490 research outputs found
Multiple-Environment Markov Decision Processes
We introduce Multi-Environment Markov Decision Processes (MEMDPs) which are
MDPs with a set of probabilistic transition functions. The goal in a MEMDP is
to synthesize a single controller with guaranteed performances against all
environments even though the environment is unknown a priori. While MEMDPs can
be seen as a special class of partially observable MDPs, we show that several
verification problems that are undecidable for partially observable MDPs, are
decidable for MEMDPs and sometimes have even efficient solutions
Diffusivity and Solubility of Si in the Al Metallization of Integrated Circuits
Si was diffused along the evaporated Al layer of an integrated-circuit structure at temperatures between 360 and 560 °C, and the resulting concentration profile analyzed by electron microprobe. The Si solubility was found to agree with literature values for Si in wrought Al. The Si diffusivity was found to be substantially enhanced, however, probably due to a high density of imperfections in the evaporated Al film. Our measured diffusivities indicate an activation energy EA ~= 0. 8 eV, about 40% less than the value for Si in wrought Al
Precipitation of Si from the Al metallization of integrated circuits
The Al metallization of integrated circuits is known to dissolve 1/2% or more Si, but the ultimate location of this Si has been uncertain. An electron microprobe operated at low beam energy so as to penetrate only the upper portion of the metallization was used to follow the movement of dissolved Si on cooling after the "forming" heat treatment. Dissolved Si substantially less than a diffusion length from the substrate was found to regrow there; elsewhere the Si forms precipitates in the Al matrix, preferentially near the free surface of the Al
Online Correlation Clustering
We study the online clustering problem where data items arrive in an online
fashion. The algorithm maintains a clustering of data items into similarity
classes. Upon arrival of v, the relation between v and previously arrived items
is revealed, so that for each u we are told whether v is similar to u. The
algorithm can create a new cluster for v and merge existing clusters.
When the objective is to minimize disagreements between the clustering and
the input, we prove that a natural greedy algorithm is O(n)-competitive, and
this is optimal.
When the objective is to maximize agreements between the clustering and the
input, we prove that the greedy algorithm is .5-competitive; that no online
algorithm can be better than .834-competitive; we prove that it is possible to
get better than 1/2, by exhibiting a randomized algorithm with competitive
ratio .5+c for a small positive fixed constant c.Comment: 12 pages, 1 figur
Assume-Admissible Synthesis
In this paper, we introduce a novel rule for synthesis of reactive systems,
applicable to systems made of n components which have each their own
objectives. It is based on the notion of admissible strategies. We compare our
novel rule with previous rules defined in the literature, and we show that
contrary to the previous proposals, our rule defines sets of solutions which
are rectangular. This property leads to solutions which are robust and
resilient. We provide algorithms with optimal complexity and also an
abstraction framework.Comment: 31 page
View subspaces for indexing and retrieval of 3D models
View-based indexing schemes for 3D object retrieval are gaining popularity
since they provide good retrieval results. These schemes are coherent with the
theory that humans recognize objects based on their 2D appearances. The
viewbased techniques also allow users to search with various queries such as
binary images, range images and even 2D sketches. The previous view-based
techniques use classical 2D shape descriptors such as Fourier invariants,
Zernike moments, Scale Invariant Feature Transform-based local features and 2D
Digital Fourier Transform coefficients. These methods describe each object
independent of others. In this work, we explore data driven subspace models,
such as Principal Component Analysis, Independent Component Analysis and
Nonnegative Matrix Factorization to describe the shape information of the
views. We treat the depth images obtained from various points of the view
sphere as 2D intensity images and train a subspace to extract the inherent
structure of the views within a database. We also show the benefit of
categorizing shapes according to their eigenvalue spread. Both the shape
categorization and data-driven feature set conjectures are tested on the PSB
database and compared with the competitor view-based 3D shape retrieval
algorithmsComment: Three-Dimensional Image Processing (3DIP) and Applications
(Proceedings Volume) Proceedings of SPIE Volume: 7526 Editor(s): Atilla M.
Baskurt ISBN: 9780819479198 Date: 2 February 201
Activity recognition from videos with parallel hypergraph matching on GPUs
In this paper, we propose a method for activity recognition from videos based
on sparse local features and hypergraph matching. We benefit from special
properties of the temporal domain in the data to derive a sequential and fast
graph matching algorithm for GPUs.
Traditionally, graphs and hypergraphs are frequently used to recognize
complex and often non-rigid patterns in computer vision, either through graph
matching or point-set matching with graphs. Most formulations resort to the
minimization of a difficult discrete energy function mixing geometric or
structural terms with data attached terms involving appearance features.
Traditional methods solve this minimization problem approximately, for instance
with spectral techniques.
In this work, instead of solving the problem approximatively, the exact
solution for the optimal assignment is calculated in parallel on GPUs. The
graphical structure is simplified and regularized, which allows to derive an
efficient recursive minimization algorithm. The algorithm distributes
subproblems over the calculation units of a GPU, which solves them in parallel,
allowing the system to run faster than real-time on medium-end GPUs
Compositional Algorithms for Succinct Safety Games
We study the synthesis of circuits for succinct safety specifications given
in the AIG format. We show how AIG safety specifications can be decomposed
automatically into sub specifications. Then we propose symbolic compositional
algorithms to solve the synthesis problem compositionally starting for the
sub-specifications. We have evaluated the compositional algorithms on a set of
benchmarks including those proposed for the first synthesis competition
organised in 2014 by the Synthesis Workshop affiliated to the CAV conference.
We show that a large number of benchmarks can be decomposed automatically and
solved more efficiently with the compositional algorithms that we propose in
this paper.Comment: In Proceedings SYNT 2015, arXiv:1602.0078
Compressively Sensed Image Recognition
Compressive Sensing (CS) theory asserts that sparse signal reconstruction is
possible from a small number of linear measurements. Although CS enables
low-cost linear sampling, it requires non-linear and costly reconstruction.
Recent literature works show that compressive image classification is possible
in CS domain without reconstruction of the signal. In this work, we introduce a
DCT base method that extracts binary discriminative features directly from CS
measurements. These CS measurements can be obtained by using (i) a random or a
pseudo-random measurement matrix, or (ii) a measurement matrix whose elements
are learned from the training data to optimize the given classification task.
We further introduce feature fusion by concatenating Bag of Words (BoW)
representation of our binary features with one of the two state-of-the-art
CNN-based feature vectors. We show that our fused feature outperforms the
state-of-the-art in both cases.Comment: 6 pages, submitted/accepted, EUVIP 201
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