Joint Bayesian Gaussian discriminant analysis for speaker verification

State-of-the-art i-vector based speaker verification relies on variants of Probabilistic Linear Discriminant Analysis (PLDA) for discriminant analysis. We are mainly motivated by the recent work of the joint Bayesian (JB) method, which is originally proposed for discriminant analysis in face verification. We apply JB to speaker verification and make three contributions beyond the original JB. 1) In contrast to the EM iterations with approximated statistics in the original JB, the EM iterations with exact statistics are employed and give better performance. 2) We propose to do simultaneous diagonalization (SD) of the within-class and between-class covariance matrices to achieve efficient testing, which has broader application scope than the SVD-based efficient testing method in the original JB. 3) We scrutinize similarities and differences between various Gaussian PLDAs and JB, complementing the previous analysis of comparing JB only with Prince-Elder PLDA. Extensive experiments are conducted on NIST SRE10 core condition 5, empirically validating the superiority of JB with faster convergence rate and 9-13% EER reduction compared with state-of-the-art PLDA.Comment: accepted by ICASSP201

A general poro-elasto-plastic model for poorly consolidated sands

Sand failure and production are likely to occur during oil and gas production, especially in poorly consolidated formations. Most past research focuses on either experimental work to understand sanding mechanisms or simple sand production models to estimate the onset of sand production. Such models are usually not enough to capture sanding behavior in complex situations in the field. In this dissertation, a numerical 3D sand production prediction model is developed based on a general poro-elasto-plastic model for multi-phase fluid flow, which can predict both the onset of sanding and the volume of sand produced. The model is thoroughly validated with multiple analytical solutions. It is also validated with experiment data for the onset of sanding, sand production volume, and cavity shape caused by sand production. The model results are shown to agree well with all these experimentally measured quantities and for the first time predict sanding behavior in complex geometries over a wide range of conditions. From extensive sand production experiments, four distinct cavity shapes have been frequently observed: spiral shear band cavity, V-shape cavity, dog-ear cavity, and slit mode cavity. However, the reasons and the sanding mechanisms responsible for this behavior have not been fully articulated. Results presented here show that the model is capable of capturing all the complicated cavity shapes, and provide qualitative guidelines to define the conditions under which each type of cavity will be formed. The effect of different well completions on sand failure and production have been investigated with the model. Results show the potential advantage of using frac-packs for reducing the fluid pressure gradients and redistributing stresses. In addition, the impact of rock and fluid properties on sanding behavior has been studied to show the importance of mechanical failure and fluid erosion on sanding. Wells with multiple oriented perforations are analyzed to study the effect of perforation design on sand production. The application of the model has been further extended to quantitatively explain some field observations, including: delayed sanding in gas wells, sanding caused by water breakthrough, and water hammer effects. Simulation results suggest that rock strengthening by water evaporation and non-Darcy effects in gas flow can delay sand production. On the other hand, sanding after water breakthrough can be explained by accelerating sand failure and fluid erosion due to an increase in the water saturation. The impact of water hammer on sand failure has been investigated to optimize subsurface valve location and shut-in procedure. Finally, the sand production model is applied to a field case for HPHT wells to study sanding mechanisms for different sanding behavior observed in two wells in similar locations in the field. Simulation results show that rock heterogeneity and natural fractures are the most likely reasons for sand production in this field. The difference in onset of sanding from the two wells can be explained by different in-situ stresses, while the difference in severity of sanding can be explained by differences in pressure drawdown and the orientation of perforations. The critical drawdown during reservoir depletion are determined under different conditions from the model to guide drawdown management so as to prevent sanding issues.Petroleum and Geosystems Engineerin

G2-MonoDepth: A General Framework of Generalized Depth Inference from Monocular RGB+X Data

Monocular depth inference is a fundamental problem for scene perception of robots. Specific robots may be equipped with a camera plus an optional depth sensor of any type and located in various scenes of different scales, whereas recent advances derived multiple individual sub-tasks. It leads to additional burdens to fine-tune models for specific robots and thereby high-cost customization in large-scale industrialization. This paper investigates a unified task of monocular depth inference, which infers high-quality depth maps from all kinds of input raw data from various robots in unseen scenes. A basic benchmark G2-MonoDepth is developed for this task, which comprises four components: (a) a unified data representation RGB+X to accommodate RGB plus raw depth with diverse scene scale/semantics, depth sparsity ([0%, 100%]) and errors (holes/noises/blurs), (b) a novel unified loss to adapt to diverse depth sparsity/errors of input raw data and diverse scales of output scenes, (c) an improved network to well propagate diverse scene scales from input to output, and (d) a data augmentation pipeline to simulate all types of real artifacts in raw depth maps for training. G2-MonoDepth is applied in three sub-tasks including depth estimation, depth completion with different sparsity, and depth enhancement in unseen scenes, and it always outperforms SOTA baselines on both real-world data and synthetic data.Comment: 18 pages, 16 figure

An original model for multi-target learning of logical rules for knowledge graph reasoning

Large-scale knowledge graphs provide structured representations of human knowledge. However, as it is impossible to collect all knowledge, knowledge graphs are usually incomplete. Reasoning based on existing facts paves a way to discover missing facts. In this paper, we study the problem of learning logical rules for reasoning on knowledge graphs for completing missing factual triplets. Learning logical rules equips a model with strong interpretability as well as the ability to generalize to similar tasks. We propose a model able to fully use training data which also considers multi-target scenarios. In addition, considering the deficiency in evaluating the performance of models and the quality of mined rules, we further propose two novel indicators to help with the problem. Experimental results empirically demonstrate that our model outperforms state-of-the-art methods on five benchmark datasets. The results also prove the effectiveness of the indicators