The influence of tourism on the sustaining of vernacular architechtural tradition embodied in the Bai and Naxi dwellings in Yunnan, China.

Yunnan is an economically underdeveloped region in south-western China, in which many ethnic settlements are preserved well. Within the last two decades, many ethnic communities at a grass-roots social level have been conducting a series of tourism-related developments of Bai and Naxi dwellings in Yunnan. They are altering, restoring, rebuilding, refurbishing and renewing ordinary Bai or Naxi dwellings into multi-function dwellings, which are not only the residential homes of families, but are also capable of providing an exotic cultural experience for tourists‘ consumption. Nevertheless, Bai and Naxi dwellings are representations of a living culture, embodying a complex set of vernacular architectural traditions which have been transmitted for many generations. When the Bai and Naxi dwellings are involved in tourism development, the transmission and adaptation of these vernacular architectural traditions are changed, and the manner in which such traditions aresustained in new circumstances becomes an interesting problem. This study explores the influence of tourism development on sustaining the vernacular architectural tradition embodied in Bai and Naxi dwellings in Yunnan, China. The researcher has conducted three rounds of fieldwork, choosing 30 Bai and Naxi dwellings involved in tourism development, from four ethnic minority settlements in Yunnan, for investigation. Observation, interview and questionnaire have been applied to collect data, and template analysis has been used to analyse the data. The results of the analysis show that if tourism development is conducted mainly at a community level, itcan enhance the sustaining of the vernacular architectural tradition embodied in Bai and Naxi dwellings. In summary, the sustaining of vernacular architectural tradition is not simply influenced by the nature of tourism, but is highly dependent on the social level of the developers, the construction pattern they choose, and the socio-cultural interaction they produce

Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of backgrounds and become hard examples during training. Compared with those proposal-based ones, real-time detectors are in far more serious trouble since they renounce the use of region-proposing stage which is used to filter a majority of backgrounds for achieving real-time rates. Though foregrounds as hard examples are in urgent need of being mined from tons of backgrounds, a considerable number of state-of-the-art real-time detectors, like YOLO series, have yet to profit from existing hard example mining methods, as using these methods need detectors fit series of prerequisites. In this paper, we propose a general hard example mining method named Loss Rank Mining (LRM) to fill the gap. LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples. By using LRM, some elements representing easy examples in final feature map are filtered and detectors are forced to concentrate on hard examples during training. Extensive experiments validate the effectiveness of our method. With our method, the improvements of YOLOv2 detector on auto-driving related dataset KITTI and more general dataset PASCAL VOC are over 5% and 2% mAP, respectively. In addition, LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.Comment: 8 pages, 6 figure

Towards human-compatible autonomous car: A study of non-verbal Turing test in automated driving with affective transition modelling

Autonomous cars are indispensable when humans go further down the hands-free route. Although existing literature highlights that the acceptance of the autonomous car will increase if it drives in a human-like manner, sparse research offers the naturalistic experience from a passenger's seat perspective to examine the human likeness of current autonomous cars. The present study tested whether the AI driver could create a human-like ride experience for passengers based on 69 participants' feedback in a real-road scenario. We designed a ride experience-based version of the non-verbal Turing test for automated driving. Participants rode in autonomous cars (driven by either human or AI drivers) as a passenger and judged whether the driver was human or AI. The AI driver failed to pass our test because passengers detected the AI driver above chance. In contrast, when the human driver drove the car, the passengers' judgement was around chance. We further investigated how human passengers ascribe humanness in our test. Based on Lewin's field theory, we advanced a computational model combining signal detection theory with pre-trained language models to predict passengers' humanness rating behaviour. We employed affective transition between pre-study baseline emotions and corresponding post-stage emotions as the signal strength of our model. Results showed that the passengers' ascription of humanness would increase with the greater affective transition. Our study suggested an important role of affective transition in passengers' ascription of humanness, which might become a future direction for autonomous driving.Comment: 16 pages, 9 figures, 3 table

Characterizing and correcting camera noise in back-illuminated sCMOS cameras

With promising properties of fast imaging speed, large field-of-view, relative low cost and many others, back-illuminated sCMOS cameras have been receiving intensive attention for low light level imaging in the past several years. However, due to the pixel-to-pixel difference of camera noise (called noise non-uniformity) in sCMOS cameras, researchers may hesitate to use them in some application fields, and sometimes wonder whether they should optimize the noise non-uniformity of their sCMOS cameras before using them in a specific application scenario. In this paper, we systematically characterize the impact of different types of sCMOS noise on image quality and perform corrections to these types of sCMOS noise using three representative algorithms (PURE, NCS and MLEsCMOS). We verify that it is possible to use appropriate correction methods to push the non-uniformity of major types of camera noise, including readout noise, offset, and photon response, to a satisfactory level for conventional microscopy and single molecule localization microscopy. We further find out that, after these corrections, global read noise becomes a major concern that limits the imaging performance of back-illuminated sCMOS cameras. We believe this study provides new insights into the understanding of camera noise in back-illuminated sCMOS cameras, and also provides useful information for future development of this promising camera technology. &nbsp;</p

1,3,4-oxadiazole-based deep-blue thermally activated delayed fluorescence emitters for organic light emitting diodes

We are grateful to the EPSRC for financial support (grants EP/P010482/1, EP/J01771X, EP/J00916 and EP/R035164/1). We gratefully acknowledge funding through the EPSRC NSF- CBET lead agency agreement (EP/R010595/1, 1706207) and a Leverhulme Trust Research Grant (RPG-2017-231). We thank the EPSRC UK National Mass Spectrometry Facility at Swansea University for analytical services. Z.L. and W. L. thank the China Scholarship Council (grant numbers 201703780004 and 201708060003)A series of four 1,3,4-oxadiazole-based thermally activated delayed fluorescence (TADF) derivatives are reported as emitters for organic light emitting diodes (OLEDs). As a function of the nature of the substituent on the weak 1,3,4-oxadiazole acceptor their emission color could be tuned from green-blue to blue. The highly twisted conformation between carbazoles and oxadiazoles results in effective separation of the HOMO and the LUMO resulting in a small singlet-triplet splitting. The corresponding singlet-triplet energy gaps (∆EST) range from 0.22 to 0.28 eV resulting in an efficient reverse intersystem crossing (RISC) process and moderate to high photoluminescence quantum yields (ΦPL), ranging from 35 to 70% in a DPEPO matrix. Organic light-emitting diodes (OLEDs) based on i-2CzdOXD4CF3Ph achieve maximum external quantum efficiency (EQEmax) of up to 12.3% with a sky-blue emission at CIE of (0.18, 0.28) while the device based on i-2CzdOXDMe shows blue emission at CIE of (0.17, 0.17) with a maximum EQE of 11.8%.PostprintPeer reviewe