Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search

Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have only been applied to standard ad hoc retrieval tasks over web pages and newswire documents. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network) a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A pooling-based similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011--2014 show that our model significantly outperforms prior feature-based as well and existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models.Comment: AAAI 2019, 10 page

Time lapsed AVAZ seismic modeling research on CO2 storage monitoring

CCUS (Carbon Capture, Utilization and Storage) now is a lead way to reduce greenhouse effect such as carbon dioxide emission in the world. This paper presents an integrated overview of seismic monitoring technology when CO2 injection process. Mainly is time-lapse seismic method .Time-lapse seismic method is a feasible way to monitor CO2 injection process when CO2 interaction with minerals, which is proved an effective method in CCUS experiments. AVAZ (Amplitude versus Azimuth) seismic method is proved a useful tool to indentify CO2 injection process, which can detect fluid-induced seismic anisotropic response and locating where CO2 flow to in reservoirs, therefore, it’s an effective way to monitor CO2 flow in CO2 monitoring process. Since we develop AVAZ modelling experiment base on rock physics theory to modeling the time-lapse AVAZ seismic reservoir response. The research show fluid saturation and pressure behave two main factors influence modeling seismic AVAZ response. Meanwhile the AVAZ response can also be detect by seismic AVAZ data

Nonexistence of Periodic Orbits for Predator-Prey System with Strong Allee Effect in Prey Populations

We use Dulac criterion to prove the nonexistence of periodic orbits for a class of general predator-prey system with strong Allee effect in the prey population growth. This completes the global bifurcation analysis of typical predator-prey systems with strong Allee effect for all possible parameters

Towards Better Data Exploitation In Self-Supervised Monocular Depth Estimation

Depth estimation plays an important role in the robotic perception system. Self-supervised monocular paradigm has gained significant attention since it can free training from the reliance on depth annotations. Despite recent advancements, existing self-supervised methods still underutilize the available training data, limiting their generalization ability. In this paper, we take two data augmentation techniques, namely Resizing-Cropping and Splitting-Permuting, to fully exploit the potential of training datasets. Specifically, the original image and the generated two augmented images are fed into the training pipeline simultaneously and we leverage them to conduct self-distillation. Additionally, we introduce the detail-enhanced DepthNet with an extra full-scale branch in the encoder and a grid decoder to enhance the restoration of fine details in depth maps. Experimental results demonstrate our method can achieve state-of-the-art performance on the KITTI benchmark, with both raw ground truth and improved ground truth. Moreover, our models also show superior generalization performance when transferring to Make3D and NYUv2 datasets. Our codes are available at https://github.com/Sauf4896/BDEdepth.Comment: 8 pages, 6 figure

DistilXLSR: A Light Weight Cross-Lingual Speech Representation Model

Multilingual self-supervised speech representation models have greatly enhanced the speech recognition performance for low-resource languages, and the compression of these huge models has also become a crucial prerequisite for their industrial application. In this paper, we propose DistilXLSR, a distilled cross-lingual speech representation model. By randomly shuffling the phonemes of existing speech, we reduce the linguistic information and distill cross-lingual models using only English data. We also design a layer-jumping initialization method to fully leverage the teacher's pre-trained weights. Experiments on 2 kinds of teacher models and 15 low-resource languages show that our method can reduce the parameters by 50% while maintaining cross-lingual representation ability. Our method is proven to be generalizable to various languages/teacher models and has the potential to improve the cross-lingual performance of the English pre-trained models.Comment: Accepted by INTERSPEECH 202

Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development

Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested in the picture of the future volume of commuters before constructing a new station, and estimate how would it affect other areas. In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. For example, an accurate PPF predictor can provide invaluable knowledge to designers, such as the advice of station scales and influences on other areas, etc. To address this problem, we propose a multi-view localized correlation learning method. The core idea of our strategy is to learn the passenger flow correlations between the target areas and their localized areas with adaptive-weight. To improve the prediction accuracy, other domain knowledge is involved via a multi-view learning process. We conduct intensive experiments to evaluate the effectiveness of our method with real-world official transportation datasets. The results demonstrate that our method can achieve excellent performance compared with other available baselines. Besides, our method can provide an effective solution to the cold-start problem in the recommender system as well, which proved by its outperformed experimental results

The Current Status and Future Prospects for Conversion Therapy in the Treatment of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related deaths worldwide. In China, most HCC patients are diagnosed with advanced disease and in these cases surgery is challenging. Conversion therapy can be used to change unresectable HCC into resectable disease and is a potential breakthrough treatment strategy. The resection rate for unresectable advanced HCC has recently improved as a growing number of patients have benefited from conversion therapy. While conversion therapy is at an early stage of development, progress in patient selection, optimum treatment methods, and the timing of surgery have the potential to deliver significant benefits. In this article, we review the current evidence and clinical experience of conversion therapy in HCC. General conversion modalities such as systemic treatments (systemic chemotherapy, targeted therapy, or immunotherapy), locoregional therapy (transarterial chemoembolization, hepatic arterial infusion chemotherapy, or selective internal radiation therapy), and combination therapy were summarized. We also discuss the current challenges of conversion therapy and provide identify areas for future research to improve the development of conversion therapy in advanced HCC

High frequency 2n pollen formation in black poplar (Populus nigra L.) induced by colchicine

Populus nigra L. is one of the most important male genetic donors in populus genetic improvement and tree breeding over the world. Many excellent Populus nigra clones are identified as triploids potentially obtaining by hybridization of 2n pollen and normal oogamete. This study revealed the cytological mechanism of 2n pollen formation in Populus nigra L. for the first time and concluded the best treated combination of colchicine treatment, which obtained 2n pollen with the highest rate reaching at 87.11% (even 100% 2n pollen in some floral buds) which made the polyploid hybridization utilizing 2n pollen in section Aigeiros possible