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Página 13 dos resultados de 471 itens digitais encontrados em 0.049 segundos

## Storing non-uniformly distributed messages in networks of neural cliques

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 24/07/2013
Português

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Associative memories are data structures that allow retrieval of stored
messages from part of their content. They thus behave similarly to human brain
that is capable for instance of retrieving the end of a song given its
beginning. Among different families of associative memories, sparse ones are
known to provide the best efficiency (ratio of the number of bits stored to
that of bits used). Nevertheless, it is well known that non-uniformity of the
stored messages can lead to dramatic decrease in performance. We introduce
several strategies to allow efficient storage of non-uniform messages in
recently introduced sparse associative memories. We analyse and discuss the
methods introduced. We also present a practical application example.; Comment: 21 pages, 8 figures, submitted to Neurocomputing

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## Understanding Locally Competitive Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

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#Computer Science - Neural and Evolutionary Computing#Computer Science - Learning#68T30, 68T10#I.2.6

Recently proposed neural network activation functions such as rectified
linear, maxout, and local winner-take-all have allowed for faster and more
effective training of deep neural architectures on large and complex datasets.
The common trait among these functions is that they implement local competition
between small groups of computational units within a layer, so that only part
of the network is activated for any given input pattern. In this paper, we
attempt to visualize and understand this self-modularization, and suggest a
unified explanation for the beneficial properties of such networks. We also
show how our insights can be directly useful for efficiently performing
retrieval over large datasets using neural networks.; Comment: 9 pages + 2 supplementary, Accepted to ICLR 2015 Conference track

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## INSTRUCT: Space-Efficient Structure for Indexing and Complete Query Management of String Databases

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

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The tremendous expanse of search engines, dictionary and thesaurus storage,
and other text mining applications, combined with the popularity of readily
available scanning devices and optical character recognition tools, has
necessitated efficient storage, retrieval and management of massive text
databases for various modern applications. For such applications, we propose a
novel data structure, INSTRUCT, for efficient storage and management of
sequence databases. Our structure uses bit vectors for reusing the storage
space for common triplets, and hence, has a very low memory requirement.
INSTRUCT efficiently handles prefix and suffix search queries in addition to
the exact string search operation by iteratively checking the presence of
triplets. We also propose an extension of the structure to handle substring
search efficiently, albeit with an increase in the space requirements. This
extension is important in the context of trie-based solutions which are unable
to handle such queries efficiently. We perform several experiments portraying
that INSTRUCT outperforms the existing structures by nearly a factor of two in
terms of space requirements, while the query times are better. The ability to
handle insertion and deletion of strings in addition to supporting all kinds of
queries including exact search...

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## A Time Efficient Indexing Scheme for Complex Spatiotemporal Retrieval

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 10/05/2008
Português

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The paper is concerned with the time efficient processing of spatiotemporal
predicates, i.e. spatial predicates associated with an exact temporal
constraint. A set of such predicates forms a buffer query or a Spatio-temporal
Pattern (STP) Query with time. In the more general case of an STP query, the
temporal dimension is introduced via the relative order of the spatial
predicates (STP queries with order). Therefore, the efficient processing of a
spatiotemporal predicate is crucial for the efficient implementation of more
complex queries of practical interest. We propose an extension of a known
approach, suitable for processing spatial predicates, which has been used for
the efficient manipulation of STP queries with order. The extended method is
supported by efficient indexing structures. We also provide experimental
results that show the efficiency of the technique.; Comment: 6 pages, 7 figures, submitted to Sigmod Record

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## Web-enabling Cache Daemon for Complex Data

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 01/10/2009
Português

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#Computer Science - Distributed, Parallel, and Cluster Computing#Computer Science - Networking and Internet Architecture

One of the most common basic techniques for improving the performance of web
applications is caching frequently accessed data in fast data stores,
colloquially known as cache daemons. In this paper we present a cache daemon
suitable for storing complex data while maintaining fine-grained control over
data storage, retrieval and expiry. Data manipulation in this cache daemon is
performed via standard SQL statements so we call it SQLcached. It is a
practical, usable solution already implemented in several large web sites.

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## Mining Associated Text and Images with Dual-Wing Harmoniums

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 04/07/2012
Português

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We propose a multi-wing harmonium model for mining multimedia data that
extends and improves on earlier models based on two-layer random fields, which
capture bidirectional dependencies between hidden topic aspects and observed
inputs. This model can be viewed as an undirected counterpart of the two-layer
directed models such as LDA for similar tasks, but bears significant difference
in inference/learning cost tradeoffs, latent topic representations, and topic
mixing mechanisms. In particular, our model facilitates efficient inference and
robust topic mixing, and potentially provides high flexibilities in modeling
the latent topic spaces. A contrastive divergence and a variational algorithm
are derived for learning. We specialized our model to a dual-wing harmonium for
captioned images, incorporating a multivariate Poisson for word-counts and a
multivariate Gaussian for color histogram. We present empirical results on the
applications of this model to classification, retrieval and image annotation on
news video collections, and we report an extensive comparison with various
extant models.; Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005)

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## Comparative Results: Group Search Optimizer and Central Force Optimization

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

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This note compares the performance of two multidimensional search and
optimization algorithms: Group Search Optimizer and Central Force Optimization.
GSO is a new state-of-the-art algorithm that has gained some notoriety,
consequently providing an excellent yardstick for measuring the performance of
other algorithms. CFO is a novel deterministic metaheuristic that has performed
well against GSO in previous tests. The CFO implementation reported here
includes architectural improvements in errant probe retrieval and decision
space adaptation that result in even better performance. Detailed results are
provided for the twenty-three function benchmark suite used to evaluate GSO.
CFO performs better than or essentially as well as GSO on twenty functions and
nearly as well on one of the remaining three. Includes update 24 February 2010.; Comment: Includes detailed numerical results and source code in appendices.
Update 02-24-10: Replaces Fig. A2(b) for improved visualization; corrects
minor typos (note that trajectory plots were removed to meet file size
restrictions - see Ver. 1 for complete set)

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## Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

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Data encoded as symmetric positive definite (SPD) matrices frequently arise
in many areas of computer vision and machine learning. While these matrices
form an open subset of the Euclidean space of symmetric matrices, viewing them
through the lens of non-Euclidean Riemannian geometry often turns out to be
better suited in capturing several desirable data properties. However,
formulating classical machine learning algorithms within such a geometry is
often non-trivial and computationally expensive. Inspired by the great success
of dictionary learning and sparse coding for vector-valued data, our goal in
this paper is to represent data in the form of SPD matrices as sparse conic
combinations of SPD atoms from a learned dictionary via a Riemannian geometric
approach. To that end, we formulate a novel Riemannian optimization objective
for dictionary learning and sparse coding in which the representation loss is
characterized via the affine invariant Riemannian metric. We also present a
computationally simple algorithm for optimizing our model. Experiments on
several computer vision datasets demonstrate superior classification and
retrieval performance using our approach when compared to sparse coding via
alternative non-Riemannian formulations.

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## Spontaneous Analogy by Piggybacking on a Perceptual System

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 10/10/2013
Português

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Most computational models of analogy assume they are given a delineated
source domain and often a specified target domain. These systems do not address
how analogs can be isolated from large domains and spontaneously retrieved from
long-term memory, a process we call spontaneous analogy. We present a system
that represents relational structures as feature bags. Using this
representation, our system leverages perceptual algorithms to automatically
create an ontology of relational structures and to efficiently retrieve analogs
for new relational structures from long-term memory. We provide a demonstration
of our approach that takes a set of unsegmented stories, constructs an ontology
of analogical schemas (corresponding to plot devices), and uses this ontology
to efficiently find analogs within new stories, yielding significant
time-savings over linear analog retrieval at a small accuracy cost.; Comment: Proceedings of the 35th Meeting of the Cognitive Science Society,
2013

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## Optimizing the performance of Lattice Gauge Theory simulations with Streaming SIMD extensions

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 02/09/2013
Português

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#Computer Science - Computational Engineering, Finance, and Science#Computer Science - Performance#Physics - Computational Physics#B.2.2#B.2.4

Two factors, which affect simulation quality are the amount of computing
power and implementation. The Streaming SIMD (single instruction multiple data)
extensions (SSE) present a technique for influencing both by exploiting the
processor's parallel functionalism. In this paper, we show how SSE improves
performance of lattice gauge theory simulations. We identified two significant
trends through an analysis of data from various runs. The speed-ups were higher
for single precision than double precision floating point numbers. Notably,
though the use of SSE significantly improved simulation time, it did not
deliver the theoretical maximum. There are a number of reasons for this:
architectural constraints imposed by the FSB speed, the spatial and temporal
patterns of data retrieval, ratio of computational to non-computational
instructions, and the need to interleave miscellaneous instructions with
computational instructions. We present a model for analyzing the SSE
performance, which could help factor in the bottlenecks or weaknesses in the
implementation, the computing architecture, and the mapping of software to the
computing substrate while evaluating the improvement in efficiency. The model
or framework would be useful in evaluating the use of other computational
frameworks...

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## A Compact Graph Model of Handwritten Images: Integration into Authentification and Recognition

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 04/07/2002
Português

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#Computer Science - Human-Computer Interaction#Computer Science - Data Structures and Algorithms#G.2.2

A novel algorithm for creating a mathematical model of curved shapes is
introduced. The core of the algorithm is based on building a graph
representation of the contoured image, which occupies less storage space than
produced by raster compression techniques. Different advanced applications of
the mathematical model are discussed: recognition of handwritten characters and
verification of handwritten text and signatures for authentification purposes.
Reducing the storage requirements due to the efficient mathematical model
results in faster retrieval and processing times. The experimental outcomes in
compression of contoured images and recognition of handwritten numerals are
given.; Comment: 9 pages, 6 figures, 1 tables, SSPR'02

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## Partitioning Graph Databases - A Quantitative Evaluation

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 22/01/2013
Português

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Electronic data is growing at increasing rates, in both size and
connectivity: the increasing presence of, and interest in, relationships
between data. An example is the Twitter social network graph. Due to this
growth demand is increasing for technologies that can process such data.
Currently relational databases are the predominant technology, but they are
poorly suited to processing connected data as they are optimized for
index-intensive operations. Conversely, graph databases are optimized for graph
computation. They link records by direct references, avoiding index lookups,
and enabling retrieval of adjacent elements in constant time, regardless of
graph size. However, as data volume increases these databases outgrow the
resources of one computer and data partitioning becomes necessary. We evaluate
the viability of using graph partitioning algorithms to partition graph
databases. A prototype partitioned database was developed. Three partitioning
algorithms explored and one implemented. Three graph datasets were used: two
real and one synthetically generated. These were partitioned in various ways
and the impact on database performance measured. We defined one synthetic
access pattern per dataset and executed each on the partitioned datasets.
Evaluation took place in a simulation environment...

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## Multimodal sparse representation learning and applications

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 19/11/2015
Português

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#Computer Science - Learning#Computer Science - Computer Vision and Pattern Recognition#Statistics - Machine Learning

Unsupervised methods have proven effective for discriminative tasks in a
single-modality scenario. In this paper, we present a multimodal framework for
learning sparse representations that can capture semantic correlation between
modalities. The framework can model relationships at a higher level by forcing
the shared sparse representation. In particular, we propose the use of joint
dictionary learning technique for sparse coding and formulate the joint
representation for concision, cross-modal representations (in case of a missing
modality), and union of the cross-modal representations. Given the accelerated
growth of multimodal data posted on the Web such as YouTube, Wikipedia, and
Twitter, learning good multimodal features is becoming increasingly important.
We show that the shared representations enabled by our framework substantially
improve the classification performance under both unimodal and multimodal
settings. We further show how deep architectures built on the proposed
framework are effective for the case of highly nonlinear correlations between
modalities. The effectiveness of our approach is demonstrated experimentally in
image denoising, multimedia event detection and retrieval on the TRECVID
dataset (audio-video), category classification on the Wikipedia dataset
(image-text)...

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## Multimodal diffusion geometry by joint diagonalization of Laplacians

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

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#Computer Science - Computer Vision and Pattern Recognition#Computer Science - Artificial Intelligence

We construct an extension of diffusion geometry to multiple modalities
through joint approximate diagonalization of Laplacian matrices. This naturally
extends classical data analysis tools based on spectral geometry, such as
diffusion maps and spectral clustering. We provide several synthetic and real
examples of manifold learning, retrieval, and clustering demonstrating that the
joint diffusion geometry frequently better captures the inherent structure of
multi-modal data. We also show that many previous attempts to construct
multimodal spectral clustering can be seen as particular cases of joint
approximate diagonalization of the Laplacians.

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## From Visual Attributes to Adjectives through Decompositional Distributional Semantics

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

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#Computer Science - Computation and Language#Computer Science - Computer Vision and Pattern Recognition

As automated image analysis progresses, there is increasing interest in
richer linguistic annotation of pictures, with attributes of objects (e.g.,
furry, brown...) attracting most attention. By building on the recent
"zero-shot learning" approach, and paying attention to the linguistic nature of
attributes as noun modifiers, and specifically adjectives, we show that it is
possible to tag images with attribute-denoting adjectives even when no training
data containing the relevant annotation are available. Our approach relies on
two key observations. First, objects can be seen as bundles of attributes,
typically expressed as adjectival modifiers (a dog is something furry, brown,
etc.), and thus a function trained to map visual representations of objects to
nominal labels can implicitly learn to map attributes to adjectives. Second,
objects and attributes come together in pictures (the same thing is a dog and
it is brown). We can thus achieve better attribute (and object) label retrieval
by treating images as "visual phrases", and decomposing their linguistic
representation into an attribute-denoting adjective and an object-denoting
noun. Our approach performs comparably to a method exploiting manual attribute
annotation, it outperforms various competitive alternatives in both attribute
and object annotation...

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## Learning hashing with affinity-based loss functions using auxiliary coordinates

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 21/01/2015
Português

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#Computer Science - Learning#Computer Science - Computer Vision and Pattern Recognition#Mathematics - Optimization and Control#Statistics - Machine Learning

In binary hashing, one wants to learn a function that maps a high-dimensional
feature vector to a vector of binary codes, for application to fast image
retrieval. This typically results in a difficult optimization problem,
nonconvex and nonsmooth, because of the discrete variables involved. Much work
has simply relaxed the problem during training, solving a continuous
optimization, and truncating the codes a posteriori. This gives reasonable
results but is suboptimal. Recent work has applied alternating optimization to
the objective over the binary codes and achieved better results, but the hash
function was still learned a posteriori, which remains suboptimal. We propose a
general framework for learning hash functions using affinity-based loss
functions that closes the loop and optimizes jointly over the hash functions
and the binary codes. The resulting algorithm can be seen as a corrected,
iterated version of the procedure of optimizing first over the codes and then
learning the hash function. Compared to this, our optimization is guaranteed to
obtain better hash functions while being not much slower, as demonstrated
experimentally in various supervised and unsupervised datasets. In addition,
the framework facilitates the design of optimization algorithms for arbitrary
types of loss and hash functions.; Comment: 18 pages...

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## Pairwise Constraint Propagation on Multi-View Data

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 18/01/2015
Português

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This paper presents a graph-based learning approach to pairwise constraint
propagation on multi-view data. Although pairwise constraint propagation has
been studied extensively, pairwise constraints are usually defined over pairs
of data points from a single view, i.e., only intra-view constraint propagation
is considered for multi-view tasks. In fact, very little attention has been
paid to inter-view constraint propagation, which is more challenging since
pairwise constraints are now defined over pairs of data points from different
views. In this paper, we propose to decompose the challenging inter-view
constraint propagation problem into semi-supervised learning subproblems so
that they can be efficiently solved based on graph-based label propagation. To
the best of our knowledge, this is the first attempt to give an efficient
solution to inter-view constraint propagation from a semi-supervised learning
viewpoint. Moreover, since graph-based label propagation has been adopted for
basic optimization, we develop two constrained graph construction methods for
interview constraint propagation, which only differ in how the intra-view
pairwise constraints are exploited. The experimental results in cross-view
retrieval have shown the promising performance of our inter-view constraint
propagation.

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## A Community Contribution Framework for Sharing Materials Data with Materials Project

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 16/10/2015
Português

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As scientific discovery becomes increasingly data-driven, software platforms
are needed to efficiently organize and disseminate data from disparate sources.
This is certainly the case in the field of materials science. For example,
Materials Project has generated computational data on over 60,000 chemical
compounds and has made that data available through a web portal and REST
interface. However, such portals must seek to incorporate community submissions
to expand the scope of scientific data sharing. In this paper, we describe
MPContribs, a computing/software infrastructure to integrate and organize
contributions of simulated or measured materials data from users. Our solution
supports complex submissions and provides interfaces that allow contributors to
share analyses and graphs. A RESTful API exposes mechanisms for book-keeping,
retrieval and aggregation of submitted entries, as well as persistent URIs or
DOIs that can be used to reference the data in publications. Our approach
isolates contributed data from a host project's quality-controlled core data
and yet enables analyses across the entire dataset, programmatically or through
customized web apps. We expect the developed framework to enhance collaborative
determination of material properties and to maximize the impact of each
contributor's dataset. In the long-term...

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## Analysis of Gait Pattern to Recognize the Human Activities

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

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Human activity recognition based on the computer vision is the process of
labelling image sequences with action labels. Accurate systems for this problem
are applied in areas such as visual surveillance, human computer interaction
and video retrieval.; Comment: This paper has been withdrawn by the author due to a crucial sign
error in equation 3

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## A Study of the Xerox XNS Filing Protocol as Implemented on Several Heterogenous Systems

Fonte: Rochester Instituto de Tecnologia
Publicador: Rochester Instituto de Tecnologia

Tipo: Tese de Doutorado

Português

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#Computer science#Distributed file systems#Network protocols#Thesis#TK5105.5.F568#Computer network protocols#File organization (Computer science)#Electronic data processing--Distributed processing

The Xerox Network System is composed of heterogeneous processors connected across a variety of transmission media A series of protocols is defined to describe the communication mechanisms between system elements. One of these protocols, the Filing Protocol, defines a general purpose file management system. Current implementations of the protocol, although derived from the Xerox specification, fall short of providing the interconnectivity between elements desired in a heterogeneous network system. The definition of an easily implemented protocol subset that provides the common file system functions of retrieval, storage, enumeration/location and deletion is derived from experiences with several implementations. This definition and an accompanying implementation document provide a mechanism to guide future implementations toward increased interconnectivity.

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