A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All

Collective entity disambiguation aims to jointly resolve multiple mentions by linking them to their associated entities in a knowledge base. Previous works are primarily based on the underlying assumption that entities within the same document are highly related. However, the extend to which these mentioned entities are actually connected in reality is rarely studied and therefore raises interesting research questions. For the first time, we show that the semantic relationships between the mentioned entities are in fact less dense than expected. This could be attributed to several reasons such as noise, data sparsity and knowledge base incompleteness. As a remedy, we introduce MINTREE, a new tree-based objective for the entity disambiguation problem. The key intuition behind MINTREE is the concept of coherence relaxation which utilizes the weight of a minimum spanning tree to measure the coherence between entities. Based on this new objective, we design a novel entity disambiguation algorithms which we call Pair-Linking. Instead of considering all the given mentions, Pair-Linking iteratively selects a pair with the highest confidence at each step for decision making. Via extensive experiments, we show that our approach is not only more accurate but also surprisingly faster than many state-of-the-art collective linking algorithms

PREDICTIONS OF WAVE INDUCED SHIP MOTIONS AND LOADS BY LARGE-SCALE MODEL MEASUREMENT AT SEA AND NUMERICAL ANALYSIS

In order to accurately predict wave induced motion and load responses of ships, a new experimental methodology is proposed. The new method includes conducting tests with large-scale models under natural environment conditions. The testing technique for large-scale model measurement proposed is quite applicable and general to a wide range of standard hydrodynamics experiments in naval architecture. In this study, a large-scale segmented self-propelling model allowed for investigating seakeeping performance and wave load behaviour as well as the testing systems were designed and experiments performed. A 2-hour voyage trial of the large-scale model aimed to perform a series of simulation exercises was carried out at Huludao harbour in October 2014. During the voyage, onboard systems, operated by crew, were used to measure and record the sea waves and the model responses. The post-voyage analysis of the measurements, both of the sea waves and the model’s responses, were made to predict the ship’s motion and load responses of short-term under the corresponding sea state. Furthermore, numerical analysis of short-term prediction was made by an in-house code and the result was compared with the experiment data. The long-term extreme prediction of motions and loads was also carried out based on the numerical results of short-term prediction

Shell-thickness-dependent photoinduced electron transfer from CuInS2/ZnS quantum dots to TiO2 films

We demonstrate the electron transfer (ET) processes from CuInS2/ZnS core/shell quantum dots (QDs) into porous anatase TiO2 films by time-resolved photoluminescence spectroscopy. The rate and efficiency of ET can be controlled by changing the core diameter and the shell thickness. It is found that the ET rates decrease exponentially at the decay constants of 1.1 and 1.4 nm–1 with increasing ZnS shell thickness for core diameters of 2.5 and 4.0 nm, respectively, in agreement with the electron tunneling model. This shows that optimized ET efficiency and QD stability can be realized by controlling the shell thickness

Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

Recent research on joint source channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch.Comment: 13 pages, 13 figures, journal pape

Design Of Power Transformer Online Monitoring System Based On GPRS

Abstract: This paper uses the design scheme which is based on of the microcontroller of C8051F580 and threephase electric energy metering chip, finished collection, processing and transmitting for the signal of the power of the transformer and the state of the switch. This system takes Server SQL 2008 as the background database, and achieves visiting by the ADO.NET data access technology. The management software of the monitoring master station is responsible for receiving, analyzing and processing, to form the graphics, reports and other types. The database access mode in this paper is based on B/S and C/S. The test results showed that the system worked stably, and realized the functions, which realizes the real-time monitoring of the transformer on operation data, remote data transmission, timely alarming and so on