Eigen-profiles of spatio-temporal fragments for adaptive region-based tracking

We propose a novel space-time descriptor for region-based tracking which is very concise and efficient. The regions represented by covariance matrices within a temporal fragment, are used to estimate this space-time descriptor which we call the Eigenprofiles(EP). EP so obtained is used in estimating the Covariance Matrix of features over spatio-temporal fragments. The Second Order Statistics of spatio-temporal fragments form our target model which can be adapted for variations across the video. The model being concise also allows the use of multiple spatially overlapping fragments to represent the target. We demonstrate good tracking results on very challenging datasets, shot under insufficient illumination conditions.

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.

Moore meets maxwell

Moore's Law has driven the semiconductor revolution enabling over four decades of scaling in frequency, size, complexity, and power. However, the limits of physics are preventing further scaling of speed, forcing a paradigm shift towards multicore computing and parallelization. In effect, the system is taking over the role that the single CPU was playing: high-speed signals running through chips but also packages and boards connect ever more complex systems. High-speed signals making their way through the entire system cause new challenges in the design of computing hardware. Inductance, phase shifts and velocity of light effects, material resonances, and wave behavior become not only prevalent but need to be calculated accurately and rapidly to enable short design cycle times. In essence, to continue scaling with Moore's Law requires the incorporation of Maxwell's equations in the design process. Incorporating Maxwell's equations into the design flow is only possible through the combined power that new algorithms, parallelization and high-speed computing provide. At the same time, incorporation of Maxwell-based models into circuit and system-level simulation presents a massive accuracy, passivity, and scalability challenge. In this tutorial, we navigate through the often confusing terminology and concepts behind field solvers, show how advances in field solvers enable integration into EDA flows, present novel methods for model generation and passivity assurance in large systems, and demonstrate the power of cloud computing in enabling the next generation of scalable Maxwell solvers and the next generation of Moore's Law scaling of systems. We intend to show the truly symbiotic growing relationship between Maxwell and Moore!

Achievable rate region of gaussian broadcast channel with finite input alphabet and quantized output

In this paper, we study the achievable rate region of two-user Gaussian broadcast channel (GBC) when the messages to be transmitted to both the users take values from finite signal sets and the received signal is quantized at both the users. We refer to this channel as quantized broadcast channel (QBC). We first observe that the capacity region defined for a GBC does not carry over as such to QBC. Also, we show that the optimal decoding scheme for GBC (i.e., high SNR user doing successive decoding and low SNR user decoding its message alone) is not optimal for QBC. We then propose an achievable rate region for QBC based on two different schemes. We present achievable rate region results for the case of uniform quantization at the receivers. We find that rotation of one of the user's input alphabet with respect to the other user's alphabet marginally enlarges the achievable rate region of QBC when almost equal powers are allotted to both the users.

Co-evolution of specific amino acid in sigma 1.2 region and nucleotide base in the discriminator to act as sensors of small molecule effectors of transcription initiation in mycobacteria

The transcription from rrn and a number of other promoters is regulated by initiating ribonucleotides (iNTPs) and guanosine tetra/penta phosphate (p)ppGpp], either by strengthening or by weakening of the RNA polymerase (RNAP)-promoter interactions during initiation. Studies in Escherichia coli revealed the importance of a sequence termed discriminator, located between -10 and the transcription start site of the responsive promoters in this mode of regulation. Instability of the open complex at these promoters is attributed to the lack of stabilizing interactions between the suboptimal discriminator and the 1.2 region of sigma 70 (Sig70) in RNAP holoenzyme. We demonstrate a different pattern of interaction between the promoters and sigma A (SigA) of Mycobacterium tuberculosis to execute similar regulation. Instead of cytosine and methionine, thymine at three nucleotides downstream to -10 element and leucine 232 in SigA are found to be essential for iNTPs and pppGpp mediated response at the rrn and gyr promoters of the organism. The specificity of the interaction is substantiated by mutational replacements, either in the discriminator or in SigA, which abolish the nucleotide mediated regulation in vitro or in vivo. Specific yet distinct bases and the amino acids appear to have co-evolved' to retain the discriminator-sigma 1.2 region regulatory switch operated by iNTPs/pppGpp during the transcription initiation in different bacteria.

FUSION OF ALGORITHMS FOR COMPRESSED SENSING

Numerous algorithms have been proposed recently for sparse signal recovery in Compressed Sensing (CS). In practice, the number of measurements can be very limited due to the nature of the problem and/or the underlying statistical distribution of the non-zero elements of the sparse signal may not be known a priori. It has been observed that the performance of any sparse signal recovery algorithm depends on these factors, which makes the selection of a suitable sparse recovery algorithm difficult. To take advantage in such situations, we propose to use a fusion framework using which we employ multiple sparse signal recovery algorithms and fuse their estimates to get a better estimate. Theoretical results justifying the performance improvement are shown. The efficacy of the proposed scheme is demonstrated by Monte Carlo simulations using synthetic sparse signals and ECG signals selected from MIT-BIH database.

Trends in aerosol optical depth over Indian region: Potential causes and impact indicators

The first regional synthesis of long-term (back to similar to 25 years at some stations) primary data (from direct measurement) on aerosol optical depth from the ARFINET (network of aerosol observatories established under the Aerosol Radiative Forcing over India (ARFI) project of Indian Space Research Organization over Indian subcontinent) have revealed a statistically significant increasing trend with a significant seasonal variability. Examining the current values of turbidity coefficients with those reported similar to 50 years ago reveals the phenomenal nature of the increase in aerosol loading. Seasonally, the rate of increase is consistently high during the dry months (December to March) over the entire region whereas the trends are rather inconsistent and weak during the premonsoon (April to May) and summer monsoon period (June to September). The trends in the spectral variation of aerosol optical depth (AOD) reveal the significance of anthropogenic activities on the increasing trend in AOD. Examining these with climate variables such as seasonal and regional rainfall, it is seen that the dry season depicts a decreasing trend in the total number of rainy days over the Indian region. The insignificant trend in AOD observed over the Indo-Gangetic Plain, a regional hot spot of aerosols, during the premonsoon and summer monsoon season is mainly attributed to the competing effects of dust transport and wet removal of aerosols by the monsoon rain. Contributions of different aerosol chemical species to the total dust, simulated using Goddard Chemistry Aerosol Radiation and Transport model over the ARFINET stations, showed an increasing trend for all the anthropogenic components and a decreasing trend for dust, consistent with the inference deduced from trend in Angstrom exponent.

Progressive fusion of reconstruction algorithms for low latency applications in compressed sensing

Recently, it has been shown that fusion of the estimates of a set of sparse recovery algorithms result in an estimate better than the best estimate in the set, especially when the number of measurements is very limited. Though these schemes provide better sparse signal recovery performance, the higher computational requirement makes it less attractive for low latency applications. To alleviate this drawback, in this paper, we develop a progressive fusion based scheme for low latency applications in compressed sensing. In progressive fusion, the estimates of the participating algorithms are fused progressively according to the availability of estimates. The availability of estimates depends on computational complexity of the participating algorithms, in turn on their latency requirement. Unlike the other fusion algorithms, the proposed progressive fusion algorithm provides quick interim results and successive refinements during the fusion process, which is highly desirable in low latency applications. We analyse the developed scheme by providing sufficient conditions for improvement of CS reconstruction quality and show the practical efficacy by numerical experiments using synthetic and real-world data. (C) 2013 Elsevier B.V. All rights reserved.

The N-terminal region containing the zinc finger domain of tobacco streak virus coat protein is essential for the formation of virus-like particles

Tobacco streak virus (TSV), a member of the genus Ilarvirus (family Bromoviridae), has a tripartite genome and forms quasi-isometric virions. All three viral capsids, encapsidating RNA 1, RNA 2 or RNA 3 and subgenomic RNA 4, are constituted of a single species of coat protein (CP). Formation of virus-like particles (VLPs) could be observed when the TSV CP gene was cloned and the recombinant CP (rCP) was expressed in E. coli. TSV VLPs were found to be stabilized by Zn2+ ions and could be disassembled in the presence of 500 mM CaCl2. Mutational analysis corroborated previous studies that showed that an N-terminal arginine-rich motif was crucial for RNA binding; however, the results presented here demonstrate that the presence of RNA is not a prerequisite for assembly of TSV VLPs. Instead, the N-terminal region containing the zinc finger domain preceding the arginine-rich motif is essential for assembly of these VLPs.

Advancing biodiversity research in developing countries: the need for changing paradigms

The world is in the midst of a biodiversity crisis, threatening essential goods and services on which humanity depends. While there is an urgent need globally for biodiversity research, growing obstacles are severely limiting biodiversity research throughout the developing world, particularly in Southeast Asia. Facilities, funding, and expertise are often limited throughout this region, reducing the capacity for local biodiversity research. Although western scientists generally have more expertise and capacity, international research has sometimes been exploitative ``parachute science,'' creating a culture of suspicion and mistrust. These issues, combined with misplaced fears of biopiracy, have resulted in severe roadblocks to biodiversity research in the very countries that need it the most. Here, we present an overview of challenges to biodiversity research and case studies that provide productive models for advancing biodiversity research in developing countries. Key to success is integration of research and education, a model that fosters sustained collaboration by focusing on the process of conducting biodiversity research as well as research results. This model simultaneously expands biodiversity research capacity while building trust across national borders. It is critical that developing countries enact policies that protect their biodiversity capital without shutting down international and local biodiversity research that is essential to achieve the long-term sustainability of biodiversity, promoting food security and economic development.

Performance evaluation of typical approximation algorithms for nonconvex l(p)-minimization in diffuse optical tomography

The sparse estimation methods that utilize the l(p)-norm, with p being between 0 and 1, have shown better utility in providing optimal solutions to the inverse problem in diffuse optical tomography. These l(p)-norm-based regularizations make the optimization function nonconvex, and algorithms that implement l(p)-norm minimization utilize approximations to the original l(p)-norm function. In this work, three such typical methods for implementing the l(p)-norm were considered, namely, iteratively reweighted l(1)-minimization (IRL1), iteratively reweighted least squares (IRLS), and the iteratively thresholding method (ITM). These methods were deployed for performing diffuse optical tomographic image reconstruction, and a systematic comparison with the help of three numerical and gelatin phantom cases was executed. The results indicate that these three methods in the implementation of l(p)-minimization yields similar results, with IRL1 fairing marginally in cases considered here in terms of shape recovery and quantitative accuracy of the reconstructed diffuse optical tomographic images. (C) 2014 Optical Society of America

Growing Length Scales and Their Relation to Timescales in Glass-Forming Liquids

The question of whether the dramatic slowing down of the dynamics of glass-forming liquids near the structural glass transition is caused by the growth of one or more correlation lengths has received much attention in recent years. Several proposals have been made for both static and dynamic length scales that may be responsible for the growth of timescales as the glass transition is approached. These proposals are critically examined with emphasis on the dynamic length scale associated with spatial heterogeneity of local dynamics and the static point-to-set or mosaic length scale of the random first order transition theory of equilibrium glass transition. Available results for these length scales, obtained mostly from simulations, are summarized, and the relation of the growth of timescales near the glass transition with the growth of these length scales is examined. Some of the outstanding questions about length scales in glass-forming liquids are discussed, and studies in which these questions may be addressed are suggested.

Efficient simulation of unitary operators by combining two numerical algorithms: An NMR simulation of the mirror-inversion propagator of an XY spin chain

Precise experimental implementation of unitary operators is one of the most important tasks for quantum information processing. Numerical optimization techniques are widely used to find optimized control fields to realize a desired unitary operator. However, finding high-fidelity control pulses to realize an arbitrary unitary operator in larger spin systems is still a difficult task. In this work, we demonstrate that a combination of the GRAPE algorithm, which is a numerical pulse optimization technique, and a unitary operator decomposition algorithm Ajoy et al., Phys. Rev. A 85, 030303 (2012)] can realize unitary operators with high experimental fidelity. This is illustrated by simulating the mirror-inversion propagator of an XY spin chain in a five-spin dipolar coupled nuclear spin system. Further, this simulation has been used to demonstrate the transfer of entangled states from one end of the spin chain to the other end.

Avoiding acidic region streaking in two-dimensional gel electrophoresis: Case study with two bacterial whole cell protein extracts

Acidic region streaking (ARS) is one of the lacunae in two-dimensional gel electrophoresis (2DE) of bacterial proteome. This streaking is primarily caused by nucleic acid (NuA) contamination and poses major problem in the downstream processes like image analysis and protein identification. Although cleanup and nuclease digestion are practiced as remedial options, these strategies may incur loss in protein recovery and perform incomplete removal of NuA. As a result, ARS has remained a common observation across publications, including the recent ones. In this work, we demonstrate how ultrasound wave can be used to shear NuA in plain ice-cooled water, facilitating the elimination of ARS in the 2DE gels without the need for any additional sample cleanup tasks. In combination with a suitable buffer recipe, IEF program and frequent paper-wick changing approach, we are able to reproducibly demonstrate the production of clean 2DE gels with improved protein recovery and negligible or no ARS. We illustrate our procedure using whole cell protein extracts from two diverse organisms, Escherichia coli and Mycobacterium smegmatis. Our designed protocols are straightforward and expected to provide good 2DE gels without ARS, with comparable times and significantly lower cost.