Optimizing Guided Traversal for Fast Learned Sparse Retrieval

Recent studies show that BM25-driven dynamic index skipping can greatly accelerate MaxScore-based document retrieval based on the learned sparse representation derived by DeepImpact. This paper investigates the effectiveness of such a traversal guidance strategy during top k retrieval when using other models such as SPLADE and uniCOIL, and finds that unconstrained BM25-driven skipping could have a visible relevance degradation when the BM25 model is not well aligned with a learned weight model or when retrieval depth k is small. This paper generalizes the previous work and optimizes the BM25 guided index traversal with a two-level pruning control scheme and model alignment for fast retrieval using a sparse representation. Although there can be a cost of increased latency, the proposed scheme is much faster than the original MaxScore method without BM25 guidance while retaining the relevance effectiveness. This paper analyzes the competitiveness of this two-level pruning scheme, and evaluates its tradeoff in ranking relevance and time efficiency when searching several test datasets.Comment: This paper is published in WWW'2

Probabilistic Results on the Architecture of Mathematical Reasoning Aligned by Cognitive Alternation

We envision a machine capable of solving mathematical problems. Dividing the quantitative reasoning system into two parts: thought processes and cognitive processes, we provide probabilistic descriptions of the architecture

Flowfield prediction of airfoil off-design conditions based on a modified variational autoencoder

Airfoil aerodynamic optimization based on single-point design may lead to poor off-design behaviors. Multipoint optimization that considers the off-design flow conditions is usually applied to improve the robustness and expand the flight envelope. Many deep learning models have been utilized for the rapid prediction or reconstruction of flowfields. However, the flowfield reconstruction accuracy may be insufficient for cruise efficiency optimization, and the model generalization ability is also questionable when facing airfoils different from the airfoils with which the model has been trained. Because a computational fluid dynamic evaluation of the cruise condition is usually necessary and affordable in industrial design, a novel deep learning framework is proposed to utilize the cruise flowfield as a prior reference for the off-design condition prediction. A prior variational autoencoder is developed to extract features from the cruise flowfield and to generate new flowfields under other free stream conditions. Physical-based loss functions based on aerodynamic force and conservation of mass are derived to minimize the prediction error of the flowfield reconstruction. The results demonstrate that the proposed model can reduce the prediction error on test airfoils by 30% compared to traditional models. The physical-based loss function can further reduce the prediction error by 4%. The proposed model illustrates a better balance of the time cost and the fidelity requirements of evaluation for cruise and off-design conditions, which makes the model more feasible for industrial applications

Fast buffet onset prediction and optimization method based on a pre-trained flowfield prediction model

The transonic buffet is a detrimental phenomenon occurs on supercritical airfoils and limits aircraft's operating envelope. Traditional methods for predicting buffet onset rely on multiple computational fluid dynamics simulations to assess a series of airfoil flowfields and then apply criteria to them, which is slow and hinders optimization efforts. This article introduces an innovative approach for rapid buffet onset prediction. A machine-learning flowfield prediction model is pre-trained on a large database and then deployed offline to replace simulations in the buffet prediction process for new airfoil designs. Unlike using a model to directly predict buffet onset, the proposed technique offers better visualization capabilities by providing users with intuitive flowfield outputs. It also demonstrates superior generalization ability, evidenced by a 32.5% reduction in average buffet onset prediction error on the testing dataset. The method is utilized to optimize the buffet performance of 11 distinct airfoils within and outside the training dataset. The optimization results are verified with simulations and proved to yield improved samples across all cases. It is affirmed the pre-trained flowfield prediction model can be applied to accelerate aerodynamic shape optimization, while further work still needs to raise its reliability for this safety-critical task.Comment: 44 pages, 20 figure

Snowball Effect of User Participation in Online Environmental Communities: Elaboration Likelihood under Social Influence

Ecological preservation and sustainable development depend on active public involvement. The emergence of online environmental communities greatly facilitates people’s participation in green endeavors. The population penetration of such platforms accelerates as existing users persuade people around them and media coverage further attracts public attention. This snowball effect plays an important role in the user base expansion, but the specific mechanism of social influence involved is yet to be examined. Based on the social influence theory, cognitive response theory, and elaboration likelihood model, this study establishes a research model depicting the relationship between persuasion in terms of social influence and outcomes in terms of behavioral intention and actual participation through the mediation of cognitive responses in terms of perceived value and perceived risk. Empirical results from survey observations show that social influence has both moderated (by education) and mediated (through perceived risk) effects on behavioral intention, which leads to actual participation. Meanwhile, social influence shapes the perceived value, which has a direct and strong impact on actual participation. These central and peripheral routes through which social influence affects individual participation yield useful theoretical and practical implications on human behavior with online environmental communities

Room-Temperature Spin-Valve Effect in Fe3_3GaTe2_2/MoS2_2/Fe3_3GaTe2_2 2D van der Waals Heterojunction Devices

Spin-valve effect has been the focus of spintronics over the last decades due to its potential in many spintronic devices. Two-dimensional (2D) van der Waals (vdW) materials are highly expected to build the spin-valve heterojunction. However, the Curie temperatures (TC) of the vdW ferromagnetic 2D crystals are mostly below room temperature (~30-220 K). It is very challenging to develop room temperature, ferromagnetic (FM) 2D crystals based spin-valve devices which are still not available to date. We report the first room temperature, FM 2D crystal based all-2D vdW Fe3GaTe2/MoS2/Fe3GaTe2 spin valve devices. The Magnetoresistance (MR) of the all- devices is up to 15.89% at 2.3 K and 11.97% at 10 K, 4-30 times of MR from the spin valves of Fe$_3$GaTe$_2$/MoS$_2$/Fe$_3$GaTe$_2$ and conventional NiFe/MoS$_2$/NiFe. Typical spin valve effect shows strong dependence on MoS2 spacer thickness in the vdW heterojunction. Importantly, the spin valve effect (0.31%) still robustly exists at 300 K with low working currents down to 10 nA (0.13 A/cm$^2$). The results provide a general vdW platform to room temperature, 2D FM crystals based 2D spin valve devices

Room-temperature and tunable tunneling magnetoresistance in Fe3GaTe2-based all-2D van der Waals heterojunctions with high spin polarization

Magnetic tunnel junctions (MTJs) based on all-two dimensional (2D) van der Waals heterostructures with sharp and clean interfaces in atomic scale are essential for the application of next-generation spintronics. However, the lack of room-temperature intrinsic ferromagnetic crystals with perpendicular magnetic anisotropy has greatly hindered the development of vertical MTJs. The discovery of room-temperature intrinsic ferromagnetic 2D crystal Fe3GaTe2 has solved the problem and greatly facilitated the realization of practical spintronic devices. Here, we demonstrate a room-temperature MTJ based on Fe3GaTe2/WS2/Fe3GaTe2 heterostructure. The tunnelling magnetoresistance (TMR) ratio is up to 213% with high spin polarization of 72% at 10 K, the highest ever reported in Fe3GaTe2-based MTJs up to now. The tunnelling spin-valve signal robustly exists at room temperature (300 K) with bias current down to 10 nA. Moreover, the spin polarization can be modulated by bias current and the TMR shows a sign reversal at large bias current. Our work sheds light on the potential application for low-energy consumption all-2D vdW spintronics and offers alternative routes for the electronic control of spintronic devices