111,481 research outputs found
The NASA Astrophysics Data System: The Search Engine and its User Interface
The ADS Abstract and Article Services provide access to the astronomical
literature through the World Wide Web (WWW). The forms based user interface
provides access to sophisticated searching capabilities that allow our users to
find references in the fields of Astronomy, Physics/Geophysics, and
astronomical Instrumentation and Engineering. The returned information includes
links to other on-line information sources, creating an extensive astronomical
digital library. Other interfaces to the ADS databases provide direct access to
the ADS data to allow developers of other data systems to integrate our data
into their system.
The search engine is a custom-built software system that is specifically
tailored to search astronomical references. It includes an extensive synonym
list that contains discipline specific knowledge about search term
equivalences.
Search request logs show the usage pattern of the various search system
capabilities. Access logs show the world-wide distribution of ADS users.
The ADS can be accessed at http://adswww.harvard.eduComment: 23 pages, 18 figures, 11 table
Stochastic Continuous Time Neurite Branching Models with Tree and Segment Dependent Rates
In this paper we introduce a continuous time stochastic neurite branching
model closely related to the discrete time stochastic BES-model. The discrete
time BES-model is underlying current attempts to simulate cortical development,
but is difficult to analyze. The new continuous time formulation facilitates
analytical treatment thus allowing us to examine the structure of the model
more closely. We derive explicit expressions for the time dependent
probabilities p(\gamma, t) for finding a tree \gamma at time t, valid for
arbitrary continuous time branching models with tree and segment dependent
branching rates. We show, for the specific case of the continuous time
BES-model, that as expected from our model formulation, the sums needed to
evaluate expectation values of functions of the terminal segment number
\mu(f(n),t) do not depend on the distribution of the total branching
probability over the terminal segments. In addition, we derive a system of
differential equations for the probabilities p(n,t) of finding n terminal
segments at time t. For the continuous BES-model, this system of differential
equations gives direct numerical access to functions only depending on the
number of terminal segments, and we use this to evaluate the development of the
mean and standard deviation of the number of terminal segments at a time t. For
comparison we discuss two cases where mean and variance of the number of
terminal segments are exactly solvable. Then we discuss the numerical
evaluation of the S-dependence of the solutions for the continuous time
BES-model. The numerical results show clearly that higher S values, i.e. values
such that more proximal terminal segments have higher branching rates than more
distal terminal segments, lead to more symmetrical trees as measured by three
tree symmetry indicators.Comment: 41 pages, 2 figures, revised structure and text improvement
Improved Attack on the Cellular Authentication and Voice Encryption Algorithm
We present new cryptanalysis of the Telecommunications hash algorithm known as Cellular Authentication and Voice Encryption Algorithm (CAVE). The previous guess-and-determine style reconstruction attack requires (resp. ) evaluations of CAVE-4 (resp. CAVE-8) to find a single valid pre-image (one which satisfies the input redundancy). Here we present a new attack that finds emph{all} valid pre-images with effort equivalent to around evaluations of the algorithm for both CAVE-4 and CAVE-8
Joint segmentation of color and depth data based on splitting and merging driven by surface fitting
This paper proposes a segmentation scheme based on the joint usage of color and depth data together with a 3D surface estimation scheme. Firstly a set of multi-dimensional vectors is built from color, geometry and surface orientation information. Normalized cuts spectral clustering is then applied in order to recursively segment the scene in two parts thus obtaining an over-segmentation. This procedure is followed by a recursive merging stage where close segments belonging to the same object are joined together. At each step of both procedures a NURBS model is fitted on the computed segments and the accuracy of the fitting is used as a measure of the plausibility that a segment represents a single surface or object. By comparing the accuracy to the one at the previous step, it is possible to determine if each splitting or merging operation leads to a better scene representation and consequently whether to perform it or not. Experimental results show how the proposed method provides an accurate and reliable segmentation
Visual Chunking: A List Prediction Framework for Region-Based Object Detection
We consider detecting objects in an image by iteratively selecting from a set
of arbitrarily shaped candidate regions. Our generic approach, which we term
visual chunking, reasons about the locations of multiple object instances in an
image while expressively describing object boundaries. We design an
optimization criterion for measuring the performance of a list of such
detections as a natural extension to a common per-instance metric. We present
an efficient algorithm with provable performance for building a high-quality
list of detections from any candidate set of region-based proposals. We also
develop a simple class-specific algorithm to generate a candidate region
instance in near-linear time in the number of low-level superpixels that
outperforms other region generating methods. In order to make predictions on
novel images at testing time without access to ground truth, we develop
learning approaches to emulate these algorithms' behaviors. We demonstrate that
our new approach outperforms sophisticated baselines on benchmark datasets.Comment: to appear at ICRA 201
Using NLP to build the hypertextuel network of a back-of-the-book index
Relying on the idea that back-of-the-book indexes are traditional devices for
navigation through large documents, we have developed a method to build a
hypertextual network that helps the navigation in a document. Building such an
hypertextual network requires selecting a list of descriptors, identifying the
relevant text segments to associate with each descriptor and finally ranking
the descriptors and reference segments by relevance order. We propose a
specific document segmentation method and a relevance measure for information
ranking. The algorithms are tested on 4 corpora (of different types and
domains) without human intervention or any semantic knowledge
Spoken content retrieval: A survey of techniques and technologies
Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR
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