The H\"older continuity of Lyapunov exponents for a class of Cos-type quasiperiodic Schr\"odinger cocycles

In this paper we obtain exact $\frac{1}{2}$-H\"older continuity of the Lyapunov exponents for quasi-periodic Sch\"odinger cocycles with $C^2$ cos-type potentials, large coupling constants, and fixed Diophantine frequency. Moreover, we prove the locally Lipschitz continuity of the Lyapunov exponent for a full measure spectral set. Furthermore, for any given $r$ between $\frac{1}{2}$ to $1$, we can find some energy on the spectrum and on which Lyapunov exponent is exactly $r$-H\"older continuous.Comment: 88 Page

The Influence of Porous Media Heterogeneity on Smouldering Remediation

Smouldering remediation is a promising technique for destroying organic contaminants in soil. Forced airflow is vital to supporting the smouldering reaction and to propagate it through the contaminated zone. This research focuses on investigating the effects of permeability heterogeneity on smouldering. A series of unique column experiments, combined with numerical model simulations, were conducted. The results suggest that smouldering can successfully propagate through layers in series despite more than a 1000:1 permeability contrast. However, extinction can occur in the finer layer when smouldering propagates through layers in parallel with a permeability ratio above 3:1. Extinction may occur due to insufficient airflow in the fine layer or due to conductive heat losses from the fine to coarse layers. However, for more complex heterogeneity, smouldering extinction can be eliminated. Overall, this research provides unique insights into managing heterogeneous soils to ensure the successful application of smouldering remediation

LSTM Pose Machines

We observed that recent state-of-the-art results on single image human pose estimation were achieved by multi-stage Convolution Neural Networks (CNN). Notwithstanding the superior performance on static images, the application of these models on videos is not only computationally intensive, it also suffers from performance degeneration and flicking. Such suboptimal results are mainly attributed to the inability of imposing sequential geometric consistency, handling severe image quality degradation (e.g. motion blur and occlusion) as well as the inability of capturing the temporal correlation among video frames. In this paper, we proposed a novel recurrent network to tackle these problems. We showed that if we were to impose the weight sharing scheme to the multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN). This property decouples the relationship among multiple network stages and results in significantly faster speed in invoking the network for videos. It also enables the adoption of Long Short-Term Memory (LSTM) units between video frames. We found such memory augmented RNN is very effective in imposing geometric consistency among frames. It also well handles input quality degradation in videos while successfully stabilizes the sequential outputs. The experiments showed that our approach significantly outperformed current state-of-the-art methods on two large-scale video pose estimation benchmarks. We also explored the memory cells inside the LSTM and provided insights on why such mechanism would benefit the prediction for video-based pose estimations.Comment: Poster in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

KERM: Knowledge Enhanced Reasoning for Vision-and-Language Navigation

Vision-and-language navigation (VLN) is the task to enable an embodied agent to navigate to a remote location following the natural language instruction in real scenes. Most of the previous approaches utilize the entire features or object-centric features to represent navigable candidates. However, these representations are not efficient enough for an agent to perform actions to arrive the target location. As knowledge provides crucial information which is complementary to visible content, in this paper, we propose a Knowledge Enhanced Reasoning Model (KERM) to leverage knowledge to improve agent navigation ability. Specifically, we first retrieve facts (i.e., knowledge described by language descriptions) for the navigation views based on local regions from the constructed knowledge base. The retrieved facts range from properties of a single object (e.g., color, shape) to relationships between objects (e.g., action, spatial position), providing crucial information for VLN. We further present the KERM which contains the purification, fact-aware interaction, and instruction-guided aggregation modules to integrate visual, history, instruction, and fact features. The proposed KERM can automatically select and gather crucial and relevant cues, obtaining more accurate action prediction. Experimental results on the REVERIE, R2R, and SOON datasets demonstrate the effectiveness of the proposed method.Comment: Accepted by CVPR 2023. The code is available at https://github.com/XiangyangLi20/KER