374 research outputs found
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn
This paper presents an image classification based approach for skeleton-based
video action recognition problem. Firstly, A dataset independent
translation-scale invariant image mapping method is proposed, which transformes
the skeleton videos to colour images, named skeleton-images. Secondly, A
multi-scale deep convolutional neural network (CNN) architecture is proposed
which could be built and fine-tuned on the powerful pre-trained CNNs, e.g.,
AlexNet, VGGNet, ResNet etal.. Even though the skeleton-images are very
different from natural images, the fine-tune strategy still works well. At
last, we prove that our method could also work well on 2D skeleton video data.
We achieve the state-of-the-art results on the popular benchmard datasets e.g.
NTU RGB+D, UTD-MHAD, MSRC-12, and G3D. Especially on the largest and challenge
NTU RGB+D, UTD-MHAD, and MSRC-12 dataset, our method outperforms other methods
by a large margion, which proves the efficacy of the proposed method
Software Testing with Large Language Model: Survey, Landscape, and Vision
Pre-trained large language models (LLMs) have recently emerged as a
breakthrough technology in natural language processing and artificial
intelligence, with the ability to handle large-scale datasets and exhibit
remarkable performance across a wide range of tasks. Meanwhile, software
testing is a crucial undertaking that serves as a cornerstone for ensuring the
quality and reliability of software products. As the scope and complexity of
software systems continue to grow, the need for more effective software testing
techniques becomes increasingly urgent, and making it an area ripe for
innovative approaches such as the use of LLMs. This paper provides a
comprehensive review of the utilization of LLMs in software testing. It
analyzes 52 relevant studies that have used LLMs for software testing, from
both the software testing and LLMs perspectives. The paper presents a detailed
discussion of the software testing tasks for which LLMs are commonly used,
among which test case preparation and program repair are the most
representative ones. It also analyzes the commonly used LLMs, the types of
prompt engineering that are employed, as well as the accompanied techniques
with these LLMs. It also summarizes the key challenges and potential
opportunities in this direction. This work can serve as a roadmap for future
research in this area, highlighting potential avenues for exploration, and
identifying gaps in our current understanding of the use of LLMs in software
testing.Comment: 20 pages, 11 figure
1xN Pattern for Pruning Convolutional Neural Networks
Though network pruning receives popularity in reducing the complexity of
convolutional neural networks (CNNs), it remains an open issue to concurrently
maintain model accuracy as well as achieve significant speedups on general
CPUs. In this paper, we propose a novel 1xN pruning pattern to break this
limitation. In particular, consecutive N output kernels with the same input
channel index are grouped into one block, which serves as a basic pruning
granularity of our pruning pattern. Our 1xN pattern prunes these blocks
considered unimportant. We also provide a workflow of filter rearrangement that
first rearranges the weight matrix in the output channel dimension to derive
more influential blocks for accuracy improvements and then applies similar
rearrangement to the next-layer weights in the input channel dimension to
ensure correct convolutional operations. Moreover, the output computation after
our 1xN pruning can be realized via a parallelized block-wise vectorized
operation, leading to significant speedups on general CPUs. The efficacy of our
pruning pattern is proved with experiments on ILSVRC-2012. For example, Given
the pruning rate of 50% and N=4, our pattern obtains about 3.0% improvements
over filter pruning in the top-1 accuracy of MobileNet-V2. Meanwhile, it
obtains 56.04ms inference savings on Cortex-A7 CPU over weight pruning. Our
project is made available at https://github.com/lmbxmu/1xN
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
Current deep networks are very data-hungry and benefit from training on
largescale datasets, which are often time-consuming to collect and annotate. By
contrast, synthetic data can be generated infinitely using generative models
such as DALL-E and diffusion models, with minimal effort and cost. In this
paper, we present DatasetDM, a generic dataset generation model that can
produce diverse synthetic images and the corresponding high-quality perception
annotations (e.g., segmentation masks, and depth). Our method builds upon the
pre-trained diffusion model and extends text-guided image synthesis to
perception data generation. We show that the rich latent code of the diffusion
model can be effectively decoded as accurate perception annotations using a
decoder module. Training the decoder only needs less than 1% (around 100
images) manually labeled images, enabling the generation of an infinitely large
annotated dataset. Then these synthetic data can be used for training various
perception models for downstream tasks. To showcase the power of the proposed
approach, we generate datasets with rich dense pixel-wise labels for a wide
range of downstream tasks, including semantic segmentation, instance
segmentation, and depth estimation. Notably, it achieves 1) state-of-the-art
results on semantic segmentation and instance segmentation; 2) significantly
more robust on domain generalization than using the real data alone; and
state-of-the-art results in zero-shot segmentation setting; and 3) flexibility
for efficient application and novel task composition (e.g., image editing). The
project website and code can be found at
https://weijiawu.github.io/DatasetDM_page/ and
https://github.com/showlab/DatasetDM, respectivel
Small Stretch Problem of the DCT Scheme and How to Fix it
DCT is a beyond-birthday-bound~(BBB) deterministic authenticated encryption~(DAE) mode proposed by Forler et al. in ACISP 2016, ensuring integrity by redundancy. The instantiation scheme of DCT employs the BRW polynomial, which is more efficient than the usual polynomial function in GCM by reducing half of the multiplication operations. However, we show that DCT suffers from a small stretch problem similar to GCM. When the stretch length is small, choosing a special -block message, we can reduce the number of queries required by a successful forgery to . We emphasize that this attack efficiently balances space and time complexity, but does not contradict the security bounds of DCT. Finally, we propose an improved scheme named Robust DCT~(RDCT) with a minor change to DCT, which improves the security when is small and makes it resist the above attack
Cell separation using tilted-angle standing surface acoustic waves
Separation of cells is a critical process for studying cell properties, disease diagnostics, and therapeutics. Cell sorting by acoustic waves offers a means to separate cells on the basis of their size and physical properties in a label-free, contactless, and biocompatible manner. The separation sensitivity and efficiency of currently available acoustic-based approaches, however, are limited, thereby restricting their widespread application in research and health diagnostics. In this work, we introduce a unique configuration of tilted-angle standing surface acoustic waves (taSSAW), which are oriented at an optimally designed inclination to the flow direction in the microfluidic channel. We demonstrate that this design significantly improves the efficiency and sensitivity of acoustic separation techniques. To optimize our device design, we carried out systematic simulations of cell trajectories, matching closely with experimental results. Using numerically optimized design of taSSAW, we successfully separated 2- and 10-µm-diameter polystyrene beads with a separation efficiency of ~99%, and separated 7.3- and 9.9-µm-polystyrene beads with an efficiency of ~97%. We illustrate that taSSAW is capable of effectively separating particles–cells of approximately the same size and density but different compressibility. Finally, we demonstrate the effectiveness of the present technique for biological–biomedical applications by sorting MCF-7 human breast cancer cells from nonmalignant leukocytes, while preserving the integrity of the separated cells. The method introduced here thus offers a unique route for separating circulating tumor cells, and for label-free cell separation with potential applications in biological research, disease diagnostics, and clinical practice.National Institutes of Health (U.S.) (Grant U01HL114476)National Institutes of Health (U.S.) (New Innovator Award 1DP2OD007209-01)National Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (Grant DMR-0820404
An improved particle swarm optimization combined with double-chaos search
Particle swarm optimization (PSO) has been successfully applied to various complex optimization problems due to its simplicity and efficiency. However, the update strategy of the standard PSO algorithm is to learn from the global best particle, making it difficult to maintain diversity in the population and prone to premature convergence due to being trapped in local optima. Chaos search mechanism is an optimization technique based on chaotic dynamics, which utilizes the randomness and nonlinearity of a chaotic system for global search and can escape from local optima. To overcome the limitations of PSO, an improved particle swarm optimization combined with double-chaos search (DCS-PSO) is proposed in this paper. In DCS-PSO, we first introduce double-chaos search mechanism to narrow the search space, which enables PSO to focus on the neighborhood of the optimal solution and reduces the probability that the swarm gets trapped into a local optimum. Second, to enhance the population diversity, the logistic map is employed to perform a global search in the narrowed search space and the best solution found by both the logistic and population search guides the population to converge. Experimental results show that DCS-PSO can effectively narrow the search space and has better convergence accuracy and speed in most cases
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