5,679 research outputs found
Michael Millemann: Putting Maryland\u27s Legal Clinics on the Map
Co-Director Michael Millemann\u27s tireless work has gained national prominence for the law school\u27s Clinical Law Program
Shaping the Landscape with Landmark Advocacy
For Stan Herr, public service is not something to do on the side
Superpixels: An Evaluation of the State-of-the-Art
Superpixels group perceptually similar pixels to create visually meaningful
entities while heavily reducing the number of primitives for subsequent
processing steps. As of these properties, superpixel algorithms have received
much attention since their naming in 2003. By today, publicly available
superpixel algorithms have turned into standard tools in low-level vision. As
such, and due to their quick adoption in a wide range of applications,
appropriate benchmarks are crucial for algorithm selection and comparison.
Until now, the rapidly growing number of algorithms as well as varying
experimental setups hindered the development of a unifying benchmark. We
present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms
utilizing a benchmark focussing on fair comparison and designed to provide new
insights relevant for applications. To this end, we explicitly discuss
parameter optimization and the importance of strictly enforcing connectivity.
Furthermore, by extending well-known metrics, we are able to summarize
algorithm performance independent of the number of generated superpixels,
thereby overcoming a major limitation of available benchmarks. Furthermore, we
discuss runtime, robustness against noise, blur and affine transformations,
implementation details as well as aspects of visual quality. Finally, we
present an overall ranking of superpixel algorithms which redefines the
state-of-the-art and enables researchers to easily select appropriate
algorithms and the corresponding implementations which themselves are made
publicly available as part of our benchmark at
davidstutz.de/projects/superpixel-benchmark/
Disentangling Adversarial Robustness and Generalization
Obtaining deep networks that are robust against adversarial examples and
generalize well is an open problem. A recent hypothesis even states that both
robust and accurate models are impossible, i.e., adversarial robustness and
generalization are conflicting goals. In an effort to clarify the relationship
between robustness and generalization, we assume an underlying, low-dimensional
data manifold and show that: 1. regular adversarial examples leave the
manifold; 2. adversarial examples constrained to the manifold, i.e.,
on-manifold adversarial examples, exist; 3. on-manifold adversarial examples
are generalization errors, and on-manifold adversarial training boosts
generalization; 4. regular robustness and generalization are not necessarily
contradicting goals. These assumptions imply that both robust and accurate
models are possible. However, different models (architectures, training
strategies etc.) can exhibit different robustness and generalization
characteristics. To confirm our claims, we present extensive experiments on
synthetic data (with known manifold) as well as on EMNIST, Fashion-MNIST and
CelebA.Comment: Conference on Computer Vision and Pattern Recognition 201
When Gravity Fails: Local Search Topology
Local search algorithms for combinatorial search problems frequently
encounter a sequence of states in which it is impossible to improve the value
of the objective function; moves through these regions, called plateau moves,
dominate the time spent in local search. We analyze and characterize plateaus
for three different classes of randomly generated Boolean Satisfiability
problems. We identify several interesting features of plateaus that impact the
performance of local search algorithms. We show that local minima tend to be
small but occasionally may be very large. We also show that local minima can be
escaped without unsatisfying a large number of clauses, but that systematically
searching for an escape route may be computationally expensive if the local
minimum is large. We show that plateaus with exits, called benches, tend to be
much larger than minima, and that some benches have very few exit states which
local search can use to escape. We show that the solutions (i.e., global
minima) of randomly generated problem instances form clusters, which behave
similarly to local minima. We revisit several enhancements of local search
algorithms and explain their performance in light of our results. Finally we
discuss strategies for creating the next generation of local search algorithms.Comment: See http://www.jair.org/ for any accompanying file
Analysis of selection pressure exerted on Plasmopara viticola by organically based fungicides
Downy mildew is one of the most important grape diseases world-wide. The pathogen is a genetically highly diversified organism with a high capacity of adaptation. A monitoring of changes in population structure of P. viticola subjected to new copper replacing products or strategies, studied and developed within REPCO (Replacement of Copper Fungicides in Organic Production of Grapevine and Apple in Europe) is important for assessing selection pressure which could lead to a reduction of efficacy of these new measures. Therefore P. viticola lesions collected on untreated and treated vines were analyzed by means of microsatellite markers. No significant differences in the populations structure were determined among untreated and treated populations, indicating that the applied products didn’t exerted any selection pressure on the P. viticola populations
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