143 research outputs found
Vibration fatigue reliability analysis of aircraft landing gear based on fuzzy theory under random vibration
The failure of aircraft landing gear (ALG) is major caused by vibration fatigue. And its main failure mode is fatigue fracture. Currently the reliability of ALG is usually calculated by the stress strength interference (SSI) model, which is based on the binary state assumption. While in reality, the strength is degraded with time and the boundary of the failure and success is blur, so the binary state assumption is deviated from the fact. To overcome this problem, this paper uses the membership function (MF) to represent fuzzy safe state which caused by the strength degradation under the failure mode of vibration fatigue. Moreover, a fuzzy reliability model (FRM) of ALG is proposed based on fuzzy failure domain (FFD). Finally, the feasibility of method is tested through a simulation example. By comparing the simulation results (SRs) of the FRM with SRs of the static SSI model and the dynamic SSI model, the rationality of the method is verified. The FRM can calculate the reliability without the gradual degradation processes, thus it is used more widely
Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao
Click-Through Rate (CTR) prediction serves as a fundamental component in
online advertising. A common practice is to train a CTR model on advertisement
(ad) impressions with user feedback. Since ad impressions are purposely
selected by the model itself, their distribution differs from the inference
distribution and thus exhibits sample selection bias (SSB) that affects model
performance. Existing studies on SSB mainly employ sample re-weighting
techniques which suffer from high variance and poor model calibration. Another
line of work relies on costly uniform data that is inadequate to train
industrial models. Thus mitigating SSB in industrial models with a
uniform-data-free framework is worth exploring. Fortunately, many platforms
display mixed results of organic items (i.e., recommendations) and sponsored
items (i.e., ads) to users, where impressions of ads and recommendations are
selected by different systems but share the same user decision rationales.
Based on the above characteristics, we propose to leverage recommendations
samples as a free lunch to mitigate SSB for ads CTR model (Rec4Ad). After
elaborating data augmentation, Rec4Ad learns disentangled representations with
alignment and decorrelation modules for enhancement. When deployed in Taobao
display advertising system, Rec4Ad achieves substantial gains in key business
metrics, with a lift of up to +6.6\% CTR and +2.9\% RPM
CAMP:Co-Attention Memory Networks for Diagnosis Prediction in Healthcare
Diagnosis prediction, which aims to predict future health information of patients from historical electronic health records (EHRs), is a core research task in personalized healthcare. Although some RNN-based methods have been proposed to model sequential EHR data, these methods have two major issues. First, they cannot capture fine-grained progression patterns of patient health conditions. Second, they do not consider the mutual effect between important context (e.g., patient demographics) and historical diagnosis. To tackle these challenges, we propose a model called Co-Attention Memory networks for diagnosis Prediction (CAMP), which tightly integrates historical records, fine-grained patient conditions, and demographics with a three-way interaction architecture built on co-attention. Our model augments RNNs with a memory network to enrich the representation capacity. The memory network enables analysis of fine-grained patient conditions by explicitly incorporating a taxonomy of diseases into an array of memory slots. We instantiate the READ/WRITE operations of the memory network so that the memory cooperates effectively with the patient demographics through co-attention mechanism. Experiments on real-world datasets demonstrate that CAMP consistently performs better than state-of-the-art methods
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
Despite the development of ranking optimization techniques, pointwise loss
remains the dominating approach for click-through rate prediction. It can be
attributed to the calibration ability of the pointwise loss since the
prediction can be viewed as the click probability. In practice, a CTR
prediction model is also commonly assessed with the ranking ability. To
optimize the ranking ability, ranking loss (e.g., pairwise or listwise loss)
can be adopted as they usually achieve better rankings than pointwise loss.
Previous studies have experimented with a direct combination of the two losses
to obtain the benefit from both losses and observed an improved performance.
However, previous studies break the meaning of output logit as the
click-through rate, which may lead to sub-optimal solutions. To address this
issue, we propose an approach that can Jointly optimize the Ranking and
Calibration abilities (JRC for short). JRC improves the ranking ability by
contrasting the logit value for the sample with different labels and constrains
the predicted probability to be a function of the logit subtraction. We further
show that JRC consolidates the interpretation of logits, where the logits model
the joint distribution. With such an interpretation, we prove that JRC
approximately optimizes the contextualized hybrid discriminative-generative
objective. Experiments on public and industrial datasets and online A/B testing
show that our approach improves both ranking and calibration abilities. Since
May 2022, JRC has been deployed on the display advertising platform of Alibaba
and has obtained significant performance improvements.Comment: Accepted at KDD 202
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