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Efficient quantum algorithms for state measurement and linear algebra applications
No full textWe present an algorithm for measurement of k-local operators in a quantum state, which scales logarithmically both in the system size and the output accuracy. The key ingredients of the algorithm are a digital representation of the quantum state, and a decomposition of the measurement operator in a basis of operators with known discrete spectra. We then show how this algorithm can be combined with (a) Hamiltonian evolution to make quantum simulations efficient, (b) the Newton-Raphson method based solution of matrix inverse to efficiently solve linear simultaneous equations, and (c) Chebyshev expansion of matrix exponentials to efficiently evaluate thermal expectation values. The general strategy may be useful in solving many other linear algebra problems efficiently
Multiterminal Secret Key Agreement at Asymptotically Zero Discussion Rate
No full textIn the multiterminal secret key agreement problem, a set of users want to discuss with each other until they share a common secret key independent of their discussion. We want to characterize the maximum secret key rate, called the secrecy capacity, asymptotically when the total discussion rate goes to zero. In the case of only two users, the capacity is equal to the Gacs-Korner common information. However, when there are more than two users, the capacity is unknown. It is plausible that a multivariate extension of the Gacs-Korner common information is the capacity, however, proving the converse is challenging. We resolved this for the hypergraphical sources and finite linear sources, and provide efficiently computable characterizations. We also give some ideas of extending the techniques to more general source models
Detection and attribution of climate change signals in South India maximum and minimum temperatures
No full textSouth India has seen significant changes in climate. Previous studies have shown that the southern part of India is more susceptible to effects of climate change than the rest of the country. We performed a rigorous climate model-based detection and attribution analysis to determine the root cause of the recent changes in climate over South India using fingerprint analysis. A modified Mann-Kendall test signalized non-stationariness in maximum and minimum temperatures (T-max and T-min) in most seasons during the period 1950-2012. The diminishing cloud cover trend may have induced significant changes in temperature during the considered time period. Significant downward trends in relative humidity during most seasons could be evidence of the recent significant warming. The observed seasonal T-max and T-min change patterns are strongly associated with the El Nino Southern Oscillation. Significant positive associations between South India temperatures and the Nino3.4 index were found in all seasons. The fingerprint approach indicated that the natural internal variability obtained from 14 climate model control simulations could not explain these significant changes in T-max (post-monsoon) and T-min (pre-monsoon and monsoon) in South India. Moreover, an experiment simulating natural external forcings (solar and volcanic) did not coincide with the observed signal strength. The dominant external factors leading to climate change are greenhouse gases, and their impact is eminent compared to other factors such as land use change and anthropogenic aerosols. Anthropogenic signals are identifiable in observed changes in T-max and T-min, of South India, and these changes can be explained only when anthropogenic forcing is involved
Microstructural properties of a dissimilar friction stir welded thick aluminum aa6082-t6 and aa7075-t6 alloy
No full textFriction stir welding is a severe plastic deformation process. This solid state welding procedure has recently received a general acceptance in the aerospace industry for joining difficult to weld aluminum alloy using the conventional method. A 10mm thick AA6082 and AA7075 in tempered form (T6) were friction stir welded in butt configuration as against the previous research on 4mm, 6mm and 8mm thicknesses respectively. The response of the stir zone (SZ) microstructure to processing parameters was evaluated using optical microscope (OM), Stereo microscope (SM) and scanning electron microscope (SEM) respectively. The magnitude forge force (kN) at the tool shoulder directly affect the flow of the extruded materials through the tool probe. The distribution of thermal energy, dissolution, precipitation and re-precipitation of solute influences the materials flow and properties evolution. Hence, equiaxed grain structures observed were due to the dynamic recrystallization mechanism at the weld nugget. Some microstructure imperfection were observed at the weld nugget when AA6082 aluminum plates were clamped on the retreating side to the backing plate. However, the material flow and mixing was not sufficient to establish good bonding and quality welds due to deviation in the positioning of the aluminum plates. (C) 2018 Elsevier Ltd. All rights reserved
A system for distributed audio classification using sparse representation over cloud for IOT
No full textIn a cloud setting where audio is intercepted from multiple nodes, it is of interest of identify the type of audio present at each node. Each node can be seen as an IOT (Internet of Things) device. Audio type can be speech by a particular speaker, different kinds of music or background noises, whose monitoring is useful for various IOT applications. A supervised audio classification system using sparse representation over a cloud network is presented. In this system, dictionaries are learnt from different audio streams on multiple nodes in the training stage. Audio classification is done separately on different nodes using distributed sparse representation, avoiding any centralized processing. Both training and testing is done in a distributed manner, and the final estimate of the audio type is arrived by consensus among the nodes. Given an audio segment at any node, distributed sparse coding is used to classify the segment into one of the audio classes using the different dictionary models learnt at different nodes
Electrical switching studies of ternary Si15Te85-xBix (0 <= x <= 2) chalcogenide glasses
No full textBulk semiconducting Si15Te85-xBix(0 <= x <= 2) chalcogenide glasses have been prepared using a well established melt-quenching technique. Electrical switching studies have been undertaken on Si15Te85-xBix(0 <= x <= 2) chalcogenide glasses. The results indicate that these samples exhibit memory type electrical switching behavior. It has been observed that the switching voltage (V-T) of the glasses decreases with the addition of Bi. In addition, OFF state resistivity of the samples have been found to decrease with the increase in Bi concentration and are related to the observed decrease in switching voltages. The switching voltage (V-T) has been found to increase with the thickness of the sample and decrease with increase in temperature confirming the thermal origin of the memory switching process. Further, scanning electron microscopy (SEM) studies reveal the formation of a crystalline channel indicating the conducting path between the two electrodes in the switched region. (C) 2018 Elsevier Ltd. All rights reserved
Reduced order modeling of random linear dynamical systems based on a new a posteriori error bound
No full textReduced order models (ROMs) are becoming increasingly useful for saving computational cost in response prediction of vibrating systems. In a number of applications such as uncertainty quantification, ROMs require robustness over a wide variation of parameters. Accordingly, often they are classified as local and global, based on their performance in the parametric domain. Availability of an error bound of a ROM helps in achieving this robustness, mainly by allowing adaptivity. In this work, for a linear random dynamical system, first, an a posteriori error bound is developed based on the residual in the governing differential equation. Next, based on this error bound, two adaptive methods are proposed for building robust ROMs, that is, one for local, and another for global. While both methods are based on a greedy search approach, a modification is proposed in the training stage of the global ROM for accelerated convergence. These methods are then applied to an uncertainty quantification problem in a statistical simulation framework, and accordingly, two algorithms are developed. A detailed numerical study on vibration of a bladed disk assembly is conducted to study the accuracy and efficiency of the proposed error bound and adaptive ROMs. It is found that these adaptive ROMs provide a considerable speed-up in estimating the probability of failure
Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network
No full textDrone systems have been deployed by various law enforcement agencies to monitor hostiles, spy on foreign drug cartels, conduct border control operations, etc. This paper introduces a real-time drone surveillance system to identify violent individuals in public areas. The system first uses the Feature Pyramid Network to detect humans from aerial images. The image region with the human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network for human pose estimation. The orientations between the limbs of the estimated pose are next used to identify the violent individuals. The proposed deep network can learn meaningful representations quickly using ScatterNet and structural priors with relatively fewer labeled examples. The system detects the violent individuals in real-time by processing the drone images in the cloud. This research also introduces the aerial violent individual dataset used for training the deep network which hopefully may encourage researchers interested in using deep learning for aerial surveillance. The pose estimation and violent individuals identification performance is compared with the state-of-the-art techniques
On Maximally Recoverable Codes for Product Topologies
No full textGiven a topology of local parity-check constraints, a maximally recoverable code (MRC) can correct all erasure patterns that are information-theoretically correctable. In a grid-like topology, there are a local constraints in every column forming a column code, b local constraints in every row forming a row code, and h global constraints in an (m x n) grid of codeword. Recently, Gopalan et al. initiated the study of MRCs under grid-like topology, and derived a necessary and sufficient condition, termed as the regularity condition, for an erasure pattern to be recoverable when a = 1; h = 0. In this paper, we consider MRCs for product topology (h = 0). First, we construct a certain bipartite graph based on the erasure pattern satisfying the regularity condition for product topology (any a; b, h = 0) and show that there exists a complete matching in this graph. We then present an alternate direct proof of the sufficient condition when a = 1; h = 0. We later extend our technique to study the topology for a = 2; h = 0, and characterize a subset of recoverable erasure patterns in that case. For both a = 1; 2, our method of proof is uniform, i.e., by constructing tensor product G(col) circle times G(row) of generator matrices of column and row codes such that certain square sub-matrices retain full rank. The full-rank condition is proved by resorting to the matching identified earlier and also another set of matchings in erasure sub-patterns
Demographic noise and cost of greenbeard can facilitate greenbeard cooperation
No full textCooperation among organisms, where cooperators suffer a personal cost to benefit others, is ubiquitous in nature. Greenbeard is a key mechanism for the evolution of cooperation, where a single gene or a set of linked genes codes for both cooperation and a phenotypic tag (metaphorically called ``green beard''). Greenbeard cooperation is typically thought to decline over time since defectors can also evolve the tag. However, models of tag-based cooperation typically ignore two key realistic features: populations are finite, and that phenotypic tags can be costly. We develop an analytical model for coevolutionary dynamics of two evolvable traits in finite populations with mutations: costly cooperation and a costly tag. We show that an interplay of demographic noise and cost of the tag can induce coevolutionary cycling, where the evolving population does not reach a steady state but spontaneously switches between cooperative tag-carrying and noncooperative tagless states. Such dynamics allows the tag to repeatedly reappear even after it is invaded by defectors. Thus, we highlight the surprising possibility that the cost of the tag, together with demographic noise, can facilitate the evolution of greenbeard cooperation. We discuss implications of these findings in the context of the evolution of quorum sensing and multicellularity