Swizzle is a data access pattern optimization technique by reorganizing the execution order of computational tasks to improve the cache locality in modern NPUs. Existing analysis and optimization techniques lack support for swizzleaware modeling on NPUs and fail to effectively capture cache behavior across diverse swizzle configurations. To this end, we propose TENET-v2, a framework for modeling and optimizing swizzle. We introduce a relation-centric notation to characterize different cache access patterns, thus exploring wider swizzle space. Then, we propose a hybrid performance model for cache analysis. The proposed performance model uses an analytical approach to quantify cache miss behavior under unsaturated cache conditions (non-saturated misses), and employs a simulation method combined with an early exiting mechanism to rapidly model cache behavior under saturated cache conditions (saturated misses). Experimental evaluations demonstrate that TENET-v2 achieves an average absolute error of 1.05 % in read hit rate compared to real-world hardware. Evaluation on a variety of DNNs shows that TENET-v2 outperforms existing tensor program optimizers by up to$\mathbf{1. 5} \times$on A100 GPUs. We also demonstrate NPU cache size optimization based on TENET-v2.

The assessment of personality is crucial not only for scientific inquiries but also for real-world applications such as personnel selection. In this article, we propose and validate a novel implicit measure to predict an individual's levels in the Big Five personality traits from 5 minutes of electroencephalography (EEG) recordings. Participants viewed Chinese words with positive, negative, and neutral emotions. The multi-channel event-related potentials elicited by these emotional words were used to train a sparse regression model for personality prediction. Results from a large test sample of 196 participants indicated that the personality scores derived from the proposed measure reached significant correlations with a commonly used questionnaire (r = .50, .60, .49, .55, and .49 for agreeableness, conscientiousness, neuroticism, openness, and extraversion, respectively). The EEG-based personality scores showed good external validity as well, capable of predicting behavioral indices and psychological adjustment similar to self-reported scores. Besides, the EEG-based scores were relatively stable across time, as reflected by the test-retest reliability of .5 ∼ .7 for the five personality traits within a cohort of 33 participants 19-78 days later. These evaluations suggest that the proposed measure can serve as a viable alternative to conventional personality questionnaires in practice.

D-S evidence theory is a method broadly applied in fusion for decision-making. However, this theory has some shortcomings in the formula of evidence combination with the exception that evidence of fully conflict can not be combined, then the probability validity is difficult to determine and sometimes the composed evidence is different from people's subjective judgments or some other issues. These confine the application of evidence to some extent. Some of them have the dubious credibility which affects the fusion result when Multi evidence are combined together. In order to expand the application of the formula of this theory and enhance the reliability of the fusion results, a new combination formula is introduced in this paper, which is also compared with other formulas in other literatures and finally the improved reliability of this combination formula is verified. At last, through data-mining of the decision-making information on a number of isolated points, a new method using combined evidence to make decisions is described. It's proven from the experimental results that the new combination method not only works well and effectively in the evidence of a high level of conflict but also is applicable to fusion for decision-making.