4,078 research outputs found
Similarity Matching Techniques For Fault Diagnosis In Automotive Infotainment Electronics
Fault diagnosis has become a very important area of research during the last decade due to the advancement of mechanical and electrical systems in industries. The automobile is a crucial field where fault diagnosis is given a special attention. Due to the increasing complexity and newly added features in vehicles, a comprehensive study has to be performed in order to achieve an appropriate diagnosis model. A diagnosis system is capable of identifying the faults of a system by investigating the observable effects (or symptoms). The system categorizes the fault into a diagnosis class and identifies a probable cause based on the supplied fault symptoms. Fault categorization and identification are done using similarity matching techniques. The development of diagnosis classes is done by making use of previous experience, knowledge or information within an application area. The necessary information used may come from several sources of knowledge, such as from system analysis. In this paper similarity matching techniques for fault diagnosis in automotive infotainment applications are discussed
Vertex similarity in networks
We consider methods for quantifying the similarity of vertices in networks.
We propose a measure of similarity based on the concept that two vertices are
similar if their immediate neighbors in the network are themselves similar.
This leads to a self-consistent matrix formulation of similarity that can be
evaluated iteratively using only a knowledge of the adjacency matrix of the
network. We test our similarity measure on computer-generated networks for
which the expected results are known, and on a number of real-world networks
The Law of Independent Legal Advice
This third edition includes the analysis of over 250 new decisions with excerpts highlighting important legal reasoning and principles. Along with the standard detailed table of contents, table of cases, and index, it offers the reader ample material to pursue further research on subtopics of independent legal advice (ILA) through extensive footnotes. Tjaden organizes each chapter in approximately the same way, beginning with an introduction to ILA as it relates to the area of law, a summary of jurisprudence reflecting both support for and criticism of the provision of ILA in certain circumstance, practical advice for lawyers, and, a new feature in the third edition, curated lists of research references at the end of each chapter.
Tjadenās dual role as lawyer and law librarian has given him keen insight into the systematic categorization of information in such a way as to simplify the navigation by his target audience. This includes lawyers, judges, law students and, presumably, the law librarians who inevitably assist these groups with their research. Tjadenās background in legal research has driven him to compile an impressively comprehensive collection of āall relevant statutory and judicial authority on the topic of independent legal adviceā
FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search
We present FLASH (\textbf{F}ast \textbf{L}SH \textbf{A}lgorithm for
\textbf{S}imilarity search accelerated with \textbf{H}PC), a similarity search
system for ultra-high dimensional datasets on a single machine, that does not
require similarity computations and is tailored for high-performance computing
platforms. By leveraging a LSH style randomized indexing procedure and
combining it with several principled techniques, such as reservoir sampling,
recent advances in one-pass minwise hashing, and count based estimations, we
reduce the computational and parallelization costs of similarity search, while
retaining sound theoretical guarantees.
We evaluate FLASH on several real, high-dimensional datasets from different
domains, including text, malicious URL, click-through prediction, social
networks, etc. Our experiments shed new light on the difficulties associated
with datasets having several million dimensions. Current state-of-the-art
implementations either fail on the presented scale or are orders of magnitude
slower than FLASH. FLASH is capable of computing an approximate k-NN graph,
from scratch, over the full webspam dataset (1.3 billion nonzeros) in less than
10 seconds. Computing a full k-NN graph in less than 10 seconds on the webspam
dataset, using brute-force (), will require at least 20 teraflops. We
provide CPU and GPU implementations of FLASH for replicability of our results
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