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