Sparse Matrix Representation for Web Opinions

Due to the advancement of Web 2.0 technologies, a large volume of Web opinions is available on social media sites such as Web forums and Weblogs. These technologies provide a platform for Internet users around the world to communicate with each other and express their opinions. Web opinions are short and sparse text messages with noisy content. In this paper, we are using a sparse matrix representation for web opinions and defining a preprocess way for it. Here, we are proposing an algorithm for matrix generation from vector of thread2019;s. Due to this representation, we use opinions in efficient way

Feto-maternal outcome in subject undergoing epidural labour analgesia with Ripovacaine and Fentanyl

Background: Epidural anaesthesia is regional anaesthesia that blocks pain in particular region of the body. The present study was done to observe the effect of epidural analgesia and active management of labour on duration of labour and mode of delivery.Methods: Main source of data were Primigravida inpatients from hospitals attached to JJM Medical College, Davangere from October 2016 to September 2017. It was a clinical cross-sectional study.Results: It was observed that duration of labour was comparatively less in epidural group than control group. There was no significant increase in duration of II stage of labour in epidural group. There was no increase in both the instrumental delivery rate and caesarean section rate and also noted higher newborn APGAR score rate in parturient who received epidural analgesia.Conclusions: The present study showed that there is no increase in the duration of labour or any increase in the rate of instrumental delivery or caesarean section in parturient receiving epidural analgesia, instead, there is a downward trend in all the above outcomes

A Coupled Compressive Sensing Scheme for Unsourced Multiple Access

This article introduces a novel paradigm for the unsourced multiple-access communication problem. This divide-and-conquer approach leverages recent advances in compressive sensing and forward error correction to produce a computationally efficient algorithm. Within the proposed framework, every active device first partitions its data into several sub-blocks, and subsequently adds redundancy using a systematic linear block code. Compressive sensing techniques are then employed to recover sub-blocks, and the original messages are obtained by connecting pieces together using a low-complexity tree-based algorithm. Numerical results suggest that the proposed scheme outperforms other existing practical coding schemes. Measured performance lies approximately $4.3$~dB away from the Polyanskiy achievability limit, which is obtained in the absence of complexity constraints

ANTIMYCOBACTERIAL ACTIVITY OF CRUDE EXTRACTS PRODUCED BY BACILLUS SP. ASSOCIATED WITH ENTOMOPATHOGENIC NEMATODE

Objective: The World Health Organization estimates that about 8 to 10 million new Tuberculosis (TB) cases occur annually worldwide and its incidence is currently increasing. There are two million deaths from TB each year. The global threat of tuberculosis demands the search for alternative antimycobacterial drugs. The aim of the present study was to determine the antimycobacterial activity of nine crude extracts from a Bacillus sp. N strain associated with entomopathogenic nematode Rhabditis (Oscheius) sp. Methods: The liquid media for fermentation was prepared in TSB alone, LB alone and TSB + LB (1:1) supplemented with six different carbon sources (fructose, maltose, dextrose, mannitol, sucrose and lactose) and after fermentation crude extract was extracted using ethyl acetate. The minimum inhibitory concentration (MIC) of extracts was determined using the broth dilution method on middle brook 7H11 against M. tuberculosis H37Rv. The cytotoxicity of the extracts was determined by 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay against VERO cell line. Results: Out of nine extract tested only two recorded activity and significant activity was recorded by TSB+LB+lactose, followed by TSB+LB+fructose. These two extracts were nontoxic to the normal cell line. Conclusion: Purification of these extract will get pure compounds with antimycobacterial activity in future

Non-Gravitating Scalar Field in the FRW Background

We study interacting scalar field theory non-minimally coupled to gravity in the FRW background. We show that for a specific choice of interaction terms, the energy-momentum tensor of the scalar field vanishes, and as a result the scalar field does not gravitate. The naive space dependent solution to equations of motion gives rise to singular field profile. We carefully analyze the energy-momentum tensor for such a solution and show that the singularity of the solution gives a subtle contribution to the energy-momentum tensor. The space dependent solution therefore is not non-gravitating. Our conclusion is applicable to other space-time dependent non-gravitating solutions as well. We study hybrid inflation scenario in this model when purely time dependent non-gravitating field is coupled to another scalar field.Comment: 7 Pages, 2 figures, RevTeX4, v2:added a section on regularized energy-momentum tensor, references and conclusions modifie

Challenges of small scale entrepreneur's in Sohar Port anyone there to listen?

Investment by its nature looks forward to the highest rate of earnings and which support the development of a country.In order to meet right investment a stable business environment is required.Sohar Industrial Port is one of the major investment and employment hubs of Oman. The emergence of SMEs in Ports of Oman paves way for higher rate of earnings and attracts lot of Entrepreneurs to set up business in the ports of Oman.As it is claimed, this investment has aimed at employment opportunities to the growing educated youngsters.Though such developments are going on, there are very less studies focused on the opportunities and challenges of SMEs in the Sohar port.Con temporarily, a study, which focuses on the challenges of entrepreneurs, that to analyze the development climate in the ports of Sultanate of OMAN, needs to be explored into. This research paper thus extends better insight into Sohar Port as employment and Investment hub of Oman: the challenges of SMEs and entrepreneurs in the “Sohar port”

Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs

We address the issue of safety in reinforcement learning. We pose the problem in an episodic framework of a constrained Markov decision process. Existing results have shown that it is possible to achieve a reward regret of $\tilde{\mathcal{O}}(\sqrt{K})$ while allowing an $\tilde{\mathcal{O}}(\sqrt{K})$ constraint violation in $K$ episodes. A critical question that arises is whether it is possible to keep the constraint violation even smaller. We show that when a strictly safe policy is known, then one can confine the system to zero constraint violation with arbitrarily high probability while keeping the reward regret of order $\tilde{\mathcal{O}}(\sqrt{K})$. The algorithm which does so employs the principle of optimistic pessimism in the face of uncertainty to achieve safe exploration. When no strictly safe policy is known, though one is known to exist, then it is possible to restrict the system to bounded constraint violation with arbitrarily high probability. This is shown to be realized by a primal-dual algorithm with an optimistic primal estimate and a pessimistic dual update.Comment: Will appear in NeurIPS 202

Machine Learning Approach for Prediction of the Online User Intention for a Product Purchase

The deployment of self-learning computer algorithms that can automatically enhance their performance via experience is referred to as machine learning in ecommerce and is a crucial trend of the retail digital transformation. Machine learning algorithms can be unambiguously trained by analysing big datasets, identifying repeating patterns, relationships, and anomalies among all of this data, and creating mathematical models resembling such associations. These models are improved when the algorithms analyse ever-increasing amounts of data, providing us with useful insights into specific ecommerce-related events and the links between all the variables that underlie them. A tool that has been quite effective in studying current affairs, predicting future trends, and making data-driven decisions. The present work investigates the implementation of machine learning algorithms to predict the user intention for purchasing a product on a specific store's website. An Online Shoppers Purchasing Intention data set from the UC Irvine Machine Learning Repository was used for this investigation. In this study, two classification-based machine learning algorithms i.e. Stochastic Gradient Descent (SGD) algorithm and Random Forest algorithm were used. SGD algorithm was used for first time in prediction of the online user intention. The results showed that the Random Forest resulted in the highest F1-Score of 0.90 in contrast to the Stochastic Gradient Descent algorithm

Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation

We study robust reinforcement learning (RL) with the goal of determining a well-performing policy that is robust against model mismatch between the training simulator and the testing environment. Previous policy-based robust RL algorithms mainly focus on the tabular setting under uncertainty sets that facilitate robust policy evaluation, but are no longer tractable when the number of states scales up. To this end, we propose two novel uncertainty set formulations, one based on double sampling and the other on an integral probability metric. Both make large-scale robust RL tractable even when one only has access to a simulator. We propose a robust natural actor-critic (RNAC) approach that incorporates the new uncertainty sets and employs function approximation. We provide finite-time convergence guarantees for the proposed RNAC algorithm to the optimal robust policy within the function approximation error. Finally, we demonstrate the robust performance of the policy learned by our proposed RNAC approach in multiple MuJoCo environments and a real-world TurtleBot navigation task