, or 135°). An ensemble approach led to a statistically significant increase of 5% in decoding accuracy and 25% increase in identification accuracy of simulated noisy neurons, when compared to a single model. Furthermore, ranking neurons based on the ensemble fractional sensitivities resulted in decoding accuracies 10%–20% greater than when randomly selecting neurons or ranking based on firing rates alone. By systematically reducing the size of the input space, we determine the optimal number of neurons needed for decoding the motor output. This selection approach has practical benefits for other BMI applications where limited number of electrodes and training datasets are available, but high decoding accuracies are desirable

Local Bupivacaine Infiltration to Reduce Pain after Tonsillectomy: A Low Cost Approach

Introduction Tonsillectomy is one of the most commonly performed surgical procedures worldwide, with the major drawback of significant post operative pain.There is no consensus regarding topical application or local infiltration of anesthetics post operatively to reduce pain. The present study was performed to evaluate the effect of bupivacaine infiltration in the tonsillar fossae after tonsillectomy. Materials and Methods A double-blinded clinical trial was performed on 75 patients undergoing tonsillectomy between January 2019 and January 2020. All patients underwent tonsillectomy under general anesthesia and were then randomly divided into 3 groups of 25 patients each. For Group I, a swab soaked in normal saline was applied to the tonsillar fossae for 5 minutes just before extubation. In Group II, a swab soaked in 5 ml of 0.5% bupivacaine was placed for 5 minutes, while in Group III, 5ml of 0.5% bupivacaine was infiltrated in the tonsillar fossae. The intensity of pain for each group was measured in the immediate post op period, at6 hours, 24 hours and 1 week by Wong Baker Faces Pain Rating Scale. Results There was a significant difference in the mean level of pain between groups I and III in the immediate post op period, at 6 hours and 24 hours. Although the average pain scores of group III were better than those of group II, the results were significant only in the 6 hour post op period. Conclusion To reduce post-tonsillectomy pain,0.5% bupivacaine can be infiltrated into the tonsillar fossa

Preparation and Evaluation of Nano-vesicles of Brimonidine Tartrate as an Ocular Drug Delivery System

The objective of the present investigation was to design a vesicular formulation of brimonidine tartrate and evaluate its ability to reduce the dosing frequency and improve the therapeutic efficacy of the drug. Nano-vesicles of brimonidine tartrate were prepared by film hydration method. The prepared vesicles were evaluated for photomicroscopic characteristics, entrapment efficiency, in vitro, and ex-in vitro drug release and in vivo intraocular pressure (IOP) lowering activity. The methods employed for preparation of vesicles produced nano vesicles of acceptable shape and size. The in vitro, and ex-in vitro drug release studies showed that there was slow and prolonged release of the drug, which followed zero-order kinetics. The IOP-lowering activity of nano vesicles was determined and compared with that of pure drug solution and showed that the IOP-lowering action of nano-vesicles sustained for a longer period of time. Stability studies revealed that the vesicle formulations were stable at the temperature range of 2-8°C, with no change in shape and drug content. The results of the study indicate that it is possible to develop a safe and physiologically effective topical formulation that is also convenient for patients

A brain-computer interface with vibrotactile biofeedback for haptic information

<p>Abstract</p> <p>Background</p> <p>It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only <it>vibrotactile feedback</it>, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy.</p> <p>Methods</p> <p>A Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance.</p> <p>Results and Conclusion</p> <p>Subjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.</p