Joint Inference on Truth/Rumor and Their Sources in Social Networks

In the contemporary era of information explosion, we are often faced with the mixture of massive \emph{truth} (true information) and \emph{rumor} (false information) flooded over social networks. Under such circumstances, it is very essential to infer whether each claim (e.g., news, messages) is a truth or a rumor, and identify their \emph{sources}, i.e., the users who initially spread those claims. While most prior arts have been dedicated to the two tasks respectively, this paper aims to offer the joint inference on truth/rumor and their sources. Our insight is that a joint inference can enhance the mutual performance on both sides. To this end, we propose a framework named SourceCR, which alternates between two modules, i.e., \emph{credibility-reliability training} for truth/rumor inference and \emph{division-querying} for source detection, in an iterative manner. To elaborate, the former module performs a simultaneous estimation of claim credibility and user reliability by virtue of an Expectation Maximization algorithm, which takes the source reliability outputted from the latter module as the initial input. Meanwhile, the latter module divides the network into two different subnetworks labeled via the claim credibility, and in each subnetwork launches source detection by applying querying of theoretical budget guarantee to the users selected via the estimated reliability from the former module. The proposed SourceCR is provably convergent, and algorithmic implementable with reasonable computational complexity. We empirically validate the effectiveness of the proposed framework in both synthetic and real datasets, where the joint inference leads to an up to 35\% accuracy of credibility gain and 29\% source detection rate gain compared with the separate counterparts

Can We `Feel' the Temperature of Knowledge? Modelling Scientific Popularity Dynamics via Thermodynamics

Just like everything in the nature, scientific topics flourish and perish. While existing literature well captures article's life-cycle via citation patterns, little is known about how scientific popularity and impact evolves for a specific topic. It would be most intuitive if we could `feel' topic's activity just as we perceive the weather by temperature. Here, we conceive knowledge temperature to quantify topic overall popularity and impact through citation network dynamics. Knowledge temperature includes 2 parts. One part depicts lasting impact by assessing knowledge accumulation with an analogy between topic evolution and isobaric expansion. The other part gauges temporal changes in knowledge structure, an embodiment of short-term popularity, through the rate of entropy change with internal energy, 2 thermodynamic variables approximated via node degree and edge number. Our analysis of representative topics with size ranging from 1000 to over 30000 articles reveals that the key to flourishing is topics' ability in accumulating useful information for future knowledge generation. Topics particularly experience temperature surges when their knowledge structure is altered by influential articles. The spike is especially obvious when there appears a single non-trivial novel research focus or merging in topic structure. Overall, knowledge temperature manifests topics' distinct evolutionary cycles

INFINITY: A Simple Yet Effective Unsupervised Framework for Graph-Text Mutual Conversion

Graph-to-text (G2T) generation and text-to-graph (T2G) triple extraction are two essential tasks for constructing and applying knowledge graphs. Existing unsupervised approaches turn out to be suitable candidates for jointly learning the two tasks due to their avoidance of using graph-text parallel data. However, they are composed of multiple modules and still require both entity information and relation type in the training process. To this end, we propose INFINITY, a simple yet effective unsupervised approach that does not require external annotation tools or additional parallel information. It achieves fully unsupervised graph-text mutual conversion for the first time. Specifically, INFINITY treats both G2T and T2G as a bidirectional sequence generation task by fine-tuning only one pretrained seq2seq model. A novel back-translation-based framework is then designed to automatically generate continuous synthetic parallel data. To obtain reasonable graph sequences with structural information from source texts, INFINITY employs reward-based training loss by leveraging the advantage of reward augmented maximum likelihood. As a fully unsupervised framework, INFINITY is empirically verified to outperform state-of-the-art baselines for G2T and T2G tasks

Synthetic and Biosynthetic Methods for Selective Cyclisations of 4,5-Epoxy Alcohols to Tetrahydropyrans

Tetrahydropyrans (THPs) are common structural motifs found in natural products and synthetic therapeutic molecules. In Nature these 6-membered oxygen heterocycles are often assembled via intramolecular reactions involving either oxy-Michael additions or ring opening of epoxy-alcohols. Indeed, the polyether natural products have been particularly widely studied due to their fascinating structures and important biological properties; these are commonly formed via endo-selective epoxide-opening cascades. In this review we outline synthetic approaches for endo-selective intramolecular epoxide ring opening (IERO) of 4,5-epoxy-alcohols and their applications in natural product synthesis. In addition, the biosynthesis of THP-containing natural products which utilise IERO reactions are reviewed

Effects of introducing low-cost high-speed rail on air-rail competition:Modelling and numerical analysis for Paris-Marseille

Given the trend of railway liberalization in Europe and Asia, we explore the effects of introducing low-cost high-speed rail as an answer to the railway reform on air-rail competition. In particular, by proposing a vertically differentiated model, we first derive the optimal pricing policies as well as the corresponding profits and market shares for low-cost high-speed rail (LCR), full-service high-speed rail (FSR) and air transport (Air). We do so for two types of LCR entrants, namely the incumbent owned entrant (to the FSR company) and the independently owned entrants. For both situations, we prove analytically that introducing LCR leads to reduced FSR and Air fares as well as to reduced Air traffic. The fare and traffic reductions increase with the passenger's time value and with the LCR travel time, while they decrease with the Air unit seat cost. Moreover, all LCR effects are stronger for an independently operated LCR. We apply our model to the Paris-Marseille route, based on data collected from publicly available sources. It is found that introducing an independently owned (incumbent owned) LCR on this route leads to 39% (33%) less air traffic, 20% (14%) less FSR traffic and a 37% (29%) increase in total rail traffic. Furthermore, this comes with increases of 2% (8%) in combined railway profit and 6% (5%) in total social welfare. These results support the decision of French policy makers to have LCR and FSR operated by the same company, as it comes with much higher combined railway profits and almost the same welfare increase as independently owned LCR. Further sensitivity analyses suggest that most LCR passengers would otherwise have traveled by FSR or Air, although LCR also attracts new passengers. In addition, offering a low-cost alternative is more effective if passengers value time more highly. Implications in terms of methodology and industry are provided.</p

iBILL: Using iBeacon and Inertial Sensors for Accurate Indoor Localization in Large Open Areas

As a key technology that is widely adopted in location-based services (LBS), indoor localization has received considerable attention in both research and industrial areas. Despite the huge efforts made for localization using smartphone inertial sensors, its performance is still unsatisfactory in large open areas, such as halls, supermarkets, and museums, due to accumulated errors arising from the uncertainty of users’ mobility and fluctuations of magnetic field. Regarding that, this paper presents iBILL, an indoor localization approach that jointly uses iBeacon and inertial sensors in large open areas. With users’ real-time locations estimated by inertial sensors through an improved particle filter, we revise the algorithm of augmented particle filter to cope with fluctuations of magnetic field. When users enter vicinity of iBeacon devices clusters, their locations are accurately determined based on received signal strength of iBeacon devices, and accumulated errors can, therefore, be corrected. Proposed by Apple Inc. for developing LBS market, iBeacon is a type of Bluetooth low energy, and we characterize both the advantages and limitations of localization when it is utilized. Moreover, with the help of iBeacon devices, we also provide solutions of two localization problems that have long remained tough due to the increasingly large computational overhead and arbitrarily placed smartphones. Through extensive experiments in the library on our campus, we demonstrate that iBILL exhibits 90% errors within 3.5 m in large open areas