60 research outputs found
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models
Pre-trained vision-language models, e.g., CLIP, working with manually
designed prompts have demonstrated great capacity of transfer learning.
Recently, learnable prompts achieve state-of-the-art performance, which however
are prone to overfit to seen classes, failing to generalize to unseen classes.
In this paper, we propose a Knowledge-Aware Prompt Tuning (KAPT) framework for
vision-language models. Our approach takes inspiration from human intelligence
in which external knowledge is usually incorporated into recognizing novel
categories of objects. Specifically, we design two complementary types of
knowledge-aware prompts for the text encoder to leverage the distinctive
characteristics of category-related external knowledge. The discrete prompt
extracts the key information from descriptions of an object category, and the
learned continuous prompt captures overall contexts. We further design an
adaptation head for the visual encoder to aggregate salient attentive visual
cues, which establishes discriminative and task-aware visual representations.
We conduct extensive experiments on 11 widely-used benchmark datasets and the
results verify the effectiveness in few-shot image classification, especially
in generalizing to unseen categories. Compared with the state-of-the-art CoCoOp
method, KAPT exhibits favorable performance and achieves an absolute gain of
3.22% on new classes and 2.57% in terms of harmonic mean.Comment: Accepted by ICCV 202
EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models
Events serve as fundamental units of occurrence within various contexts. The
processing of event semantics in textual information forms the basis of
numerous natural language processing (NLP) applications. Recent studies have
begun leveraging large language models (LLMs) to address event semantic
processing. However, the extent that LLMs can effectively tackle these
challenges remains uncertain. Furthermore, the lack of a comprehensive
evaluation framework for event semantic processing poses a significant
challenge in evaluating these capabilities. In this paper, we propose an
overarching framework for event semantic processing, encompassing
understanding, reasoning, and prediction, along with their fine-grained
aspects. To comprehensively evaluate the event semantic processing abilities of
models, we introduce a novel benchmark called EVEVAL. We collect 8 datasets
that cover all aspects of event semantic processing. Extensive experiments are
conducted on EVEVAL, leading to several noteworthy findings based on the
obtained results
Total meat (flesh) supply may be a significant risk factor for cardiovascular diseases worldwide
Abstract Consumption of red meat instead of white meat has typically been associated with cardiovascular diseases (CVDs). Reflecting actual diet patterns, this study explored the role of total meat (red + white) in predicting CVD incidence. Data from 217 countries were extracted from United Nations agencies for the analyses in five steps. Bivariate correlations were applied to examine the relationship between total meat and CVD incidence globally and regionally. Partial correlation was applied to identify that total meat was an independent predictor of CVD incidence while socioeconomic status, obesity, and urbanization were statistically constant. Stepwise linear regression was conducted for selecting the significant predictor of CVD incidence. SPSS 28 and Microsoft Excel were used for correlation analyses. Globally, total meat correlated to CVD incidence strongly and significantly in bivariate correlation models. This relationship remained significant in partial correlation when socioeconomic status, obesity, and urbanization were statistically kept constant. Stepwise multiple regression identified that, second to socioeconomic status, total meat was a significant predictor of CVD incidence. Total meat correlated to CVD incidence in different country groupings. However, the correlations between total meat and CVD incidence were significantly stronger in developing countries than in developed countries. Worldwide, total meat (flesh) consumption correlated to CVD incidence independently, but significantly stronger in developing countries than in developed countries. This correlation is worth exploring further in longitudinal cohort studies
Protection Principle for a DC Distribution System with a Resistive Superconductive Fault Current Limiter
A DC distribution system, which is suitable for access to distributed power generation and DC loads, is one of the development directions in power systems. Furthermore, it could greatly improve the energy efficiency and reduce the loss of power transportation. The huge short circuit current is always a great threat to the safety of the components, especially the capacitors and diodes. A resistive superconductive fault current limiter (SFCL), which could respond quickly once a fault happens and limit the fault current to a relatively low level, becomes a good solution to this problem. In this paper, the operational principle of the resistive SFCL is introduced first, and then, the DC short-circuit fault characteristic of the DC distribution system with the SFCL is analyzed and the effectiveness of the SFCL verified. In order to realize the selectivity of the protection in the DC distribution system with SFCL, a new transient current protection principle based on Ip (the peak value of the current) and tp (the transient time that the current takes to reach its peak value) is proposed. Finally, a model of a 10-kV DC distribution system with an SFCL is established and simulated in PSCAD/METDC. Simulation results have demonstrated the validity of the analysis and protection principle
Protection Principle for a DC Distribution System with a Resistive Superconductive Fault Current Limiter
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