Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification

Spatio-temporal point processes (STPPs) are potent mathematical tools for modeling and predicting events with both temporal and spatial features. Despite their versatility, most existing methods for learning STPPs either assume a restricted form of the spatio-temporal distribution, or suffer from inaccurate approximations of the intractable integral in the likelihood training objective. These issues typically arise from the normalization term of the probability density function. Moreover, current techniques fail to provide uncertainty quantification for model predictions, such as confidence intervals for the predicted event's arrival time and confidence regions for the event's location, which is crucial given the considerable randomness of the data. To tackle these challenges, we introduce SMASH: a Score MAtching-based pSeudolikeliHood estimator for learning marked STPPs with uncertainty quantification. Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of marked STPPs through score-matching and offers uncertainty quantification for the predicted event time, location and mark by computing confidence regions over the generated samples. The superior performance of our proposed framework is demonstrated through extensive experiments in both event prediction and uncertainty quantification

Multi-view 3D Face Reconstruction Based on Flame

At present, face 3D reconstruction has broad application prospects in various fields, but the research on it is still in the development stage. In this paper, we hope to achieve better face 3D reconstruction quality by combining multi-view training framework with face parametric model Flame, propose a multi-view training and testing model MFNet (Multi-view Flame Network). We build a self-supervised training framework and implement constraints such as multi-view optical flow loss function and face landmark loss, and finally obtain a complete MFNet. We propose innovative implementations of multi-view optical flow loss and the covisible mask. We test our model on AFLW and facescape datasets and also take pictures of our faces to reconstruct 3D faces while simulating actual scenarios as much as possible, which achieves good results. Our work mainly addresses the problem of combining parametric models of faces with multi-view face 3D reconstruction and explores the implementation of a Flame based multi-view training and testing framework for contributing to the field of face 3D reconstruction

A Novel Hierarchically Porous Polypyrrole Sphere Modified Separator for Lithium-Sulfur Batteries

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6723804/The commercialization of Lithium-sulfur batteries was limited by the polysulfide shuttle effect, and modifying the routine separator was an effective method to solve this problem. In this work, a novel hierarchically porous polypyrrole sphere (PPS) was successfully prepared by using silica as hard-templates. As-prepared PPS was slurry-coated on the separator, which could reduce the polarization phenomenon of the sulfur cathode, and efficiently immobilize polysulfides. As expected, high sulfur utilization was achieved by suppressing the shuttle effect. When tested in the lithium-sulfur battery, it exhibited a high capacity of 855 mAh·g−1 after 100 cycles at 0.2 C, and delivered a reversible capacity of 507 mAh·g−1 at 3 C, showing excellent electrochemical performance

Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability

Because of its streaming nature, recurrent neural network transducer (RNN-T) is a very promising end-to-end (E2E) model that may replace the popular hybrid model for automatic speech recognition. In this paper, we describe our recent development of RNN-T models with reduced GPU memory consumption during training, better initialization strategy, and advanced encoder modeling with future lookahead. When trained with Microsoft's 65 thousand hours of anonymized training data, the developed RNN-T model surpasses a very well trained hybrid model with both better recognition accuracy and lower latency. We further study how to customize RNN-T models to a new domain, which is important for deploying E2E models to practical scenarios. By comparing several methods leveraging text-only data in the new domain, we found that updating RNN-T's prediction and joint networks using text-to-speech generated from domain-specific text is the most effective.Comment: Accepted by Interspeech 202