2,432 research outputs found
Inequality, Inequity Aversion, and the Provision of Public Goods
We investigate the effects of inequality in wealth on the incentives to contribute to a public good when agents are inequity averse and may differ in ability. We show that equality may lead to a reduction of public good provision below levels generated by purely selfish agents. But introducing inequality motivates more productive agents to exert higher efforts and help the group to coordinate on equilibria with less free-riding. As a result, less able agents may benefit from initially disadvantageous inequality. Moreover, the more inequity averse the agents, the more inequality should be imposed even by an egalitarian social planner.public goods, inequality, inequity aversion, social welfare, voluntary provision, income distribution, heterogeneity
A deep learning framework for quality assessment and restoration in video endoscopy
Endoscopy is a routine imaging technique used for both diagnosis and
minimally invasive surgical treatment. Artifacts such as motion blur, bubbles,
specular reflections, floating objects and pixel saturation impede the visual
interpretation and the automated analysis of endoscopy videos. Given the
widespread use of endoscopy in different clinical applications, we contend that
the robust and reliable identification of such artifacts and the automated
restoration of corrupted video frames is a fundamental medical imaging problem.
Existing state-of-the-art methods only deal with the detection and restoration
of selected artifacts. However, typically endoscopy videos contain numerous
artifacts which motivates to establish a comprehensive solution.
We propose a fully automatic framework that can: 1) detect and classify six
different primary artifacts, 2) provide a quality score for each frame and 3)
restore mildly corrupted frames. To detect different artifacts our framework
exploits fast multi-scale, single stage convolutional neural network detector.
We introduce a quality metric to assess frame quality and predict image
restoration success. Generative adversarial networks with carefully chosen
regularization are finally used to restore corrupted frames.
Our detector yields the highest mean average precision (mAP at 5% threshold)
of 49.0 and the lowest computational time of 88 ms allowing for accurate
real-time processing. Our restoration models for blind deblurring, saturation
correction and inpainting demonstrate significant improvements over previous
methods. On a set of 10 test videos we show that our approach preserves an
average of 68.7% which is 25% more frames than that retained from the raw
videos.Comment: 14 page
On the Complexity of Nucleolus Computation for Bipartite b-Matching Games
We explore the complexity of nucleolus computation in b-matching games on
bipartite graphs. We show that computing the nucleolus of a simple b-matching
game is NP-hard even on bipartite graphs of maximum degree 7. We complement
this with partial positive results in the special case where b values are
bounded by 2. In particular, we describe an efficient algorithm when a constant
number of vertices satisfy b(v) = 2 as well as an efficient algorithm for
computing the non-simple b-matching nucleolus when b = 2
WHAT YOU SEE IS WHAT YOU GET (WYSIWYG) STAGE EDITOR FOR USERS WHO SHARE APPLICATIONS
Techniques are provided herein for a What You See Is What You Get (WYSIWYG) stage editor for the presenter in a meeting. These techniques no longer require more than two people (one as a presenter to share content and another to take on the producer role as stage editor). These techniques also resolve infinity effects caused by screen-sharing (one display only)
Replicability in Reinforcement Learning
We initiate the mathematical study of replicability as an algorithmic
property in the context of reinforcement learning (RL). We focus on the
fundamental setting of discounted tabular MDPs with access to a generative
model. Inspired by Impagliazzo et al. [2022], we say that an RL algorithm is
replicable if, with high probability, it outputs the exact same policy after
two executions on i.i.d. samples drawn from the generator when its internal
randomness is the same. We first provide an efficient -replicable
algorithm for -optimal policy estimation with sample and
time complexity ,
where is the number of state-action pairs. Next, for the subclass of
deterministic algorithms, we provide a lower bound of order
.
Then, we study a relaxed version of replicability proposed by Kalavasis et al.
[2023] called TV indistinguishability. We design a computationally efficient TV
indistinguishable algorithm for policy estimation whose sample complexity is
.
At the cost of running time, we transform these TV indistinguishable
algorithms to -replicable ones without increasing their sample
complexity. Finally, we introduce the notion of approximate-replicability where
we only require that two outputted policies are close under an appropriate
statistical divergence (e.g., Renyi) and show an improved sample complexity of
.Comment: to be published in neurips 202
Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics
State-of-the-art approaches in computer vision heavily rely on sufficiently
large training datasets. For real-world applications, obtaining such a dataset
is usually a tedious task. In this paper, we present a fully automated pipeline
to generate a synthetic dataset for instance segmentation in four steps. In
contrast to existing work, our pipeline covers every step from data acquisition
to the final dataset. We first scrape images for the objects of interest from
popular image search engines and since we rely only on text-based queries the
resulting data comprises a wide variety of images. Hence, image selection is
necessary as a second step. This approach of image scraping and selection
relaxes the need for a real-world domain-specific dataset that must be either
publicly available or created for this purpose. We employ an object-agnostic
background removal model and compare three different methods for image
selection: Object-agnostic pre-processing, manual image selection and CNN-based
image selection. In the third step, we generate random arrangements of the
object of interest and distractors on arbitrary backgrounds. Finally, the
composition of the images is done by pasting the objects using four different
blending methods. We present a case study for our dataset generation approach
by considering parcel segmentation. For the evaluation we created a dataset of
parcel photos that were annotated automatically. We find that (1) our dataset
generation pipeline allows a successful transfer to real test images (Mask AP
86.2), (2) a very accurate image selection process - in contrast to human
intuition - is not crucial and a broader category definition can help to bridge
the domain gap, (3) the usage of blending methods is beneficial compared to
simple copy-and-paste. We made our full code for scraping, image composition
and training publicly available at https://a-nau.github.io/parcel2d.Comment: Accepted at ICMLA 202
Replicable Clustering
We design replicable algorithms in the context of statistical clustering
under the recently introduced notion of replicability from Impagliazzo et al.
[2022]. According to this definition, a clustering algorithm is replicable if,
with high probability, its output induces the exact same partition of the
sample space after two executions on different inputs drawn from the same
distribution, when its internal randomness is shared across the executions. We
propose such algorithms for the statistical -medians, statistical -means,
and statistical -centers problems by utilizing approximation routines for
their combinatorial counterparts in a black-box manner. In particular, we
demonstrate a replicable -approximation algorithm for statistical
Euclidean -medians (-means) with sample
complexity. We also describe an -approximation algorithm with an
additional -additive error for statistical Euclidean -centers, albeit
with sample complexity. In addition, we provide experiments on
synthetic distributions in 2D using the -means++ implementation from sklearn
as a black-box that validate our theoretical results.Comment: to be published in NeurIPS 202
Local Detection of Topical Entities Using Machine Learning
Computer-implemented systems and methods for determining topics of displayed content are provided while maintaining user data privacy and security. Entity identification and topic determination models may be stored within a user computing device such that the user computing device may perform topic detection of content presently displayed on the user computing device to maintain user data privacy. Once a topic(s) is determined from the content, features within the user computing device may be enabled or tailored to a user based on the content being displayed
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