다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

Distribution System Modeling and Analysis

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Numerical Methods For Partial Differential Equations

Bipartite Graph Partitioning and Data Clustering* Hongyuan Zha Xiaofeng He Dept. of Comp. Sci. & Eng. Penn State Univ. State College, PA 16802 {zha,xhe}@cse.psu.edu Chris Ding Horst Simon NERSC Division Berkeley National Lab. Berkeley, CA 94720 {chqding,hdsimon} Qlbl. gov Ming Gu Dept. of Math. U.C. Berkeley Berkeley, CA 94720 mgu@math.berkeley.edu ABSTRACT M a n y data types arising from data mining applications can be modeled as bipartite graphs, examples include terms and documents in a text corpus, customers and purchasing items in market basket analysis and reviewers and movies in a movie recommender system. In this paper, we propose a new data clustering method based on partitioning the underlying bipartite graph. The partition is constructed by minimizing a normalized sum of edge weights between unmatched pairs of vertices of the bipartite graph. We show that an approxi­ mate solution to the minimization problem can be obtained by computing a partial singular value decomposition ( S V D ) of the associated edge weight matrix of the bipartite graph. We point out the connection of our clustering algorithm to correspondence analysis used in multivariate analysis. We also briefly discuss the issue of assigning data objects to multiple clusters. In the experimental results, we apply our clustering algorithm to the problem of document clustering to illustrate its effectiveness and efficiency. 1. INTRODUCTION Cluster analysis is an important tool for exploratory data mining applications arising from many diverse disciplines. Informally, cluster analysis seeks to partition a given data set into compact clusters so that data objects within a clus­ ter are more similar than those in distinct clusters. The liter­ ature on cluster analysis is enormous including contributions from many research communities, (see [6, 9] for recent sur­ veys of some classical approaches.) M a n y traditional clus­ tering algorithms are based on the assumption that the given dataset consists of covariate information (or attributes) for each individual data object, and cluster analysis can be cast as a problem of grouping a set of n-dimensional vectors each representing a data object in the dataset. A familiar ex­ ample is document clustering using the vector space model [1]. Here each document is represented by an n-dimensional vector, and each coordinate of the vector corresponds to a term in a vocabulary of size n. This formulation leads to the so-called term-document matrix A = (oy) for the rep­ resentation of the collection of documents, where o y is the so-called term frequency, i.e., the number of times term i occurs in document j. In this vector space model terms and documents are treated asymmetrically with terms consid­ ered as the covariates or attributes of documents. It is also possible to treat both terms and documents as first-class citizens in a symmetric fashion, and consider a y as the fre­ quency of co-occurrence of term i and document j as is done, for example, in probabilistic latent semantic indexing [12]. In this paper, we follow this basic principle and propose a new approach to model terms and documents as vertices in a bipartite graph with edges of the graph indicating the co-occurrence of terms and documents. In addition we can optionally use edge weights to indicate the frequency of this co-occurrence. Cluster analysis for document collections in this context is based on a very intuitive notion: documents are grouped by topics, on one hand documents in a topic tend to more heavily use the same subset of terms which form a term cluster, and on the other hand a topic usually is characterized by a subset of terms and those documents heavily using those terms tend to be about that particular topic. It is this interplay of terms and documents which gives rise to what we call bi-clustering by which terms and documents are simultaneously grouped into semantically co- Categories and Subject Descriptors 11.3.3 [ I n f o r m a t i o n S e a r c h a n d R e t r i e v a l ] : Clustering; G.1.3 [ N u m e r i c a l L i n e a r A l g e b r a ] : Singular value de­ composition; G.2.2 [ G r a p h T h e o r y ] : G r a p h algorithms General Terms Algorithms, theory Keywords document clustering, bipartite graph, graph partitioning, spectral relaxation, singular value decomposition, correspon­ dence analysis *Part of this work was done while Xiaofeng He was a grad­ uate research assistant at N E R S C , Berkeley National Lab. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. CIKM '01 November 5-10, 2001, Atlanta, Georgia. U S A Copyright 2001 A C M X - X X X X X - X X - X / X X / X X ...$5.00. O u r clustering algorithm computes an approximate global optimal solution while probabilistic latent semantic indexing relies on the E M algorithm and therefore might be prone to local m i n i m a even with the help of some annealing process. x

Bipartite graph partitioning and data clustering

Network on Chip (NoC) is an emerging solution to the existing scalability problems with System on Chip (SoC). However, it is exposed to security threats like extraction of secret information from IP cores. In this paper we present an Authenticated Encryption (AE)-based security framework for NoC based systems. The security framework resides in Network Interface (NI) of every IP core allowing secure communication among such IP cores. The secure cores can communicate using permanent keys whereas temporary session keys are used for communication between secure and non-secure cores. A traffic limiting counter is used to prevent bandwidth denial and access rights table avoids unauthorized memory accesses. We simulated and implemented our framework using Verilog/VHDL modules on top of NoCem emulator. The results showed tolerable area overhead and did not affect the network performance apart from some initial latency.

A Security Framework for NoC Using Authenticated Encryption and Session Keys

The concept of approximate computing, that is, to sacrifice computation quality for computation efforts, has recently emerged as a promising design approach. Over the past decade, several research works have explored approximate computing at both the software level and hardware level of abstraction with encouraging results. At the hardware level of abstraction, adders (being the fundamental and most widely used data operators in digital systems) have attracted a significant attention for approximation. In this article, we first explain briefly the need/significance of approximate adders. We then propose four Approximate Full Adders (AFAs) for high-performance energy-efficient approximate computing. The key design objective behind the proposed AFAs is to curtail the length of carry propagation subjected to minimal error rate. Next, we exploit one of the proposed AFAs (optimal one) to construct an N-bit approximate adder that hereinafter is referred as “ApproxADD.” An emergent property of ApproxADD is that carries do not propagate in it, and, consequently, it provides bit-width-aware constant delay (O(1)). ApproxADD also provides improvement in dynamic power consumption by 46.31% and in area by 28.57% w.r.t. Ripple Carry Adder (RCA), which exhibits the lowest power and area. Although ApproxADD provides a significant improvement in delay, power, and area, it may not be preferred for some of the error-resilient applications because its: (i) Error Distance (ED) is too high; and (ii) Error Rate (ER) increases rapidly with bit-width (N). To improve ED and ER, we exploit the concept of carry-lifetime and Error Detection and Correction logic, respectively. In this way, we introduce two more (improved) versions of ApproxADD--ApproxADDu1 and ApproxADD. We call these as ApproxADDu1 and ApproxADDu2 with existing approximate adders based on conventional design metrics and approximate computing design metrics. Furthermore, to inspect effectiveness of the proposed approach in real-life applications, we demonstrate image compression and decompression by replacing the conventional addition operations in Discrete Cosine Transform (DCT) and Inverse Discrete Cosine Transform (IDCT) modules with ApproxADDu2.

Analysis and Design of Adders for Approximate Computing

Human beings have a natural ability to see, listen and interact with their external environment. Unfortunately, there are some people who are differently abled and do not have the ability to use their senses to the best extent possible. Such people depend on other means of communication like sign language. This presents a major roadblock for people in the deaf and dumb communities when they try to engage in interaction with others, especially in their educational, social and professional environments. Therefore, it is necessary to have an advance gesture recognition or sign language detection system to bridge this communication gap. Here an effort has been made to develop a smart glove using Intel Galileo Gen 2 IoT kit for real-time gesture recognition. The objective is to create a device which helps the hearing or speech impaired persons to communicate with others.

Smart glove for Sign Language communications

Thermal performance evaluation of multi-tube cylindrical LHS system

The gap between capabilities of CMOS technology scaling and requirements of future application workloads is increasing rapidly. There are several promising design approaches that jointly can reduce this gap significantly. Approximate computing is one of them and in recent years, has attracted the strongest attention of the scientific community. Approximate computing exploits inherent error-resilience of applications and features high-performance energy-efficient software and hardware implementations by trading-off computational quality (e.g., accuracy) for computational efforts (e.g., performance and energy). Over the decade, several research efforts have explored approximate computing throughout all the layers of computing stack, however, most of the work at hardware level of abstraction has been proposed on adders. In this paper, we first explain briefly - why approximate adders have attracted the strongest attention? We then provide a comparative survey of state-of-the-art approximate adders. We provide comparison based on both conventional design metrics as well as approximate computing design metrics.

Gaurav Trivedi

Papers

A Security Framework for NoC Using Authenticated Encryption and Session Keys

Analysis and Design of Adders for Approximate Computing

Smart glove for Sign Language communications

Thermal performance evaluation of multi-tube cylindrical LHS system

A comparative survey of approximate adders