Quantum Cloning, Bell's Inequality and Teleportation

We analyze a possibility of using the two qubit output state from Buzek-Hillery quantum copying machine (not necessarily universal quantum cloning machine) as a teleportation channel. We show that there is a range of values of the machine parameter $\xi$ for which the two qubit output state is entangled and violates Bell-CHSH inequality and for a different range it remains entangled but does not violate Bell-CHSH inequality. Further we observe that for certain values of the machine parameter the two-qubit mixed state can be used as a teleportation channel. The use of the output state from the Buzek-Hillery cloning machine as a teleportation channel provides an additional appeal to the cloning machine and motivation of our present work.Comment: 7 pages and no figures, Accepted in Journal of Physics

Proteinopathy, oxidative stress and mitochondrial dysfunction: cross talk in alzheimer’s disease and parkinson’s disease

Alzheimer's disease and Parkinson's disease are two common neurodegenerative diseases of the elderly people that have devastating effects in terms of morbidity and mortality. The predominant form of the disease in either case is sporadic with uncertain etiology. The clinical features of Parkinson's disease are primarily motor deficits, while the patients of Alzheimer's disease present with dementia and cognitive impairment. Though neuronal death is a common element in both the disorders, the postmortem histopathology of the brain is very characteristic in each case and different from each other. In terms of molecular pathogenesis, however, both the diseases have a significant commonality, and proteinopathy (abnormal accumulation of misfolded proteins), mitochondrial dysfunction and oxidative stress are the cardinal features in either case. These three damage mechanisms work in concert, reinforcing each other to drive the pathology in the aging brain for both the diseases; very interestingly, the nature of interactions among these three damage mechanisms is very similar in both the diseases, and this review attempts to highlight these aspects. In the case of Alzheimer's disease, the peptide amyloid beta (A beta) is responsible for the proteinopathy, while alpha-synuclein plays a similar role in Parkinson's disease. The expression levels of these two proteins and their aggregation processes are modulated by reactive oxygen radicals and transition metal ions in a similar manner. In turn, these proteins - as oligomers or in aggregated forms - cause mitochondrial impairment by apparently following similar mechanisms. Understanding the common nature of these interactions may, therefore, help us to identify putative neuroprotective strategies that would be beneficial in both the clinical conditions

Deep Neural Network Cloud-Type Classification (DeepCTC) model and its application in evaluating PERSIANN-CCS

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation

Neural reactivations during sleep determine network credit assignment.

A fundamental goal of motor learning is to establish the neural patterns that produce a desired behavioral outcome. It remains unclear how and when the nervous system solves this 'credit assignment' problem. Using neuroprosthetic learning, in which we could control the causal relationship between neurons and behavior, we found that sleep-dependent processing was required for credit assignment and the establishment of task-related functional connectivity reflecting the casual neuron-behavior relationship. Notably, we observed a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non-causal activity. Decoupling of spiking to slow oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep is essential for establishing sparse activation patterns that reflect the causal neuron-behavior relationship

Electrically modulated photoluminescence in ferroelectric liquid crystal

Electrical modulation and switching of photoluminescence (PL) have been demonstrated in pure deformed helix ferroelectric liquid crystal (DHFLC) material. The PL intensity increases and peak position shifts towards lower wavelength above a threshold voltage which continues up to a saturation voltage. This is attributed to the helix unwinding phenomenon in the DHFLC on the application of an electric field. Moreover, the PL intensity could be switched between high intensity (field-on) and low intensity (field-off) positions. These studies would add a new dimension to ferroelectric liquid crystal's application in the area of optical devices.Comment: 4 figure

Statistical Significance of Lorentz invariance violation from GRB 160625B

Recently Wei et al (arXiv:1612.09425) have found evidence for a transition from positive time lags to negative time lags in the spectral lag data of GRB 160625B. They have fit these observed lags to a sum of two components: an intrinsic time lag due to astrophysical mechanisms and an energy-dependent speed of light due to violation of Lorentz invariance, which could be a signature of quantum gravity. Here, we examine the statistical significance of the evidence for this claim using the same data by comparing it against the null hypothesis, viz. the time-lags are induced only by intrinsic delays. We use three different model comparison techniques: a frequentist test and two information based criteria (AIC and BIC). From the frequentist model comparison test, we find that evidence for Lorentz violation is favoured at 3.05σ and 3.74σ for linear and quadratic models respectively and do not cross the 5σ discovery threshold. We find that ΔAIC and ΔBIC have values ≳ 10 for the quadratic Lorentz violating model pointing to "decisive evidence" against Lorentz invariance violation compared to only astrophysically induced intrinsic emission. Another concern however is that none of the three models (including the null hypothesis) provide a good fit to the data, which implies that there is additional physics or systematic errors, which are not accounted for while fitting the data to these models