On a fractional Nirenberg problem, part II: existence of solutions

This paper is a continuation of our earlier work "[T. Jin, Y.Y. Li and J. Xiong, On a fractional Nirenberg problem, part I: blow up analysis and compactness of solutions, to appear in J. Eur. Math. Soc.]", where compactness results were given on a fractional Nirenberg problem. We prove two existence results stated there. We also obtain a fractional Aubin inequality

A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data

It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.Comment: 8 Pages, accepted at AAAI 201

3DCFS : Fast and robust joint 3D semantic-instance segmentation via coupled feature selection

We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features from two tasks in a coupled manner. To further boost the performance of the instance segmentation task in our 3DCFS, we investigate a loss function that helps the model learn to balance the magnitudes of the output embedding dimensions during training, which makes calculating the Euclidean distance more reliable and enhances the generalizability of the model. Extensive experiments demonstrate that our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost

Shoe–Floor Interactions in Human Walking With Slips: Modeling and Experiments

Shoe–floor interactions play a crucial role in determining the possibility of potential slip and fall during human walking. Biomechanical and tribological parameters influence the friction characteristics between the shoe sole and the floor and the existing work mainly focus on experimental studies. In this paper, we present modeling, analysis, and experiments to understand slip and force distributions between the shoe sole and floor surface during human walking. We present results for both soft and hard sole material. The computational approaches for slip and friction force distributions are presented using a spring-beam networks model. The model predictions match the experimentally observed sole deformations with large soft sole deformation at the beginning and the end stages of the stance, which indicates the increased risk for slip. The experiments confirm that both the previously reported required coefficient of friction (RCOF) and the deformation measurements in this study can be used to predict slip occurrence. Moreover, the deformation and force distribution results reported in this study provide further understanding and knowledge of slip initiation and termination under various biomechanical conditions

Optothermal approaches to architected functional nanomaterials and nanostructures

The manipulation and engineering of nanomaterials are the core techniques in modern nanotechnology and have been extensively investigated over the past decades. Many optical techniques were developed to manipulate, assemble, and pattern nanomaterials, which have inspired numerous progress in various fields, such as microrobotics, bottom-up nanofabrication, nanomedicine, and microelectronics. With the entropically favorable photon-to-phonon conversion and tailorable opto-thermo-matter coupling, various thermal forces in the light-controlled temperature field can be harnessed to achieve the precise control of nanomaterials at a high spatial and temporal resolution. This Dissertation focuses on optothermal approaches for optical manipulation and structuring of functional nanomaterials and nanostructures on solid substrates for the development of on-chip devices. First, thermophoresis of colloidal species is exploited to optically trap various nanoparticles and biological objects. The assembly and printing of colloidal matter on the substrate from the suspension are demonstrated with the assistance of optothermally controlled depletion forces. Next, by optothermally modulating the nanomaterial-substrate interactions, versatile manipulation and assembly of nanomaterials on solid substrates can be achieved. Precise manipulation of nanomaterials, orbital rotation of nanomotors, and reconfigurable assembly of functional nanostructures on the solid substrate are demonstrated. Last, opto-thermoplasmonic nanolithography is developed for on-demand patterning of a variety of two-dimensional materials through plasmon-enhanced thermal oxidation and sublimation. It is anticipated that the studies presented here provide an ideal platform for studying colloidal science, materials science, and nanophotonics, and the optothermally architected nanomaterials and nanostructures are expected to stimulate more advances in a broad range of fields.Materials Science and Engineerin