Comprehensive evaluation of extracellular small RNA isolation methods from serum in high throughput sequencing

Supplementary Tables S1-S9. (XLSX 576 kb

Revisiting the Information Capacity of Neural Network Watermarks: Upper Bound Estimation and Beyond

To trace the copyright of deep neural networks, an owner can embed its identity information into its model as a watermark. The capacity of the watermark quantify the maximal volume of information that can be verified from the watermarked model. Current studies on capacity focus on the ownership verification accuracy under ordinary removal attacks and fail to capture the relationship between robustness and fidelity. This paper studies the capacity of deep neural network watermarks from an information theoretical perspective. We propose a new definition of deep neural network watermark capacity analogous to channel capacity, analyze its properties, and design an algorithm that yields a tight estimation of its upper bound under adversarial overwriting. We also propose a universal non-invasive method to secure the transmission of the identity message beyond capacity by multiple rounds of ownership verification. Our observations provide evidence for neural network owners and defenders that are curious about the tradeoff between the integrity of their ownership and the performance degradation of their products.Comment: Accepted by AAAI 202

Pullout Performance and Branching Effect of Radial Cables to Reinforce the Steep Fill–Bedrock Interfaces: Investigation of a Pullout Test and a Numerical Simulation

Steep fill–bedrock interfaces usually appear in many filling soil infrastructures, such as airports, houses, and road embankments in mountainous areas, when the excavation of rock slopes is constrained. These interfaces are prone to be tensioned up to failure, which easily triggers landslides of fill slopes. The anchor system buried in the fill soil, named radial cable system, was proposed for effectively enhancing the stability of steep fill–bedrock interfaces. At the interface, the steel ropes of the anchor section cable were equally divided into three subcables with a radial distribution. The pullout performance, failure evolution, and branching effect of the radial cable coupled with anchor plates were studied by a pullout test (in a laboratory setup) and a numerical simulation. The results showed that (1) the ultimate pullout capacities (Pu) of the radial cables were 193.53%–312.94% (for the 7 mm diameter of the anchor plate) and 141.25%–247.50% (for the 10 mm diameter of the anchor plate) greater than those of the single cables; (2) the pullout performance of the radial cable was significantly improved with an increase in the diameter of the anchor plate, and the optimal radial inclined angle of subcables coupled with anchor plates was 15°; (3) the soil surrounding the radial cable showed a progressive failure pattern, and its failure area was basically a symmetric conical; and (4) the radial cable can better reinforce the steep fill–rock interface than the conventional cable, as verified by a hill-fill project. The results of this study provide some new and important guidelines for the design and application of the radial cable system.This work is supported by the National Natural Science Foundation of China (41972297), the Natural Science Foundation of Hebei Province (D2021202002), scientific research project from the Education Department of Hunan Province (21C0753), the Changsha Municipal Natural Science Foundation (kq2202065), and Natural Science Foundation of Hunan Province (2022JJ40521). The work of author Roberto Tomás is supported by the ESA-MOST China DRAGON-5 project (ref. 59339)

PyramidInfer: Pyramid KV Cache Compression for High-throughput LLM Inference

Large Language Models (LLMs) have shown remarkable comprehension abilities but face challenges in GPU memory usage during inference, hindering their scalability for real-time applications like chatbots. To accelerate inference, we store computed keys and values (KV cache) in the GPU memory. Existing methods study the KV cache compression to reduce memory by pruning the pre-computed KV cache. However, they neglect the inter-layer dependency between layers and huge memory consumption in pre-computation. To explore these deficiencies, we find that the number of crucial keys and values that influence future generations decreases layer by layer and we can extract them by the consistency in attention weights. Based on the findings, we propose PyramidInfer, a method that compresses the KV cache by layer-wise retaining crucial context. PyramidInfer saves significant memory by computing fewer keys and values without sacrificing performance. Experimental results show PyramidInfer improves 2.2x throughput compared to Accelerate with over 54% GPU memory reduction in KV cache.Comment: Accepted by ACL 202

Multi-objective Dwarf Mongoose Optimization Algorithm with Leader Guidance and Dominated Solution Evolution Mechanism

In the face of the increasingly complex multi-objective optimization problems, it is necessary to develop novel multi-objective optimization algorithms to meet the challenges. This paper proposes a multi-objective dwarf mongoose optimization algorithm (MODMO) with leader guidance and dominated solution dynamic reduction evolution mechanism. In the leader guidance mechanism, a dynamic trade-off factor is introduced to regulate the search radius of the scout mongoose exploring the mound. At the same time, an external archive is constructed with a non-inferior solution set and the leader is determined according to the non-dominated ranking level, and then the scout mongoose is guided to advance to the multi-objective frontier to improve the convergence of the algorithm. The dominant solution dynamic reduction evolution strategy is constructed to overcome the redundancy problem in the process of maintaining the external archive of non-inferior solutions. It dynamically selects the dominant solutions based on the dominance relationship and crowding distance and stores them in the external archive. The dominant solution information is integrated into the population evolution to realize the mining of multi-objective potential frontier and enhance the diversity of the algorithm. Compared with five representative algorithms on ZDT, DTLZ and WFG benchmark functions, experimental results show that MODMO algorithm has significant advantages in convergence and diversity

Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schr\"odinger equation (NLSE) has long been regarded as extremely time-consuming, especially for designing and optimizing experiments. The recurrent neural network (RNN) has been implemented as an accurate intensity prediction tool with reduced complexity and good generalization capability. However, the complexity of long grid input points and the flexibility of neural network structure should be further optimized for broader applications. Here, we propose a convolutional feature separation modeling method to predict full-field ultrafast nonlinear dynamics with low complexity and high flexibility, where the linear effects are firstly modeled by NLSE-derived methods, then a convolutional deep learning method is implemented for nonlinearity modeling. With this method, the temporal relevance of nonlinear effects is substantially shortened, and the parameters and scale of neural networks can be greatly reduced. The running time achieves a 94% reduction versus NLSE and an 87% reduction versus RNN without accuracy deterioration. In addition, the input pulse conditions, including grid point numbers, durations, peak powers, and propagation distance, can be flexibly changed during the predicting process. The results represent a remarkable improvement in the ultrafast nonlinear dynamics prediction and this work also provides novel perspectives of the feature separation modeling method for quickly and flexibly studying the nonlinear characteristics in other fields.Comment: 15 pages,9 figure

An Open and Comprehensive Pipeline for Unified Object Grounding and Detection

Grounding-DINO is a state-of-the-art open-set detection model that tackles multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC). Its effectiveness has led to its widespread adoption as a mainstream architecture for various downstream applications. However, despite its significance, the original Grounding-DINO model lacks comprehensive public technical details due to the unavailability of its training code. To bridge this gap, we present MM-Grounding-DINO, an open-source, comprehensive, and user-friendly baseline, which is built with the MMDetection toolbox. It adopts abundant vision datasets for pre-training and various detection and grounding datasets for fine-tuning. We give a comprehensive analysis of each reported result and detailed settings for reproduction. The extensive experiments on the benchmarks mentioned demonstrate that our MM-Grounding-DINO-Tiny outperforms the Grounding-DINO-Tiny baseline. We release all our models to the research community. Codes and trained models are released at https://github.com/open-mmlab/mmdetection/tree/main/configs/mm_grounding_dino.Comment: 10 pages, 6 figure

Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers

The segmentation of kidney layer structures, including cortex, outer stripe, inner stripe, and inner medulla within human kidney whole slide images (WSI) plays an essential role in automated image analysis in renal pathology. However, the current manual segmentation process proves labor-intensive and infeasible for handling the extensive digital pathology images encountered at a large scale. In response, the realm of digital renal pathology has seen the emergence of deep learning-based methodologies. However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation. Addressing this gap, this paper assesses the feasibility of performing deep learning based approaches on kidney layer structure segmetnation. This study employs the representative convolutional neural network (CNN) and Transformer segmentation approaches, including Swin-Unet, Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We quantitatively evaluated six prevalent deep learning models on renal cortex layer segmentation using mice kidney WSIs. The empirical results stemming from our approach exhibit compelling advancements, as evidenced by a decent Mean Intersection over Union (mIoU) index. The results demonstrate that Transformer models generally outperform CNN-based models. By enabling a quantitative evaluation of renal cortical structures, deep learning approaches are promising to empower these medical professionals to make more informed kidney layer segmentation

Gene and isoform expression signatures associated with tumor stage in kidney renal clear cell carcinoma

BACKGROUND: Identification of expression alternations between early and late stage cancers is helpful for understanding cancer development and progression. Much research has been done focusing on stage-dependent gene expression profiles. In contrast, relatively fewer studies on isoform expression profiles have been performed due to the difficulty of quantification and noisy splicing. Here we conducted both gene- and isoform-level analysis on RNA-seq data of 234 stage I and 81 stage IV kidney renal clear cell carcinoma patients, aiming to uncover the stage-dependent expression signatures and investigate the advantage of isoform expression profiling for identifying advanced stage cancers and predicting clinical outcome. RESULTS: Both gene and isoform expression signatures are useful for distinguishing cancer stages. They provide common and unique information associated with cancer progression and metastasis. Combining gene and isoform signatures even improves the classification performance and reveals additional important biological processes, such as angiogenesis and TGF−beta signaling pathway. Moreover, expression abundance of a number of genes and isoforms is predictive of the risk of cancer death in an independent dataset, such as gene and isoform expression of ITPKA, the expression of a functional important isoform of UPS19. CONCLUSION: Isoform expression profiling provides unique and important information which cannot be detected by gene expression profiles. Combining gene and isoform expression signatures helps to identify advanced stage cancers, predict clinical outcome, and present a comprehensive view of cancer development and progression