Robust vector quantization for noisy channels

The paper briefly discusses techniques for making vector quantizers more tolerant to tranmsission errors. Two algorithms are presented for obtaining an efficient binary word assignment to the vector quantizer codewords without increasing the transmission rate. It is shown that about 4.5 dB gain over random assignment can be achieved with these algorithms. It is also proposed to reduce the effects of error propagation in vector-predictive quantizers by appropriately constraining the response of the predictive loop. The constrained system is shown to have about 4 dB of SNR gain over an unconstrained system in a noisy channel, with a small loss of clean-channel performance

Health economics of targeted intraoperative radiotherapy (TARGIT-IORT) for early breast cancer: a cost-effectiveness analysis in the United Kingdom

Objective: The clinical effectiveness of targeted intraoperative radiotherapy (TARGIT-IORT) has been confirmed in the randomised TARGIT-A (targeted intraoperative radiotherapy-alone) trial to be similar to a several weeks’ course of whole-breast external-beam radiation therapy (EBRT) in patients with early breast cancer. This study aims to determine the cost effectiveness of TARGIT-IORT to inform policy decisions about its wider implementation. Setting TARGIT-A randomised clinical trial (ISRCTN34086741) which compared TARGIT with traditional EBRT and found similar breast cancer control, particularly when TARGIT was given simultaneously with lumpectomy. Methods: Cost-utility analysis using decision analytic modelling by a Markov model. A cost-effectiveness Markov model was developed using TreeAge Pro V.2015. The decision analytic model compared two strategies of radiotherapy for breast cancer in a hypothetical cohort of patients with early breast cancer based on the published health state transition probability data from the TARGIT-A trial. Analysis was performed for UK setting and National Health Service (NHS) healthcare payer’s perspective using NHS cost data and treatment outcomes were simulated for both strategies for a time horizon of 10 years. Model health state utilities were drawn from the published literature. Future costs and effects were discounted at the rate of 3.5%. To address uncertainty, one-way and probabilistic sensitivity analyses were performed. Main outcome measures: Quality-adjusted life-years (QALYs). Results: In the base case analysis, TARGIT-IORT was a highly cost-effective strategy yielding health gain at a lower cost than its comparator EBRT. Discounted TARGITIORT and EBRT costs for the time horizon of 10 years were £12 455 and £13 280, respectively. TARGIT-IORT gained 0.18 incremental QALY as the discounted QALYs gained by TARGIT-IORT were 8.15 and by EBRT were 7.97 showing TARGIT-IORT as a dominant strategy over EBRT. Model outputs were robust to one-way and probabilistic sensitivity analyses. Conclusions: TARGIT-IORT is a dominant strategy over EBRT, being less costly and producing higher QALY gain

Nifty Auto Index prediction on the basis of tweets sentiments

In this paper we investigate the complex relationshipbetween tweet sentiments with the Nifty Stock market AutoIndex (esp. stock prices). We will analyze sentiments for nearly 1million tweets from December 2015 to March 2016 for Nifty AutoIndex which includes 15 auto industries stocks. Our results willshow high correlation between stock prices and twittersentiments. A Granger causality analysis and a Neural Networkare then used to investigate the hypothesis that public moodstates are predictive of changes in Nifty Auto Index closingvalues

On the interconnection structure of cellular networks

This paper presents a model which can be used to represent many of the interconnection patterns commonly found in cellular networks. This model is then used to classify cellular networks according to the degree of regularity in their interconnection patterns. Specifically, three classes of cellular networks, corresponding to three forms of interconnection regularity, are defined. A concept of network realization is then developed to detect structural similarities in different networks and is used to compare the computational capabilities of these three classes