A negative emotional context disrupts the framing effect on outcome evaluation in decision making under uncertainty: An ERP study.

The framing effect refers to the phenomenon that different descriptions of the same option lead to a shift in the choice of the decision maker. Several studies have found that emotional contexts irrelevant to a decision in progress still influence the framing effect on decision making. However, little is known about the potential role of emotional contexts in the framing effect on outcome evaluation under uncertainty and the related neural mechanisms. The present study measured event-related potentials (ERPs) to capture the time series of brain activities during the processing of gain- and loss-framed choices and outcomes primed with neutral and negative emotional contexts. The results revealed that in the neutral emotional context, the P300 amplitudes following both positive and negative feedback were greater in the gain-framed condition than those in the loss-framed condition, demonstrating a framing effect, whereas in the negative emotional context, this effect was unstable and observed only following negative feedback. In contrast, regardless of whether the feedback was positive or negative, the framing effect on the feedback-related negativity (FRN) amplitudes was insensitive to neutral and negative emotional contexts. Furthermore, the time-frequency analysis showed that the framing effect on the theta power related to the FRN was also insensitive to neutral and negative emotional contexts. Our findings suggest that brain responses to framing effects on outcome evaluation in a later cognitive appraisal stage of decision making under uncertainty may depend on the emotional context, as the effects were observed only following negative feedback in the negative emotional context.

Towards greenhouse cultivation of Artemisia annua: The application of LEDs in regulating plant growth and secondary metabolism.

Artemisinin is a sesquiterpene lactone produced in glandular trichomes of Artemisia annua, and is extensively used in the treatment of malaria. Growth and secondary metabolism of A. annua are strongly regulated by environmental conditions, causing unstable supply and quality of raw materials from field grown plants. This study aimed to bring A. annua into greenhouse cultivation and to increase artemisinin production by manipulating greenhouse light environment using LEDs. A. annua plants were grown in a greenhouse compartment for five weeks in vegetative stage with either supplemental photosynthetically active radiation (PAR) (blue, green, red or white) or supplemental radiation outside PAR wavelength (far-red, UV-B or both). The colour of supplemental PAR hardly affected plant morphology and biomass, except that supplemental green decreased plant biomass by 15% (both fresh and dry mass) compared to supplemental white. Supplemental far-red increased final plant height by 23% whereas it decreased leaf area, plant fresh and dry weight by 30%, 17% and 7%, respectively, compared to the treatment without supplemental radiation. Supplemental UV-B decreased plant leaf area and dry weight (both by 7%). Interestingly, supplemental green and UV-B increased leaf glandular trichome density by 11% and 9%, respectively. However, concentrations of artemisinin, arteannuin B, dihydroartemisinic acid and artemisinic acid only exhibited marginal differences between the light treatments. There were no interactive effects of far-red and UV-B on plant biomass, morphology, trichome density and secondary metabolite concentrations. Our results illustrate the potential of applying light treatments in greenhouse production of A. annua to increase trichome density in vegetative stage. However, the trade-off between light effects on plant growth and trichome initiation needs to be considered. Moreover, the underlying mechanisms of light spectrum regulation on artemisinin biosynthesis need further clarification to enhance artemisinin yield in greenhouse production of A. annua.

Enhancing the robustness of recommender systems against spammers.

The accuracy and diversity of recommendation algorithms have always been the research hotspot of recommender systems. A good recommender system should not only have high accuracy and diversity, but also have adequate robustness against spammer attacks. However, the issue of recommendation robustness has received relatively little attention in the literature. In this paper, we systematically study the influences of different spammer behaviors on the recommendation results in various recommendation algorithms. We further propose an improved algorithm by incorporating the inner-similarity of user's purchased items in the classic KNN approach. The new algorithm effectively enhances the robustness against spammer attacks and thus outperforms traditional algorithms in recommendation accuracy and diversity when spammers exist in the online commercial systems.

Photometric Stereo for General BRDFs via Reflection Sparsity Modeling.

This paper proposes a pixelwise photometric stereo method for object surfaces with general bidirectional reflectance distribution functions (BRDFs) via appropriate reflection modeling. The modeling is based on three general characteristics of reflection components, i.e., the smooth variation of diffuse reflection, the concentration of specular reflection, and the low-intensity nature of shadow. A graph, whose nodes are light directions, is introduced to model these characteristics. In the graph, the neighborhood of nodes is determined by finding the light sources with close directions. The smoothness of the diffuse component is termed as the summation of local variations under all light sources. The specular reflection is modeled by group sparsity, and the shadow is determined via weighted l1 -norm modeling. The optimization problem, which incorporates these three modeling terms, is cast as a second-order cone programming problem. The proposed method is evaluated on both synthetic and real-world scenes with both isotropic and anisotropic materials. The experimental results show that the method is effective for object surfaces with general BRDFs and outperforms the state-of-the-arts.