58 research outputs found
Finite-time self-similar rupture in a generalized elastohydrodynamic lubrication model
Thin film rupture is a type of nonlinear instability that causes the solution
to touch down to zero at finite time. We investigate the finite-time rupture
behavior of a generalized elastohydrodynamic lubrication model. This model
features the interplay between destabilizing disjoining pressure and
stabilizing elastic bending pressure and surface tension. The governing
equation is a sixth-order nonlinear degenerate parabolic partial differential
equation parameterized by exponents in the mobility function and the disjoining
pressure, respectively. Asymptotic self-similar finite-time rupture solutions
governed by a sixth-order leading-order equation are analyzed. In the weak
elasticity limit, transient self-similar dynamics governed by a fourth-order
similarity equation are also identified
Identifying the Source of Vulnerability in Explanation Discrepancy: A Case Study in Neural Text Classification
Some recent works observed the instability of post-hoc explanations when
input side perturbations are applied to the model. This raises the interest and
concern in the stability of post-hoc explanations. However, the remaining
question is: is the instability caused by the neural network model or the
post-hoc explanation method? This work explores the potential source that leads
to unstable post-hoc explanations. To separate the influence from the model, we
propose a simple output probability perturbation method. Compared to prior
input side perturbation methods, the output probability perturbation method can
circumvent the neural model's potential effect on the explanations and allow
the analysis on the explanation method. We evaluate the proposed method with
three widely-used post-hoc explanation methods (LIME (Ribeiro et al., 2016),
Kernel Shapley (Lundberg and Lee, 2017a), and Sample Shapley (Strumbelj and
Kononenko, 2010)). The results demonstrate that the post-hoc methods are
stable, barely producing discrepant explanations under output probability
perturbations. The observation suggests that neural network models may be the
primary source of fragile explanations.Comment: EMNLP BlackboxNLP 202
Visual SLAM based on dynamic object removal
Visual simultaneous localization and mapping (SLAM) is the core of intelligent robot navigation system. Many traditional SLAM algorithms assume that the scene is static. When a dynamic object appears in the environment, the accuracy of visual SLAM can degrade due to the interference of dynamic features of moving objects. This strong hypothesis limits the SLAM applications for service robot or driverless car in the real dynamic environment. In this paper, a dynamic object removal algorithm that combines object recognition and optical flow techniques is proposed in the visual SLAM framework for dynamic scenes. The experimental results show that our new method can detect moving object effectively and improve the SLAM performance compared to the state of the art methods
Simultaneous monocular visual odometry and depth reconstruction with scale recovery
In this paper, we propose a deep neural net-work that can estimate camera poses and reconstruct thefull resolution depths of the environment simultaneously usingonly monocular consecutive images. In contrast to traditionalmonocular visual odometry methods, which cannot estimatescaled depths, we here demonstrate the recovery of the scaleinformation using a sparse depth image as a supervision signalin the training step. In addition, based on the scaled depth,the relative poses between consecutive images can be estimatedusing the proposed deep neural network. Another novelty liesin the deployment of view synthesis, which can synthesize anew image of the scene from a different view (camera pose)given an input image. The view synthesis is the core techniqueused for constructing a loss function for the proposed neuralnetwork, which requires the knowledge of the predicted depthsand relative poses, such that the proposed method couples thevisual odometry and depth prediction together. In this way,both the estimated poses and the predicted depths from theneural network are scaled using the sparse depth image as thesupervision signal during training. The experimental results onthe KITTI dataset show competitive performance of our methodto handle challenging environments
REV: Information-Theoretic Evaluation of Free-Text Rationales
Generating free-text rationales is a promising step towards explainable NLP,
yet evaluating such rationales remains a challenge. Existing metrics have
mostly focused on measuring the association between the rationale and a given
label. We argue that an ideal metric should focus on the new information
uniquely provided in the rationale that is otherwise not provided in the input
or the label. We investigate this research problem from an
information-theoretic perspective using conditional V-information (Hewitt et
al., 2021). More concretely, we propose a metric called REV (Rationale
Evaluation with conditional V-information), to quantify the amount of new,
label-relevant information in a rationale beyond the information already
available in the input or the label. Experiments across four benchmarks with
reasoning tasks, including chain-of-thought, demonstrate the effectiveness of
REV in evaluating rationale-label pairs, compared to existing metrics. We
further demonstrate REV is consistent with human judgments on rationale
evaluations and provides more sensitive measurements of new information in
free-text rationales. When used alongside traditional performance metrics, REV
provides deeper insights into models' reasoning and prediction processes.Comment: ACL 202
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