Strong-coupling expansion for the momentum distribution of the Bose-Hubbard model with benchmarking against exact numerical results

A strong-coupling expansion for the Green's functions, self-energies, and correlation functions of the Bose-Hubbard model is developed. We illustrate the general formalism, which includes all possible (normal-phase) inhomogeneous effects in the formalism, such as disorder or a trap potential, as well as effects of thermal excitations. The expansion is then employed to calculate the momentum distribution of the bosons in the Mott phase for an infinite homogeneous periodic system at zero temperature through third order in the hopping. By using scaling theory for the critical behavior at zero momentum and at the critical value of the hopping for the Mott insulator–to–superfluid transition along with a generalization of the random-phase-approximation-like form for the momentum distribution, we are able to extrapolate the series to infinite order and produce very accurate quantitative results for the momentum distribution in a simple functional form for one, two, and three dimensions. The accuracy is better in higher dimensions and is on the order of a few percent relative error everywhere except close to the critical value of the hopping divided by the on-site repulsion. In addition, we find simple phenomenological expressions for the Mott-phase lobes in two and three dimensions which are much more accurate than the truncated strong-coupling expansions and any other analytic approximation we are aware of. The strong-coupling expansions and scaling-theory results are benchmarked against numerically exact quantum Monte Carlo simulations in two and three dimensions and against density-matrix renormalization-group calculations in one dimension. These analytic expressions will be useful for quick comparison of experimental results to theory and in many cases can bypass the need for expensive numerical simulations.

Antenna Selection in Interference-Constrained Underlay Cognitive Radios: SEP-Optimal Rule and Performance Benchmarking

In the underlay mode of cognitive radio, secondary users are allowed to transmit when the primary is transmitting, but under tight interference constraints that protect the primary. However, these constraints limit the secondary system performance. Antenna selection (AS)-based multiple antenna techniques, which exploit spatial diversity with less hardware, help improve secondary system performance. We develop a novel and optimal transmit AS rule that minimizes the symbol error probability (SEP) of an average interference-constrained multiple-input-single-output secondary system that operates in the underlay mode. We show that the optimal rule is a non-linear function of the power gain of the channel from the secondary transmit antenna to the primary receiver and from the secondary transmit antenna to the secondary receive antenna. We also propose a simpler, tractable variant of the optimal rule that performs as well as the optimal rule. We then analyze its SEP with L transmit antennas, and extensively benchmark it with several heuristic selection rules proposed in the literature. We also enhance these rules in order to provide a fair comparison, and derive new expressions for their SEPs. The results bring out new inter-relationships between the various rules, and show that the optimal rule can significantly reduce the SEP.

Benchmarking recognition results on camera captured word image data sets

We have benchmarked the maximum obtainable recognition accuracy on five publicly available standard word image data sets using semi-automated segmentation and a commercial OCR. These images have been cropped from camera captured scene images, born digital images (BDI) and street view images. Using the Matlab based tool developed by us, we have annotated at the pixel level more than 3600 word images from the five data sets. The word images binarized by the tool, as well as by our own midline analysis and propagation of segmentation (MAPS) algorithm are recognized using the trial version of Nuance Omnipage OCR and these two results are compared with the best reported in the literature. The benchmark word recognition rates obtained on ICDAR 2003, Sign evaluation, Street view, Born-digital and ICDAR 2011 data sets are 83.9%, 89.3%, 79.6%, 88.5% and 86.7%, respectively. The results obtained from MAPS binarized word images without the use of any lexicon are 64.5% and 71.7% for ICDAR 2003 and 2011 respectively, and these values are higher than the best reported values in the literature of 61.1% and 41.2%, respectively. MAPS results of 82.8% for BDI 2011 dataset matches the performance of the state of the art method based on power law transform.

Hybrid Energy Harvesting Wireless Systems: Performance Evaluation and Benchmarking

Energy harvesting sensor (EHS) nodes provide an attractive and green solution to the problem of limited lifetime of wireless sensor networks (WSNs). Unlike a conventional node that uses a non-rechargeable battery and dies once it runs out of energy, an EHS node can harvest energy from the environment and replenish its rechargeable battery. We consider hybrid WSNs that comprise of both EHS and conventional nodes; these arise when legacy WSNs are upgraded or due to EHS deployment cost issues. We compare conventional and hybrid WSNs on the basis of a new and insightful performance metric called k-outage duration, which captures the inability of the nodes to transmit data either due to lack of sufficient battery energy or wireless fading. The metric overcomes the problem of defining lifetime in networks with EHS nodes, which never die but are occasionally unable to transmit due to lack of sufficient battery energy. It also accounts for the effect of wireless channel fading on the ability of the WSN to transmit data. We develop two novel, tight, and computationally simple bounds for evaluating the k-outage duration. Our results show that increasing the number of EHS nodes has a markedly different effect on the k-outage duration than increasing the number of conventional nodes.

Benchmarking sustainability of Indian electricity system: An indicator approach

India needs to significantly increase its electricity consumption levels, in a sustainable manner, if it has to ensure rapid economic development, a goal that remains the most potent tool for delivering adaptation capacity to its poor who will suffer the worst consequences of climate change. Resource/supply constraints faced by conventional energy sources, techno-economic constraints faced by renewable energy sources, and the bounds imposed by climate change on fossil fuel use are likely to undermine India's quest for having a robust electricity system that can effectively contribute to achieving accelerated, sustainable and inclusive economic growth. One possible way out could be transitioning into a sustainable electricity system, which is a trade-off solution having taken into account the economic, social and environmental concerns. As a first step toward understanding this transition, we contribute an indicator based hierarchical multidimensional framework as an analytical tool for sustainability assessment of electricity systems, and validate it for India's national electricity system. We evaluate Indian electricity system using this framework by comparing it with a hypothetical benchmark sustainable electrical system, which was created using best indicator values realized across national electricity systems in the world. This framework, we believe, can be used to examine the social, economic and environmental implications of the current Indian electricity system as well as setting targets for future development. The analysis with the indicator framework provides a deeper understanding of the system, identify and quantify the prevailing sustainability gaps and generate specific targets for interventions. We use this framework to compute national electricity system sustainability index (NESSI) for India. (C) 2014 Elsevier Ltd. All rights reserved.

HFinFET: A Scalable, High Performance, Low Leakage Hybrid n-Channel FET

In this letter, we propose the design and simulation study of a novel transistor, called HFinFET, which is a hybrid of an HEMT and a FinFET, to obtain excellent performance and good OFF-state control. Followed by the description of the design, 3-D device simulation has been performed to predict the characteristics of the device. The device has been benchmarked against published state of the art HEMT as well as planar and nonplanar Si n-MOSFET data of comparable gate length using standard benchmarking techniques.

Combining Bayes Classification and Point Group Symmetry under Boolean Framework for Enhanced Protein Quaternary Structure Inference

Our ability to infer the protein quaternary structure automatically from atom and lattice information is inadequate, especially for weak complexes, and heteromeric quaternary structures. Several approaches exist, but they have limited performance. Here, we present a new scheme to infer protein quaternary structure from lattice and protein information, with all-around coverage for strong, weak and very weak affinity homomeric and heteromeric complexes. The scheme combines naive Bayes classifier and point group symmetry under Boolean framework to detect quaternary structures in crystal lattice. It consistently produces >= 90% coverage across diverse benchmarking data sets, including a notably superior 95% coverage for recognition heteromeric complexes, compared with 53% on the same data set by current state-of-the-art method. The detailed study of a limited number of prediction-failed cases offers interesting insights into the intriguing nature of protein contacts in lattice. The findings have implications for accurate inference of quaternary states of proteins, especially weak affinity complexes.

DockYard-a repository to assist modeling of protein-protein docking

In the absence of interlogs, building docking models is a time intensive task, involving generation of a large pool of docking decoys followed by refinement and screening to identify near native docking solutions. This limits the researcher interested in building docking methods with the choice of benchmarking only a limited number of protein complexes. We have created a repository called dockYard (http://pallab.serc.iisc.ernet.in/dockYard), that allows modelers interested in protein-protein interaction to access large volume of information on protein dimers and their interlogs, and also download decoys for their work if they are interested in building modeling methods. dockYard currently offers four categories of docking decoys derived from: Bound (native dimer co-crystallized), Unbound (individual subunits are crystallized, as well as the target dimer), Variants (match the previous two categories in at least one subunit with 100% sequence identity), and Interlogs (match the previous categories in at least one subunit with >= 90% or >= 50% sequence identity). The web service offers options for full or selective download based on search parameters. Our portal also serves as a repository to modelers who may want to share their decoy sets with the community.

Assessing design creativity

Creativity is crucial for designing products and enabling innovation. Assessing creativity can help identify innovative designers and products, and support improvement of both. The literature variously defines creativity as a function of degree of novelty, usefulness, or both. Most methods for assessing creativity, however, focus only on assessing novelty of products. This research proposes a new method for assessing the creativity of products as a function of their novelty and usefulness. We develop individual methods for assessing novelty and usefulness of products. and then combine these into a method for assessing creativity of products. The proposed methods have been evaluated by benchmarking them, and other methods available from literature, against the collective, intuitive assessment of product creativity of experienced designers. (C) 2011 Elsevier Ltd. All rights reserved.

Underlap Optimization in HFinFET in Presence of Interface Traps

In this work, using 3-D device simulation, we perform an extensive gate to source/drain underlap optimization for the recently proposed hybrid transistor, HFinFET, to show that the underlap lengths can be suitably tuned to improve the ON-OFF ratio as well as the subthreshold characteristics in an ultrashort channel n-type device without significantON performance degradation. We also show that the underlap knob can be tuned to mitigate the device quality degradation in presence of interface traps. The obtained results are shown to be promising when compared against ITRS 2009 performance projections, as well as published state of the art planar and nonplanar Silicon MOSFET data of comparable gate lengths using standard benchmarking techniques.

Indicator-based urban sustainability-A review

Sustainability has emerged as one of the important planning concepts from its beginnings in economics and ecological thinking, and has widely been applied to assessing urban development. Different methods, techniques and instruments for urban sustainability assessment that help determine how cities can become more sustainable have emerged over a period of time. Among these, indicator-based approaches contribute to building of sustainable self-regulated systems that integrate development and environment protection. Hence, these provide a solid foundation for decision-making at all levels and are being increasingly used. The present paper builds on the background of the available literature and suggests the need for benchmarking indicator-based approach in a given urban area and incorporating various local issues, thus enhancing the long-term sustainability of cities which can be developed by introducing sustainability indicators into the urban planning process. (C) 2013 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

Antenna Selection with Power Adaptation in Interference-Constrained Cognitive Radios

The performance of an underlay cognitive radio (CR) system, which can transmit when the primary is on, is curtailed by tight constraints on the interference it can cause to the primary receiver. Transmit antenna selection (AS) improves the performance of underlay CR by exploiting spatial diversity but with less hardware. However, the selected antenna and its transmit power now both depend on the channel gains to the secondary and primary receivers. We develop a novel Chernoffbound based optimal AS and power adaptation (CBBOASPA) policy that minimizes an upper bound on the symbol error probability (SEP) at the secondary receiver, subject to constraints on the average transmit power and the average interference to the primary. The optimal antenna and its power are presented in an insightful closed form in terms of the channel gains. We then analyze the SEP of CBBOASPA. Extensive benchmarking shows that the SEP of CBBOASPA for both MPSK and MQAM is one to two orders of magnitude lower than several ad hoc AS policies and even optimal AS with on-off power control.

Exploring Transfer Learning Approaches for Head Pose Classification from Multi-view Surveillance Images

Head pose classification from surveillance images acquired with distant, large field-of-view cameras is difficult as faces are captured at low-resolution and have a blurred appearance. Domain adaptation approaches are useful for transferring knowledge from the training (source) to the test (target) data when they have different attributes, minimizing target data labeling efforts in the process. This paper examines the use of transfer learning for efficient multi-view head pose classification with minimal target training data under three challenging situations: (i) where the range of head poses in the source and target images is different, (ii) where source images capture a stationary person while target images capture a moving person whose facial appearance varies under motion due to changing perspective, scale and (iii) a combination of (i) and (ii). On the whole, the presented methods represent novel transfer learning solutions employed in the context of multi-view head pose classification. We demonstrate that the proposed solutions considerably outperform the state-of-the-art through extensive experimental validation. Finally, the DPOSE dataset compiled for benchmarking head pose classification performance with moving persons, and to aid behavioral understanding applications is presented in this work.

CANDO and the infinite drug discovery frontier

The Computational Analysis of Novel Drug Opportunities (CANDO) platform (http://protinfo.org/cando) uses similarity of compound-proteome interaction signatures to infer homology of compound/drug behavior. We constructed interaction signatures for 3733 human ingestible compounds covering 48,278 protein structures mapping to 2030 indications based on basic science methodologies to predict and analyze protein structure, function, and interactions developed by us and others. Our signature comparison and ranking approach yielded benchmarking accuracies of 12-25% for 1439 indications with at least two approved compounds. We prospectively validated 49/82 `high value' predictions from nine studies covering seven indications, with comparable or better activity to existing drugs, which serve as novel repurposed therapeutics. Our approach may be generalized to compounds beyond those approved by the FDA, and can also consider mutations in protein structures to enable personalization. Our platform provides a holistic multiscale modeling framework of complex atomic, molecular, and physiological systems with broader applications in medicine and engineering.

Experimental studies on the influence of intermediate principal stress and inclination on the mechanical behaviour of angular sands

A comprehensive experimental study has been made on angular sand to investigate various aspects of mechanical behavior. A hollow cylinder torsion testing apparatus is used in this program to apply a range of stress conditions on this angular quartzitic fine sand under monotonic drained shear. The effect of the magnitude and inclination of the principal stresses on an element of sand is studied through these experiments. This magnitude and inclination of the principal stresses are presented as an ``ensemble measure of fabric in sands''. This ensemble measure of fabric in the sands evolves through the shearing process, and reaches the final state, which indeed has a unique fabric. The sand shows significant variation in strength with changing inclination of the principal stresses. The locus of the final stress state in principal stress space is also mapped from these series of experiments. Additional aspects of non-coaxiality, a benchmarking exercise with a few constitutive models is presented here. This experimental approach albeit indirect shows that a unique state which is dependent on the fabric, density and confining stress exists. This suite of experiments provides a well-controlled data set for a clear understanding on the mechanical behavior of sands.