Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

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A translation apparatus is provided which comprises: an inputting section for inputting a source document in a natural language; a layout analyzing section for analyzing layout information including cascade information, itemization information, numbered itemization information, labeled itemization information and separator line information in the source document inputted by the inputting section and specifying a translation range on the basis of the layout information; a translation processing section for translating a source document text in the specified translation range into a second language; and an outputting section for outputting a translated text provided by the translation processing section.

Automated Planning: Theory and Practice.

Autonomous driving, including intelligent decision-making and path planning, in dynamic environments (like highway) is significantly more difficult than the navigation in static scenarios because of the additional time dimension. Therefore, correlating the time dimension and the space dimension through prediction to create a spatio-temporal navigation map can make decision-making and path planning in such kinds of environment much easier. In this article, NGSIM data is analysed and processed from the perspective of the ego-vehicle (using the data as an ego-vehicle’s perception results). Based on the data, we develop an LSTM (Long-Short Term Memory)-based framework to predict possible trajectories of multiple surrounding vehicles within a certain range of the ego-vehicle. Then, the multiple predicted trajectories in a series of continuous dynamic highway scenes are projected into a spatio-temporal domain to create an octree map. Thus, dynamic targets and static obstacles can be unified into the same domain or map so that the dynamic disturbance problem for autonomous driving in highway environments can be resolved. Experimental results show that the proposed model is capable of predicting all the future trajectories around the ego-vehicle efficiently and the corresponding spatio-temporal map can be generated accurately in different dynamic scenarios. 

Trajectory Prediction-Based Local Spatio-Temporal Navigation Map for Autonomous Driving in Dynamic Highway Environments

Hybrid offline and online task planning for service robot using object-level semantic map and probabilistic inference

Modal separation logics are formalisms that combine modal operators to reason locally, with separating connectives that allow to perform global updates on the models. In this work, we design Hilbert-style proof systems for the modal separation logics $\text {MSL} ^{}(*,\langle \ne \rangle )$ and $\text {MSL} ^{}(*,\Diamond )$, where $*$ is the separating conjunction, $\Diamond $ is the standard modal operator and $\langle \ne \rangle $ is the difference modality. The calculi only use the logical languages at hand (no external features such as labels) and take advantage of new normal forms and of their axiomatisation.

https://hal.archives-ouvertes.fr/hal-02362648/document

Axiomatising Logics with Separating Conjunction and Modalities

Knowledge based approaches developed for AI planning can convert an intractable planning problem to a tractable one. Current techniques often use temporal logics to express Search Control Knowledge (SCK) in logic based planning. However, traditional temporal logics are limited in expressiveness since they are unable to express spatial constraints which are as important as temporal ones in many planning domains. To this end, we propose a two-dimensional (spatial and temporal) logic namely PPTL $^{\mathrm{SL}}$ by temporalizing separation logic with PPTL (Propositional Projection Temporal Logic) which is well-suited to specify SCK involving both spatial and temporal constraints in planning. We prove that PPTL $^{\mathrm{SL}}$ is decidable essentially via an equisatisfiable translation from PPTL $^{\mathrm{SL}}$ to its restricted form. Moreover, we implement a tool, S-TSolver , which effectively computes plans under the guidance of the spatio-temporal SCK expressed by PPTL $^{\mathrm{SL}}$ formulas. The effectiveness of the tool is evaluated on selected benchmark domains from the International Planning Competition.

Planning with Spatio-Temporal Search Control Knowledge

Temporal logics are widely adopted in Artificial Intelligence (AI) planning for specifying Search Control Knowledge (SCK). However, traditional temporal logics are limited in expressive power since they are unable to express spatial constraints which are as important as temporal ones in many planning domains. To this end, we propose a twodimensional (spatial and temporal) logic namely PPTL by temporalising separation logic with Propositional Projection Temporal Logic (PPTL). The new logic is well-suited for specifying SCK containing both spatial and temporal constraints which are useful in AI planning. We show that PPTL is decidable and present a decision procedure. With this basis, a planner namely S-TSolver for computing plans based on the spatio-temporal SCK expressed in PPTL formulas is developed. Evaluation on some selected benchmark domains shows the effectiveness of S-TSolver.

/pdf/temporalising-separation-logic-for-planning-with-search-450kofoed7.pdf

Temporalising Separation Logic for Planning with Search Control Knowledge

Smart parking techniques are widely used in smart city and intelligent transportation systems. However, how to build a friendly and effective smart parking system in a large city is still a challenge. This paper proposes a P2P network based smart parking system using Edge Computing. It utilizes Cloud Computing, Edge Computing, and P2P network techniques to provide plenty of services including parking space inquiry, navigation, vehicle license plate recognition, payment, and accident query. The architecture of the system and the related protocols and algorithms are presented in detail. Compared with the existing smart parking systems, the proposed system is more friendly and effective.

P2P Network Based Smart Parking System Using Edge Computing

Verify heaps via unified model checking

In this paper, we integrate separation logic with Propositional Projection Temporal Logic (PPTL) to obtain a two-dimensional logic, namely PPTL$^{\tiny\mbox{SL}}$. The spatial dimension is realized by a decidable fragment of separation logic which can be used to describe linked lists, and the temporal dimension is expressed by PPTL. We show that PPTL and PPTL$^{\tiny\mbox{SL}}$ are closely related in their syntax structures. That is, for any PPTL$^{\tiny\mbox{SL}}$ formula in a restricted form, there exists an "isomorphic" PPTL formula. The "isomorphic" PPTL formulas can be obtained by first an equisatisfiable translation and then an isomorphic mapping. As a result, existing theory of PPTL, such as decision procedure for satisfiability and model checking algorithm, can be reused for PPTL$^{\tiny\mbox{SL}}$.

Extending PPTL for Verifying Heap Evolution Properties.

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