Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing

The advent of high-capacity pre-trained models has revolutionized problem-solving in computer vision, shifting the focus from training task-specific models to adapting pre-trained models. Consequently, effectively adapting large pre-trained models to downstream tasks in an efficient manner has become a prominent research area. Existing solutions primarily concentrate on designing lightweight adapters and their interaction with pre-trained models, with the goal of minimizing the number of parameters requiring updates. In this study, we propose a novel Adapter Re-Composing (ARC) strategy that addresses efficient pre-trained model adaptation from a fresh perspective. Our approach considers the reusability of adaptation parameters and introduces a parameter-sharing scheme. Specifically, we leverage symmetric down-/up-projections to construct bottleneck operations, which are shared across layers. By learning low-dimensional re-scaling coefficients, we can effectively re-compose layer-adaptive adapters. This parameter-sharing strategy in adapter design allows us to significantly reduce the number of new parameters while maintaining satisfactory performance, thereby offering a promising approach to compress the adaptation cost. We conduct experiments on 24 downstream image classification tasks using various Vision Transformer variants to evaluate our method. The results demonstrate that our approach achieves compelling transfer learning performance with a reduced parameter count. Our code is available at \href{https://github.com/DavidYanAnDe/ARC}{https://github.com/DavidYanAnDe/ARC}.Comment: Paper is accepted to NeurIPS 202

Comprehensive profiling of rhizome-associated alternative splicing and alternative polyadenylation in moso bamboo (Phyllostachys edulis).

Moso bamboo (Phyllostachys edulis) represents one of the fastest-spreading plants in the world, due in part to its well-developed rhizome system. However, the post-transcriptional mechanism for the development of the rhizome system in bamboo has not been comprehensively studied. We therefore used a combination of single-molecule long-read sequencing technology and polyadenylation site sequencing (PAS-seq) to re-annotate the bamboo genome, and identify genome-wide alternative splicing (AS) and alternative polyadenylation (APA) in the rhizome system. In total, 145 522 mapped full-length non-chimeric (FLNC) reads were analyzed, resulting in the correction of 2241 mis-annotated genes and the identification of 8091 previously unannotated loci. Notably, more than 42 280 distinct splicing isoforms were derived from 128 667 intron-containing full-length FLNC reads, including a large number of AS events associated with rhizome systems. In addition, we characterized 25 069 polyadenylation sites from 11 450 genes, 6311 of which have APA sites. Further analysis of intronic polyadenylation revealed that LTR/Gypsy and LTR/Copia were two major transposable elements within the intronic polyadenylation region. Furthermore, this study provided a quantitative atlas of poly(A) usage. Several hundred differential poly(A) sites in the rhizome-root system were identified. Taken together, these results suggest that post-transcriptional regulation may potentially have a vital role in the underground rhizome-root system

Static stiffness modeling and sensitivity analysis for geared system used for rotary feeding

The positioning accuracy of rotary feed system under load greatly depends on the static stiffness of mechanical transmission system. This paper proposes a unified static stiffness model of rotary feed system with geared transmission system. Taking the torsional stiffness of transmission shaft and mesh stiffness of gear pairs into account, the motion equations of the whole transmission system are presented. Based on the static equilibrium, a unified expression for the relationship between torsional angles of two adjacent elements is derived. Then a unified static stiffness model is presented. Furthermore, analytical expressions for sensitivity analysis of the static stiffness on the individual element’s stiffness and design parameters are derived. The presented model is verified by a traditional model, and a good agreement is obtained. The influence of phase angle of meshing gear pairs on the resultant static stiffness is investigated. An example transmission system is employed to perform the sensitivity analysis and the results are analyzed. The proposed model provides an essential tool for the design of rotary feed system satisfying requirement of static stiffness

Improved Acid Resistance of a Metal-Organic Cage Enables Cargo Release and Exchange between Hosts.

The use of di(2-pyridyl)ketone in subcomponent self-assembly is introduced. When combined with a flexible triamine and zinc bis(trifluoromethanesulfonyl)imide, this ketone formed a new Zn4 L4 tetrahedron 1 bearing twelve uncoordinated pyridyl units around its metal-ion vertices. The acid stability of 1 was found to be greater than that of the analogous tetrahedron 2 built from 2-formylpyridine. Intriguingly, the peripheral presence of additional pyridine rings in 1 resulted in distinct guest binding behavior from that of 2, affecting guest scope as well as binding affinities. The different stabilities and guest affinities of capsules 1 and 2 enabled the design of systems whereby different cargoes could be moved between cages using acid and base as chemical stimuli.European Research Council (695009), UK Engineering and Physical Sciences Research Council (EPSRC EP/P027067/1

Post-Training Quantization for Object Detection

Efficient inference for object detection networks is a major challenge on edge devices. Post-Training Quantization (PTQ), which transforms a full-precision model into low bit-width directly, is an effective and convenient approach to reduce model inference complexity. But it suffers severe accuracy drop when applied to complex tasks such as object detection. PTQ optimizes the quantization parameters by different metrics to minimize the perturbation of quantization. The p-norm distance of feature maps before and after quantization, Lp, is widely used as the metric to evaluate perturbation. For the specialty of object detection network, we observe that the parameter p in Lp metric will significantly influence its quantization performance. We indicate that using a fixed hyper-parameter p does not achieve optimal quantization performance. To mitigate this problem, we propose a framework, DetPTQ, to assign different p values for quantizing different layers using an Object Detection Output Loss (ODOL), which represents the task loss of object detection. DetPTQ employs the ODOL-based adaptive Lp metric to select the optimal quantization parameters. Experiments show that our DetPTQ outperforms the state-of-the-art PTQ methods by a significant margin on both 2D and 3D object detectors. For example, we achieve 31.1/31.7(quantization/full-precision) mAP on RetinaNet-ResNet18 with 4-bit weight and 4-bit activation

PD-Quant: Post-Training Quantization based on Prediction Difference Metric

Post-training quantization (PTQ) is a neural network compression technique that converts a full-precision model into a quantized model using lower-precision data types. Although it can help reduce the size and computational cost of deep neural networks, it can also introduce quantization noise and reduce prediction accuracy, especially in extremely low-bit settings. How to determine the appropriate quantization parameters (e.g., scaling factors and rounding of weights) is the main problem facing now. Existing methods attempt to determine these parameters by minimize the distance between features before and after quantization, but such an approach only considers local information and may not result in the most optimal quantization parameters. We analyze this issue and ropose PD-Quant, a method that addresses this limitation by considering global information. It determines the quantization parameters by using the information of differences between network prediction before and after quantization. In addition, PD-Quant can alleviate the overfitting problem in PTQ caused by the small number of calibration sets by adjusting the distribution of activations. Experiments show that PD-Quant leads to better quantization parameters and improves the prediction accuracy of quantized models, especially in low-bit settings. For example, PD-Quant pushes the accuracy of ResNet-18 up to 53.14% and RegNetX-600MF up to 40.67% in weight 2-bit activation 2-bit. The code is released at https://github.com/hustvl/PD-Quant