Lightweight super resolution network for point cloud geometry compression

This paper presents an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network. The proposed method involves decomposing a point cloud into a base point cloud and the interpolation patterns for reconstructing the original point cloud. While the base point cloud can be efficiently compressed using any lossless codec, such as Geometry-based Point Cloud Compression, a distinct strategy is employed for handling the interpolation patterns. Rather than directly compressing the interpolation patterns, a lightweight super-resolution network is utilized to learn this information through overfitting. Subsequently, the network parameter is transmitted to assist in point cloud reconstruction at the decoder side. Notably, our approach differentiates itself from lookup table-based methods, allowing us to obtain more accurate interpolation patterns by accessing a broader range of neighboring voxels at an acceptable computational cost. Experiments on MPEG Cat1 (Solid) and Cat2 datasets demonstrate the remarkable compression performance achieved by our method.Comment: 10 pages, 3 figures, 2 tables, and 27 reference

Infrared High-Index Coating Materials, PbTe and Pb1−xGexTe: Properties and Applications

The greater value of refractive index for high-index layers in thin-film interference filters operating in the infrared has an incomparable advantage. Lead telluride (PbTe), which is much superior to other infrared high-index coating materials due to its high index and advantage of fundamental absorption edges, has played an important role in filters employed in the infrared radiometer and other instruments launched in space atmosphere sounding research projects. In this chapter, we summarized some recent achievements in the investigations into another infrared high-index coating material—lead germanium telluride (Pb1−xGexTe), a pseudo-binary alloy of PbTe and GeTe. It can be revealed that the layers of Pb1−xGexTe exhibit the tunable optical properties, such as temperature coefficient of refractive index and fundamental absorption edge, as well as mechanical properties, such as the hardness and Young’s modulus, corresponding to its intrinsic ferroelectric phase transition. Some important applications in thin-film interference filters were also demonstrated for its tremendous potential, such as a stable narrow bandpass interference filter without temperature-induced wavelength shift and a tunable infrared short wavelength cutoff filter. Furthermore, it is also revealed that electron beam evaporation is a more effective congruent-transfer technique to deposit the layers of Pb1−xGexTe

Double Policy Network for Aspect Sentiment Triplet Extraction (Student Abstract)

Aspect Sentiment Triplet Extraction (ASTE) is the task to extract aspects, opinions and associated sentiments from sentences. Previous studies do not adequately consider the complicated interactions between aspect and opinion terms in both extraction logic and strategy. We present a novel Double Policy Network with Multi-Tag based Reward model (DPN-MTR), which adopts two networks ATE, TSOTE and a Trigger Mechanism to execute ASTE task following a more logical framework. A Multi-Tag based reward is also proposed to solve the limitations of existing studies for identifying aspect/opinion terms with multiple tokens (one term may consist of two or more tokens) to a certain extent. Extensive experiments are conducted on four widely-used benchmark datasets, and demonstrate the effectiveness of our model in generally improving the performance on ASTE significantly

A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization

Sales forecasting is a highly practical application of time series prediction. It is used to help enterprises identify and utilize information to reduce costs and maximize profits. For example, in numerous manufacturing enterprises, sales forecasting serves as a key indicator for inventory optimization and directly influences the level of cost savings. However, existing research methods mainly focus on detecting sequences and local correlations from multivariate time series (MTS), but seldom consider modeling the distinct information among the time series within MTS. The prediction accuracy of sales time series is significantly influenced by the dynamic and complex environment, so identifying the distinct signals between different time series within a sales MTS is more important. In order to extract more valuable information from sales series and to enhance the accuracy of sales prediction, we devised a universality–distinction mechanism (UDM) framework that can predict future multi-step sales. Universality represents the instinctive features of sequences and correlation patterns of sales with similar contexts. Distinction corresponds to the fluctuations in a specific time series due to complex or unobserved influencing factors. In the mechanism, a query-sparsity measurement (QSM)-based attention calculation method is proposed to improve the efficiency of the proposed model in processing large-scale sales MTS. In addition, to improve the specific decision-making scenario of inventory optimization and ensure stable accuracy in multi-step prediction, we use a joint Pin-DTW (Pinball loss and Dynamic Time Warping) loss function. Through experiments on the public Cainiao dataset, and via our cooperation with Galanz, we are able to demonstrate the effectiveness and practical value of the model. Compared with the best baseline, the improvements are 57.27%, 50.68%, and 35.26% on the Galanz dataset and 16.58%, 6.07%, and 5.27% on the Cainiao dataset, in terms of the MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Squared Error)

Continual Learning for Blind Image Quality Assessment

The explosive growth of image data facilitates the fast development of image processing and computer vision methods for emerging visual applications, meanwhile introducing novel distortions to the processed images. This poses a grand challenge to existing blind image quality assessment (BIQA) models, failing to continually adapt to such subpopulation shift. Recent work suggests training BIQA methods on the combination of all available human-rated IQA datasets. However, this type of approach is not scalable to a large number of datasets, and is cumbersome to incorporate a newly created dataset as well. In this paper, we formulate continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data. We first identify five desiderata in the new setting with a measure to quantify the plasticity-stability trade-off. We then propose a simple yet effective method for learning BIQA models continually. Specifically, based on a shared backbone network, we add a prediction head for a new dataset, and enforce a regularizer to allow all prediction heads to evolve with new data while being resistant to catastrophic forgetting of old data. We compute the quality score by an adaptive weighted summation of estimates from all prediction heads. Extensive experiments demonstrate the promise of the proposed continual learning method in comparison to standard training techniques for BIQA. We made the code publicly available at https://github.com/zwx8981/BIQA_CL.Comment: Accepted to IEEE TPAM

Gadolinium-based MR cisternography with prepontine cisternal routine for evaluating distribution pattern of intrathecal targeted drug delivery in pain management

AbstractGadolinium-based MR cisternography has been mainly applied in clinical evaluation of cerebrospinal fluid leaking, that is conducted by intrathecal administration of contrast media. Recently, we have reported one novel technique of intrathecal targeted drug delivery with prepontine cisternal routine to treat orofacial cancer pain. The aim of this study was to examine the distribution pattern of this intrathecal drug delivery strategy. Here, we introduce one case who suffered severe orofacial pain caused by sublingual gland tumor, and successfully attenuated by prepontine cisternal administration of analgesic agents. To assess the distribution of intrathecal drugs, postoperative MR images of brain, cervical, thoracic, and lumbar segments in axial, coronal, and sagittal planes were obtained after application of gadolinium. The perfusion rate of contrast medium was set at 0.01 mmol per hour for 24 hours prior to MR scanning. In the T1-weighted images, we can identify contrast spread not only locating around the site of the intrathecal catheter tip, but also concentrated to the lateral sides. None obvious side effect was found after intrathecal injection of contrast media. Thus, our finding demonstrated the local distribution phenomenon of intrathecal drugs through prepontine cisternal access, and the bilateral perfusion pattern may provide insights underlying the analgesic mechanism of trigeminal pain provided by this novel intrathecal therapy. Gadolinium-based MR cisternography may serve as a potential tool to confirm the therapeutic effect of intrathecal targeted drug delivery via prepontine cisternal routine in orofacial pain management