32 research outputs found
Risk preferences in self–other decisions: The effect of payoff allocation framing
No full textThe effect of framing is well established: Decision makers' preferences are influenced by how outcomes or attributes are phrased. In the financial domain, individuals often make decisions for themselves and for others. Therefore, decisions in a two-person context with the outcome equally allocated can be framed in two ways defined as theallocation framing: (1) self-allocation frame: making a decision for oneself, with half the payoffs shared by another person; and (2) other-allocation frame: making a decision for the other person and sharing half the payoffs. The results of six studies provided consistent evidence that people are more risk seeking in the self-allocation frame than in the other-allocation frame, and the effect was only noteworthy in the gain domain-not the loss domain. Our findings on allocation framing provide a meaningful contribution to studies of self-other decision making
Data from: Social attention triggered by eye gaze and walking direction is resistant to temporal decay
No full textConscious and unconscious processing of ensemble statistics oppositely modulate perceptual decision-making
No full textData and figure
Conscious and unconscious processing of ensemble statistics oppositely modulate perceptual decision-making
No full textData and figure
Conscious and unconscious processing of ensemble statistics oppositely modulate perceptual decision-making.
No full textOur visual system possesses a remarkable ability to extract summary statistical information from groups of similar objects, known as ensemble perception. It remains elusive whether the processing of ensemble statistics exerts influences on our perceptual decision-making and what roles consciousness and attention play in this process. In a series of experiments, we demonstrated that the processing of ensemble statistics can exert significant modulation effects on our perceptual decision-making, which is independent of consciousness but relies on attentional resources. More intriguingly, the conscious and unconscious ensemble representations respectively induce repulsive and attractive modulation effects, with the unconscious effect susceptible to the temporal separation and the distinction between the inducers and the targets. These results not only suggest that the conscious and unconscious ensemble representations engage different visual processing mechanisms but also highlight the distinct roles of consciousness and attention in ensemble perception.</p
Laminar Burning Speed of Aviation Kerosene at Low Pressures
No full textAero-engine combustors may experience extreme low pressures in the case of an in-flight shutdown, which makes the study of aviation kerosene flame propagation characteristics at low pressures important. The present work examined flame propagation during the combustion of aviation kerosene over the pressure range from 25 to 100 kPa using a constant-volume bomb apparatus. The laminar burning speeds at different initial pressures, temperatures and equivalence ratios were measured and compared. In addition, numerical simulations were used to examine the reaction sensitivity of the laminar burning speed at low pressure. In trials at the lean flammability limit, the data indicated that it was more difficult to ignite the fuel under a lower pressure condition of 25 kPa and a lower temperature condition of 420 K. The experimental results of laminar burning speed were fitted to an equation providing the laminar burning speeds expected at different pressures (25–100 kPa), temperatures (400–480 K) and equivalence ratios (0.8–1.5). The temperature index (α=1.76) and pressure index (β=−0.15) of the fitting equation were obtained. Both hydrodynamic and diffusional thermal flame instabilities were found to be suppressed at low pressures. The negative effects of two specific reactions on laminar burning speed were greatly reduced at these same low pressures of 25 kPa
Video based Children's Social Behavior Classification in Peer-play Scenarios
No full textLabeling children's play behavior is an important process in children's social behavior analysis which is traditionally done by experienced coders. With the growing volume of data, automatic methods for labeling are increasingly required. This paper presents a novel method to label children's social behavior automatically in peer-play scenarios based on visual attention and mutual interaction computation. In this method, the discrete distribution of children's visual attention is computed based on face pose estimation. Then, the mutual interaction among children is calculated by "Attention Processes", which are continuous periods of time during which a certain child pays attention to the same target. After that, "Solitary Feature" and "Group Feature" are extracted by the mutual interaction of children and children's play behaviors will be classified into 3 types ("Solitary Play", "Parallel Play" and "Group Play") by these two kinds of features. Finally, this method is evaluated by a dataset of children's peer-play scenarios and the results show this method has a good performance in our dataset.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000339501000159&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Artificial IntelligenceEICPCI-S(ISTP)
Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions
Full text linkAccurate segmentation of lesions is crucial for diagnosis and treatment of
early esophageal cancer (EEC). However, neither traditional nor deep
learning-based methods up to today can meet the clinical requirements, with the
mean Dice score - the most important metric in medical image analysis - hardly
exceeding 0.75. In this paper, we present a novel deep learning approach for
segmenting EEC lesions. Our approach stands out for its uniqueness, as it
relies solely on a single image coming from one patient, forming the so-called
"You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network"
learning ensures complete patient privacy as it does not use any images from
other patients as the training data. On the other hand, it avoids nearly all
generalization-related problems since each trained network is applied only to
the input image itself. In particular, we can push the training to
"over-fitting" as much as possible to increase the segmentation accuracy. Our
technical details include an interaction with clinical physicians to utilize
their expertise, a geometry-based rendering of a single lesion image to
generate the training set (the \emph{biggest} novelty), and an edge-enhanced
UNet. We have evaluated YOHO over an EEC data-set created by ourselves and
achieved a mean Dice score of 0.888, which represents a significant advance
toward clinical applications
Experimental Study on the Microstructural Characterization of Retardation Capacity of Microbial Inhibitors to Spontaneous Lignite Combustion
No full textMine fires are one of the common major disasters in underground mining. In addition to the external fire sources generated by mining equipment and mechanical and electrical equipment during operations, coal is exposed to air during mining, and spontaneous combustion is also the main cause of mine fires. In order to reduce the hidden danger of coal mines caused by spontaneous coal combustion during lignite mining, the microbial inhibition of coal spontaneous combustion is proposed in this paper. Via SEM, pore size analysis, and NMR and FT-IR experiments, the mechanism of coal spontaneous combustion is discussed and revealed. The modification of lignite before and after the addition of retardants is analyzed from the perspective of microstructure, and the change in flame retardancy of the lignite treated with two retardants compared with raw coal is explored. The results show that, compared with raw coal, a large number of calcium carbonate particles are attached to the surface of the coal sample after bioinhibition treatment, and the total pore volume and specific surface area of the coal sample after bioinhibition treatment are decreased by 68.49% and 74.01%, respectively, indicating that bioinhibition can effectively plug the primary pores. The results of NMR and Fourier infrared spectroscopy show that the chemical structure of the coal sample is mainly composed of aromatic carbon, followed by fatty carbon and carbonyl carbon. In addition, the contents of active groups (hydroxyl, carboxyl, and methyl/methylene) in lignite after bioretardation are lower than those in raw coal, and methyl/methylene content is decreased by 96.5%. The comparison shows that the flame-retardant performance of biological retardants is better than that of chemical retardants, which provides an effective solution for the efficient prevention and control of spontaneous combustion disasters in coal mines
