The Sonogashira-type coupling of 2,2-difluoroethenyl tosylate with a variety of aliphatic and aromatic terminal alkynes proceeds smoothly even at room temperature to produce the corresponding difluorinated enyne derivatives. 2,2-Difluoroethenyl tosylate is a useful difluoroethenyl source because of its ready availability from 2,2,2-trifluoroethanol. Some of the obtained enynes exhibit strong fluorescence in the solid state. Further derivatization of a difluorinated enyne through Rh(III)-catalyzed oxidative coupling has also been examined.

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This article proposes a method to find intersections at which cars tend to deviate from the optimal route based on global positioning system (GPS) tracking data under the assumption that such deviations indicate that car navigation systems (CNSs) and road signage are not readily available. If the intended route is known, deviations can be enumerated by comparing the intended route with the vehicle’s actual route as observed by a GPS; however, the intended route is unknown and can differ from the route suggested by a CNS. To identify intersections with high deviation rates without knowing intended routes, we exhaustively sampled subsequences from each vehicular GPS track, and detected deviations from the optimal route for the subsequences. Although the detected deviations are not always caused by driver confusion, accumulating such erroneous detection results would yield a meaningful difference in the number of accumulated deviations at each intersection. We applied the proposed method to 3,843 GPS tracks collected from visitor drivers in the city of Kyoto. Thresholding the estimated deviation rate yielded 39 intersections from 14,543 candidates. The results show a certain level of correlation between obtained deviations and rerouting locations from actual CNS data. We also found several intersections where faulty route suggestions are provided by CNSs.

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In the deployment of scene-text spotting systems on mobile platforms, lightweight models with low computation are preferable. In concept, end-to-end (E2E) text spotting is suitable for such purposes because it performs text detection and recognition in a single model. However, current state-of-the-art E2E methods rely on heavy feature extractors, recurrent sequence modellings, and complex shape aligners to pursue accuracy, which means their computations are still heavy. We explore the opposite direction: How far can we go without bells and whistles in E2E text spotting? To this end, we propose a text-spotting method that consists of simple convolutions and a few post-processes, named Context-Free TextSpotter. Experiments using standard benchmarks show that Context-Free TextSpotter achieves real-time text spotting on a GPU with only three million parameters, which is the smallest and fastest among existing deep text spotters, with an acceptable transcription quality degradation compared to heavier ones. Further, we demonstrate that our text spotter can run on a smartphone with affordable latency, which is valuable for building stand-alone OCR applications.