GeneFormer: Learned Gene Compression using Transformer-based Context Modeling

With the development of gene sequencing technology, an explosive growth of gene data has been witnessed. And the storage of gene data has become an important issue. Traditional gene data compression methods rely on general software like G-zip, which fails to utilize the interrelation of nucleotide sequence. Recently, many researchers begin to investigate deep learning based gene data compression method. In this paper, we propose a transformer-based gene compression method named GeneFormer. Specifically, we first introduce a modified transformer structure to fully explore the nucleotide sequence dependency. Then, we propose fixed-length parallel grouping to accelerate the decoding speed of our autoregressive model. Experimental results on real-world datasets show that our method saves 29.7% bit rate compared with the state-of-the-art method, and the decoding speed is significantly faster than all existing learning-based gene compression methods

ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression

Recently, deep learning methods have shown promising results in point cloud compression. For octree-based point cloud compression, previous works show that the information of ancestor nodes and sibling nodes are equally important for predicting current node. However, those works either adopt insufficient context or bring intolerable decoding complexity (e.g. >600s). To address this problem, we propose a sufficient yet efficient context model and design an efficient deep learning codec for point clouds. Specifically, we first propose a window-constrained multi-group coding strategy to exploit the autoregressive context while maintaining decoding efficiency. Then, we propose a dual transformer architecture to utilize the dependency of current node on its ancestors and siblings. We also propose a random-masking pre-train method to enhance our model. Experimental results show that our approach achieves state-of-the-art performance for both lossy and lossless point cloud compression. Moreover, our multi-group coding strategy saves 98% decoding time compared with previous octree-based compression method

Flexible Neural Image Compression via Code Editing

Neural image compression (NIC) has outperformed traditional image codecs in rate-distortion (R-D) performance. However, it usually requires a dedicated encoder-decoder pair for each point on R-D curve, which greatly hinders its practical deployment. While some recent works have enabled bitrate control via conditional coding, they impose strong prior during training and provide limited flexibility. In this paper we propose Code Editing, a highly flexible coding method for NIC based on semi-amortized inference and adaptive quantization. Our work is a new paradigm for variable bitrate NIC. Furthermore, experimental results show that our method surpasses existing variable-rate methods, and achieves ROI coding and multi-distortion trade-off with a single decoder.Comment: NeurIPS 202

Correcting the Sub-optimal Bit Allocation

In this paper, we investigate the problem of bit allocation in Neural Video Compression (NVC). First, we reveal that a recent bit allocation approach claimed to be optimal is, in fact, sub-optimal due to its implementation. Specifically, we find that its sub-optimality lies in the improper application of semi-amortized variational inference (SAVI) on latent with non-factorized variational posterior. Then, we show that the corrected version of SAVI on non-factorized latent requires recursively applying back-propagating through gradient ascent, based on which we derive the corrected optimal bit allocation algorithm. Due to the computational in-feasibility of the corrected bit allocation, we design an efficient approximation to make it practical. Empirical results show that our proposed correction significantly improves the incorrect bit allocation in terms of R-D performance and bitrate error, and outperforms all other bit allocation methods by a large margin. The source code is provided in the supplementary material

Tracking of Human Arm Based on MEMS Sensors

Abstract. This paper studied the method for motion tracking of arm using triaxial accelerometer, triaxial gyroscope and electronic compass. The motion model of arm is established. The hardware of tracking system of arm is designed. The track method of arm gesture based on multi-sensors data fusion is analyzed. The compensation algorithm for motion accelerations is researched. The experimental results demonstrate that the motion acceleration compensation algorithm is validity, which can improve the dynamic measure precision of arm gesture angle

Conditional Perceptual Quality Preserving Image Compression

We propose conditional perceptual quality, an extension of the perceptual quality defined in \citet{blau2018perception}, by conditioning it on user defined information. Specifically, we extend the original perceptual quality $d(p_{X},p_{\hat{X}})$ to the conditional perceptual quality $d(p_{X|Y},p_{\hat{X}|Y})$, where $X$ is the original image, $\hat{X}$ is the reconstructed, $Y$ is side information defined by user and $d(.,.)$ is divergence. We show that conditional perceptual quality has similar theoretical properties as rate-distortion-perception trade-off \citep{blau2019rethinking}. Based on these theoretical results, we propose an optimal framework for conditional perceptual quality preserving compression. Experimental results show that our codec successfully maintains high perceptual quality and semantic quality at all bitrate. Besides, by providing a lowerbound of common randomness required, we settle the previous arguments on whether randomness should be incorporated into generator for (conditional) perceptual quality compression. The source code is provided in supplementary material

A ROP GTPase-Dependent Auxin Signaling Pathway Regulates the Subcellular Distribution of PIN2 in Arabidopsis Roots

SummaryPIN-FORMED (PIN) protein-mediated auxin polar transport is critically important for development, pattern formation, and morphogenesis in plants. Auxin has been implicated in the regulation of polar auxin transport by inhibiting PIN endocytosis [1, 2], but how auxin regulates this process is poorly understood. Our genetic screen identified the Arabidopsis SPIKE1 (SPK1) gene whose loss-of-function mutations increased lateral root density and retarded gravitropic responses, as do pin2 knockout mutations [3]. SPK1 belongs to the conserved DHR2-Dock family of Rho guanine nucleotide exchange factors [4–6]. The spk1 mutations induced PIN2 internalization that was not suppressed by auxin, as did the loss-of-function mutations for Rho-like GTPase from Plants 6 (ROP6)-GTPase or its effector RIC1. Furthermore, SPK1 was required for auxin induction of ROP6 activation. Our results have established a Rho GTPase-based auxin signaling pathway that maintains PIN2 polar distribution to the plasma membrane via inhibition of its internalization in Arabidopsis roots. Our findings provide new insights into signaling mechanisms that underlie the regulation of the dynamic trafficking of PINs required for long-distance auxin transport and that link auxin signaling to PIN-mediated pattern formation and morphogenesis

Bit Allocation using Optimization

In this paper, we consider the problem of bit allocation in neural video compression (NVC). Due to the frame reference structure, current NVC methods using the same R-D (Rate-Distortion) trade-off parameter $\lambda$ for all frames are suboptimal, which brings the need for bit allocation. Unlike previous methods based on heuristic and empirical R-D models, we propose to solve this problem by gradient-based optimization. Specifically, we first propose a continuous bit implementation method based on Semi-Amortized Variational Inference (SAVI). Then, we propose a pixel-level implicit bit allocation method using iterative optimization by changing the SAVI target. Moreover, we derive the precise R-D model based on the differentiable trait of NVC. And we show the optimality of our method by proofing its equivalence to the bit allocation with precise R-D model. Experimental results show that our approach significantly improves NVC methods and outperforms existing bit allocation methods. Our approach is plug-and-play for all differentiable NVC methods, and it can be directly adopted on existing pre-trained models

Deep Mouse: An End-to-end Auto-context Refinement Framework for Brain Ventricle and Body Segmentation in Embryonic Mice Ultrasound Volumes

High-frequency ultrasound (HFU) is well suited for imaging embryonic mice due to its noninvasive and real-time characteristics. However, manual segmentation of the brain ventricles (BVs) and body requires substantial time and expertise. This work proposes a novel deep learning based end-to-end auto-context refinement framework, consisting of two stages. The first stage produces a low resolution segmentation of the BV and body simultaneously. The resulting probability map for each object (BV or body) is then used to crop a region of interest (ROI) around the target object in both the original image and the probability map to provide context to the refinement segmentation network. Joint training of the two stages provides significant improvement in Dice Similarity Coefficient (DSC) over using only the first stage (0.818 to 0.906 for the BV, and 0.919 to 0.934 for the body). The proposed method significantly reduces the inference time (102.36 to 0.09 s/volume around 1000x faster) while slightly improves the segmentation accuracy over the previous methods using slide-window approaches.Comment: Full Paper Submission to ISBI 202