DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration

We present DeepICP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods. Different from other keypoint based methods where a RANSAC procedure is usually needed, we implement the use of various deep neural network structures to establish an end-to-end trainable network. Our keypoint detector is trained through this end-to-end structure and enables the system to avoid the inference of dynamic objects, leverages the help of sufficiently salient features on stationary objects, and as a result, achieves high robustness. Rather than searching the corresponding points among existing points, the key contribution is that we innovatively generate them based on learned matching probabilities among a group of candidates, which can boost the registration accuracy. Our loss function incorporates both the local similarity and the global geometric constraints to ensure all above network designs can converge towards the right direction. We comprehensively validate the effectiveness of our approach using both the KITTI dataset and the Apollo-SouthBay dataset. Results demonstrate that our method achieves comparable or better performance than the state-of-the-art geometry-based methods. Detailed ablation and visualization analysis are included to further illustrate the behavior and insights of our network. The low registration error and high robustness of our method makes it attractive for substantial applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results updated, accepted by ICCV 201

PULP AND FIBER CHARACTERIZATION OF WHEAT STRAW AND EUCALUPTUS PULPS - A COMPARISON

The response to refining of wheat straw and eucalyptus pulps as well as the relationships between refining, fiber properties, and paper properties are described in this paper. Pulps were bleached applying different bleaching sequences and thereafter refined to varying degrees. Pulp and fiber properties were investigated and set into relation to the final sheet properties. The results show that wheat straw pulps respond to refining more easily than eucalyptus pulps and that the differences are due mainly to morphological and ultrastructural differences as well as fines content and xylan content. The development of strength properties of the different pulps was found to be strongly correlated to the number of dislocations, i.e. weak points in the fiber wall, as well as to the morphological appearance of the pulp fibers after refining. A higher initial number and a faster development of dislocations together with the creation of large amounts of fines explain the slower and lower development of strength properties of wheat straw pulps than of eucalyptus pulps. Removal of fines from wheat straw pulps improved not only the drainability of the pulp suspension but also the mechanical and optical sheet properties. This indicates that the fines in the wheat straw pulps act mainly as filler with low bonding properties. The fact that fractionated D(EOP)D wheat straw pulps can deliver good mechanical sheet properties at very good drainability with no or only minor refining is very interesting when evaluating the potential of replacing or partially replacing eucalyptus with domestic Chinese raw materials in furnishes for production of different paper products

Evidence for Majorana bound state in an iron-based superconductor

The search for Majorana bound state (MBS) has recently emerged as one of the most active research areas in condensed matter physics, fueled by the prospect of using its non-Abelian statistics for robust quantum computation. A highly sought-after platform for MBS is two-dimensional topological superconductors, where MBS is predicted to exist as a zero-energy mode in the core of a vortex. A clear observation of MBS, however, is often hindered by the presence of additional low-lying bound states inside the vortex core. By using scanning tunneling microscope on the newly discovered superconducting Dirac surface state of iron-based superconductor FeTe1-xSex (x = 0.45, superconducting transition temperature Tc = 14.5 K), we clearly observe a sharp and non-split zero-bias peak inside a vortex core. Systematic studies of its evolution under different magnetic fields, temperatures, and tunneling barriers strongly suggest that this is the case of tunneling to a nearly pure MBS, separated from non-topological bound states which is moved away from the zero energy due to the high ratio between the superconducting gap and the Fermi energy in this material. This observation offers a new, robust platform for realizing and manipulating MBSs at a relatively high temperature.Comment: 27 pages, 11 figures, supplementary information include

Nearly quantized conductance plateau of vortex zero mode in an iron-based superconductor

Majorana zero-modes (MZMs) are spatially-localized zero-energy fractional quasiparticles with non-Abelian braiding statistics that hold a great promise for topological quantum computing. Due to its particle-antiparticle equivalence, an MZM exhibits robust resonant Andreev reflection and 2e2/h quantized conductance at low temperature. By utilizing variable-tunnel-coupled scanning tunneling spectroscopy, we study tunneling conductance of vortex bound states on FeTe0.55Se0.45 superconductors. We report observations of conductance plateaus as a function of tunnel coupling for zero-energy vortex bound states with values close to or even reaching the 2e2/h quantum conductance. In contrast, no such plateau behaviors were observed on either finite energy Caroli-de Genne-Matricon bound states or in the continuum of electronic states outside the superconducting gap. This unique behavior of the zero-mode conductance reaching a plateau strongly supports the existence of MZMs in this iron-based superconductor, which serves as a promising single-material platform for Majorana braiding at a relatively high temperature

Complexation With Polysaccharides Enhanced Polyphenol Gastrointestinal Stability and Activity

Fruits and vegetables contain dietary polyphenols and polysaccharides. Accumulating evidence suggests that polyphenol- containing whole foods are protective against inflammation-promoted chronic colonic diseases. However, isolated polyphenols are less stable and may not confer the same gastrointestinal health benefits as that of the whole food matrix. Therefore, we hypothesized that the complex- ation of anthocyanins, a class of polypheonols, with polysaccharides would enhance colonic concentration and stability of anthocyanins, and attenuate impaired barrier function

Multi-Granularity Representation Learning for Sketch-based Dynamic Face Image Retrieval

In specific scenarios, face sketch can be used to identify a person. However, drawing a face sketch often requires exceptional skill and is time-consuming, limiting its widespread applications in actual scenarios. The new framework of sketch less face image retrieval (SLFIR)[1] attempts to overcome the barriers by providing a means for humans and machines to interact during the drawing process. Considering SLFIR problem, there is a large gap between a partial sketch with few strokes and any whole face photo, resulting in poor performance at the early stages. In this study, we propose a multigranularity (MG) representation learning (MGRL) method to address the SLFIR problem, in which we learn the representation of different granularity regions for a partial sketch, and then, by combining all MG regions of the sketches and images, the final distance was determined. In the experiments, our method outperformed state-of-the-art baselines in terms of early retrieval on two accessible datasets. Codes are available at https://github.com/ddw2AIGROUP2CQUPT/MGRL.Comment: 5 pages,5 figure

Graph Out-of-Distribution Generalization with Controllable Data Augmentation

Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. However, due to the selection bias of training and testing data (e.g., training on small graphs and testing on large graphs, or training on dense graphs and testing on sparse graphs), distribution deviation is widespread. More importantly, we often observe \emph{hybrid structure distribution shift} of both scale and density, despite of one-sided biased data partition. The spurious correlations over hybrid distribution deviation degrade the performance of previous GNN methods and show large instability among different datasets. To alleviate this problem, we propose \texttt{OOD-GMixup} to jointly manipulate the training distribution with \emph{controllable data augmentation} in metric space. Specifically, we first extract the graph rationales to eliminate the spurious correlations due to irrelevant information. Secondly, we generate virtual samples with perturbation on graph rationale representation domain to obtain potential OOD training samples. Finally, we propose OOD calibration to measure the distribution deviation of virtual samples by leveraging Extreme Value Theory, and further actively control the training distribution by emphasizing the impact of virtual OOD samples. Extensive studies on several real-world datasets on graph classification demonstrate the superiority of our proposed method over state-of-the-art baselines.Comment: Under revie

Fiber Evolution during Alkaline Treatment and Its Impact on Handsheet Properties

To understand the swelling effects of alkaline treatment on the morphological properties of fibers and physical properties of handsheets, bleached softwood kraft pulp was treated with NaOH at different concentrations. The results showed that the fiber swelling increased, but the shrinkage and elongation of the paper at a NaOH concentration of 6% or higher did not improve. Dissolution of amorphous material occurred during the treatment together with peeling reactions. The fiber length and shape factor decreased and the fines content increased with an increasing alkali concentration. The cellulose crystallinity decreased with an increasing NaOH concentration. This was confirmed by X-ray diffractometry, which also showed that some cellulose I was converted to cellulose II, especially at higher NaOH concentrations (\u3e 9%). The fiber curl and kink indices increased and the handsheet density decreased with an increasing NaOH concentration. However, the tensile index decreased more steeply than the density with an increasing NaOH concentration, possibly because of the lower number and strength of the interfiber bonds, increased kinks, and reduced fiber strength and length. The handsheet extensibility first increased and subsequently decreased as the NaOH concentration increased, which indicated that well-controlled NaOH treatment could be used to improve the extensibility of paper