Many 3D generative models rely on variational autoencoders (VAEs) to learn compact shape representations. However, existing methods encode all shapes into a fixed-size token, disregarding the inherent variations in scale and complexity across 3D data. This leads to inefficient latent representations that can compromise downstream generation. We address this challenge by introducing Octree-based Adaptive Tokenization, a novel framework that adjusts the dimension of latent representations according to shape complexity. Our approach constructs an adaptive octree structure guided by a quadric-error-based subdivision criterion and allocates a shape latent vector to each octree cell using a query-based transformer. Building upon this tokenization, we develop an octree-based autoregressive generative model that effectively leverages these variable-sized representations in shape generation. Extensive experiments demonstrate that our approach reduces token counts by 50% compared to fixed-size methods while maintaining comparable visual quality. When using a similar token length, our method produces significantly higher-quality shapes. When incorporated with our downstream generative model, our method creates more detailed and diverse 3D content than existing approaches.

In software development, bug report reproduction is a challenging task. This paper introduces ReBL, a novel feedback-driven approach that leverages GPT-4, a large-scale language model (LLM), to automatically reproduce Android bug reports. Unlike traditional methods, ReBL bypasses the use of Step to Reproduce (S2R) entities. Instead, it leverages the entire textual bug report and employs innovative prompts to enhance GPT’s contextual reasoning. This approach is more flexible and context-aware than the traditional step-by-step entity matching approach, resulting in improved accuracy and effectiveness. In addition to handling crash reports, ReBL has the capability of handling non-crash functional bug reports. Our evaluation of 96 Android bug reports (73 crash and 23 non-crash) demonstrates that ReBL successfully reproduced 90.63% of these reports, averaging only 74.98 seconds per bug report. Additionally, ReBL outperformed three existing tools in both success rate and speed.

In this paper, we propose a novel deep sparse coding network (SCN) capable of efficiently adapting its own regularization parameters for a given application. The network is trained end-to-end with a supervised task-driven learning algorithm via error backpropagation. During training, the network learns both the dictionaries and the regularization parameters of each sparse coding layer so that the reconstructive dictionaries are smoothly transformed into increasingly discriminative representations. In addition, the adaptive regularization also offers the network more flexibility to adjust sparsity levels. Furthermore, we have devised a sparse coding layer utilizing a 'skinny' dictionary. Integral to computational efficiency, these skinny dictionaries compress the high dimensional sparse codes into lower dimensional structures. The adaptivity and discriminability of our fifteen-layer sparse coding network are demonstrated on five benchmark datasets, namely Cifar-10, Cifar-100, STL-10, SVHN and MNIST, most of which are considered difficult for sparse coding models. Experimental results show that our architecture overwhelmingly outperforms traditional one-layer sparse coding architectures while using much fewer parameters. Moreover, our multilayer architecture exploits the benefits of depth with sparse coding's characteristic ability to operate on smaller datasets. In such data-constrained scenarios, our technique demonstrates highly competitive performance compared to the deep neural networks.

In this paper, we present the deep sparse coding network (DSCN) - a novel deep learning framework that encodes intermediate representations with nonnegative sparse coding. DSCN is constructed from a cascade of bottleneck modules, each of which consists of two sparse coding layers with relatively wide and slim dictionaries that are specialized to produce high dimensional discriminative features and low dimensional clustered representations, respectively. During training, all dictionaries at all depth levels along with all regularization parameters are optimized jointly with an end-to-end supervised learning algorithm based on multilevel optimization. The effectiveness of the proposed DSCN with seven bottleneck modules11Consisting 14 sparse coding layers. is verified on several popular benchmark datasets Remarkably, with few parameters to learn, our SCN achieves 5.81 % and 19.93% classification error rate on CIFAR-10 and CIFAR-100, respectively.

Sparse representation-based classifiers have shown outstanding accuracy and robustness in image classification tasks even with the presence of intense noise and occlusion. However, it has been discovered that the performance degrades significantly either when test image is not aligned with the dictionary atoms or the dictionary atoms themselves are not aligned with each other, in which cases the sparse linear representation assumption fails. In this paper, having both training and test images misaligned, we introduce a novel sparse coding framework that is able to efficiently adapt the dictionary atoms to the test image via large displacement optical flow. In the proposed algorithm, every dictionary atom is automatically aligned with the input image and the sparse code is then recovered using the adapted dictionary atoms. A corresponding supervised dictionary learning algorithm is also developed for the proposed framework. Experimental results on digit datasets recognition verify the efficacy and robustness of the proposed algorithm.

Sparse representation models a signal as a linear combination of a small number of dictionary atoms. As a generative model, it requires the dictionary to be highly redundant in order to ensure both a stable high sparsity level and a low reconstruction error for the signal. However, in practice, this requirement is usually impaired by the lack of labeled training samples. Fortunately, previous research has shown that the requirement for a redundant dictionary can be less rigorous if simultaneous sparse approximation is employed, which can be carried out by enforcing various structured sparsity constraints on the sparse codes of the neighboring pixels. In addition, numerous works have shown that applying a variety of dictionary learning methods for the sparse representation model can also improve the classification performance. In this paper, we highlight the task-driven dictionary learning (TDDL) algorithm, which is a general framework for the supervised dictionary learning method. We propose to enforce structured sparsity priors on the TDDL method in order to improve the performance of the hyperspectral classification. Our approach is able to benefit from both the advantages of the simultaneous sparse representation and those of the supervised dictionary learning. We enforce two different structured sparsity priors, the joint and Laplacian sparsities, on the TDDL method and provide the details of the corresponding optimization algorithms. Experiments on numerous popular hyperspectral images demonstrate that the classification performance of our approach is superior to that of the sparse representation classifier with structured priors or the TDDL method.

Recently, the separability assumption turns the nonnegative matrix factorization (NMF) into a tractable problem. The assumption coincides with the pixel purity assumption and provides new insights for the hyperspectral unmixing problem. In this paper, we present a quasi-greedy algorithm for solving the problem by employing a back-tracking strategy. Unlike the current greedy methods, the proposed method can refresh the endmember index set in every iteration. Therefore, our method has two important characteristics: (i) low computational complexity comparable to state-of-the-art greedy methods but (ii) empirically enhanced robustness against noise. Finally, computer simulations on synthetic hyperspectral data demonstrate the effectiveness of the proposed method.

In hyperspectral pixel classification, previous research have shown that the sparse representation classifier can achieve a better performance when exploiting the neighboring test pixels through enforcing different structured sparsity priors. In this paper, we propose a supervised sparse-representation-based dictionary learning method with joint or Laplacian s-parsity priors. The proposed method has numerous advantages over the existing dictionary learning techniques. It uses a structured sparsity and provides a more robust and stable sparse coefficients. Besides, it is capable of reducing the classification error by jointly optimizing the dictionary and the classifier's parameters during the dictionary training stage.

Pixelwise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis. By representing a test pixel as a linear combination of a small subset of labeled pixels, a sparse representation classifier (SRC) gives rather plausible results compared with that of traditional classifiers such as the support vector machine. Recently, by incorporating additional structured sparsity priors, the second-generation SRCs have appeared in the literature and are reported to further improve the performance of HSI. These priors are based on exploiting the spatial dependences between the neighboring pixels, the inherent structure of the dictionary, or both. In this letter, we review and compare several structured priors for sparse-representation-based HSI classification. We also propose a new structured prior called the low-rank (LR) group prior, which can be considered as a modification of the LR prior. Furthermore, we will investigate how different structured priors improve the result for the HSI classification.

The router is required to provide not only high forwarding performance, but also advanced quality of service. Packet scheduling in buffered queues is required in all algorithms to support QoS, such as fair queueing. Output queueing systems can achieve desired quality of service, but lack of essential scalability. Input buffered systems, while scalable, lack the necessary quality of service features. In this paper we design and implement DF/sup 2/Q (distributed feedback fair queueing), on the basis of CIOQ (combined input output queueing) architecture. The most important feature of this algorithm is the feedback mechanism. It can avoid internal congestion effectively and improve the efficiency of resource utilization. We discuss the performance of DF/sup 2/Q in different cases. Finally research directions and open problems in this area are proposed.

The performance-evaluating criterion is one of the most important issues for the design of network traffic control strategy and algorithms. The design will be varied with the evaluating criterion. Because the network traffic control strategy has multiple performance objects, how to reach multiple objects simultaneously is one difficult problem. This paper provides a set of integrated performance evaluating criteria from the network traffic control model, and the criteria integrates several objects, such as the network performance, user's QoS requirement and system fairness. Then it is discussed and analyzed in details. Moreover, the integrated performance evaluating criteria can be applied to performance evaluation and improvement in network traffic control strategy and algorithms.