Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs

Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines

On the Stability of Coherent States for Pais-Uhlenbeck Oscillator

We have constructed coherent states for the higher derivative Pais-Uhlenbeck Oscillator. In the process we have suggested a novel way to construct coherent states for the oscillator having only negative energy levels. These coherent states have negative energies in general but their coordinate and momentum expectation values and dispersions behave in an identical manner as that of normal (positive energy) oscillator. The coherent states for the Pais-Uhlenbeck Oscillator have constant dispersions and a modified Heisenberg Uncertainty Relation. Moreover, under reasonable assumptions on parameters these coherent states can have positive energies.Comment: Title changed, modified version with no major change in results and conclusions, to appear in Mod.Phys.Lett.

Magnetic-field-induced propulsion of jellyfish-inspired soft robotic swimmers

The multifaceted appearance of soft robots in the form of swimmers, catheters, surgical devices, and drug-carrier vehicles in biomedical and microfluidic applications is ubiquitous today. Jellyfish-inspired soft robotic swimmers (jellyfishbots) have been fabricated and experimentally characterized by several researchers that reported their swimming kinematics and multimodal locomotion. However, the underlying physical mechanisms that govern magnetic-field-induced propulsion are not yet fully understood. Here, we use a robust and efficient computational framework to study the jellyfishbot swimming kinematics and the induced flow field dynamics through numerical simulation. We consider a two-dimensional model jellyfishbot that has flexible lappets, which are symmetric about the jellyfishbot center. These lappets exhibit flexural deformation when subjected to external magnetic fields to displace the surrounding fluid, thereby generating the thrust required for propulsion. We perform a parametric sweep to explore the jellyfishbot kinematic performance for different system parameters—structural, fluidic, and magnetic. In jellyfishbots, the soft magnetic composite elastomeric lappets exhibit temporal and spatial asymmetries when subjected to unsteady external magnetic fields. The average speed is observed to be dependent on both these asymmetries, quantified by the glide magnitude and the net area swept by the lappet tips per swimming cycle, respectively. We observe that a judicious choice of the applied magnetic field and remnant magnetization profile in the jellyfishbot lappets enhances both these asymmetries. Furthermore, the dependence of the jellyfishbot swimming speed upon the net area swept (spatial asymmetry) is twice as high as the dependence of speed on the glide ratio (temporal asymmetry). Finally, functional relationships between the swimming speed and different kinematic parameters and nondimensional numbers are developed. Our results provide guidelines for the design of improved jellyfish-inspired magnetic soft robotic swimmers

Bi-directional locomotion of a magnetically-actuated jellyfish-inspired soft robot

Biomimetic compliant untethered robots find a plethora of applications in biomedical engineering, microfluidics, soft robotics, and deep-sea exploration. Flexible miniature robots in the form of magnetically actuated compliant swimmers are increasingly used for targeted drug delivery, robotic surgery, laparoscopy, and microfluidic device design. These applications require an in-depth understanding of the nonlinear large deformation structural mechanics, non-invasive remote-control and untethered actuation mechanisms, and associated fluid-structure interactions that arise between a soft smart robot and its surrounding fluid. The present work obtains numerical solutions for the temporal evolution of structural and flow variables using a fictitious domain method that employs a robust multi-physics computational model involving both fluid-structure interaction and magneto-elasto-dynamics. The magnetically-actuated soft robotic swimmer (jellyfishbot) is inspired by the most efficient aquatic swimmer, the jellyfish. The swimming kinematics and bi-directional locomotion are obtained for different waveforms and gradients of the external magnetic actuation. The breaking of temporal symmetry and its relative dominance is discussed as well

Editorial: Manufacturing, Characterisation and Properties of Advanced Nanocomposites

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