Improving Reasoning Efficiency in ASPIC+ with Backwards Chaining and Partial Arguments

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Worst Case Matters for Few-Shot Recognition

Few-shot recognition learns a recognition model with very few (e.g., 1 or 5) images per category, and current few-shot learning methods focus on improving the average accuracy over many episodes. We argue that in real-world applications we may often only try one episode instead of many, and hence maximizing the worst-case accuracy is more important than maximizing the average accuracy. We empirically show that a high average accuracy not necessarily means a high worst-case accuracy. Since this objective is not accessible, we propose to reduce the standard deviation and increase the average accuracy simultaneously. In turn, we devise two strategies from the bias-variance tradeoff perspective to implicitly reach this goal: a simple yet effective stability regularization (SR) loss together with model ensemble to reduce variance during fine-tuning, and an adaptability calibration mechanism to reduce the bias. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed strategies, which outperforms current state-of-the-art methods with a significant margin in terms of not only average, but also worst-case accuracy. Our code is available at https://github.com/heekhero/ACSR.Comment: Accepted by ECCV202

Research on the Running Speed Prediction Model of Interchange Ramp

AbstractRamp is an important part of the interchange. The ramp design concept based on the running speed is a new design idea of the highway and interchange. By analysis of the influence factors of the interchange ramp running speed, this paper formulates the orthogonal experiment, and collects 14 ramps, in total of 147 groups of the running speed as the samples, and then analyzes the characteristics of the running speed of the vehicles on interchange ramp. According to the change trend of the running speed, the ramp is divided into three sections: deceleration section, uniform section, acceleration section. This paper mainly analyzes the relationship between the interchange ramp running speed of the three sections and the influence factors; and then this paper builds the interchange ramp running speed prediction model of the three sections. At last, correlation test will be validated, and verify the applicability of the model

Gravity Variation in Siberia: GRACE Observation and Possible Causes

We report the finding, from the GRACE observation, of an increasing trend in the gravity anomaly in Siberia at the rate of up to 0.5 ugal yr-1 during 2003/1 - 2009/12, in the backdrop of a negative anomaly of magnitude on the order of ~-10 mgal. In consideration of the non-uniqueness of the gravitational inverse problem, we examine in some detail the various possible geophysical causes to explain the increasing gravity signal. We find two geophysical mechanisms being the most plausible, namely the melting of permafrost and the GIA post-glacial rebound. We conclude that these two mechanisms cannot be ruled out as causes for the regional gravity increase in Siberia, based on gravity data and in want of ancillary geophysical data in the region. More definitive identification of the contributions of the various causes awaits further studies

Synergistic Self-supervised and Quantization Learning

With the success of self-supervised learning (SSL), it has become a mainstream paradigm to fine-tune from self-supervised pretrained models to boost the performance on downstream tasks. However, we find that current SSL models suffer severe accuracy drops when performing low-bit quantization, prohibiting their deployment in resource-constrained applications. In this paper, we propose a method called synergistic self-supervised and quantization learning (SSQL) to pretrain quantization-friendly self-supervised models facilitating downstream deployment. SSQL contrasts the features of the quantized and full precision models in a self-supervised fashion, where the bit-width for the quantized model is randomly selected in each step. SSQL not only significantly improves the accuracy when quantized to lower bit-widths, but also boosts the accuracy of full precision models in most cases. By only training once, SSQL can then benefit various downstream tasks at different bit-widths simultaneously. Moreover, the bit-width flexibility is achieved without additional storage overhead, requiring only one copy of weights during training and inference. We theoretically analyze the optimization process of SSQL, and conduct exhaustive experiments on various benchmarks to further demonstrate the effectiveness of our method. Our code is available at https://github.com/megvii-research/SSQL-ECCV2022.Comment: Accepted to ECCV 2022 ora

An Unsupervised Three-way Decisions Framework of Overload Preference Based on Adjusted Weight Multi-attribute Decision-making Model

AbstractIn the process of traffic control, law-enforcement officials are required to accurately evaluate the potential probability of freight-driver's overloading behavior. This study establishes a model of overloading preference assessment on the basis of freight-driver's individual variation. With indexes selecting, the equal-weight and AHP-based adjusted weight decision-making model are used respectively to evaluate freight-driver's overload preference. Synthesizing the results from two models, we present a three-way decisions model to make judgment

KINEMATICS OF SOCCER DRIBBLING IN DIFFERENT TASKS

The purpose of this study was to find the differences in kinematics between different speeds and cutting directions. Ten male university division 1 soccer players served as the subjects in this study. The Vicon Motion System and the KISTLER force platform were used synchronously to collect data. The length of projection vector was normalized by leg length. 2way ANOVA was used for statistics. Simple main effect was tested if no significant interaction effect was noted. The significant level was set as .05. The length of projection vector between COM and the heel of pivot leg onto X-Y plane in high speed tasks were longer than that in low speed tasks (p \u3c .05). The angle between the X axis and the projection vector between COM and the heel of pivot leg onto X-Y plane had significant interaction effect (p \u3c .05). In low speed tasks, players’ pivot legs landed more laterally and that might enhance lateral motion of body, especially when players cut to the dominate side (right). It was concluded that players would change their cutting tactics at different speeds and in different directions. Landing position of pivot leg might be a factor that would help defender to know the cutting side of attacker at low dribbling speed