183 research outputs found
Preference-grounded Token-level Guidance for Language Model Fine-tuning
Aligning language models (LMs) with preferences is an important problem in
natural language generation. A key challenge is that preferences are typically
provided at the sequence level while LM training and generation both occur at
the token level. There is, therefore, a granularity mismatch between the
preference and the LM training losses, which may complicate the learning
problem. In this paper, we address this issue by developing an alternate
training process, where we iterate between grounding the sequence-level
preference into token-level training guidance, and improving the LM with the
learned guidance. For guidance learning, we design a framework that extends the
pairwise-preference learning in imitation learning to both variable-length LM
generation and utilizing the preference among multiple generations. For LM
training, based on the amount of supervised data, we present two minimalist
learning objectives that utilize the learned guidance. In experiments, our
method performs competitively on two distinct representative LM tasks --
discrete-prompt generation and text summarization
An analytical modeling for high-velocity impacts on woven Kevlar composite laminates
In this paper, an analytical model, which based on energy balance, is built to study the process of high velocity impacts on woven Kevlar composite laminates by a cylindrical projectile. Four different mechanisms, such as laminate crushing, linear momentum transfer and tensile fiber failure, and shear plugging, is absorbed by the laminate while impacting. Then, simplification of the model is done to obtain the residual velocity and ballistic limit. The analytical results are validated with the results of experiment, and the perturbation analysis is done to analyze the reason of error
A Comparative Study of Image Restoration Networks for General Backbone Network Design
Despite the significant progress made by deep models in various image
restoration tasks, existing image restoration networks still face challenges in
terms of task generality. An intuitive manifestation is that networks which
excel in certain tasks often fail to deliver satisfactory results in others. To
illustrate this point, we select five representative image restoration networks
and conduct a comparative study on five classic image restoration tasks. First,
we provide a detailed explanation of the characteristics of different image
restoration tasks and backbone networks. Following this, we present the
benchmark results and analyze the reasons behind the performance disparity of
different models across various tasks. Drawing from this comparative study, we
propose that a general image restoration backbone network needs to meet the
functional requirements of diverse tasks. Based on this principle, we design a
new general image restoration backbone network, X-Restormer. Extensive
experiments demonstrate that X-Restormer possesses good task generality and
achieves state-of-the-art performance across a variety of tasks
FIRST: A Million-Entry Dataset for Text-Driven Fashion Synthesis and Design
Text-driven fashion synthesis and design is an extremely valuable part of
artificial intelligence generative content(AIGC), which has the potential to
propel a tremendous revolution in the traditional fashion industry. To advance
the research on text-driven fashion synthesis and design, we introduce a new
dataset comprising a million high-resolution fashion images with rich
structured textual(FIRST) descriptions. In the FIRST, there is a wide range of
attire categories and each image-paired textual description is organized at
multiple hierarchical levels. Experiments on prevalent generative models
trained over FISRT show the necessity of FIRST. We invite the community to
further develop more intelligent fashion synthesis and design systems that make
fashion design more creative and imaginative based on our dataset. The dataset
will be released soon.Comment: 11 pages, 8 figure
Ideal Unconventional Weyl Point in a Chiral Photonic Metamaterial
Unconventional Weyl points (WPs), carrying topological charge 2 or higher,
possess interesting properties different from ordinary charge-1 WPs, including
multiple Fermi arcs that stretch over a large portion of the Brillouin zone.
Thus far, such WPs have been observed in chiral materials and acoustic
metamaterials, but there has been no clean demonstration in photonics in which
the unconventional photonic WPs are separated from trivial bands. We
experimentally realize an ideal symmetry-protected photonic charge-2 WP in a
three-dimensional topological chiral microwave metamaterial. We use field
mapping to directly observe the projected bulk dispersion, as well as the two
long surface arcs that form a noncontractible loop wrapping around the surface
Brillouin zone. The surface states span a record-wide frequency window of
around 22.7% relative bandwidth. We demonstrate that the surface states exhibit
a novel topological self-collimation property and are robust against disorder.
This work provides an ideal photonic platform for exploring fundamental physics
and applications of unconventional WPs.Comment: 6 pages, 4 figure
Moderating effect of classroom sociable norm on the relations between unsociability and internalizing problems in Chinese adolescents
ObjectivesThe goal of the present study was to examine the moderating effect of classroom sociable norm on the relations between unsociability and internalizing problems (the indicators included depression, loneliness and self-esteem) in Chinese adolescents.MethodsParticipants were N = 1,160 adolescents in Grade 4–8 from Shanghai, People’s Republic of China. They completed questionnaires about unsociability, sociability, and social preference via peer nominations, while depression, loneliness, and self-esteem were collected via self-report.ResultsIt was found that unsociability was positively associated with depression and loneliness, and negatively associated with self-esteem. Moreover, the relations between unsociability and indicators of internalizing problems were moderated by classroom sociable norm. More specifically, the significant positive associations between unsociability and depression and loneliness were stronger in classrooms with high sociable norm, and the negative association between unsociability and self-esteem was only significant in such classrooms.ConclusionThe findings suggest that classroom sociable norm plays an important role in unsociable adolescents’ psychological adjustment in China. Researchers should focus more on the influence of classroom environment on adolescents’ development in future
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