Learning Deep and Compact Models for Gesture Recognition

We look at the problem of developing a compact and accurate model for gesture recognition from videos in a deep-learning framework. Towards this we propose a joint 3DCNN-LSTM model that is end-to-end trainable and is shown to be better suited to capture the dynamic information in actions. The solution achieves close to state-of-the-art accuracy on the ChaLearn dataset, with only half the model size. We also explore ways to derive a much more compact representation in a knowledge distillation framework followed by model compression. The final model is less than $1~MB$ in size, which is less than one hundredth of our initial model, with a drop of $7\%$ in accuracy, and is suitable for real-time gesture recognition on mobile devices.Comment: Accepted at 2017 IEEE International Conference on Image Processing (ICIP 2017

Case Reports : Non-traumatic muscle pain in a diabetic

The prevalence of diabetes in the adult population in United States is approximately 10[percent] and is expected to rise. The myriad of complexities of this entity, both microvascular and macrovascular is anticipated to follow suit, adding to the morbidity and mortality. Diabetic muscle infarction (DMI) is one such incompletely understood complication of long-standing uncontrolled diabetes and seems to play a crucial role in risk stratification in those with microvascular involvement. DMI has shown to be a poor prognosticator of long-term survival, a grim reality given the mean age of presentation is only 43 years. Further investigation into improving this outlook and whether tighter glycemic control changes outcome would be of significant interest and benefit

“Multicarrier Modulation for Wireless Communication using Wavelet Packets

Success of OFDM has proved that Multi carrier modulation is an efficient solution for wireless communications. Wavelet Packet Modulation (WPM) is a new type of modulation for transmission of multicarrier signal on wireless channel that uses orthogonal wavelet bases other than sine functions. Though this modulation is over all similar to that of OFDM, it provides interesting additional features. In this thesis, a detailed study is given on Wavelets and WPM and the BER performance comparison between the OFDM systems and WPM systems and equalization techniques are analysed. The analysis is done for different types of wavelet generating families, various number of modulations QAM constellation points (16 to 64), and simulated over AWGN channel, and other Multipath fading channels

Some heretical thoughts on the federal budget

The author discusses the present budget of Pakistan, particularly its effects on development. Although it compares favourably to the former ones and offers a number of advantages mainly in the fields of agriculture and taxation, there are serious shortcomings, too. Therefore the Government would be well advised to employ better trained experts who are already available

Understanding Psycholinguistic Behavior of predominant drunk texters in Social Media

In the last decade, social media has evolved as one of the leading platform to create, share, or exchange information; it is commonly used as a way for individuals to maintain social connections. In this online digital world, people use to post texts or pictures to express their views socially and create user-user engagement through discussions and conversations. Thus, social media has established itself to bear signals relating to human behavior. One can easily design user characteristic network by scraping through someone's social media profiles. In this paper, we investigate the potential of social media in characterizing and understanding predominant drunk texters from the perspective of their social, psychological and linguistic behavior as evident from the content generated by them. Our research aims to analyze the behavior of drunk texters on social media and to contrast this with non-drunk texters. We use Twitter social media to obtain the set of drunk texters and non-drunk texters and show that we can classify users into these two respective sets using various psycholinguistic features with an overall average accuracy of 96.78% with very high precision and recall. Note that such an automatic classification can have far-reaching impact - (i) on health research related to addiction prevention and control, and (ii) in eliminating abusive and vulgar contents from Twitter, borne by the tweets of drunk texters.Comment: 6 pages, 8 Figures, ISCC 2018 Workshops - ICTS4eHealth 201

Reverse genetics in Candida albicans predicts ARF cycling is essential for drug resistance and virulence

Peer reviewedPublisher PD