Promoting Metro Operation Safety by Exploring Metro Operation Accident Network

Understanding the causation of accidents is essential to promote metro operation safety. In terms of 243 reported metro operation accident cases in China, a directed weighted network was constructed based on complex network theory, where nodes and directed edges denotes factors and event chains respectively. To reveal the key causal factors, the topological characteristics of metro operation accident network (MOAN) were analyzed from both global and local views. The results show that facility-type factors are more closely related to the occurrence of the accidents from the perspectives of average path length and cascading effects. Accident types like train delay and train suspension are the great risk recipients. Key causal factors with large out-degree, out-strength, betweenness centrality and cluster coefficient, such as communication and signal failure, vehicle failure and piling into the train should be noticed. The research framework proposed in the paper is not only applicable to China’s metro operation system, but also appropriate for other transportation system safety studies.

Modeling the predictive potential of extralinguistic context with script knowledge: The case of fragments

We describe a novel approach to estimating the predictability of utterances given extralinguistic context in psycholinguistic research. Predictability effects on language production and comprehension are widely attested, but so far predictability has mostly been manipulated through local linguistic context, which is captured with n-gram language models. However, this method does not allow to investigate predictability effects driven by extralinguistic context. Modeling effects of extralinguistic context is particularly relevant to discourse-initial expressions, which can be predictable even if they lack linguistic context at all. We propose to use script knowledge as an approximation to extralinguistic context. Since the application of script knowledge involves the generation of prediction about upcoming events, we expect that scrips can be used to manipulate the likelihood of linguistic expressions referring to these events. Previous research has shown that script-based discourse expectations modulate the likelihood of linguistic expressions, but script knowledge has often been operationalized with stimuli which were based on researchers’ intuitions and/or expensive production and norming studies. We propose to quantify the likelihood of an utterance based on the probability of the event to which it refers. This probability is calculated with event language models trained on a script knowledge corpus and modulated with probabilistic event chains extracted from the corpus. We use the DeScript corpus of script knowledge to obtain empirically founded estimates of the likelihood of an event to occur in context without having to resort to expensive pre-tests of the stimuli. We exemplify our method at a case study on the usage of nonsentential expressions (fragments), which shows that utterances that are predictable given script-based extralinguistic context are more likely to be reduced.

Using enriched semantic event chains to model human action prediction based on (minimal) spatial information

Predicting other people’s upcoming action is key to successful social interactions. Previous studies have started to disentangle the various sources of information that action observers exploit, including objects, movements, contextual cues and features regarding the acting person’s identity. We here focus on the role of static and dynamic inter-object spatial relations that change during an action. We designed a virtual reality setup and tested recognition speed for ten different manipulation actions. Importantly, all objects had been abstracted by emulating them with cubes such that participants could not infer an action using object information. Instead, participants had to rely only on the limited information that comes from the changes in the spatial relations between the cubes. In spite of these constraints, participants were able to predict actions in, on average, less than 64% of the action’s duration. Furthermore, we employed a computational model, the so-called enriched Semantic Event Chain (eSEC), which incorporates the information of different types of spatial relations: (a) objects’ touching/untouching, (b) static spatial relations between objects and (c) dynamic spatial relations between objects during an action. Assuming the eSEC as an underlying model, we show, using information theoretical analysis, that humans mostly rely on a mixed-cue strategy when predicting actions. Machine-based action prediction is able to produce faster decisions based on individual cues. We argue that human strategy, though slower, may be particularly beneficial for prediction of natural and more complex actions with more variable or partial sources of information. Our findings contribute to the understanding of how individuals afford inferring observed actions’ goals even before full goal accomplishment, and may open new avenues for building robots for conflict-free human-robot cooperation.

How to consider history in landscape ecology: patterns, processes, and pathways

Context Landscape ecology early on developed the awareness that central objects of investigation are not stable over time and therefore the historical dimension must be included, or at least considered. Objectives This paper considers the importance of history in landscape ecology in terms of its impact on patterns and processes and proposes to complement these with the notion of pathways in order to provide a comprehensive analysis of landscape change. Methods We develop a conceptual framework distinguishing between legacy effects, which include pattern and processes, and path dependence, with a focus of development pathways and we illustrate these perspectives by empirical examples. Results Combined short- to long-lasting imprints and legacies of historical patterns and processes reveal how present patterns and processes are in various ways influenced by legacies of the past. The focus on inherent dynamics of development pathways sheds light on the process of change itself, and its trajectories, and reveals the role of event chains and institutional reproduction. Conclusions Understanding patterns, processes, and pathways over time, allows a more complete analysis of landscape change, and forms the base to preserve vital ecosystem services of both human-made and natural landscapes for the future.

Enhancing Dialog Coherence with Event Graph Grounded Content Planning

How to generate informative, coherent and sustainable open-domain conversations is a non-trivial task. Previous work on knowledge grounded conversation generation focus on improving dialog informativeness with little attention on dialog coherence. In this paper, to enhance multi-turn dialog coherence, we propose to leverage event chains to help determine a sketch of a multi-turn dialog. We first extract event chains from narrative texts and connect them as a graph. We then present a novel event graph grounded Reinforcement Learning (RL) framework. It conducts high-level response content (simply an event) planning by learning to walk over the graph, and then produces a response conditioned on the planned content. In particular, we devise a novel multi-policy decision making mechanism to foster a coherent dialog with both appropriate content ordering and high contextual relevance. Experimental results indicate the effectiveness of this framework in terms of dialog coherence and informativeness.

Defying chronology: Crosslinguistic variation in reverse order reports

Much of how we sequence events in speech mirrors the order of their natural occurrence. While event chains that conform to chronology may be easier to process, languages offer substantial freedom to manipulate temporal order. This article explores to what extent digressions from chronology are attributable to differences in grammatical aspect systems. We compared reverse order reports (RORs) in event descriptions elicited from native speakers of four languages, two with (Spanish, Modern Standard Arabic [MSA]) and two without grammatical aspect (German, Hungarian). In the Arabic group, all participants were highly competent MSA speakers from Palestine and Jordan. Standardized frequency counts showed significantly more RORs expressed by non-aspect groups than by aspect groups. Adherence to chronology changing as a function of contrast in grammatical aspect signal that languages without obligatory marking of ongoingness may provide more flexibility for event reordering. These findings bring novel insights about the dynamic interplay between language structure and temporal sequencing in the discourse stream.