Search

Imaging is of great importance in everyday life and various fields of science and technology. Conventional imaging is achieved by bending light rays originating from an object with a lens. Such ray bending requires space-variant structures, inevitably introducing a geometric center to the lens. To overcome the limitations arising from the conventional imaging mechanism, we consider imaging elements that employ a different mechanism, which we call reciprocal lenses. This type of imaging element relies on ray shifting, enabled by momentum-space-variant phase modulations in periodic structures. As such, it has the distinct advantage of not requiring alignment with a geometric center. Moreover, upright real images can be produced directly with a single reciprocal lens as the directions of rays are not changed. We realized an ultra-thin reciprocal lens based on a photonic crystal slab. We characterized the ray shifting behavior of the reciprocal lens and demonstrated imaging. Our work gives an alternative mechanism for imaging, and provides a new way to modulate electromagnetic waves.

Testing is the most direct and effective technique to ensure software quality. However, it is a burden for developers to understand the poorly-commented tests, which are common in industry environment projects. Mobile applications (app) are GUI-intensive and event-driven, so test scripts focusing on GUI interactions play a more important role in mobile app testing besides the test cases for the source code. Therefore, more attention should be paid to the user interactions and the corresponding user event responses. However, test scripts are loosely linked to apps under test (AUT) based on widget selectors, making it hard to map the operations to the functionality code of AUT. In such a situation, code understanding algorithms may lose efficacy if directly applied to mobile app test scripts. We present a novel approach, TestIntent, to infer the intent of mobile app test scripts. TestIntent combines the GUI image understanding and code understanding technologies. The test script is transferred into an operation sequence model. For each operation, TestIntent extracts the operated widget selector and link the selector to the UI layout structure, which stores the detailed information of the widgets, including coordinates, type, etc. With code understanding technologies, TestIntent can locate response methods in the source code. Afterwards, NLP algorithms are adopted to understand the code and generate descriptions. Also, TestIntent can locate widgets on the app GUI images. Then, TestIntent can understand the widget intent with an encoder-decoder model. With the combination of the results from GUI and code understanding, TestIntent generates the test intents in natural language format. We also conduct an empirical experiment, and the results prove the outstanding performance of TestIntent. A user study also declares that TestIntent can save developers' time to understand test scripts.

Crowdsourced testing is increasingly dominant in mobile application (app) testing, but it is a great burden for app developers to inspect the incredible number of test reports. Many researches have been proposed to deal with test reports based only on texts or additionally simple image features. However, in mobile app testing, texts contained in test reports are condensed and the information is inadequate. Many screenshots are included as complements that contain much richer information beyond texts. This trend motivates us to prioritize crowdsourced test reports based on a deep screenshot understanding. In this paper, we present a novel crowdsourced test report prioritization approach, namely DeepPrior. We first represent the crowdsourced test reports with a novelly introduced feature, namely DeepFeature, that includes all the widgets along with their texts, coordinates, types, and even intents based on the deep analysis of the app screenshots, and the textual descriptions in the crowdsourced test reports. DeepFeature includes the Bug Feature, which directly describes the bugs, and the Context Feature, which depicts the thorough context of the bug. The similarity of the DeepFeature is used to represent the test reports' similarity and prioritize the crowdsourced test reports. We formally define the similarity as DeepSimilarity. We also conduct an empirical experiment to evaluate the effectiveness of the proposed technique with a large dataset group. The results show that DeepPrior is promising, and it outperforms the state-of-the-art approach with less than half the overhead.

Considerable progress has been made in organic light-emitting diodes (OLEDs) to achieve high external quantum efficiency (EQE), among which the dipole orientation of OLED emitters has a remarkable effect. In most cases, EQE of the OLED emitter is theoretically predicted with only one orientation factor to match with corresponding experiments. Here, we develop a distribution theory with three independent parameters to fully describe the relationship between dipole orientations and power densities. Furthermore, we propose an optimal experiment configuration for measuring such distribution parameters. Measuring the unpolarized spectrum can dig more information of dipole orientation distributions with a rather simple way. Our theory provides a universal plot of the OLED dipole orientation, paving the way for designing more complicated OLED structures.