5,481 research outputs found
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering
In this paper, we propose a novel end-to-end neural architecture for ranking
candidate answers, that adapts a hierarchical recurrent neural network and a
latent topic clustering module. With our proposed model, a text is encoded to a
vector representation from an word-level to a chunk-level to effectively
capture the entire meaning. In particular, by adapting the hierarchical
structure, our model shows very small performance degradations in longer text
comprehension while other state-of-the-art recurrent neural network models
suffer from it. Additionally, the latent topic clustering module extracts
semantic information from target samples. This clustering module is useful for
any text related tasks by allowing each data sample to find its nearest topic
cluster, thus helping the neural network model analyze the entire data. We
evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic
domain question answering dataset, which is related to Samsung products. The
proposed model shows state-of-the-art results for ranking question-answer
pairs.Comment: 10 pages, Accepted as a conference paper at NAACL 201
Diffusion-Stego: Training-free Diffusion Generative Steganography via Message Projection
Generative steganography is the process of hiding secret messages in
generated images instead of cover images. Existing studies on generative
steganography use GAN or Flow models to obtain high hiding message capacity and
anti-detection ability over cover images. However, they create relatively
unrealistic stego images because of the inherent limitations of generative
models. We propose Diffusion-Stego, a generative steganography approach based
on diffusion models which outperform other generative models in image
generation. Diffusion-Stego projects secret messages into latent noise of
diffusion models and generates stego images with an iterative denoising
process. Since the naive hiding of secret messages into noise boosts visual
degradation and decreases extracted message accuracy, we introduce message
projection, which hides messages into noise space while addressing these
issues. We suggest three options for message projection to adjust the trade-off
between extracted message accuracy, anti-detection ability, and image quality.
Diffusion-Stego is a training-free approach, so we can apply it to pre-trained
diffusion models which generate high-quality images, or even large-scale
text-to-image models, such as Stable diffusion. Diffusion-Stego achieved a high
capacity of messages (3.0 bpp of binary messages with 98% accuracy, and 6.0 bpp
with 90% accuracy) as well as high quality (with a FID score of 2.77 for 1.0
bpp on the FFHQ 6464 dataset) that makes it challenging to distinguish
from real images in the PNG format
Arithmetic properties of orders in imaginary quadratic fields
Let be an imaginary quadratic field. For an order in
and a positive integer , let be the ray class field of
modulo . We deal with various subjects related to
, mainly about Galois representations attached to elliptic
curves with complex multiplication, form class groups and -functions for
orders
Class fields arising from the form class groups of order O and level N
Let be an imaginary quadratic field and be an order in .
We construct class fields associated with form class groups which are
isomorphic to certain -ideal class groups in terms of the theory
of canonical models due to Shimura. By utilizing these form class groups, we
first derive a congruence relation on special values of a modular function of
higher level as an analogue of Kronecker's congruence relation. Furthermore, as
an application of such class fields, for a positive integer we examine
primes of the form with some additional conditions.Comment: 30 page
The Mediating and moderating effects of teacher-child relationships on social behavior and peer preference
Abstract The purpose of this study was to investigate the mediating and moderating effects of teacher-child relationships on children's social behavior and peer preference. The participants were 508 children and 28 head teachers of their classes. Teachers measured the children's social behavior and the teacher-child relationships. Peer preference was measured by peer nomination. The association between prosocial behavior and peer preference was partially mediated by teacher-child conflict. The association between withdrawal, aggression and peer preference was fully mediated by teacher-child conflict. The moderating effects of teacher-child conflict were found between prosocial behavior and peer preference. In addition, teacher-child conflict moderated the association between physical aggression and peer preference. (peer preference), -(teacher-child relationships), (prosocial behavior), (withdrawal)
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