Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space

Diverse human motion prediction aims at predicting multiple possible future pose sequences from a sequence of observed poses. Previous approaches usually employ deep generative networks to model the conditional distribution of data, and then randomly sample outcomes from the distribution. While different results can be obtained, they are usually the most likely ones which are not diverse enough. Recent work explicitly learns multiple modes of the conditional distribution via a deterministic network, which however can only cover a fixed number of modes within a limited range. In this paper, we propose a novel sampling strategy for sampling very diverse results from an imbalanced multimodal distribution learned by a deep generative model. Our method works by generating an auxiliary space and smartly making randomly sampling from the auxiliary space equivalent to the diverse sampling from the target distribution. We propose a simple yet effective network architecture that implements this novel sampling strategy, which incorporates a Gumbel-Softmax coefficient matrix sampling method and an aggressive diversity promoting hinge loss function. Extensive experiments demonstrate that our method significantly improves both the diversity and accuracy of the samplings compared with previous state-of-the-art sampling approaches. Code and pre-trained models are available at https://github.com/Droliven/diverse_sampling.Comment: Paper and Supp of our work accepted by ACM MM 202

Weakly supervised deep semantic segmentation using CNN and ELM with semantic candidate regions.

The task of semantic segmentation is to obtain strong pixel-level annotations for each pixel in the image. For fully supervised semantic segmentation, the task is achieved by a segmentation model trained using pixel-level annotations. However, the pixel-level annotation process is very expensive and time-consuming. To reduce the cost, the paper proposes a semantic candidate regions trained extreme learning machine (ELM) method with image-level labels to achieve pixel-level labels mapping. In this work, the paper casts the pixel mapping problem into a candidate region semantic inference problem. Specifically, after segmenting each image into a set of superpixels, superpixels are automatically combined to achieve segmentation of candidate region according to the number of image-level labels. Semantic inference of candidate regions is realized based on the relationship and neighborhood rough set associated with semantic labels. Finally, the paper trains the ELM using the candidate regions of the inferred labels to classify the test candidate regions. The experiment is verified on the MSRC dataset and PASCAL VOC 2012, which are popularly used in semantic segmentation. The experimental results show that the proposed method outperforms several state-of-the-art approaches for deep semantic segmentation

Door and window detection in 3D point cloud of indoor scenes.

This paper proposes a 3D-2D-3D algorithm for doors and windows detection in 3D indoor environment of point cloud data. Firstly, by setting up a virtual camera in the middle of this 3D environment, a set of pictures are taken from different angles by rotating the camera, so that corresponding 2D images can be generated. Next, these images are used to detect and identify the positions of doors and windows in the space. To obtain point cloud data containing the doors and windows position information, the 2D information are then mapped back to the origin 3D point cloud environment. Finally, by processing the contour lines and crossing points, the features of doors and windows through the position information are optimized. The experimental results show that this "global-local" approach is efficient when detecting and identifying the location of doors and windows in 3D point cloud environment

Multiple-Crop Human Mesh Recovery with Contrastive Learning and Camera Consistency in A Single Image

We tackle the problem of single-image Human Mesh Recovery (HMR). Previous approaches are mostly based on a single crop. In this paper, we shift the single-crop HMR to a novel multiple-crop HMR paradigm. Cropping a human from image multiple times by shifting and scaling the original bounding box is feasible in practice, easy to implement, and incurs neglectable cost, but immediately enriches available visual details. With multiple crops as input, we manage to leverage the relation among these crops to extract discriminative features and reduce camera ambiguity. Specifically, (1) we incorporate a contrastive learning scheme to enhance the similarity between features extracted from crops of the same human. (2) We also propose a crop-aware fusion scheme to fuse the features of multiple crops for regressing the target mesh. (3) We compute local cameras for all the input crops and build a camera-consistency loss between the local cameras, which reward us with less ambiguous cameras. Based on the above innovations, our proposed method outperforms previous approaches as demonstrated by the extensive experiments

Palladium, platinum, selenium and tellurium enrichment in the Jiguanzui-Taohuazui Cu-Au Deposit, Edong Ore District: Distribution and comparison with Cu-Mo deposits

The Jiguanzui-Taohuazui Cu-Au deposit is located in the Edong ore district, Middle–Lower Yangtze River metallogenic belt, eastern China. The deposit is palladium, platinum, selenium and tellurium enriched; however, the distribution of these metals is unclear. Three mineral assemblages of ore in the deposit have been identified, namely: a magnetite-bornite-chalcopyrite-(hematite) assemblage (Mt-Bn-Cp-Hm), a chalcopyrite-pyrite assemblage (Cp-Py), and a pyrite-chalcopyrite-(sphalerite) assemblage (Py-Cp-Sph). Forty-eight bulk ore assay results show high concentrations of up to 66.9 ppb for Pd, 5.9 ppb for Pt, 150 ppm for Se and 249 ppm for Te. The high temperature Mt-Bn-Cp-Hm assemblage (530–380 °C) is enriched in Pt and Pd, whereas the Py-Cp-Sph assemblage in the marble-replacement ore (300–220 °C) hosts the major Se and Te mineralization. Palladium, Pt, and Se are mostly hosted in sulfide minerals, whereas Te is hosted in tellurides and Bi-Te-S sulfosalt minerals. Building on previous experimental and thermodynamic calculations, we propose the major controls on the Pd and Pt distribution in the deposit are temperature and salinity, whereas the Se and Te mineralization is promoted by the precipitation of major sulfide phases such as pyrite, chalcopyrite and sphalerite. A comparison of the ores from the Jiguanzui-Taohuazui Cu-Au and Tongshankou Cu-Mo deposits in the Edong ore district shows that the Cu-Au deposit has higher PGE and Te, but similar Se concentrations. This scenario is consistent with the average grades and bulk ore contents of these elements from global (oxidized) porphyry (±skarn) Cu deposits. This suggests that the saturation of magmatic sulfides in the magma chamber as a result of higher proportion of crustal S-rich and/or reduced material contamination can be detrimental for PGE and Te enrichment processes, and thus, Cu-Au porphyry (±skarn) deposits have more potential for higher Pd and Te concentrations than the Cu-Mo deposits

Treatment of vulval condyloma with a combination of paiteling and cryotherapy, and its effect on late recurrence

Purpose: To study the clinical effectiveness of a combination of Paiteling and cryotherapy in the treatment of vulval condyloma acuminatum (VCA), and its effect on late recurrence. Methods: Eighty-six VCA patients were chosen as research subjects, and were randomized into group A and group B. Group A patients were treated with combination of Paiteling and cryotherapy, while group B patients received cryotherapy only. The clinical effects of the two treatment methods on VCA were evaluated by measuring area of damaged skin, levels of interleukin-6 (IL-6) and C-reactive protein (CRP), as well as degree of recurrence of VCA in the two groups, before and after treatment. Results: Total clinical treatment effectiveness in group A was significantly higher compared with group B (p < 0.05). After treatment, the area of damaged skin, and levels of IL-6 and CRP were markedly lower in group A than in group B (p < 0.001). After 6 months of treatment, disease control was higher in group A than in group B (p < 0.05). There was also a lower incidence of adverse reactions in group A than in group B (p < 0.05). Conclusion: These results indicate that the combination of Paiteling and cryotherapy is more effective than cryotherapy alone in improving treatment effectiveness and reducing late recurrence of VCA. Therefore, the combined treatment has potentials clinical application in the management of VCA

Pan-cancer analysis of super enhancer-induced PRR7-AS1 as a potential prognostic and immunological biomarker

Introduction: Systematic pan-cancer analysis of the roles and regulatory mechanisms for PRR7-AS1 is currently not available.Methods: In the present study, a comprehensive bioinformatic approach was used to mine the underlying oncogenic effects of PRR7-AS1, including expression status, prognostic value and immune characteristics.Results: We discovered that PRR7-AS1 expression was remarkably upregulated in most cancer types and exhibited a negative correlation with the prognosis. Furthermore, PRR7-AS1 expression was inversely connected with the majority of tumor-infiltrating immune cells, immune scores and immune checkpoint gene expression in pancancer. There was also a significant correlation between PRR7-AS1 expression status and tumor mutational burden, microsatellite instability, and neoantigens in certain tumors. PRR7-AS1 had the best predictive power for immune checkpoint blockade efficacy compared to other well-recognized biomarkers. PRR7-AS1 overexpression could affect cytotoxic T cells-mediated antitumor responses. Functional enrichment analysis revealed that PRR7-AS1 might be involved in the metabolic pathways. Super enhancer activity might have participated in the regulation of PRR7-AS1 expression. And we constructed the competitive endogenous RNA networks for PRR7-AS1.Discussion: In general, PRR7-AS1 had the potential to be a diagnostic, prognostic and immune biomarker for pan cancer. PRR7-AS1 was correlated with an immunosuppressive microenvironment and was a new potential target for immunotherapy. Epigenetic factors were the driving forces for PRR7-AS1 overexpression in tumors

Accurate Forecasting of Building Energy Consumption Via A Novel Ensembled Deep Learning Method Considering the Cyclic Feature

Short-term forecasting of building energy consumption (BEC) is significant for building energy reduction and real-time demand response. In this study, we propose a new method to realize half-hourly BEC prediction. In this new method, to fully utilize the existing data features and to further promote the forecasting performance, we divide the BEC data into the stable (cyclic) and stochastic components, and propose a novel hybrid model to model the stable and stochastic components respectively. The cyclic feature (CF) is extracted via the spectrum analysis, while the stochastic component is approximated by a novel Deep Belief Network (DBN) and Extreme Learning Machine (ELM) based ensembled model (DEEM). This novel hybrid model is named DEEM&nbsp;+&nbsp;CF. Furthermore, two real-world BEC experiments are performed to verify the proposed method. Also, to display the superiorities of the proposed DEEM&nbsp;+&nbsp;CF, this model is compared with the DBN, DBN&nbsp;+&nbsp;CF, ELM, ELM&nbsp;+&nbsp;CF, Support Vector Regression (SVR) and SVR&nbsp;+&nbsp;CF. Experimental results indicate that the CF has a great influence on the promotion of forecasting accuracy for approximately 20%, and DEEM&nbsp;+&nbsp;CF performance is the best among the comparative models, with at least 3%, 6%, 10% better accuracy than the DBN&nbsp;+&nbsp;CF, ELM&nbsp;+&nbsp;CF and SVR&nbsp;+&nbsp;CF respectively under the criteria of MAE. &nbsp;</p