4,783 research outputs found
Filtering algorithms for global chance constraints
Stochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a PSPACE task. The only complete solution approach to date — scenario-based stochastic constraint programming — compiles SCSPs down into classical CSPs. This allows the reuse of classical constraint solvers to solve SCSPs, but at the cost of increased space requirements and weak constraint propagation. This paper tries to overcome these drawbacks by automatically synthesizing filtering algorithms for global chance constraints. These filtering algorithms are parameterized by propagators for the deterministic version of the chance constraints. This approach allows the reuse of existing propagators in current constraint solvers and it has the potential to enhance constraint propagation. Our results show that, for the test bed considered in this work, our approach is superior to scenario-based stochastic constraint programming. For these instances, our approach is more scalable, it produces more compact formulations, it is more efficient in terms of run time and more effective in terms of pruning for both stochastic constraint satisfaction and optimization problems
Depth Assisted Full Resolution Network for Single Image-based View Synthesis
Researches in novel viewpoint synthesis majorly focus on interpolation from
multi-view input images. In this paper, we focus on a more challenging and
ill-posed problem that is to synthesize novel viewpoints from one single input
image. To achieve this goal, we propose a novel deep learning-based technique.
We design a full resolution network that extracts local image features with the
same resolution of the input, which contributes to derive high resolution and
prevent blurry artifacts in the final synthesized images. We also involve a
pre-trained depth estimation network into our system, and thus 3D information
is able to be utilized to infer the flow field between the input and the target
image. Since the depth network is trained by depth order information between
arbitrary pairs of points in the scene, global image features are also involved
into our system. Finally, a synthesis layer is used to not only warp the
observed pixels to the desired positions but also hallucinate the missing
pixels with recorded pixels. Experiments show that our technique performs well
on images of various scenes, and outperforms the state-of-the-art techniques
ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System
Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems
recently achieve promising progress to take over tedious examinations by
cardiologists. However, their vulnerability to adversarial attacks still lack
comprehensive investigation. The existing attacks in image domain could not be
directly applicable due to the distinct properties of ECGs in visualization and
dynamic properties. Thus, this paper takes a step to thoroughly explore
adversarial attacks on the DNN-powered ECG diagnosis system. We analyze the
properties of ECGs to design effective attacks schemes under two attacks models
respectively. Our results demonstrate the blind spots of DNN-powered diagnosis
systems under adversarial attacks, which calls attention to adequate
countermeasures.Comment: Accepted by AAAI 202
Confidence-based Reasoning in Stochastic Constraint Programming
In this work we introduce a novel approach, based on sampling, for finding
assignments that are likely to be solutions to stochastic constraint
satisfaction problems and constraint optimisation problems. Our approach
reduces the size of the original problem being analysed; by solving this
reduced problem, with a given confidence probability, we obtain assignments
that satisfy the chance constraints in the original model within prescribed
error tolerance thresholds. To achieve this, we blend concepts from stochastic
constraint programming and statistics. We discuss both exact and approximate
variants of our method. The framework we introduce can be immediately employed
in concert with existing approaches for solving stochastic constraint programs.
A thorough computational study on a number of stochastic combinatorial
optimisation problems demonstrates the effectiveness of our approach.Comment: 53 pages, working draf
On the role of pre and post-processing in environmental data mining
The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed
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