Context-aware Event Forecasting via Graph Disentanglement

Event forecasting has been a demanding and challenging task throughout the entire human history. It plays a pivotal role in crisis alarming and disaster prevention in various aspects of the whole society. The task of event forecasting aims to model the relational and temporal patterns based on historical events and makes forecasting to what will happen in the future. Most existing studies on event forecasting formulate it as a problem of link prediction on temporal event graphs. However, such pure structured formulation suffers from two main limitations: 1) most events fall into general and high-level types in the event ontology, and therefore they tend to be coarse-grained and offers little utility which inevitably harms the forecasting accuracy; and 2) the events defined by a fixed ontology are unable to retain the out-of-ontology contextual information. To address these limitations, we propose a novel task of context-aware event forecasting which incorporates auxiliary contextual information. First, the categorical context provides supplementary fine-grained information to the coarse-grained events. Second and more importantly, the context provides additional information towards specific situation and condition, which is crucial or even determinant to what will happen next. However, it is challenging to properly integrate context into the event forecasting framework, considering the complex patterns in the multi-context scenario. Towards this end, we design a novel framework named Separation and Collaboration Graph Disentanglement (short as SeCoGD) for context-aware event forecasting. Since there is no available dataset for this novel task, we construct three large-scale datasets based on GDELT. Experimental results demonstrate that our model outperforms a list of SOTA methods.Comment: KDD 2023, 9 pages, 7 figures, 4 table

Neural topic modeling with bidirectional adversarial training

Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA). However, these models either typically assume improper prior (e.g. Gaussian or Logistic Normal) over latent topic space or could not infer topic distribution for a given document. To address these limitations, we propose a neural topic modeling approach, called Bidirectional Adversarial Topic (BAT) model, which represents the first attempt of applying bidirectional adversarial training for neural topic modeling. The proposed BAT builds a two-way projection between the document-topic distribution and the document-word distribution. It uses a generator to capture the semantic patterns from texts and an encoder for topic inference. Furthermore, to incorporate word relatedness information, the Bidirectional Adversarial Topic model with Gaussian (Gaussian-BAT) is extended from BAT. To verify the effectiveness of BAT and Gaussian-BAT, three benchmark corpora are used in our experiments. The experimental results show that BAT and Gaussian-BAT obtain more coherent topics, outperforming several competitive baselines. Moreover, when performing text clustering based on the extracted topics, our models outperform all the baselines, with more significant improvements achieved by Gaussian-BAT where an increase of near 6% is observed in accuracy

SCTc-TE: A Comprehensive Formulation and Benchmark for Temporal Event Forecasting

Temporal complex event forecasting aims to predict the future events given the observed events from history. Most formulations of temporal complex event are unstructured or without extensive temporal information, resulting in inferior representations and limited forecasting capabilities. To bridge these gaps, we innovatively introduce the formulation of Structured, Complex, and Time-complete temporal event (SCTc-TE). Following this comprehensive formulation, we develop a fully automated pipeline and construct a large-scale dataset named MidEast-TE from about 0.6 million news articles. This dataset focuses on the cooperation and conflict events among countries mainly in the MidEast region from 2015 to 2022. Not limited to the dataset construction, more importantly, we advance the forecasting methods by discriminating the crucial roles of various contextual information, i.e., local and global contexts. Thereby, we propose a novel method LoGo that is able to take advantage of both Local and Global contexts for SCTc-TE forecasting. We evaluate our proposed approach on both our proposed MidEast-TE dataset and the original GDELT-TE dataset. Experimental results demonstrate the effectiveness of our forecasting model LoGo. The code and datasets are released via https://github.com/yecchen/GDELT-ComplexEvent.Comment: pre-print, 6 figures, 7 table

Empirical Dependency-Based Head Finalization for Statistical Chinese-, English-, and French-to-Myanmar (Burmese) Machine Translation

Abstract We conduct dependency-based head finalization for statistical machine translation (SMT) for Myanmar (Burmese). Although Myanmar is an understudied language, linguistically it is a head-final language with similar syntax to Japanese and Korean. So, applying the efficient techniques of Japanese and Korean processing to Myanmar is a natural idea. Our approach is a combination of two approaches. The first is a head-driven phrase structure grammar (HPSG) based head finalization for English-to-Japanese translation, the second is dependency-based pre-ordering originally designed for English-to-Korean translation. We experiment on Chinese-, English-, and French-to-Myanmar translation, using a statistical pre-ordering approach as a comparison method. Experimental results show the dependency-based head finalization was able to consistently improve a baseline SMT system, for different source languages and different segmentation schemes for the Myanmar language

Platform success in the international marketplace: reconfiguring digital resources for marketing agility

PurposeThis paper explores how platforms reconfigure versatile digital resources to achieve marketing agility in international markets. Design/methodology/approachWe draw on a case study of a Chinese digital platform to explore the processes and mechanisms of reconfiguring during marketing agility development. Data from different sources are collected, including interviews, informal dialogue and archival data. FindingsVersatile digital resources create productive applications for previously less amendable marketing and nonmarketing resources to be malleable, editable and reconfigurable in marketing agility development. This study identifies and clarifies three versatile digital resource-enabled reconfiguration activities in marketing agility building: recombining digital artifacts, repurposing human capital and cross-pollinating markets. Research limitations/implicationsSince our study adopts a case study method, future research can extend our insights by using quantitative methods to test and verify our theoretical framework. Practical implicationsFirst, we provide insights into how organizations can reconfigure versatile digital resources to achieve the benefits of marketing agility in international markets. Second, while recruiting new employees during internationalization is vital, we suggest that assisted by digital artifacts, firms can repurpose the existing workforce, such as via multitasking, swift task-switching and flexible job redirecting to satisfy dynamic international business requirements with lower adjustment costs. Third, we offer two localization approaches in which firms can use digital artifacts as the enabler to remix sociocultural elements with local adaptations to develop glocal content and decentralize content production to generate inclusive local content. Originality/valueWe provide a process model that specifies how platforms reconfigure versatile digital resources to achieve marketing agility in international markets. Furthermore, we provide novel insights into the literature on marketing agility in international markets and localization

Structural tuning and catalysis of tungsten carbides for the regioselective cleavage of C-O bonds

Tungsten carbides exhibit excellent performance in many heterogeneous processes because of their distinctive catalytic properties. Preparation of tungsten carbides with controllable phase composition relevant to their catalytic behavior is essential yet challenging. In this study, tungsten carbides embedded in carbon spheres (WxC@CS) were fabricated through carburization of organic–inorganic hybrid precursors. W1.25C@CS with rational structure-tuning properties exhibits promising regioselectivity (reaching 91.5%) toward aryl CO bond cleavage, specifically during hydrogenolysis of guaiacol to phenol. A structure reconstruction strategy was adopted to elucidate structure–performance relationship by transforming commercially available bulk WC from inert phase to composition-dependent active catalysts. Combined catalytic and characteristic analyses illustrate that the catalyst performance is dependent on the C-defect structure. The intimate connection between the phenol space time yield and the C/W atomic ratio on the exterior interface of the catalyst was verified. The C/W atomic ratio of 7.2 leads to the optimal catalytic performance. Density functional theory calculations were performed to define the catalytic mechanism at the atomic level. The theoretical analysis suggests an appropriate configuration of surface W and C atoms for activation of hydrogen and guaiacol molecules, rendering the intrinsic active sites for phenol production. This work provides insights into controlling the surface compositions of tungsten carbides to develop efficient CO bond cleavage catalysts, which verifies the importance of hydrogenolysis catalysis in lignin-derived compounds involving complex O-containing guaiacols and phenolics

Genetic identification and expression optimization of a novel protease HapR from Bacillus velezensis

Due to the broad application and substantial market demand for proteases, it was vital to explore the novel and efficient protease resources. The aim of this study was to identify the novel protease for tobacco protein degradation and optimize the expression levels. Firstly, the tobacco protein was used as the sole nitrogen resource for isolation of protease-producing strains, and a strain with high protease production ability was obtained, identified as Bacillus velezensis WH-7. Then, the whole genome sequencing was conducted on the strain B. velezensis WH-7, and 7 proteases genes were mined by gene annotation analysis. By further heterologous expression of the 7 protease genes, the key protease HapR was identified with the highest protease activity (144.19 U/mL). Moreover, the catalysis mechanism of HapR was explained by amino acid sequence analysis. The expression levels of protease HapR were further improved through optimization of promoter, signal peptide and host strain, and the maximum protease activity reaced 384.27 U/mL in WX-02/pHY-P43-SPyfkD-hapR, increased by 167% than that of initial recombinant strain HZ/pHY-P43-SPhapR-hapR. This study identified a novel protease HapR and the expression level was significantly improved, which provided an important enzyme resource for the development of enzyme preparations in tobacco protein degradation