20,220 research outputs found
The Bidder's Curse
We employ a novel approach to identify overbidding in the field. We compare auction prices to fixed prices for the same item on the same webpage. In detailed board-game data, 42 percent of auctions exceed the simultaneous fixed price. The result replicates in a broad cross-section of auctions (48 percent). A small fraction of overbidders, 17 percent, suffices to generate the overbidding. The observed behavior is inconsistent with rational behavior, even allowing for uncertainty and switching costs, since also the expected auction price exceeds the fixed price. Limited attention to outside options is most consistent with our results.
Refining the Spin Hamiltonian in the Spin-1/2 Kagome Lattice Antiferromagnet ZnCu(OH)Cl using Single Crystals
We report thermodynamic measurements of the S=1/2 kagome lattice
antiferromagnet ZnCu(OH)Cl, a promising candidate system with
a spin-liquid ground state. Using single crystal samples, the magnetic
susceptibility both perpendicular and parallel to the kagome plane has been
measured. A small, temperature-dependent anisotropy has been observed, where
at high temperatures and at
low temperatures. Fits of the high-temperature data to a Curie-Weiss model also
reveal an anisotropy. By comparing with theoretical calculations, the presence
of a small easy-axis exchange anisotropy can be deduced as the primary
perturbation to the dominant Heisenberg nearest neighbor interaction. These
results have great bearing on the interpretation of theoretical calculations
based on the kagome Heisenberg antiferromagnet model to the experiments on
ZnCu(OH)Cl.Comment: 4 pages, 4 figure
Distinctive-attribute Extraction for Image Captioning
Image captioning, an open research issue, has been evolved with the progress
of deep neural networks. Convolutional neural networks (CNNs) and recurrent
neural networks (RNNs) are employed to compute image features and generate
natural language descriptions in the research. In previous works, a caption
involving semantic description can be generated by applying additional
information into the RNNs. In this approach, we propose a distinctive-attribute
extraction (DaE) which explicitly encourages significant meanings to generate
an accurate caption describing the overall meaning of the image with their
unique situation. Specifically, the captions of training images are analyzed by
term frequency-inverse document frequency (TF-IDF), and the analyzed semantic
information is trained to extract distinctive-attributes for inferring
captions. The proposed scheme is evaluated on a challenge data, and it improves
an objective performance while describing images in more detail.Comment: 14 main pages, 4 supplementary page
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