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Understanding the mechanisms of unidirectional magnetoresistance (UMR) has become an important topic for its potential application of a two-terminal spin–orbit torque device. Field sweep DC measurements have been proposed and adopted to measure the value of UMR instead of second harmonic measurements. In this paper, potential measurement errors in conventional DC measurements are investigated. Oersted field and field-like torque usually do not influence the measurement, but a large field-like torque was found to lead to an anisotropic magnetoresistance difference when the sample is not perfectly aligned with the external field. The existence of ordinary magnetoresistance was also found to contribute to a large background. In this paper, an alternative measurement method for UMR was proposed and demonstrated to address those issues related to previous DC measurements. Our work may broaden the understanding of the error sources of UMR measurements and provide a reliable DC measurement method for the characterization of UMR. 

Understanding spatial expressions and using them appropriately is necessary for seamless and natural human-machine interaction. However, capturing the semantics and appropriate usage of spatial prepositions is notoriously difficult, because of their vagueness and polysemy. Although modern data-driven approaches are good at capturing statistical regularities in the usage, they usually require substantial sample sizes, often do not generalize well to unseen instances and, most importantly, their structure is essentially opaque to analysis, which makes diagnosing problems and understanding their reasoning process difficult. In this work, we discuss our attempt at modeling spatial senses of prepositions in English using a combination of rule-based and statistical learning approaches. Each preposition model is implemented as a tree where each node computes certain intuitive relations associated with the preposition, with the root computing the final value of the prepositional relation itself. The models operate on a set of artificial 3D “room world” environments, designed in Blender, taking the scene itself as an input. We also discuss our annotation framework used to collect human judgments employed in the model training. Both our factored models and black-box baseline models perform quite well, but the factored models will enable reasoned explanations of spatial relation judgements. 

Modern cloud databases adopt a storage-disaggregation architecture that separates the management of computation and storage. A major bottleneck in such an architecture is the network connecting the computation and storage layers. Two solutions have been explored to mitigate the bottleneck: caching and computation pushdown. While both techniques can significantly reduce network traffic, existing DBMSs consider them as orthogonal techniques and support only one or the other, leaving potential performance benefits unexploited. In this paper we present FlexPushdownDB (FPDB), an OLAP cloud DBMS prototype that supports fine-grained hybrid query execution to combine the benefits of caching and computation pushdown in a storage-disaggregation architecture. We build a hybrid query executor based on a new concept called separable operators to combine the data from the cache and results from the pushdown processing. We also propose a novel Weighted-LFU cache replacement policy that takes into account the cost of pushdown computation. Our experimental evaluation on the Star Schema Benchmark shows that the hybrid execution outperforms both the conventional caching- only architecture and pushdown-only architecture by 2.2×. In the hybrid architecture, our experiments show that Weighted-LFU can outperform the baseline LFU by 37%.