Implementation Intentions Reduce Implicit Stereotype Activation and Application.

Research has found that implementation intentions, if-then action plans (e.g., "if I see a Black face, I will think safe"), reduce stereotyping on implicit measures. However, it is unknown by what process(es) implementation intentions reduce implicit stereotyping. The present research examines the effects of implementation intentions on stereotype activation (e.g., extent to which stereotypic information is accessible) and stereotype application (e.g., extent to which accessible stereotypes are applied in judgment). In addition, we assessed the efficiency of implementation intentions by manipulating cognitive resources (e.g., digit-span, restricted response window) while participants made judgments on an implicit stereotyping measure. Across four studies, implementation intentions reduced implicit stereotyping. This decrease in stereotyping was associated with reductions in both stereotype activation and application. In addition, these effects of implementation intentions were highly efficient and associated with reduced stereotyping even for groups for which people may have little practice inhibiting stereotypes (e.g., gender)

Investigation Of Green Strawberry Detection Using R-Cnn With Various Architectures

Traditional image processing solutions have been applied in the past to detect and count strawberries. These methods typically involve feature extraction followed by object detection using one or more features. Some object detection problems can be ambiguous as to what features are relevant and the solutions to many problems are only fully realized when the modern approach has been applied and tested, such as deep learning. In this work, we investigate the use of R-CNN for green strawberry detection. The object detection involves finding regions of interest (ROIs) in field images using the selective segmentation algorithm and inputting these regions into a pre-trained deep neural network (DNN) model. The convolutional neural networks VGG, MobileNet and ResNet were implemented to detect subtle differences between green strawberries and various background elements. Downscaling factors, intersection over union (IOU) thresholds and non-maxima suppression (NMS) values can be tweaked to increase recall and reduce false positives while data augmentation and negative hardminging can be used to increase the amount of input data. The state of the art model is sufficient in locating the green strawberries with an overall model accuracy of 74%. The R-CNN model can then be used for crop yield prediction to forecast the actual red strawberry count one week in advance with a 90% accuracy

A submillimeter interference spectrometer - Characteristics, performance and measurements

Performance characteristics of interference spectrometer using submillimeter wave

National Technology Transfer Center

Viewgraphs on the National Technology Transfer Center (NTTC) are provided. The NTTC mission is to serve as a hub for the nationwide technology-transfer network to expedite the movement of federally developed technology into the stream of commerce. A description of the Center is provided

Spontaneous Fluxoid Formation in Superconducting Loops

We report on the first experimental verification of the Zurek-Kibble scenario in an isolated superconducting ring over a wide parameter range. The probability of creating a single flux quantum spontaneously during the fast normal-superconducting phase transition of a wide Nb loop clearly follows an allometric dependence on the quenching time $\tau_{Q}$, as one would expect if the transition took place as fast as causality permits. However, the observed Zurek-Kibble scaling exponent $\sigma = 0.62\pm0.15$ is two times larger than anticipated for large loops. Assuming Gaussian winding number densities we show that this doubling is well-founded for small annuli.Comment: 10 pages, 2 figures, submitted to Phys. Rev. Lett

Experimental Psychology in Relation to Insanity

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