Venue2Vec: An efficient embedding model for fine-grained user location prediction in geo-social networks

Geo-Social Networks (GSN) significantly improve location-aware capability of services by offering geo-located content based on the huge volumes of data generated in the GSN. The problem of user location prediction based on user-generated data in GSN has been extensively studied. However, existing studies are either concerning predicting users' next check-in location or predicting their future check-in location at a given time with coarse granularity. A unified model that can predict both scenarios with fine granularity is quite rare. Also, due to the heterogeneity of multiple factors associated with both locations and users, how to efficiently incorporate these information still remains challenging. Inspired by the recent success of word embedding in natural language processing, in this paper, we propose a novel embedding model called Venue2Vec which automatically incorporates temporal-spatial context, semantic information, and sequential relations for fine-grained user location prediction. Locations of the same type, and those that are geographically close or often visited successively by users will be situated closer within the embedding space. Based on our proposed Venue2Vec model, we design techniques that allow for predicting a user's next check-in location, and also their future check-in location at a given time. We conduct experiments on three real-world GSN datasets to verify the performance of the proposed model. Experimental results on both tasks show that Venue2Vec model outperforms several state-of-the-art models on various evaluation metrics. Furthermore, we show how the Venue2Vec model can be more time-efficient due to being parallelizable

Progress of Chinese research in physical oceanography of the Southern Ocean

Oceanographic surveying has been one of the key missions of the Chinese National Antarctic Research Expedition since 1984. Using the field data obtained in these surveys and the results from remote sensing and numerical models, Chinese physical oceanographers have investigated the water masses, fronts and circulation patterns in the Southern Ocean. The results of nearly 30 years of research are summarized in this paper. Most oceanographic observations by Chinese researchers have been conducted in Prydz Bay and the adjacent seas. CTD (Conductivity Temperature and Depth) data, collected during the past 20 years, have been applied to study several features of the water masses in this region: The spatial variation of warm summer surface water, the northward extension of shelf water, the flow of ice shelf water from the cavity beneath the Amery Ice Shelf, the upwelling of the Circumpolar Deep Water, and the formation of the Antarctic Bottom Water. The circulation and its dynamic factors have been analyzed with dynamic heights calculated from CTD data as well as by numerical models. The structure and strength of the fronts in the southeast Indian Ocean and the Drake Passage were investigated with underway XBT/XCTD (Expendable Bathythermograph/ Expendable CTD) and ADCP (Acoustic Doppler Current Profiler) data. Their interannual variations have been determined and the factors of influence, especially the atmospheric forcing and mesoscale oceanic processes, were studied using remote sensing data. The dynamic mechanism of the Antarctic Circumpolar Current (ACC) was analyzed by theoretical models. The transport and pattern of the ACC have been well reproduced by coupled sea ice-ocean models. Additional details of ACC variability were identified based on satellite altimeter data. The response of the ACC to climate change was studied using reanalysis data. Prospects for future research are presented at the end of this paper

No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation

Online Social Network (OSN) has become a hotbed of fake news due to the low cost of information dissemination. Although the existing methods have made many attempts in news content and propagation structure, the detection of fake news is still facing two challenges: one is how to mine the unique key features and evolution patterns, and the other is how to tackle the problem of small samples to build the high-performance model. Different from popular methods which take full advantage of the propagation topology structure, in this paper, we propose a novel framework for fake news detection from perspectives of semantic, emotion and data enhancement, which excavates the emotional evolution patterns of news participants during the propagation process, and a dual deep interaction channel network of semantic and emotion is designed to obtain a more comprehensive and fine-grained news representation with the consideration of comments. Meanwhile, the framework introduces a data enhancement module to obtain more labeled data with high quality based on confidence which further improves the performance of the classification model. Experiments show that the proposed approach outperforms the state-of-the-art methods

MC-SpEx: Towards Effective Speaker Extraction with Multi-Scale Interfusion and Conditional Speaker Modulation

The previous SpEx+ has yielded outstanding performance in speaker extraction and attracted much attention. However, it still encounters inadequate utilization of multi-scale information and speaker embedding. To this end, this paper proposes a new effective speaker extraction system with multi-scale interfusion and conditional speaker modulation (ConSM), which is called MC-SpEx. First of all, we design the weight-share multi-scale fusers (ScaleFusers) for efficiently leveraging multi-scale information as well as ensuring consistency of the model's feature space. Then, to consider different scale information while generating masks, the multi-scale interactive mask generator (ScaleInterMG) is presented. Moreover, we introduce ConSM module to fully exploit speaker embedding in the speech extractor. Experimental results on the Libri2Mix dataset demonstrate the effectiveness of our improvements and the state-of-the-art performance of our proposed MC-SpEx.Comment: Accepted by InterSpeech 202

Numerical simulation of the dynamic effects of grounding icebergs on summer circulation in Prydz Bay, Antarctica

The Regional Ocean Modeling System (ROMS) is employed to create a three-dimensional numerical model of the summer circulation in the Prydz Bay region, Antarctica. Consistent with the currents measured using an underway acoustic Doppler current profiler during a Chinese cruise, the simulated current field illustrates the major features of the Prydz Bay circulation, including the Antarctic Slope Current (ASC) along the continental shelf break, the cyclonic Prydz Bay Gyre, and the Prydz Bay Eastern Coastal Current (PBECC). The effects of grounding icebergs D15 and B15 on the circulation in Prydz Bay are investigated via numerical simulations. The results indicate that these giant grounding icebergs substantially affect the flows into and within the bay, which may differ with the different grounding locations. As grounding iceberg D15 is located close to the southwestern part of the West Ice Shelf (WIS), it cuts off the coastal current along the outer edge of the WIS, and the ASC can only enter Prydz Bay from the west side of iceberg D15, whereupon it becomes a main source of the PBECC. Iceberg D15 also weakens the circulation in the bay in general. The relatively small iceberg B15 entered Prydz Bay from 2007 to 2009 and grounded on the southwestern section of the Four Ladies Bank. The numerical experiments indicate that iceberg B15 guides the ASC flowing into the bay around its west side and reduces the width of the inflow on the eastern side of the Prydz Bay Channel. The grounding of iceberg B15 has also led to adjustments of the circulation within the bay, among which the most significant is that the outflow along the western flank of Fram Bank has shifted to the west and become more intensive

Summer freshwater content variability of the upper ocean in the Canada Basin during recent sea ice rapid retreat

Freshwater content (FWC) in the Arctic Ocean has changed rapidly in recent years, in response to significant decreases in sea ice extent. Research on freshwater content variability in the Canada Basin, the main storage area of fresh water is very important to understand the input-output freshwater in the Arctic Ocean. The FWC in the Canada Basin was calculated using data from the Chinese National Arctic Research Expeditions of 2003 and 2008, and from expeditions of the Canadian icebreaker Louis S. St-Laurent (LSSL) from 2004 to 2007. Results show that the upper ocean in the Canada Basin became continuously fresher from 2003 to 2008, except during 2006. The FWC increased at a rate of more than 1 m¢a¡1, and the maximum increase, 7 m, was in the central basin compared between 2003 and 2008. Variability of the FWC was almost entirely limited to the layer above the winter Bering Sea Water (wBSW), below which the FWC remained around 3 m during the study period. Contributors to the FWC increase are generally considered to be net precipitation, runoff changes, Pacific water inflow through the Bering Strait, sea ice extent, and the Arctic Oscillation(AO). However, we determined that the first three contributors did not have apparent impact on the FWC changes. Therefore, this paper focuses on analysis of the latter two factors and the results indicate that they were the major contributors to the FWC variability in the basin