We choose the dual-path transformer-based full-subband network (DPT-FSNet) [12] to explore compression methods for Echo cancellation and noise reduction are essential for fullduplex three reasons. First, the model has exhibited high wide-band communication, yet most existing neural networks have perceptual evaluation of speech quality (WB-PESQ) scores on high computational costs and are inflexible in tuning model the NS task with a small number of parameters but suffers from complexity. In this paper, we introduce time-frequency dualpath large computational cost. Second, DPT-FSNet is conducted compression to achieve a wide range of compression ratios on complete time-frequency (T-F) feature maps, indicating its on computational cost. Specifically, for frequency compression, complexity being closely related to the number of frames and trainable filters are used to replace manually designed frequency bands. Third, the model involves a 2D convolution filters for dimension reduction. For time compression, only using encoder, a dual-path transformer and a 2D convolution decoder, frame skipped prediction causes large performance degradation, implying that compression methods should be applicable to different which can be alleviated by a post-processing network modules.

We present a lightweight post-processing method to refine the semantic segmentation results of point cloud sequences. Most existing methods usually segment frame by frame and encounter the inherent ambiguity of the problem: based on a measurement in a single frame, labels are sometimes difficult to predict even for humans. To remedy this problem, we propose to explicitly train a network to refine these results predicted by an existing segmentation method. The network, which we call the P2Net, learns the consistency constraints between coincident points from consecutive frames after registration. We evaluate the proposed post-processing method both qualitatively and quantitatively on the SemanticKITTI dataset that consists of real outdoor scenes. The effectiveness of the proposed method is validated by comparing the results predicted by two representative networks with and without the refinement by the post-processing network. Specifically, qualitative visualization validates the key idea that labels of the points that are difficult to predict can be corrected with P2Net. Quantitatively, overall mIoU is improved from 10.5% to 11.7% for PointNet [1] and from 10.8% to 15.9% for PointNet++ [2].

So now, we have a clear sense of why we need to do it and how we can. From now, we're going to dive deeper in details. But before realizing if it works, let's see how it works. Well, each PPN is fed by two streams (O and S); the first is directed to the InAdapter, and the latter bypasses InAdapter to MLP. Let's see the PPN in more detail: In Figure 4. We can see that InAdapter transforms output o (output of previous module (in this figure, it is NLU)) to vector v, and then MLP converts the vector v to another vector v (you can see some numbers cannot be encoded so they'll copy to v) These equations are all we need to understand what's going on in PPNs.

In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem. The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints. With comprehensive experiments on benchmark datasets, we demonstrate the superior performance of E2Efold: it predicts significantly better structures compared to previous SOTA (especially for pseudoknotted structures), while being as efficient as the fastest algorithms in terms of inference time.