Answering complex questions that involve multiple entities and multiple relations using a standard knowledge base is an open and challenging task. Most existing KBQA approaches focus on simpler questions and do not work very well on complex questions because they were not able to simultaneously represent the question and the corresponding complex query structure. In this work, we encode such complex query structure into a uniform vector representation, and thus successfully capture the interactions between individual semantic components within a complex question. This approach consistently outperforms existing methods on complex questions while staying competitive on simple questions.

The majority of existing Linear Temporal Logic (LTL) planning methods rely on the construction of a discrete product automaton, that combines a discrete abstraction of robot mobility and a Büchi automaton that captures the LTL specification. Representing this product automaton as a graph and using graph search techniques, optimal plans that satisfy the LTL task can be synthesized. However, constructing expressive discrete abstractions makes the synthesis problem computationally intractable. In this paper, we propose a new samplingbased LTL planning algorithm that does not require any discrete abstraction of robot mobility. Instead, it builds incrementally trees that explore the product state-space, until a maximum number of iterations is reached or a feasible plan is found. The use of trees makes data storing and manipulation tractable, which significantly increases the scalability of our algorithm. To accelerate the construction of feasible plans, we introduce bias in the sampling process which is guided by transitions in the Büchi automaton that belong to the shortest path to the accepting states. We show that our planning algorithm is probabilistically complete and asymptotically optimal. Finally, we present numerical experiments showing that our method outperforms relevant temporal logic planning methods. I. INTRODUCTIONM OTION planning traditionally consists of generating robot trajectories that reach a goal region from a starting point while avoiding obstacles [1]. Methods for pointto-point navigation range from using potential fields and navigation functions [2], [3] to sampling-based algorithms [4]- [6]. More recently, a new class of planning approaches emerges that can handle a richer class of tasks, than the classical pointto-point navigation, and can capture temporal goals. Such tasks can be, e.g., sequencing or coverage [7], data gathering [8], intermittent communication [9], or persistent surveillance [10], and can be captured using formal languages, such as Linear Temporal Logic (LTL) [11], developed in concurrency theory.Control synthesis for mobile robots under complex tasks, captured by Linear Temporal Logic (LTL) formulas, builds upon either bottom-up approaches when independent LTL expressions are assigned to robots [12]-[14] or top-down approaches when a global LTL formula describing a collaborative task is assigned to a team of robots [15], [16], as in this work. Common in the above works is that they rely on model

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Cell behaviors are dictated by epigenetic and transcriptional programs. Little is known about how extracellular stimuli modulate these programs to reshape gene expression and control cell behavioral responses. Here, we interrogated the epigenetic and transcriptional response of endothelial cells to VEGFA treatment and found rapid chromatin changes that mediate broad transcriptomic alterations. VEGFA-responsive genes were associated with active promoters, but changes in promoter histone marks were not tightly linked to gene expression changes. VEGFA altered transcription factor occupancy and the distal epigenetic landscape, which profoundly contributed to VEGFA-dependent changes in gene expression. Integration of gene expression, dynamic enhancer, and transcription factor occupancy changes induced by VEGFA yielded a VEGFA-regulated transcriptional regulatory network, which revealed that the small MAF transcription factors are master regulators of the VEGFA transcriptional program and angiogenesis. Collectively these results revealed that extracellular stimuli rapidly reconfigure the chromatin landscape to coordinately regulate biological responses.

Slot filling is a critical task in natural language understanding (NLU) for dialog systems. State-of-the-art approaches treat it as a sequence labeling problem and adopt such models as BiLSTM-CRF. While these models work relatively well on standard benchmark datasets, they face challenges in the context of E-commerce where the slot labels are more informative and carry richer expressions. In this work, inspired by the unique structure of E-commerce knowledge base, we propose a novel multi-task model with cascade and residual connections, which jointly learns segment tagging, named entity tagging and slot filling. Experiments show the effectiveness of the proposed cascade and residual structures. Our model has a 14.6% advantage in F1 score over the strong baseline methods on a new Chinese E-commerce shopping assistant dataset, while achieving competitive accuracies on a standard dataset. Furthermore, online test deployed on such dominant E-commerce platform shows 130% improvement on accuracy of understanding user utterances. Our model has already gone into production in the E-commerce platform.