Establishing a Gross Primary Productivity Model by SIF and PRI on the Rice Canopy.

Solar-induced chlorophyll fluorescence (SIF) has shown remarkable results in estimating vegetation carbon cycles, and combining it with the photochemical reflectance index (PRI) has great potential for estimating gross primary productivity (GPP). However, few studies have used SIF combined with PRI to estimate crop canopy GPP. Large temporal and spatial variability between SIF, PRI, and GPP has also been found in remote sensing observations, and the observed PRI and SIF are influenced by the ratio of different observed information (e.g., background, direct sunlit, and shaded leaves) and the physiological state of the vegetation. In this study, the PRI and SIF from a multi-angle spectrometer and the GPP from an eddy covariance system were used to assess the ability of the PRI to enhance the SIF-GPP estimation model. A semi-empirical kernel-driven Bidirectional Reflectance Distribution Function (BRDF) model was used to describe the hotspot PRI/SIF (PRIhs/SIFhs), and a modified two-leaf model was used to calculate the total canopy PRI/SIF (PRItot/SIFtot). We compared the accuracies of PRIhs/SIFhs and PRItot/SIFtot in estimating GPP. The results indicated that the PRItot+SIFtot-GPP model performed the best, with a correlation coefficient (R2) of the validation dataset of 0.88, a root mean square error (RMSE) of 3.74, and relative prediction deviation (RPD) of 2.71. The leaf area index (LAI) had a linear effect on the PRI/SIF estimation of GPP, but the temperature and vapor pressure differences had nonlinear effects. Compared with hotspot PRIhs/SIFhs, PRItot/SIFtot exhibited better consistency with GPP across different time series. Our research demonstrates that PRI is effective in enhancing SIF and PRI for estimating GPP on the rice canopy and also suggests that the two-leaf model would contribute to the vegetation index tracking the real-time crop productivity.

Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction.

Predicting human motion from historical pose sequence is crucial for a machine to succeed in intelligent interactions with humans. One aspect that has been obviated so far, is the fact that how we represent the skeletal pose has a critical impact on the prediction results. Yet there is no effort that investigates across different pose representation schemes. We conduct an indepth study on various pose representations with a focus on their effects on the motion prediction task. Moreover, recent approaches build upon off-the-shelf RNN units for motion prediction. These approaches process input pose sequence sequentially and inherently have difficulties in capturing long-term dependencies. In this paper, we propose a novel RNN architecture termed AHMR (Attentive Hierarchical Motion Recurrent network) for motion prediction which simultaneously models local motion contexts and a global context. We further explore a geodesic loss and a forward kinematics loss for the motion prediction task, which have more geometric significance than the widely employed L2 loss. Interestingly, we applied our method to a range of articulate objects including human, fish, and mouse. Empirical results show that our approach outperforms the state-of-the-art methods in short-term prediction and achieves much enhanced long-term prediction proficiency, such as retaining natural human-like motions over 50 seconds predictions. Our codes are released.

[Clinical significance of bone marrow morphological examination and tumor marker detection for lymphoma diagnosis and prognosis].

OBJECTIVE: This study was aimed to evaluate the significance of bone marrow(BM) morphological examination and many tumor marker(TM) detection, especially carcinoembryonic antigen (CEA), cancer antigen 125(CA125), cancer antigen 15-3 (CA15-3) and serum ferritin (SF) for lymphoma diagnosis and prognosis. METHODS: A total of 47 confirmed patients with lymphoma in our hospital from January 2012 to October 2013 and 20 health peoplels as normal controls were performed with bone marrow morphological examination, at the same time, the electrochemistry luminescent technique was applied for detecting levels of TM (especially CEA, CA125, CA15-3 and SF) in serum samples of lymphoma patient and normal controls, then the BM immature lymphocyte counts of these people and clinical parameters were analyzed for diagnosis and prognosis. RESULTS: There was significant differences in all the four TM levels between serum samples of lymphoma patients and normal control (P=0.029, P=0.000, P=0.005, P=0.000). These TM levels had no correlation with age, sex white blood cell, lymphocyte, platelet counts and anemia of lymphoma patients (P>0.05). It was also found that the patients with elevated TM levels had high BM immature lymphocytes (lymphoma cells) counts, B symptoms, advanced clinical stage and high IPI index (P<0.05). The CA15-3 and SF levels in serum samples of lymphoma patients with BM infiltration were higher than that in lymphoma patients without BM infiltration (P=0.002, P=0.000). CONCLUSION: Combination of BM morphological examination with serum TM level detection plays an important role in diagnosis, clinical stage and prognosis evaluation of lymphoma patients. It is also very important for assessing BM infiltration status of lymphoma patients.

Comprehensive quantitative analysis on privacy leak behavior.

Privacy information is prone to be leaked by illegal software providers with various motivations. Privacy leak behavior has thus become an important research issue of cyber security. However, existing approaches can only qualitatively analyze privacy leak behavior of software applications. No quantitative approach, to the best of our knowledge, has been developed in the open literature. To fill this gap, in this paper we propose for the first time four quantitative metrics, namely, possibility, severity, crypticity, and manipulability, for privacy leak behavior analysis based on Privacy Petri Net (PPN). In order to compare the privacy leak behavior among different software, we further propose a comprehensive metric, namely, overall leak degree, based on these four metrics. Finally, we validate the effectiveness of the proposed approach using real-world software applications. The experimental results demonstrate that our approach can quantitatively analyze the privacy leak behaviors of various software types and reveal their characteristics from different aspects.