Self-Supervised Deep Visual Odometry with Online Adaptation

Self-supervised VO methods have shown great success in jointly estimating camera pose and depth from videos. However, like most data-driven methods, existing VO networks suffer from a notable decrease in performance when confronted with scenes different from the training data, which makes them unsuitable for practical applications. In this paper, we propose an online meta-learning algorithm to enable VO networks to continuously adapt to new environments in a self-supervised manner. The proposed method utilizes convolutional long short-term memory (convLSTM) to aggregate rich spatial-temporal information in the past. The network is able to memorize and learn from its past experience for better estimation and fast adaptation to the current frame. When running VO in the open world, in order to deal with the changing environment, we propose an online feature alignment method by aligning feature distributions at different time. Our VO network is able to seamlessly adapt to different environments. Extensive experiments on unseen outdoor scenes, virtual to real world and outdoor to indoor environments demonstrate that our method consistently outperforms state-of-the-art self-supervised VO baselines considerably.Comment: Accepted by CVPR 2020 ora

Scanning Tunneling Microscopy and Spectroscopy of Wet-Chemically Prepared Chlorinated Si(111) Surfaces

Chlorine-terminated Si(111) surfaces prepared through the wet-chemical treatment of H-terminated Si(111) surfaces with PCl_5 (in chlorobenzene) were investigated using ultrahigh vacuum scanning tunneling microscopy (UHV cryo-STM) and tunneling spectroscopy. STM images, collected at 77 K, revealed an unreconstructed 1 × 1 structure for the chlorination layer, consistent with what has been observed for the gas phase chlorination of H-terminated Si(111). However, the wet-chemical chlorination is shown to generate etch pits in the Si(111) surface, with an increase in etch pit density correlating with increasing PCl_5 exposure temperatures. These etch pits were assumed to stabilize the edge structure through the partial removal of the 〈112̄〉 step edges. Tunneling spectroscopy revealed a nonzero density of states at zero bias. This is in contrast to the cases of H-, methyl-, or ethyl-terminated Si(111), in which similar measurements have revealed the presence of a large conductance gap

Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations

Top-$k$ predictions are used in many real-world applications such as machine learning as a service, recommender systems, and web searches. $\ell_0$-norm adversarial perturbation characterizes an attack that arbitrarily modifies some features of an input such that a classifier makes an incorrect prediction for the perturbed input. $\ell_0$-norm adversarial perturbation is easy to interpret and can be implemented in the physical world. Therefore, certifying robustness of top-$k$ predictions against $\ell_0$-norm adversarial perturbation is important. However, existing studies either focused on certifying $\ell_0$-norm robustness of top-$1$ predictions or $\ell_2$-norm robustness of top-$k$ predictions. In this work, we aim to bridge the gap. Our approach is based on randomized smoothing, which builds a provably robust classifier from an arbitrary classifier via randomizing an input. Our major theoretical contribution is an almost tight $\ell_0$-norm certified robustness guarantee for top-$k$ predictions. We empirically evaluate our method on CIFAR10 and ImageNet. For instance, our method can build a classifier that achieves a certified top-3 accuracy of 69.2\% on ImageNet when an attacker can arbitrarily perturb 5 pixels of a testing image

The unfolding and folding dynamics of TNfnALL probed by single molecule force-ramp spectroscopy

Abstract Tenascin, an important extracellular matrix protein, is subject to stretching force under physiological conditions and plays important roles in regulating the cell-matrix interactions. Using the recently developed single molecule force-ramp spectroscopy, we investigated the unfoldingfolding kinetics of a recombinant tenascin fragment TNfnALL. Our results showed that all the 15 FnIII domains in TNfnALL have similar spontaneous unfolding rate constant at zero force, but show great difference in their folding rate constants. Our results demonstrated that single molecule force-ramp spectroscopy is a powerful tool for accurate determination of the kinetic parameters that characterize the unfolding and folding reactions. We anticipate that single molecule force-ramp spectroscopy will become a versatile addition to the single molecule manipulation tool box and greatly expand the scope of single molecule force spectroscopy.

Face Restoration via Plug-and-Play 3D Facial Priors

State-of-the-art face restoration methods employ deep convolutional neural networks (CNNs) to learn a mapping between degraded and sharp facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and only deal with task-specific face restoration (e.g.,face super-resolution or deblurring). In this paper, we propose cross-tasks and cross-models plug-and-play 3D facial priors to explicitly embed the network with the sharp facial structures for general face restoration tasks. Our 3D priors are the first to explore 3D morphable knowledge based on the fusion of parametric descriptions of face attributes (e.g., identity, facial expression, texture, illumination, and face pose). Furthermore, the priors can easily be incorporated into any network and are very efficient in improving the performance and accelerating the convergence speed. Firstly, a 3D face rendering branch is set up to obtain 3D priors of salient facial structures and identity knowledge. Secondly, for better exploiting this hierarchical information (i.e., intensity similarity, 3D facial structure, and identity content), a spatial attention module is designed for image restoration problems. Extensive face restoration experiments including face super-resolution and deblurring demonstrate that the proposed 3D priors achieve superior face restoration results over the state-of-the-art algorithm

The NS1 protein of influenza a virus interacts with heat shock protein Hsp90 in human alveolar basal epithelial cells: Implication for virus-induced apoptosis

<p>Abstract</p> <p>Background</p> <p>Our previous study showed that the NS1 protein of highly pathogenic avian influenza A virus H5N1 induced caspase-dependent apoptosis in human alveolar basal epithelial cells (A549), supporting its function as a proapoptotic factor during viral infection, but the mechanism is still unknown.</p> <p>Results</p> <p>To characterize the mechanism of NS1-induced apoptosis, we used a two-hybrid system to isolate the potential NS1-interacting partners in A549 cells. We found that heat shock protein 90 (Hsp90) was able to interact with the NS1 proteins derived from both H5N1 and H3N2 viruses, which was verified by co-immunoprecitation assays. Significantly, the NS1 expression in the A549 cells dramatically weakened the interaction between Apaf-1 and Hsp90 but enhanced its interaction with cytochrome c (Cyt c), suggesting that the competitive binding of NS1 to Hsp90 might promote the Apaf-1 to associate with Cyt c and thus facilitate the activation of caspase 9 and caspase 3.</p> <p>Conclusions</p> <p>The present results demonstrate that NS1 protein of Influenza A Virus interacts with heat hock protein Hsp90 and meidates the apoptosis induced by influenza A virus through the caspase cascade.</p