Differential Diffractive Reflectance Modulation Sensing

We fabricated a reflectance based sensor which relies on the diffraction pattern generated from a bio-microarray where an underlying thin film structure enhances the diffracted intensity from molecular layers. The zero order diffraction represents the background signal and the higher orders represent the phase difference between the array elements and the background. By taking the differential ratio of the first and zero order diffraction signals we get a quantitative measure of molecular binding while simultaneously rejecting common mode fluctuations. We improved the signal-to-noise ratio by an order of magnitude with this ratiometric approach compared to conventional single channel detection. In addition, we use a lithography based approach for fabricating microarrays which results in spot sizes as small as 5 micron diameter unlike the 100 micron spots from inkjet printing and is therefore capable of a high degree of multiplexing. We will describe the real-time measurement of adsorption of charged polymers and bulk refractometry using this technique. The lack of moving parts for point scanning of the microarray and the differential ratiometric measurements using diffracted orders from the same probe beam allows us to make real-time measurements in spite of noise arising from thermal or mechanical fluctuations in the fluid sample above the sensor surface. Further, the lack of moving parts leads to considerable simplification in the readout hardware permitting the use of this technique in compact point of care sensors.

Photoresist functionalisation method for high-density protein microarrays using photolithography

Since the last decade, there is a growing need for patterned biomolecules for various applications ranging from diagnostic devices to enabling fundamental biological studies with high throughput. Protein arrays facilitate the study of protein-protein, protein-drug or protein-DNA interactions as well as highly multiplexed immunosensors based on antibody-antigen recognition. Protein microarrays are typically fabricated using piezoelectric inkjet printing with resolution limit of similar to 70-100 mu m limiting the array density. A considerable amount of research has been done on patterning biomolecules using customised biocompatible photoresists. Here, a simple photolithographic process for fabricating protein microarrays on a commercially available diazo-naphthoquinone-novolac-positive tone photoresist functionalised with 3-aminopropyltriethoxysilane is presented. The authors demonstrate that proteins immobilised using this procedure retain their activity and therefore form functional microarrays with the array density limited only by the resolution of lithography, which is more than an order of magnitude compared with inkjet printing. The process described here may be useful in the integration of conventional semiconductor manufacturing processes with biomaterials relevant for the creation of next-generation bio-chips.

Virtually cool ternary content addressable memory

Fast content addressable data access mechanisms have compelling applications in today's systems. Many of these exploit the powerful wildcard matching capabilities provided by ternary content addressable memories. For example, TCAM based implementations of important algorithms in data mining been developed in recent years; these achieve an an order of magnitude speedup over prevalent techniques. However, large hardware TCAMs are still prohibitively expensive in terms of power consumption and cost per bit. This has been a barrier to extending their exploitation beyond niche and special purpose systems. We propose an approach to overcome this barrier by extending the traditional virtual memory hierarchy to scale up the user visible capacity of TCAMs while mitigating the power consumption overhead. By exploiting the notion of content locality (as opposed to spatial locality), we devise a novel combination of software and hardware techniques to provide an abstraction of a large virtual ternary content addressable space. In the long run, such abstractions enable applications to disassociate considerations of spatial locality and contiguity from the way data is referenced. If successful, ideas for making content addressability a first class abstraction in computing systems can open up a radical shift in the way applications are optimized for memory locality, just as storage class memories are soon expected to shift away from the way in which applications are typically optimized for disk access locality.

A sequential dual method for structural SVMs

In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to compu- tational biology or computer vision and have been tackled using algorithms, referred to as structured output learning algorithms. We consider the problem of structured classifi- cation. In the last few years, large margin classifiers like sup-port vector machines (SVMs) have shown much promise for structured output learning. The related optimization prob -lem is a convex quadratic program (QP) with a large num-ber of constraints, which makes the problem intractable for large data sets. This paper proposes a fast sequential dual method (SDM) for structural SVMs. The method makes re-peated passes over the training set and optimizes the dual variables associated with one example at a time. The use of additional heuristics makes the proposed method more efficient. We present an extensive empirical evaluation of the proposed method on several sequence learning problems.Our experiments on large data sets demonstrate that the proposed method is an order of magnitude faster than state of the art methods like cutting-plane method and stochastic gradient descent method (SGD). Further, SDM reaches steady state generalization performance faster than the SGD method. The proposed SDM is thus a useful alternative for large scale structured output learning.

Pressure induced structural phase transformation in TiN: a first-principles study

Titanium nitride (TiN), which is widely used for hard coatings, reportedly undergoes a pressure-induced structural phase transformation, from a NaCl to a CsCl structure, at similar to 7 GPa. In this paper, we use first-principles calculations based on density functional theory with a generalized gradient approximation of the exchange correlation energy to determine the structural stability of this transformation. Our results show that the stress required for this structural transformation is substantially lower (by more than an order of magnitude) when it is deviatoric in nature vis-a-vis that under hydrostatic pressure. Local stability of the structure is assessed with phonon dispersion determined at different pressures, and we find that CsCl structure of TiN is expected to distort after the transformation. From the electronic structure calculations, we estimate the electrical conductivity of TiN in the CsCl structure to be about 5 times of that in NaCl structure, which should be observable experimentally. (C) 2013 American Institute of Physics. http://dx.doi.org/10.1063/1.4798591]

Can Superflares Occur on Our Sun?

Recent observations of Sun-like stars, similar to our Sun in their surface temperature (5600-6000 K) and slow rotation (rotational period > 10 d), using the Kepler satellite by Maehara et al. (2012, Nature, 485, 478) have revealed the existence of superflares (with energy of 10(33)-10(35) erg). From statistical analyses of these superflares, it was found that superflares with energy of 10(34) erg occur once in 800 yr, and superflares with 10(35) erg occur once in 5000 yr. In this paper, we examine whether superflares with energy of 10(33)-10(35) erg could occur on the present Sun through the use of simple order-of-magnitude estimates based on current ideas related to the mechanisms of the solar dynamo. If magnetic flux is generated by differential rotation at the base of the convection zone, as assumed in typical dynamo models, it is possible that the present Sun would generate a large sunspot with a total magnetic flux of similar to 2 x 10(23) Mx (= G cm(2)) within one solar cycle period, and lead to superflares with an energy of 10(34) erg. To store a total magnetic flux of similar to 10(24) Mx, necessary for generating 10(35) erg superflares, it would take similar to 40 yr. Hot Jupiters have often been argued to be a necessary ingredient for the generation of superflares, but we found that they do not play any essential role in the generation of magnetic flux in the star itself, if we consider only the magnetic interaction between the star and the hot Jupiter. This seems to be consistent with Maehara et al.'s finding of 148 superflare-generating solar-type stars that do not have a hot Jupiter-like companion. Altogether, our simple calculations, combined with Maehara et al.'s analysis of superflares on Sun-like stars, show that there is a possibility that superflares of 10(34) erg would occur once in 800 yr on our present Sun.

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.

Optimal Joint Antenna Selection and Power Adaptation in Underlay Cognitive Radios

Transmit antenna selection (AS) is a popular, low hardware complexity technique that improves the performance of an underlay cognitive radio system, in which a secondary transmitter can transmit when the primary is on but under tight constraints on the interference it causes to the primary. The underlay interference constraint fundamentally changes the criterion used to select the antenna because the channel gains to the secondary and primary receivers must be both taken into account. We develop a novel and optimal joint AS and transmit power adaptation policy that minimizes a Chernoff upper bound on the symbol error probability (SEP) at the secondary receiver subject to an average transmit power constraint and an average primary interference constraint. Explicit expressions for the optimal antenna and power are provided in terms of the channel gains to the primary and secondary receivers. The SEP of the optimal policy is at least an order of magnitude lower than that achieved by several ad hoc selection rules proposed in the literature and even the optimal antenna selection rule for the case where the transmit power is either zero or a fixed value.

On the k(1)(-1) scaling in sink-flow turbulent boundary layers

Scaling of the streamwise velocity spectrum phi(11)(k(1)) in the so-called sink-flow turbulent boundary layer is investigated in this work. The present experiments show strong evidence for the k(1)(-1) scaling i.e. phi(11)(k(1)) = Lambda(1)U(tau)(2)k(1)(-1), where k(1)(-1) is the streamwise wavenumber and U-tau is the friction velocity. Interestingly, this k(1)(-1) scaling is observed much farther from the wall and at much lower flow Reynolds number (both differing by almost an order of magnitude) than what the expectations from experiments on a zero-pressure-gradient turbulent boundary layer flow would suggest. Furthermore, the coefficient A(1) in the present sink-flow data is seen to be non-universal, i.e. A(1) varies with height from the wall; the scaling exponent -1 remains universal. Logarithmic variation of the so-called longitudinal structure function, which is the physical-space counterpart of spectral k(1)(-1) scaling, is also seen to be non-universal, consistent with the non-universality of A(1). These observations are to be contrasted with the universal value of A(1) (along with the universal scaling exponent of 1) reported in the literature on zero-pressure-gradient turbulent boundary layers. Theoretical arguments based on dimensional analysis indicate that the presence of a streamwise pressure gradient in sink-flow turbulent boundary layers makes the coefficient A(1) non-universal while leaving the scaling exponent -1 unaffected. This effect of the pressure gradient on the streamwise spectra, as discussed in the present study (experiments as well as theory), is consistent with other recent studies in the literature that are focused on the structural aspects of turbulent boundary layer flows in pressure gradients (Harun etal., J. Flui(d) Mech., vol. 715, 2013, pp. 477-498); the present paper establishes the link between these two. The variability of A(1) accommodated in the present framework serves to clarify the ideas of universality of the k(1)(-1) scaling.

Note on modelling directionality in piezoresistivity

When computing the change in electrical resistivity of a piezoresistive cubic material embedded in a deforming structure, the piezoresistive and the stress tensors should be in the same coordinate system. While the stress tensor is usually calculated in a coordinate system aligned with the principal axes of a regular structure, the specified piezoresistive coefficients may not be in that coordinate system. For instance, piezoresistive coefficients are usually given in an orthogonal cartesian coordinate system aligned with the <100> crystallographic directions and designers sometimes deliberately orient a crystallographic direction other than <100> along the principal directions of the structure to increase the gauge factor. In such structures, it is advantageous to calculate the piezoresistivity tensor in the coordinate system along which the stress tensors are known rather than the other way around. This is because the transformation of stress will have to be done at every point in the structure but piezoresistivity tensor needs to be transformed only once. Here, using tensor transformation relations, we show how to calculate the piezoresistive tensor along any arbitrary Cartesian coordinate system from the piezoresistive coefficients for the <100> coordinate system. Some of the software packages that simulate the piezoresistive effect do not have interfaces for calculation of the entire piezoresistive tensor for arbitrary directions. This warrants additional work for the user because not considering the complete piezoresisitive tensor can lead to large errors. This is illustrated with an example where the error is as high as 33%. Additionally, for elastic analysis, we used hybrid finite element formulation that estimates stresses more accurately than displacement-based formulation. Therefore, as shown in an example where the change in resistance can be calculated analytically, the percentage error of our piezoresistive program is an order of magnitude lower relative to displacement-based finite element method.

Lovasz theta function, SVMs and Finding Dense Subgraphs

In this paper we establish that the Lovasz theta function on a graph can be restated as a kernel learning problem. We introduce the notion of SVM-theta graphs, on which Lovasz theta function can be approximated well by a Support vector machine (SVM). We show that Erdos-Renyi random G(n, p) graphs are SVM-theta graphs for log(4)n/n <= p < 1. Even if we embed a large clique of size Theta(root np/1-p) in a G(n, p) graph the resultant graph still remains a SVM-theta graph. This immediately suggests an SVM based algorithm for recovering a large planted clique in random graphs. Associated with the theta function is the notion of orthogonal labellings. We introduce common orthogonal labellings which extends the idea of orthogonal labellings to multiple graphs. This allows us to propose a Multiple Kernel learning (MKL) based solution which is capable of identifying a large common dense subgraph in multiple graphs. Both in the planted clique case and common subgraph detection problem the proposed solutions beat the state of the art by an order of magnitude.