(E)-2-(4-Fluoro­benzyl­idene)cyclo­octanone

The title compound, C15H17FO, was prepared directly from the aldol condensation of cyclo­octa­none with 4-fluoro­benz­aldehyde, catalysed by Pd(Ni,Ce) in the presence of trimethyl­silyl chloride. The eight-membered ring adopts a boat-chair conformation

An LSTM Based Generative Adversarial Architecture for Robotic Calligraphy Learning System

Robotic calligraphy is a very challenging task for the robotic manipulators, which can sustain industrial manufacturing. The active mechanism of writing robots require a large sized training set including sequence information of the writing trajectory. However, manual labelling work on those training data may cause the time wasting for researchers. This paper proposes a machine calligraphy learning system using a Long Short-Term Memory (LSTM) network and a generative adversarial network (GAN), which enables the robots to learn and generate the sequences of Chinese character stroke (i.e., writing trajectory). In order to reduce the size of the training set, a generative adversarial architecture combining an LSTM network and a discrimination network is established for a robotic manipulator to learn the Chinese calligraphy regarding its strokes. In particular, this learning system converts Chinese character stroke image into the trajectory sequences in the absence of the stroke trajectory writing sequence information. Due to its powerful learning ability in handling motion sequences, the LSTM network is used to explore the trajectory point writing sequences. Each generation process of the generative adversarial architecture contains a number of loops of LSTM. In each loop, the robot continues to write by following a new trajectory point, which is generated by LSTM according to the previously written strokes. The written stroke in an image format is taken as input to the next loop of the LSTM network until the complete stroke is finally written. Then, the final output of the LSTM network is evaluated by the discriminative network. In addition, a policy gradient algorithm based on reinforcement learning is employed to aid the robot to find the best policy. The experimental results show that the proposed learning system can effectively produce a variety of high-quality Chinese stroke writing

Multiple Human Papillomavirus Infections among Chinese Women with and without Cervical Abnormalities: A Population-Based Multi-Center Cross-Sectional Study

Background: Despite an increase in the number of studies conducted in recent years on human papillomavirus (HPV) and cervical cancer epidemiology, the profile of multiple HPV infections remain obscure, particularly among Chinese women. During 2004–2005, a series of population-based HPV prevalence surveys were performed by Cancer Institute and Hospital of Chinese Academy of Medical Sciences (CIHCAMS) and International Agency for Research on Cancer (IARC). Based on these surveys, we evaluated the prevalence and risk factors of multiple HPV infections, and explored its association with cervical abnormalities among Chinese women. Methods: A total of 2374 women from three study centers underwent gynecological examinations with valid cytology and their HPV results were included in the analysis. Forty-four HPV types were detected using the GP5+/6+ PCR-based enzyme immunoassay. An unconditional logistic regression model was used to evaluate the effect of multiple HPV infections on cervical lesions and its risk factors adjusting for confounders. The between-groups difference was evaluated by a heterogeneity test based on the Q test. Results: One hundred and eleven women of multiple HPV infections was found among 2374 Chinese women with a prevalence of 5.28% (95% CI = 3.86–5.60%), which attributed to 28.98% (95% CI = 24.49–33.81%) of all of the 383 HPV-positive women. A significantly increased risk of multiple HPV infections was found in the older women (≥45 years; adjusted OR = 1.52, 95% CI = 1.02–2.27) and those having more than three sexual partners (adjusted OR = 2.10, 95% CI = 1.05–4.17) after adjustment for age-group, study area, and number of sexual partner. We also found that the risk of high-grade lesions was significantly higher than that of low-grade lesions with the multiple HPV infections (Pheterogeneity = 0.044), but not as significantly with the single HPV infection (Pheterogeneity = 0.108). Conclusion: Multiple HPV Infections, especially with high-risk HPV types, may be a substantial indicator either for public cervical cancer prevention or clinical implications

Single snapshot multiple frequency modulated imaging of subsurface optical properties of turbid media with structured light

We report a novel demodulation method that enables single snapshot wide field imaging of optical properties of turbid media in the Spatial Frequency Domain (SFD). This Single Snapshot Multiple frequency Demodulation (SSMD) method makes use of the orthogonality of harmonic functions to extract the modulation transfer function (MTF) at multiple modulation frequencies simultaneously from a single structured-illuminated image at once. The orientation, frequency, and amplitude of each modulation can be set arbitrarily subject to the limitation of the implementation device. We first validate and compare SSMD to the existing demodulation methods by numerical simulations. The performance of SSMD is then demonstrated with experiments on both tissue mimicking phantoms and in vivo for recovering optical properties by comparing to the standard three-phase demodulation approach. The results show that SSMD increases significantly the data acquisition speed and reduces motion artefacts. SSMD exhibits excellent noise suppression in imaging as well at the rate proportional to the square root of the number of pixels contained in its kernel. SSMD is ideal in the implementation of a real-time spatial frequency domain imaging platform and will open up SFDI for vast applications in imaging and monitoring dynamic turbid medium and processes

BigraphTalk: verified design of IoT applications

Graphical IoT device management platforms, such as IoTtalk, make it easy to describe interactions between IoT devices. Applications are defined by dragging-and-dropping devices and specifying how they are connected, e.g. a door sensor controlling a light. While this allows simple and rapid development, it remains possible to specify unwanted device configurations – such as using the same device to drive a motor up and down simultaneously, risking damaging the motor. We propose , a verification framework for IoTtalk that utilizes formal techniques, based on bigraphs, to statically guarantee that unwanted configurations do not arise. In particular, we check for invalid connections between devices, as well as type errors, e.g. passing a float to a boolean switch. To the best of our knowledge, is the first platform to support the graphical specification of correct-by-design IoT applications. provides fully automated verification and feedback without end-users ever needing to specify a bigraph. This means any application, specifiable in IoTtalk, is guaranteed, so long as verification succeeds, not to violate the given configuration constraints when deployed; with no extra cost to the user