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The proliferation of big data and analytic workloads has driven the need for cloud compute and cluster-based job processing. With Apache Spark, users can process terabytes of data at ease with hundreds of parallel executors. At Microsoft, we aim at providing a fast and succinct interface for users to run Spark applications, such as through creating simple notebook "sessions" by abstracting the underlying complexity of the cloud. Providing low latency access to Spark clusters and sessions is a challenging problem due to the large overheads of cluster creation and session startup. In this paper, we introduce Intelligent Pooling, a system for proactively provisioning compute resources to combat the aforementioned overheads. To reduce the COGS (cost-of-goods-sold), our system (1) predicts usage patterns using an innovative hybrid Machine Learning (ML) model with low latency and high accuracy; and (2) optimizes the pool size dynamically to meet customer demand while reducing extraneous COGS. The proposed system auto-tunes its hyper-parameters to balance between performance and operational cost with minimal to no engineering input. Evaluated using large-scale production data, Intelligent Pooling achieves up to 43% reduction in cluster idle time compared to static pooling when targeting 99% pool hit rate. Currently deployed in production, Intelligent Pooling is on track to save tens of million dollars in COGS per year as compared to traditional pre-provisioned pools.

Chirality, a characteristic handedness that distinguishes 'left' from 'right', cuts widely across all of nature$^1$, from the structure of DNA$^2$ to opposite chirality of particles and antiparticles$^3$. In condensed matter chiral fermions have been identified in Weyl semimetals$^4$ through their unconventional electrodynamics arising from 'axial' charge imbalance between chiral Weyl nodes of topologically nontrivial electronic bands. Up to now it has been challenging or impossible to create transport channels of Weyl fermions in a single material that could be easily configured for advancing chiral logic or spintronics$^{5,6}$. Here we generate chirality-directed conduction channels in inversion-symmetric Weyl ferromagnet (FM) $MnSb_2Te_4$, emergent from a deep connection between chirality in reciprocal and real space. We alter the bandstructure on-demand with an intake and a subsequent release of ionic hydrogen ($H^+$) $-$ a process we show to induce the tilt and rotation of Weyl bands. The transformed Weyl FM states feature a doubled Curie temperature $\geq50K$ and an enhanced angular transport chirality synchronous with a rare field-antisymmetric longitudinal resistance $-$ a low-field tunable 'chiral switch' that roots in the interplay of Berry curvature$^7$, chiral anomaly$^8$ and hydrogen-engendered mutation of Weyl nodes.

Globally, cataract is a common eye disease and one of the leading causes of blindness and vision impairment. The traditional process of detecting cataracts involves eye examination using a slit-lamp microscope or ophthalmoscope by an ophthalmologist, who checks for clouding of the normally clear lens of the eye. The lack of resources and unavailability of a sufficient number of experts pose a burden to the healthcare system throughout the world, and researchers are exploring the use of AI solutions for assisting the experts. Inspired by the progress in iris recognition, in this research, we present a novel algorithm for cataract detection using near-infrared eye images. The NIR cameras, which are popularly used in iris recognition, are of relatively low cost and easy to operate compared to ophthalmoscope setup for data capture. However, such NIR images have not been explored for cataract detection. We present deep learning-based eye segmentation and multitask network classification networks for cataract detection using NIR images as input. The proposed segmentation algorithm efficiently and effectively detects non-ideal eye boundaries and is cost-effective, and the classification network yields very high classification performance on the cataract dataset.

While the study of space charge potentials has a long history, present models are largely based on the notion of steady state equilibrium, ill-suited to describe wide bandgap semiconductors with moderate to low concentrations of defects. Here we build on color centers in diamond both to locally inject carriers into the crystal and probe their evolution as they propagate in the presence of external and internal potentials. We witness the formation of metastable charge patterns whose shape - and concomitant field - can be engineered through the timing of carrier injection and applied voltages. With the help of previously crafted charge patterns, we unveil a rich interplay between local and extended sources of space charge field, which we then exploit to show space-charge-induced carrier guiding.

Cataract is caused due to various factors such as age, trauma, genetics, smoking and substance consumption, and radiation. It is one of the major common ophthalmic diseases worldwide which can potentially affect iris-based biometric systems. India, which hosts the largest biometrics project in the world, has about 8 million people undergoing cataract surgery annually. While existing research shows that cataract does not have a major impact on iris recognition, our observations suggest that the iris segmentation approaches are not well equipped to handle cataract or post cataract surgery cases. Therefore, failure in iris segmentation affects the overall recognition performance. This paper presents an efficient iris segmentation algorithm with variations due to cataract and post cataract surgery. The proposed algorithm, termed as SegDenseNet, is a deep learning algorithm based on DenseNets. The experiments on the IIITD Cataract database show that improving the segmentation enhances the identification by up to 25% across different sensors and matchers., Comment: Corrected diagrams. Results remain the same!