Small-Scale Variations of HI Spectra from Interstellar Scintillatio

I suggest that radio-wave scattering by the interstellar plasma, in combination with subsonic gradients in the Doppler velocity of interstellar HI, is responsible for the observed small-scale variation in HI absorption spectra of pulsars. Velocity gradients on the order of 0.05 to 0.3 km/s across 1 AU can produce the observed variations. I suggest observational tests to distinguish between this model and the traditional picture of small-scale opacity variations from cloudlets.Comment: 24 pages, 2 figures, Latex, uses AASTe

Canonical Quantization Inside the Schwarzschild Black Hole

We propose a scheme for quantizing a scalar field over the Schwarzschild manifold including the interior of the horizon. On the exterior, the timelike Killing vector and on the horizon the isometry corresponding to restricted Lorentz boosts can be used to enforce the spectral condition. For the interior we appeal to the need for CPT invariance to construct an explicitly positive definite operator which allows identification of positive and negative frequencies. This operator is the translation operator corresponding to the inexorable propagation to smaller radii as expected from the classical metric. We also propose an expression for the propagator in the interior and express it as a mode sum.Comment: 8 pages, LaTex. Title altered. One reference added. A few typos esp. eq.(7),(38) corrected. To appear in Class.Q.Gra

Testing LSND at long-baseline neutrino experiments

Recently it was suggested that two very different mass-squared differences play a role in atmospheric neutrino oscillations. The larger of these also accounts for the LSND result and the smaller of these also drives the solar neutrino oscillations. We consider the predictions of this scheme for long-baseline experiments. We find that high statistics experiments, such as MINOS, can observe a clean signal for this scheme, which is clearly distinguishable from the usual scheme of atmospheric neutrino oscillations driven by a single mass-squared difference

AN EVALUATION OF CPU EFFICIENCY UNDER DYNAMIC QUANTUM ALLOCATION

A model for a time-sharing operating system is developed in order to assess the effects of dynamic quantum allocation and overhead variability on central processing unit (CPU) efficiency. CPU efficiency is determined by the proportion of time devoted to user-oriented (problem state) tasks within a busy period. Computational results indicate that a dynamic quantum allocation strategy produces significant differences in CPU efficiency compared to a constant quantum. The differences are affected significantly by the variability among allocated quantum values and the demand on the system. Overhead variability also has a pronounced effect. A function that depicts overhead as decreasing with demand produces more stable values of CPU efficiency. The interaction between demand and the amount of overhead is observed to be significant

Unsupervised Early Exit in DNNs with Multiple Exits

Deep Neural Networks (DNNs) are generally designed as sequentially cascaded differentiable blocks/layers with a prediction module connected only to its last layer. DNNs can be attached with prediction modules at multiple points along the backbone where inference can stop at an intermediary stage without passing through all the modules. The last exit point may offer a better prediction error but also involves more computational resources and latency. An exit point that is `optimal' in terms of both prediction error and cost is desirable. The optimal exit point may depend on the latent distribution of the tasks and may change from one task type to another. During neural inference, the ground truth of instances may not be available and error rates at each exit point cannot be estimated. Hence one is faced with the problem of selecting the optimal exit in an unsupervised setting. Prior works tackled this problem in an offline supervised setting assuming that enough labeled data is available to estimate the error rate at each exit point and tune the parameters for better accuracy. However, pre-trained DNNs are often deployed in new domains for which a large amount of ground truth may not be available. We model the problem of exit selection as an unsupervised online learning problem and use bandit theory to identify the optimal exit point. Specifically, we focus on Elastic BERT, a pre-trained multi-exit DNN to demonstrate that it `nearly' satisfies the Strong Dominance (SD) property making it possible to learn the optimal exit in an online setup without knowing the ground truth labels. We develop upper confidence bound (UCB) based algorithm named UEE-UCB that provably achieves sub-linear regret under the SD property. Thus our method provides a means to adaptively learn domain-specific optimal exit points in multi-exit DNNs. We empirically validate our algorithm on IMDb and Yelp datasets.Comment: To be presented at International conference on AI-ML system