High-Dimension Linear System Computation Based on Efficient Integrated Online Arithmetic

Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase‐level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two‐thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self‐formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2‐second response times at the highest loads tested).

One of the algorithms used for sampling complex networks is the classical random walk algorithm, which has been considered due to its good performance. But speed and energy consumption can also be improved by reducing size of input data. In this study, two random walk algorithms inspired by two methods, choosing seed node, and no-retracing algorithm which obtained by changing the classical random walk algorithm, and combining these three algorithms with google page rank algorithm, are discussed. This is done to preserve important nodes and reduce the size of the input data. This sampling was done from the United States flight network database. Also, important characteristics obtained in sampling, such as sampling efficiency, degree distribution, average degree, and average clustering coefficient have been investigated. The algorithms studied in this research each have their own advantages and disadvantages. For example, the no-retracing shows better performance in terms of time and average clustering coefficient. This efficiency is even greater when we use a combination of no-retracing algorithm with google page ranking algorithm. These algorithms can be used when speed is important in decision making, such as deciding on airlines and public transportation, etc. These algorithms are also more energy efficient than the studied algorithms.

A Novel Biosignature for Potential Stratification and Elucidation of Newly Diagnosed Multiple Myeloma Patients at-Risk

In the recent financial crises, attention has shifted towards "too-central-to-fail" to recognize the sources of systemic risk. The NBFC Crisis of 2018-19 adversely affected other financial institutions and the real economy of India. The NBFCs crisis highlighted the role of smaller institutions in perpetuating and amplifying the crisis. Thus, the present study models the interconnection of NBFCs with the rest of financial institutions using a complex Granger-causality network based on returns data. The PageRank algorithm identifies the central and important nodes and ranks financial institutions in pre-crisis and crisis periods. The financial institutions are also ranked based on the maximum percentage loss suffered during the crises. Using non-parametric rank-based regression, the PageRank ranking of financial institutions in the pre-crises period (explanatory variable) is regressed with the ranking of financial institutions based on maximum percentage loss suffered by them during the crises period (dependent variable) along with Leverage and Size as control variables. We found that PageRank from pre-crisis can significantly identify most financial institutions that suffered loss during NBFCs crises even in the presence of control variables.

Pathways are the functional building blocks of complex diseases such as cancer. Identifying disease‐associated pathways is of great importance to the development of novel therapeutics, as it provides functional insights into the pathogenesis of a disease. Existing methods for pathway ranking, however, are mostly based on an enrichment score assigned to each pathway independently, which could be biased by overlooking the interactions between pathways. In this paper, we consider a modification of Google's PageRank algorithm in order to fully incorporate the pathway dependencies into the pathway ranking. The proposed measurement is a trade‐off between two important aspects, namely, the phenotype–pathway association and pathway coexpression. We propose to use a projection correlation to quantify pathway coexpression, and a generalized R2 for phenotype–pathway association. Our simulation study on real pathways shows the competitive performance of the new measure compared with enrichment‐based analyses in pathway ranking.

This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. Representing much of the foundational research in this field, the book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data. Sep Kamvar focuses on eigenvector-based techniques in Web search, introducing a personalized variant of Google's PageRank algorithm, and he outlines algorithms--such as the now-famous quadratic extrapolation technique--that speed up computation, making personalized PageRank feasible. Kamvar suggests that Power Method-related techniques ultimately should be the basis for improving the PageRank algorithm, and he presents algorithms that exploit the convergence behavior of individual components of the PageRank vector. Kamvar then extends the ideas of reputation management and personalized search to distributed networks like peer-to-peer and social networks. He highlights locality and computational considerations related to the structure of the network, and considers such unique issues as malicious peers. He describes the EigenTrust algorithm and applies various PageRank concepts to P2P settings. Discussion chapters summarizing results conclude the book's two main sections. Clear and thorough, this book provides an authoritative look at central innovations in search for all of those interested in the subject.

🔗Why care about patents? <strong> 1. Patents play a critical role in incentivizing innovation, withoutwhich we wouldn't have much of the technology we rely on everyday </strong> What does your iPhone, Google's PageRank algorithm, and a buttersubstitute called Smart Balance all have in common? …They all probably wouldn't be here if not for patents.

Hierarchical organization is a common feature of many directed networks arising in nature and technology. For example, a well-defined message-passing framework based on managerial status typically exists in a business organization. However, in many real-world networks, such patterns of hierarchy are unlikely to be quite so transparent. Due to the nature in which empirical data are collated, the nodes will often be ordered so as to obscure any underlying structure. In addition, the possibility of even a small number of links violating any overall “chain of command” makes the determination of such structures extremely challenging. Here we address the issue of how to reorder a directed network to reveal this type of hierarchy. In doing so, we also look at the task of quantifying the level of hierarchy, given a particular node ordering. We look at a variety of approaches. Using ideas from the graph Laplacian literature, we show that a relevant discrete optimization problem leads to a natural hierarchical node ranking. We also show that this ranking arises via a maximum likelihood problem associated with a new range-dependent hierarchical random-graph model. This random-graph insight allows us to compute a likelihood ratio that quantifies the overall tendency for a given network to be hierarchical. We also develop a generalization of this node-ordering algorithm based on the combinatorics of directed walks. In passing, we note that Google's PageRank algorithm tackles a closely related problem, and may also be motivated from a combinatoric, walk-counting viewpoint. We illustrate the performance of the resulting algorithms on synthetic network data, and on a real-world network from neuroscience where results may be validated biologically.

The notion of "centrality" within graph analytics has led to the creation of well-known metrics such as Google's Page Rank [1], which is an extension of eigenvector centrality [2]. Triangle centrality is a related metric [3] that utilizes the presence of triangles, which play an important role in network analysis, to quantitatively determine the relative "importance" of a node in a network. Efficiently counting and enumerating these triangles are a major backbone to understanding network characteristics, and linear algebraic methods have utilized the correspondence between sparse adjacency matrices and graphs to perform such calculations, with sparse matrix-matrix multiplication as the main computational kernel. In this paper, we use an intersection representation of graph data implemented as a sparse matrix, and engineer an algorithm to compute the triangle centrality of each vertex within a graph. The main computational task of calculating these sparse matrix-vector products is carefully crafted by employing compressed vectors as accumulators. As with other state-of-the-art algorithms [4], our method avoids redundant work by counting and enumerating each triangle exactly once. We present results from extensive computational experiments on large-scale real-world and synthetic graph in-stances that demonstrate good scalability of our method. We also present a shared memory parallel implementation of our algorithm.

The efficacy and mechanisms of therapeutic action are largely described by atomic bonds and interactions local to drug binding sites. Here we introduce global connectivity analysis as a high-throughput computational assay of therapeutic action – inspired by the Google page rank algorithm that unearths most “globally connected” websites from the information-dense world wide web (WWW). We execute short timescale (30 ps) molecular dynamics simulations with high sampling frequency (0.01 ps), to identify amino acid residue hubs whose global connectivity dynamics are characteristic of the ligand or mutation associated with the target protein. We find that unexpected allosteric hubs – up to 20Å from the ATP binding site, but within 5Å of the phosphorylation site – encode the Gibbs free energy of inhibition (ΔGinhibition) for select protein kinase-targeted cancer therapeutics. We further find that clinically relevant somatic cancer mutations implicated in both drug resistance and personalized drug sensitivity can be predicted in a high-throughput fashion. Our results establish global connectivity analysis as a potent assay of protein functional modulation. This sets the stage for unearthing disease-causal exome mutations and motivates forecast of clinical drug response on a patient-by-patient basis. We suggest incorporation of structure-guided genetic inference assays into pharmaceutical and healthcare Oncology workflows.

Identification of the most influential areas for air pollution control using XGBoost and Grid Importance Rank

One of the key components which ensures the acceptance of web search service is the web page ranker - a component which is said to have been the main contributing factor to the early successes of Google. It is well established that a machine learning method such as the Graph Neural Network (GNN) is able to learn and estimate Google's page ranking algorithm. This paper shows that the GNN can successfully learn many other Web page ranking methods e.g. TrustRank, HITS and OPIC. Experimental results show that GNN may be suitable to learn any arbitrary web page ranking scheme, and hence, may be more flexible than any other existing web page ranking scheme. The significance of this observation lies in the fact that it is possible to learn ranking schemes for which no algorithmic solution exists or is known.

Automatic text summarization is a popular area in Natural Language Processing and Machine Learning. In this work, we adopt a graph-based text summarization approach, using PageRank algorithm, for automatically summarizing Konkani text documents. Konkani is an Indo-Aryan language spoken primarily in the state of Goa, which is on the west coast of India. It is a low-resource language with limited language processing tools. Such tools are readily available in other popular languages of choice for automatic text summarization, like English. The Konkani language dataset used for this purpose is based on Konkani folktales. We examine the impact of various language-independent and language-dependent similarity measures on the construction of the graph. The language-dependent similarity measures use pre-trained fastText word embeddings. A fully connected undirected graph is constructed for each document with the sentences represented as the graph's vertices. The vertices are connected to each other based on how strongly they are related to one another. Thereafter, PageRank algorithm is used for ranking the scores of the vertices. The top-ranking sentences are used to generate the summary. ROUGE toolkit was used for evaluating the quality of these system-generated summaries, and the performance was evaluated against human generated “gold-standard” abstracts and also compared with baselines and benchmark systems. The experimental results show that language-independent similarity measures performed well compared to language-dependent similarity measures despite not using language-specific tools, such as stop-words list, stemming, and word embeddings.

Wireless Sensor Network (WSN) presents significant challenges for the application of distributed signal processing and distributed control. These systems will challenge us to apply appropriate techniques to construct capable processing units with sensing nodes considering energy constraints. Digital Signal Processing (DSP) is one of the capable processing units, but it is not commonly used in WSN because of the power constraint. The Multiply-Accumulate Unit (MAC) is the main computational kernel in DSP architectures. The MAC unit determines the power and the speed of the overall system; it always lies in the critical path. Developing high speed and low power MAC is crucial to use DSP in the future WSN. In this work, a fast and low power MAC Unit is proposed. The proposed architecture is based on examination of the critical delays and hardware complexities of merged MAC architectures to design a unit with a low critical path delay and low hardware complexity. The new architecture reduces the hardware complexity of the summation network, thus reduces the overall power. Increasing the speed of operation is achieved by feeding the bits of the accumulated operand into the summation tree before the final adder instead of going through the entire summation network. The ASIC implementation of the proposed 32-bit MAC unit saves 5.5% of the area, 9% of the energy, and reduces the delay by 13% compared to the regular merged MAC unit.

Quantum dot cellular automata (QCA) is a hopeful technology in the field of nanotechnology that seems to suite well with signal‐processing needs. It is concerned with great interest because of its benefits such as ultra‐low power consumption, small size and can operate at one Terahertz. The multiply accumulator (MAC) unit is considered as one of the essential operations in digital signal processing (DSP). In the real‐time DSP systems, several applications like speech processing, video coding, and digital filtering etc. require MAC operations. However, the power dissipation and area are the most significant aspects in these systems. Here, the authors design low power MAC unit based on QCA technology. QCADesigner version 2.0.3 is used to validate the accuracy of the proposed circuit. The reliability of this unit is taken at different temperatures. The power dissipation is estimated using QCAPro tool. The total power consumed by this unit is 2.183 μW. The proposed circuit has 90% improvement in terms of power over complementary metal–oxide–semiconductor (CMOS) circuits. Since the works in the field of QCA logic signal processing has started to progress, the suggested contribution will give rise to a new thread of research in the field of real‐time signal and image treatment.