Incomplete Augmented Lagrangian Preconditioner for Steady Incompressible Navier-Stokes Equations

An incomplete augmented Lagrangian preconditioner, for the steady incompressible Navier-Stokes equations discretized by stable finite elements, is proposed. The eigenvalues of the preconditioned matrix are analyzed. Numerical experiments show that the incomplete augmented Lagrangian-based preconditioner proposed is very robust and performs quite well by the Picard linearization or the Newton linearization over a wide range of values of the viscosity on both uniform and stretched grids

Scale Attention for Learning Deep Face Representation: A Study Against Visual Scale Variation

Human face images usually appear with wide range of visual scales. The existing face representations pursue the bandwidth of handling scale variation via multi-scale scheme that assembles a finite series of predefined scales. Such multi-shot scheme brings inference burden, and the predefined scales inevitably have gap from real data. Instead, learning scale parameters from data, and using them for one-shot feature inference, is a decent solution. To this end, we reform the conv layer by resorting to the scale-space theory, and achieve two-fold facilities: 1) the conv layer learns a set of scales from real data distribution, each of which is fulfilled by a conv kernel; 2) the layer automatically highlights the feature at the proper channel and location corresponding to the input pattern scale and its presence. Then, we accomplish the hierarchical scale attention by stacking the reformed layers, building a novel style named SCale AttentioN Conv Neural Network (\textbf{SCAN-CNN}). We apply SCAN-CNN to the face recognition task and push the frontier of SOTA performance. The accuracy gain is more evident when the face images are blurry. Meanwhile, as a single-shot scheme, the inference is more efficient than multi-shot fusion. A set of tools are made to ensure the fast training of SCAN-CNN and zero increase of inference cost compared with the plain CNN

In situ epicatechin-loaded hydrogel implants for local drug delivery to spinal column for effective management of post-traumatic spinal injuries

Purpose: To prepare hydrogels loaded with epicatechin, a strong antioxidant,  anti-inflammatory, and neuroprotective tea flavonoid, and characterise them in situ as a vehicle for prolonged and safer drug delivery in patients with post-traumatic spinal cord injury.Methods: Five in situ gel formulations were prepared using chitosan and evaluated in terms of their visual appearance, clarity, pH, viscosity, and in vitro drug release. In vivo anti-inflammatory activity was determined and compared with 2 % piroxicam gel as standard. Motor function activity in a rat model of spinal injury was examined comparatively with i.v. methylprednisolone as standard.Results: The N-methyl pyrrolidone solution (containing 1 % w/w epicatechin with 2 to 10 % w/w chitosan) of the in situ gel formulation had a uniform pH in the range of 4.01 ± 0.12 to 4.27 ± 0.02. High and uniform drug loading, ranging from 94.48 ± 1.28 to 98.08 ± 1.24 %, and good in vitro drug release (79.48 ± 2.84 to 96.48 ± 1.02 % after 7 days) were achieved. The in situ gel prepared from 1 % epicatechin and 2 % chitosan (E5) showed the greatest in vivo anti-inflammatory activity  (60.58 % inhibition of paw oedema in standard carrageenan-induced hind rat paw oedema model, compared with 48.08 % for the standard). The gels showed  significant therapeutic effectiveness against post-traumainduced spinal injury in rats. E5 elicited maximum motor activity (horizontal bar test) in the spinal injuryrat model; the rats that received E5 treatment produced an activity score of 3.62 ± 0.02 at the end of 7 days, compared with 5.0 ± 0.20 following treatment with the standard.Conclusion: In situ epicatechin-loaded gel exhibits significant neuroprotective and anti-inflammatory effects, and therefore can potentially be used for prolonged and safe drug delivery in patients with traumatic spinal cord injury.Keywords: Epicatechin, In situ gel, Chitosan, Spinal injury, Post-traumatic, Motor activity, Antiinflammator

Adaptive loose optimization for robust question answering

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favorable out-of-distribution generalizability, while non-debiasing methods sacrifice a considerable amount of out-of-distribution performance in order to obtain high in-distribution performance. Therefore, it is challenging for them to deal with the complicated changing real-world situations. In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and current optimization state on mini-batch training data. This loose optimization can be used to prevent non-debiasing methods from overlearning data bias while enabling debiasing methods to maintain slight bias learning. Experiments on the visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2, GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases. The source code has been released publicly in \url{https://github.com/reml-group/ALO}.Comment: 13 pages,8 figure

A splitting preconditioner for the incompressible navier–stokes equations

In this paper, a splitting preconditioner based on the relaxed dimensional factorization (RDF) preconditioner and the modified augmented Lagrangian (MAL) preconditioner for the incompressible Navier–Stokes equations is presented. The preconditioned matrix is analyzed, and similar results arising from the RDF and the MAL preconditioners are obtained. The corresponding details of the spectrum analysis are given. Finally, we compare the three preconditioners and numerical experiments are implemented by using the IFISS package

Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution

Click-through rate (CTR) prediction is an essential task in industrial applications such as video recommendation. Recently, deep learning models have been proposed to learn the representation of users' overall interests, while ignoring the fact that interests may dynamically change over time. We argue that it is necessary to consider the continuous-time information in CTR models to track user interest trend from rich historical behaviors. In this paper, we propose a novel Deep Time-Stream framework (DTS) which introduces the time information by an ordinary differential equations (ODE). DTS continuously models the evolution of interests using a neural network, and thus is able to tackle the challenge of dynamically representing users' interests based on their historical behaviors. In addition, our framework can be seamlessly applied to any existing deep CTR models by leveraging the additional Time-Stream Module, while no changes are made to the original CTR models. Experiments on public dataset as well as real industry dataset with billions of samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with existing methods.Comment: 8 pages. arXiv admin note: text overlap with arXiv:1809.03672 by other author