Ds- S₁ S2 S3 D₁ T₁ Encoder Output (shifted right) Output Embedding Input Embedding Attention Muti-Head Add & Norm Feed Forward Add & Norm Muti-Head Add & Attention Norm Encoder #N Muti-Head Add & Attention Norm Feed Forward Add & Norm Decoder #N Linear T₁ S₁ T₁ S₁ S₂ S3 Linear Кт VT Qs Vs Ks Cross Adaptive Layer Sigmoid Muti-Head Cross Attention Muti-Head Attention Add & Norm Add & Norm S₂ ypred T₁ S₁ S₁ LMMD LMSE Feed Forward Feed Forward Ldistillation S3 Ylabel Add & Norm Add & Norm ΤΙ S₁ Cross Adaptive Layer |Ltotal = arg min (WaLdistillation+ WMLMMD + W,Lregression) Fig. 6. Architecture of the proposed MSCATN. Inputs Teacher Model (Pretrained) Internal Features!! Input C (Complete Data) Transformer Encoder T Teacher Prediction y_! Input M (Missing Data) Prediction Loss (y s'Avs Total Loss A Knowledge Distillation (Student B) Knowledge Distillation (Student A) Feature Alignment (Avs Backpropagation Total Loss B Backpropagation Prediction Loss (y "B vs y_0) Student ModeA (Handles MissingInput) Transformer Encoder S_A Ground Truth RUL RULLabels Student A Prediction y "A Student Model B (Handles Missing Labels) Transformer Encoder S B Student B Prediction y_s^8 Final Output Final RUL Prediction (y_s)
The knowledge distillation part is not very clear in the diagram. Please create two new diagrams by separating the two student models:
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First Diagram (Student A - Missing Values):
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Clearly illustrate the student training process.
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Show how knowledge distillation happens between the teacher and Student A.
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Explain what the teacher teaches Student A (e.g., handling missing values) and how this teaching occurs (e.g., through logits, features, or attention).
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Second Diagram (Student B - Missing Labels):
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Similarly, detail the training process for Student B.
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Clarify how knowledge distillation works between the teacher and Student B.
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Specify what the teacher teaches Student B (e.g., dealing with missing labels) and how the knowledge is transferred.
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Since these are two distinct challenges (missing values vs. missing labels), they should not be combined in the same diagram. Instead, create two separate diagrams for clarity.
For reference, I will attach a second image (architecture of the proposed MSCATNN) as an example of the level of detail I expect for both cases (Student A and Student B).



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