Grammar Boosting: A New Technique for Proving Lower Bounds for Computation over Compressed Data

Grammar compression is a general compression framework in which a string $T$ of length $N$ is represented as a context-free grammar of size $n$ whose language contains only $T$. In this paper, we focus on studying the limitations of algorithms and data structures operating on strings in grammar-compressed form. Previous work focused on proving lower bounds for grammars constructed using algorithms that achieve the approximation ratio $\rho=\mathcal{O}(\text{polylog }N)$. Unfortunately, for the majority of grammar compressors, $\rho$ is either unknown or satisfies $\rho=\omega(\text{polylog }N)$. In their seminal paper, Charikar et al. [IEEE Trans. Inf. Theory 2005] studied seven popular grammar compression algorithms: RePair, Greedy, LongestMatch, Sequential, Bisection, LZ78, and $\alpha$-Balanced. Only one of them ($\alpha$-Balanced) is known to achieve $\rho=\mathcal{O}(\text{polylog }N)$. We develop the first technique for proving lower bounds for data structures and algorithms on grammars that is fully general and does not depend on the approximation ratio $\rho$ of the used grammar compressor. Using this technique, we first prove that $\Omega(\log N/\log \log N)$ time is required for random access on RePair, Greedy, LongestMatch, Sequential, and Bisection, while $\Omega(\log\log N)$ time is required for random access to LZ78. All these lower bounds hold within space $\mathcal{O}(n\text{ polylog }N)$ and match the existing upper bounds. We also generalize this technique to prove several conditional lower bounds for compressed computation. For example, we prove that unless the Combinatorial $k$-Clique Conjecture fails, there is no combinatorial algorithm for CFG parsing on Bisection (for which it holds $\rho=\tilde{\Theta}(N^{1/2})$) that runs in $\mathcal{O}(n^c\cdot N^{3-\epsilon})$ time for all constants $c>0$ and $\epsilon>0$. Previously, this was known only for $c<2\epsilon$

Interval based fuzzy systems for identification of important genes from microarray gene expression data: Application to carcinogenic development

AbstractIn the present article, we develop two interval based fuzzy systems for identification of some possible genes mediating the carcinogenic development in various tissues. The methodology involves dimensionality reduction, classifying the genes through incorporation of the notion of linguistic fuzzy sets low, medium and high, and finally selection of some possible genes mediating a particular disease, obtained by a rule generation/grouping technique. The effectiveness of the proposed methodology, is demonstrated using five microarray gene expression datasets dealing with human lung, colon, sarcoma, breast cancer and leukemia. Moreover, the superior capability of the methodology in selecting important genes, over five other existing gene selection methods, viz., Significance Analysis of Microarrays (SAM), Signal-to-Noise Ratio (SNR), Neighborhood analysis (NA), Bayesian Regularization (BR) and Data-adaptive (DA) is demonstrated, in terms of the enrichment of each GO category of the important genes based on P-values. The results are appropriately validated by earlier investigations, gene expression profiles and t-test. The proposed methodology has been able to select genes that are more biologically significant in mediating the development of a disease than those obtained by the others

Fuzzy feature evaluation index and connectionist realization

A new feature evaluation index based on fuzzy set theory and a connectionist model for its evaluation are provided. A concept of flexible membership function incorporating weighting factors, is introduced which makes the modeling of the class structures more appropriate. A neuro-fuzzy algorithm is developed for determining the optimum weighting coefficients representing the feature importance. The overall importance of the features is evaluated both individually and in a group considering their dependence as well as independence. Effectiveness of the algorithms along with comparison is demonstrated on speech and Iris data

Unsupervised feature extraction using neuro-fuzzy approach

The present article demonstrates a way of formulating a neuro-fuzzy approach for feature extraction under unsupervised training. A fuzzy feature evaluation index for a set of features is newly defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces. A concept of flexible membership function incorporating weighted distance is introduced for computing membership values in the transformed space that is obtained by a set of linear transformation on the original space. A layered network is designed for performing the task of minimization of the evaluation index through unsupervised learning process. This extracts a set of optimum transformed features, by projecting n-dimensional original space directly to n'-dimensional (n'&lt;n) transformed space, along with their relative importance. The extracted features are found to provide better classification performance than the original ones for different real life data with dimensions 3, 4, 9, 18 and 34. The superiority of the method over principal component analysis network, nonlinear discriminant analysis network and Kohonen self-organizing feature map is also established

Enabling Big Science in a Small Satellite - The Global L-band Observatory for Water Cycle Studies (GLOWS) Mission

The SMOS and SMAP radiometers have demonstrated the ability to monitor soil moisture and sea surface salinity. It is important to maintain data continuity for these science measurements. The proposed instrument concept (Global L-band active/passive Observatory for Water cycle Studies - GLOWS) will enable low-cost L-band data continuity (that includes both L-band radar and radiometer measurements). The objective of this project is to develop key instrument technology to enable L-band observations using an Earth Venture class satellite. Specifically, a new deployable meta-lens antenna is being developed that will enable a smaller EELV Secondary Payload Adapter (ESPA) Grande-class satellite mission to continue the L-band observations at SMAP and SMOS resolution and accuracy at substantially lower cost, size, and weight. The key to maintaining the scientific value of the observations is the retention of the full 6-meter antenna aperture, while packaging that aperture on a small ESPA Grande satellite platform. The meta-lens antenna is lightweight, has a simplified flat deployed surface geometry, and stows in a compact form factor. This dramatic aperture packaging reduction enables the GLOWS sensor to fit on an Earth Venture class satellite

Polyphosphoinositides-dependent regulation of the osteoclast actin cytoskeleton and bone resorption

BACKGROUND: Gelsolin, an actin capping protein of osteoclast podosomes, has a unique function in regulating assembly and disassembly of the podosome actin filament. Previously, we have reported that osteopontin (OPN) binding to integrin α(v)β(3 )increased the levels of gelsolin-associated polyphosphoinositides, podosome assembly/disassembly, and actin filament formation. The present study was undertaken to identify the possible role of polyphosphoinositides and phosphoinositides binding domains (PBDs) of gelsolin in the osteoclast cytoskeletal structural organization and osteoclast function. RESULTS: Transduction of TAT/full-length gelsolin and PBDs containing gelsolin peptides into osteoclasts demonstrated: 1) F-actin enriched patches; 2) disruption of actin ring; 3) an increase in the association polyphosphoinositides (PPIs) with the transduced peptides containing PBDs. The above-mentioned effects were more pronounced with gelsolin peptide containing 2 tandem repeats of PBDs (PBD (2)). Binding of PPIs to the transduced peptides has resulted in reduced levels of PPIs association with the endogenous gelsolin, and thereby disrupted the actin remodeling processes in terms of podosome organization in the clear zone area and actin ring formation. These peptides also exhibited a dominant negative effect in the formation of WASP-Arp2/3 complex indicating the role of phosphoinositides in WASP activation. The TAT-PBD gelsolin peptides transduced osteoclasts are functionally defective in terms of motility and bone resorption. CONCLUSIONS: Taken together, these data demonstrate that transduction of PBD gelsolin peptides into osteoclasts produced a dominant negative effect on actin assembly, motility, and bone resorption. These findings indicate that phosphoinositide-mediated signaling mechanisms regulate osteoclast cytoskeleton, podosome assembly/disassembly, actin ring formation and bone resorption activity of osteoclasts

Association among plasma levels of monocyte chemoattractant protein-1, traditional cardiovascular risk factors, and subclinical atherosclerosis

ObjectivesWe sought to evaluate the association between plasma levels of monocyte chemoattractant protein (MCP)-1 and the risk for subclinical atherosclerosis.BackgroundMonocyte chemoattractant protein is a chemokine that recruits monocytes into the developing atheroma and may contribute to atherosclerotic disease development and progression. Plasma levels of MCP-1 are independently associated with prognosis in patients with acute coronary syndromes, but few population-based data are available from subjects in earlier stages of atherosclerosis.MethodsIn the Dallas Heart Study, a population-based probability sample of adults in Dallas County ≤65 years old, plasma levels of MCP-1 were measured in 3,499 subjects and correlated with traditional cardiovascular risk factors, high-sensitivityC-reactive protein (hs-CRP), and coronary artery calcium (CAC) measured by electron beam computed tomography.ResultsHigher MCP-1 levels were associated with older age, white race, family history of premature coronary disease, smoking, hypertension, diabetes, hypercholesterolemia, and higher levels of hs-CRP (p &lt; 0.01 for each). Similar associations were observed between MCP-1 and risk factors in the subgroup of participants without detectable CAC. Compared with the subjects in the lowest quartile of MCP-1, the odds of prevalent CAC (CAC score ≥10) for subjects in the second, third, and fourth quartiles were 1.30 (95% confidence interval [CI] 0.99 to 1.73), 1.60 (95% CI 1.22 to 2.11), and 2.02 (95% CI 1.54 to 2.63), respectively. The association between MCP-1 and CAC remained significant when adjusted for traditional cardiovascular risk factors, but not when further adjusted for age.ConclusionsIn a large population-based sample, plasma levels of MCP-1 were associated with traditional risk factors for atherosclerosis, supporting the hypothesis that MCP-1 may mediate some of the atherogenic effects of these risk factors. These findings support the potential role of MCP-1 as a biomarker target for drug development

Unsupervised neural network for single cell Multi-omics INTegration (UMINT): an application to health and disease

Multi-omics studies have enabled us to understand the mechanistic drivers behind complex disease states and progressions, thereby providing novel and actionable biological insights into health status. However, integrating data from multiple modalities is challenging due to high dimensionality and diverse nature of data, and noise associated with each platform. Sparsity in data, non-overlapping features and technical batch effects make the task of learning more complicated. Conventional machine learning (ML) tools are not quite effective against such data integration hazards due to their simplistic nature with less capacity. In addition, existing methods for single cell multi-omics integration are computationally expensive. Therefore, in this work, we have introduced a novel Unsupervised neural network for single cell Multi-omics INTegration (UMINT). UMINT serves as a promising model for integrating variable number of single cell omics layers with high dimensions. It has a light-weight architecture with substantially reduced number of parameters. The proposed model is capable of learning a latent low-dimensional embedding that can extract useful features from the data facilitating further downstream analyses. UMINT has been applied to integrate healthy and disease CITE-seq (paired RNA and surface proteins) datasets including a rare disease Mucosa-Associated Lymphoid Tissue (MALT) tumor. It has been benchmarked against existing state-of-the-art methods for single cell multi-omics integration. Furthermore, UMINT is capable of integrating paired single cell gene expression and ATAC-seq (Transposase-Accessible Chromatin) assays as well