Private Data Transfer over a Broadcast Channel

We study the following private data transfer problem: Alice has a database of files. Bob and Cathy want to access a file each from this database (which may or may not be the same file), but each of them wants to ensure that their choices of file do not get revealed even if Alice colludes with the other user. Alice, on the other hand, wants to make sure that each of Bob and Cathy does not learn any more information from the database than the files they demand (the identities of which will be unknown to her). Moreover, they should not learn any information about the other files even if they collude. It turns out that it is impossible to accomplish this if Alice, Bob, and Cathy have access only to private randomness and noiseless communication links. We consider this problem when a binary erasure broadcast channel with independent erasures is available from Alice to Bob and Cathy in addition to a noiseless public discussion channel. We study the file-length-per-broadcast-channel-use rate in the honest-but-curious model. We focus on the case when the database consists of two files, and obtain the optimal rate. We then extend to the case of larger databases, and give upper and lower bounds on the optimal rate.Comment: To be presented at IEEE International Symposium on Information Theory (ISIT 2015), Hong Kon

Inverse cascading for initial MHD turbulence spectra between Saffman and Batchelor

In decaying magnetohydrodynamic (MHD) turbulence with a strong magnetic field, the spectral magnetic energy density is known to increase with time at small wavenumbers $k$, provided the spectrum at low $k$ is sufficiently steep. This is inverse cascading and occurs for an initial Batchelor spectrum, where the magnetic energy per linear wavenumber interval increases like $k^4$. For an initial Saffman spectrum that is proportional to $k^2$, however, inverse cascading has not been found in the past. We study here the case of an intermediate $k^3$ spectrum, which may be relevant for magnetogenesis in the early Universe during the electroweak epoch. This case is not well understood in view of the standard Taylor expansion of the magnetic energy spectrum for small $k$. Using high resolution MHD simulations, we show that also in this case there is inverse cascading with a strength just as expected from the conservation of the Hosking integral, which governs the decay of an initial Batchelor spectrum. Even for shallower $k^\alpha$ spectra with spectral index $\alpha>3/2$, our simulations suggest a spectral increase at small $k$ with time $t$ proportional to $t^{4\alpha/9-2/3}$. The critical spectral index of $\alpha=3/2$ is related to the slope of the spectral envelope in the Hosking phenomenology. Although we cannot exclude the possibility of artifacts from the finite size of the computational domain, our simulations with $2048^3$ mesh points now suggest inverse cascading even for an initial Saffman spectrum.Comment: 16 pages, 7 figures, 3 tables, submitted to J. Plasma Physic

Twin Studies: Revealing the Genetic Basis of Malocclusion

The relative contribution of genes and the environment to the etiology of malocclusion has been a matter of controversy throughout the twentieth century and the first decace of twentyfirst century. Twin studies provide important insights into how genetic and environmental factors contribute to variation in dental and craniofacial morphology. This review describes research models involving twins, apart from the traditional comparison of similarities in monozygotic (identical) and dizygotic (nonidentical) pairs, throws some light on zygositydetermination, summarizes some landmark twin studies in orthodontics and future directions in dental research involving twins are outlined

Orthopedic Combination Pull Headgear with an Expanded Inner Bow for Class II Correction

A successful orthodontic treatment depends upon proper diagnosis and treatment plan, as in this case combination pull headgear was use for correction of skeletal class II discrepancy.At the end of treatment an improvement in the facial profile was observed and skeletal as well as occlusal correction was achieved

Clinical profile of patients with prosthetic heart valve thrombosis undergoing fibrinolytic therapy and NYHA class as a predictor of outcome

Background: Prosthetic heart valve thrombosis (PHVT) is a potentially fatal complication of heart valve replacement with mechanical prostheses mainly due to thrombosis.Aim: The study aimed to evaluate the clinical profile of the patients presenting with PHVT undergoing fibrinolytic therapy and analyzing patients with respect to New York Heart Association (NYHA) functional class on presentation and its association with outcome of fibrinolytic therapy.Settings & design: This was prospective, observational study conducted from June, 2016 to April, 2017. Total 133 patients with prosthetic heart valve thrombosis were included. Materials and methods: Routine blood investigations included complete hemogram, liver and renal function tests. Prothrombin time with INR was done on admission. The diagnosis of PHVT was assessed by fluoroscopy and/or echocardiography (transthoracic/transesophageal). Follow-up at 6 months was scheduled for all patients.Statistical analysis: Parametric values between two groups were performed using the independent sample t-test or chi-square test, as appropriate. Univariate and multivariate logistic regression was used to find out factors associated with outcome.Results: All patients received fibrinolytic therapy in which 108 (81.2%) were treated with streptokinase and 25 (18.8%) were treated with urokinase. On presentation, 48.9% patients were in NYHA class III, 41.4% in NYHA class IV and 9.77% in NYHA class II. Fibrinolytic therapy was successful in 105 patients (78.9%) and it failed in 28 patients (21.1%). Mortality in NYHA class II was 0%, NYHA class III was 4.6% and in NYHA class IV was 23.6%. During 6 months follow up prosthetic heart valve thrombosis recurred in 12 (11.43%) patients.Conclusion: From our single centre experience, fibrinolytic therapy is fairly effective first line therapy for prosthetic heart valve thrombosis and NYHA functional class on presentation can predict the outcome of fibrinolytic therapy

Cognitive Based Attention Deficit Hyperactivity Disorder Detection with Ability Assessment Using Auto Encoder Based Hidden Markov Model

Attention deficit hyperactivity disorder (ADHD) is a frequent Neuro-generative mental disorder. It can persist in adulthood and be expressed as a cognitive complaint. Behavioural analysis of ADHD consumes more time. This is a multi-informant complex procedure due to the overlaps in symptomatology which is the cause for delay in diagnosis and treatment. Dur to these behavioural consequences and various causes, no single test is utilized till now for diagnosing this disorder. Hence, a diagnosing model of ADHD based on Continuous Ability Assessment Test (CAAT) can enhance and balance behavioural assessment. The objective behind this study is to use a deep learning based model with CAAT for predicting ADHD. The proposed Auto Encoder Based Hidden Markov Model (AE-HMM) produces low-dimensional features of brain structures, and a novel Pearson Correlation Coefficient (PCC) is employed for normalizing these features in order to minimize batch effects over populations and datasets. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like CogniLearn and 3-D Convolutional Neural Networks (3DCNN). It is found that the proposed AE-HMM method achieves 93.68% of accuracy, 90.66% of sensitivity, 87.72% of specificity, 87.78% of F1-score and 74.22% of kappa score

Disruptive Technology in Digital Notice Board using Flutter

In early days, notice boards were used to place notices which is a very hard process. First a teacher has to make a notice on his computer and edit to make it attractive then he must get it approved by the HOD. Then when it gets approved, he must print it then the teacher must go to every notice board and physically pin the notice on the notice board Which is a very hard and time-consuming process. In order to make this experience easy we are proposing a digital notice board Which will make the process very easy and very efficient. Our main aim is to make information easy to access to students and at the same time make it easy for teachers to build a notice using their phones and the templates provided on the app

Sliding Window along with EEGNet based Prediction of EEG Motor Imagery

The need for repeated calibration and accounting for inter subject variability is a major challenge for the practical applications of a brain-computer interface. The problem becomes more severe when it is applied for the neurorehabilitation of stroke patients where the brain-activation pattern may differ quite a bit as compared to healthy individuals due to the altered neurodynamics because of a lesion. There were several approaches to handle this problem in the past that depend on creating customized features that can be generalized among the individual subjects. Recently, several deep learning architectures came into the picture although they often failed to produce superior accuracy as compared to the traditional approaches and mostly do not follow an end-to-end architecture as they depend on custom features. However, a few of them have the promising ability to create more generalizable features in an end-to-end fashion such as the popular EEGNet architecture. Although EEGNet was applied for decoding stroke patient’s motor imagery (MI) data with limited success it failed to achieve superior performance over the traditional methods. In this study, we have augmented the EEGNet based decoding by introducing a post-processing step called the longest consecutive repetition (LCR) in a sliding window-based approach and named it EEGNet+LCR. The proposed approach was tested on a dataset of 10 hemiparetic stroke patients’ MI data set yielding superior performance against the only EEGNet and a more traditional approach such as common spatial pattern (CSP)+support vector machine (SVM) for both within- and cross-subject decoding of MI signals. We also observed comparable and satisfactory performance of the EEGNet+LCR in both the within- and cross-subject categories which are rarely found in literature making it a promising candidate to realize practically feasible BCI for stroke rehabilitation