Scalable Preference Aggregation in Social Networks

In social choice theory, preference aggregation refers to computing an aggregate preference over a set of alternatives given individual preferences of all the agents. In real-world scenarios, it may not be feasible to gather preferences from all the agents. Moreover, determining the aggregate preference is computationally intensive. In this paper, we show that the aggregate preference of the agents in a social network can be computed efficiently and with sufficient accuracy using preferences elicited from a small subset of critical nodes in the network. Our methodology uses a model developed based on real-world data obtained using a survey on human subjects, and exploits network structure and homophily of relationships. Our approach guarantees good performance for aggregation rules that satisfy a property which we call expected weak insensitivity. We demonstrate empirically that many practically relevant aggregation rules satisfy this property. We also show that two natural objective functions in this context satisfy certain properties, which makes our methodology attractive for scalable preference aggregation over large scale social networks. We conclude that our approach is superior to random polling while aggregating preferences related to individualistic metrics, whereas random polling is acceptable in the case of social metrics.

Analysis of large-amplitude stratospheric mountain wave event observed from the AIRS and MLS sounders over the western Himalayan region

Mountain waves in the stratosphere have been observed over elevated topographies using both nadir-looking and limb-viewing satellites. However, the characteristics of mountain waves generated over the Himalayan Mountain range and the adjacent Tibetan Plateau are relatively less explored. The present study reports on three-dimensional (3-D) properties of a mountain wave event that occurred over the western Himalayan region on 9 December 2008. Observations made by the Atmospheric Infrared Sounder on board the Aqua and Microwave Limb Sounder on board the Aura satellites are used to delineate the wave properties. The observed wave properties such as horizontal (lambda(x), lambda(y)) and vertical (lambda(z)) wavelengths are 276 km (zonal), 289 km (meridional), and 25 km, respectively. A good agreement is found between the observed and modeled/analyzed vertical wavelength for a stationary gravity wave determined using the Modern Era Retrospective Analysis for Research and Applications (MERRA) reanalysis winds. The analysis of both the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis and MERRA winds shows that the waves are primarily forced by strong flow across the topography. Using the 3-D properties of waves and the corrected temperature amplitudes, we estimated wave momentum fluxes of the order of similar to 0.05 Pa, which is in agreement with large-amplitude mountain wave events reported elsewhere. In this regard, the present study is considered to be very much informative to the gravity wave drag schemes employed in current general circulation models for this region.

Synthesis of pure iron magnetic nanoparticles in large quantity

Free nanoparticles of iron (Fe) and their colloids with high saturation magnetization are in demand for medical and microfluidic applications. However, the oxide layer that forms during processing has made such synthesis a formidable challenge. Lowering the synthesis temperature decreases rate of oxidation and hence provides a new way of producing pure metallic nanoparticles prone to oxidation in bulk amount (large quantity). In this paper we have proposed a methodology that is designed with the knowledge of thermodynamic imperatives of oxidation to obtain almost oxygen-free iron nanoparticles, with or without any organic capping by controlled milling at low temperatures in a specially designed high-energy ball mill with the possibility of bulk production. The particles can be ultrasonicated to produce colloids and can be bio-capped to produce transparent solution. The magnetic properties of these nanoparticles confirm their superiority for possible biomedical and other applications.

A Novel Monte-Carlo-Sampling-Based Receiver for Large-Scale Uplink Multiuser MIMO Systems

In this paper, we propose low-complexity algorithms based on Monte Carlo sampling for signal detection and channel estimation on the uplink in large-scale multiuser multiple-input-multiple-output (MIMO) systems with tens to hundreds of antennas at the base station (BS) and a similar number of uplink users. A BS receiver that employs a novel mixed sampling technique (which makes a probabilistic choice between Gibbs sampling and random uniform sampling in each coordinate update) for detection and a Gibbs-sampling-based method for channel estimation is proposed. The algorithm proposed for detection alleviates the stalling problem encountered at high signal-to-noise ratios (SNRs) in conventional Gibbs-sampling-based detection and achieves near-optimal performance in large systems with M-ary quadrature amplitude modulation (M-QAM). A novel ingredient in the detection algorithm that is responsible for achieving near-optimal performance at low complexity is the joint use of a mixed Gibbs sampling (MGS) strategy coupled with a multiple restart (MR) strategy with an efficient restart criterion. Near-optimal detection performance is demonstrated for a large number of BS antennas and users (e. g., 64 and 128 BS antennas and users). The proposed Gibbs-sampling-based channel estimation algorithm refines an initial estimate of the channel obtained during the pilot phase through iterations with the proposed MGS-based detection during the data phase. In time-division duplex systems where channel reciprocity holds, these channel estimates can be used for multiuser MIMO precoding on the downlink. The proposed receiver is shown to achieve good performance and scale well for large dimensions.

Taming of large diameter triaxial setup

Triaxial tests are essential to estimate the shear strength properties of the soil or rock. Normally triaxial tests are carried out on samples of 38 mm diameter and 76 mm height. Granular materials, predominantly used in base/sub-base construction of pavements or in railways have size range of 60-75 mm. Determination of shear strength parameters of those materials can be made possible only through triaxial tests on large diameter samples. This paper describes a large diameter cyclic triaxial testing facility set up in the Geotechnical Engineering lab of Indian Institute of Science. This setup consists of 100 kN capacity dynamic loading frame, which facilitates testing of samples of up to 300 mm diameter and 600 mm height. The loading ram can be actuated up to a maximum frequency of 10 Hz, with maximum amplitude of 100 mm. The setup is capable of carrying out static as well as dynamic triaxial tests under isotropic, anisotropic conditions with a maximum confining pressure of 1 MPa. Working with this setup is a difficult task because of the size of the sample. In this paper, a detailed discussion on the various problems encountered during the initial testing using the equipment, the ideas and solutions adopted to solve them are presented. Pilot experiments on granular sub-base material of 53 mm down size are also presented.

Information content of molecular graph and prediction of gas phase thermal entropy of organic compounds

Entropy is a fundamental thermodynamic property that has attracted a wide attention across domains, including chemistry. Inference of entropy of chemical compounds using various approaches has been a widely studied topic. However, many aspects of entropy in chemical compounds remain unexplained. In the present work, we propose two new information-theoretical molecular descriptors for the prediction of gas phase thermal entropy of organic compounds. The descriptors reflect the bulk and size of the compounds as well as the gross topological symmetry in their structures, all of which are believed to determine entropy. A high correlation () between the entropy values and our information-theoretical indices have been found and the predicted entropy values, obtained from the corresponding statistically significant regression model, have been found to be within acceptable approximation. We provide additional mathematical result in the form of a theorem and proof that might further help in assessing changes in gas phase thermal entropy values with the changes in molecular structures. The proposed information-theoretical molecular descriptors, regression model and the mathematical result are expected to augment predictions of gas phase thermal entropy for a large number of chemical compounds.

Scaling of turbulent flame speed for expanding flames with Markstein diffusion considerations

In this paper we clarify the role of Markstein diffusivity, which is the product of the planar laminar flame speed and the Markstein length, on the turbulent flame speed and its scaling, based on experimental measurements on constant-pressure expanding turbulent flames. Turbulent flame propagation data are presented for premixed flames of mixtures of hydrogen, methane, ethylene, n-butane, and dimethyl ether with air, in near-isotropic turbulence in a dual-chamber, fan-stirred vessel. For each individual fuel-air mixture presented in this work and the recently published iso-octane data from Leeds, normalized turbulent flame speed data of individual fuel-air mixtures approximately follow a Re-T,f(0.5) scaling, for which the average radius is the length scale and thermal diffusivity is the transport property of the turbulence Reynolds number. At a given Re-T,Re-f, it is experimentally observed that the normalized turbulent flame speed decreases with increasing Markstein number, which could be explained by considering Markstein diffusivity as the leading dissipation mechanism for the large wave number flame surface fluctuations. Consequently, by replacing thermal diffusivity with the Markstein diffusivity in the turbulence Reynolds number definition above, it is found that normalized turbulent flame speeds could be scaled by Re-T,M(0.5) irrespective of the fuel, equivalence ratio, pressure, and turbulence intensity for positive Markstein number flames.

Large-area Piezoceramic Coating with IDT Electrodes for Ultrasonic Sensing Applications

In the present paper, the ultrasonic strain sensing performance of large-area piezoceramic coating with Inter Digital Transducer (IDT) electrodes is studied. The piezoceramic coating is prepared using slurry coating technique and the piezoelectric phase is achieved by poling under DC field. To study the sensing performance of the piezoceramic coating with IDT electrodes for strain induced by the guided waves, the piezoceramic coating is fabricated on the surface of a beam specimen at one end and the ultrasonic guided waves are launched with a piezoelectric wafer bonded on another end. Often a wider frequency band of operation is needed for the effective implementation of the sensors in the Structural Health Monitoring (SHM) of various structures, for different types of damages. A wider frequency band of operation is achieved in the present study by considering the variation in the number of IDT electrodes in the contribution of voltage for the induced dynamic strain. In the present work, the fabricated piezoceramic coatings with IDT electrodes have been characterized for dynamic strain sensing applications using guided wave technique at various different frequencies. Strain levels of the launched guided wave are varied by varying the magnitude of the input voltage sent to the actuator. Sensitivity variation with the variation in the strain levels of guided wave is studied for the combination of different number of IDT electrodes. Piezoelectric coefficient e(11) is determined at different frequencies and at different strain levels using the guided wave technique.

The Lovasz θ function, SVMs and finding large dense subgraphs

The Lovasz θ function of a graph, is a fundamental tool in combinatorial optimization and approximation algorithms. Computing θ involves solving a SDP and is extremely expensive even for moderately sized graphs. In this paper we establish that the Lovasz θ function is equivalent to a kernel learning problem related to one class SVM. This interesting connection opens up many opportunities bridging graph theoretic algorithms and machine learning. We show that there exist graphs, which we call SVM−θ graphs, on which the Lovasz θ function can be approximated well by a one-class SVM. This leads to a novel use of SVM techniques to solve algorithmic problems in large graphs e.g. identifying a planted clique of size Θ(n√) in a random graph G(n,12). A classic approach for this problem involves computing the θ function, however it is not scalable due to SDP computation. We show that the random graph with a planted clique is an example of SVM−θ graph, and as a consequence a SVM based approach easily identifies the clique in large graphs and is competitive with the state-of-the-art. Further, we introduce the notion of a ''common orthogonal labeling'' which extends the notion of a ''orthogonal labelling of a single graph (used in defining the θ function) to multiple graphs. The problem of finding the optimal common orthogonal labelling is cast as a Multiple Kernel Learning problem and is used to identify a large common dense region in multiple graphs. The proposed algorithm achieves an order of magnitude scalability compared to the state of the art.