Time-Sensitive Networking (TSN) has been recognized as one of the key enabling technologies for Industry 4.0 and has been deployed in many time- and mission-critical industrial applications, e.g., automotive and aerospace systems. Given the stringent real-time communication requirements raised by these applications, the Time-Aware Shaper (TAS) draws special attention among the many traffic shapers developed for TSN, due to its ability to achieve deterministic latency guarantees. Extensive efforts on the designs of scheduling methods for TAS shapers have been reported in recent years to improve the system schedulability, each with their own distinct focuses and concerns. However, these scheduling methods have yet to be thoroughly evaluated, especially through experimental comparisons, to provide a systematical understanding on their performance using different evaluation metrics in various application scenarios. In this paper, we fill this gap by presenting a comprehensive experimental study on the existing TAS-based scheduling methods for TSN. We first categorize the system models employed in these work along with their formulated problems, and outline the fundamental considerations in the designs of TAS-based scheduling methods. We then perform extensive evaluation on 16 representative solutions and compare their performance under both synthetic scenarios and real-life industrial use cases. Through these experimental studies, we identify the limitations of individual scheduling methods and highlight several important findings. This work will provide foundational knowledge for the future studies on TSN real-time scheduling problems, and serve as the performance benchmarking for scheduling method development in TSN.
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The rise in popularity of ChatGPT and GPT-4 has significantly accelerated the development of large models, leading to the creation of numerous impressive large language models(LLMs) and multimodal large language models (MLLMs). These cutting-edge models owe their remarkable performance to high-quality data. However, the details of the training data used in leading paradigms are often kept confidential. This lack of transparency, coupled with the scarcity of open-source data, impedes further developments within the community. As a response, this paper presents "Wan Juan", a large-scale multimodal dataset composed of both Chinese and English data, collected from a wide range of web sources. The dataset incorporates text, image-text, and video modalities, with a total volume exceeding 2TB. It was utilized in the training of InternLM, a model that demonstrated significant advantages in multi-dimensional evaluations when compared to models of a similar scale. All data can be accessed at //opendatalab.org.cn/WanJuan1.0.
Gaussian processes (GPs) have emerged as a prominent technique for machine learning and signal processing. A key component in GP modeling is the choice of kernel, and linear multiple kernels (LMKs) have become an attractive kernel class due to their powerful modeling capacity and interpretability. This paper focuses on the grid spectral mixture (GSM) kernel, an LMK that can approximate arbitrary stationary kernels. Specifically, we propose a novel GSM kernel formulation for multi-dimensional data that reduces the number of hyper-parameters compared to existing formulations, while also retaining a favorable optimization structure and approximation capability. In addition, to make the large-scale hyper-parameter optimization in the GSM kernel tractable, we first introduce the distributed SCA (DSCA) algorithm. Building on this, we propose the doubly distributed SCA (D$^2$SCA) algorithm based on the alternating direction method of multipliers (ADMM) framework, which allows us to cooperatively learn the GSM kernel in the context of big data while maintaining data privacy. Furthermore, we tackle the inherent communication bandwidth restriction in distributed frameworks, by quantizing the hyper-parameters in D$^2$SCA, resulting in the quantized doubly distributed SCA (QD$^2$SCA) algorithm. Theoretical analysis establishes convergence guarantees for the proposed algorithms, while experiments on diverse datasets demonstrate the superior prediction performance and efficiency of our methods.
The application of Physics-Informed Neural Networks (PINNs) is investigated for the first time in solving the one-dimensional Countercurrent spontaneous imbibition (COUCSI) problem at both early and late time (i.e., before and after the imbibition front meets the no-flow boundary). We introduce utilization of Change-of-Variables as a technique for improving performance of PINNs. We formulated the COUCSI problem in three equivalent forms by changing the independent variables. The first describes saturation as function of normalized position X and time T; the second as function of X and Y=T^0.5; and the third as a sole function of Z=X/T^0.5 (valid only at early time). The PINN model was generated using a feed-forward neural network and trained based on minimizing a weighted loss function, including the physics-informed loss term and terms corresponding to the initial and boundary conditions. All three formulations could closely approximate the correct solutions, with water saturation mean absolute errors around 0.019 and 0.009 for XT and XY formulations and 0.012 for the Z formulation at early time. The Z formulation perfectly captured the self-similarity of the system at early time. This was less captured by XT and XY formulations. The total variation of saturation was preserved in the Z formulation, and it was better preserved with XY- than XT formulation. Redefining the problem based on the physics-inspired variables reduced the non-linearity of the problem and allowed higher solution accuracies, a higher degree of loss-landscape convexity, a lower number of required collocation points, smaller network sizes, and more computationally efficient solutions.
In computation pathology, the pyramid structure of gigapixel Whole Slide Images (WSIs) has recently been studied for capturing various information from individual cell interactions to tissue microenvironments. This hierarchical structure is believed to be beneficial for cancer diagnosis and prognosis tasks. However, most previous hierarchical WSI analysis works (1) only characterize local or global correlations within the WSI pyramids and (2) use only unidirectional interaction between different resolutions, leading to an incomplete picture of WSI pyramids. To this end, this paper presents a novel Hierarchical Interaction Graph-Transformer (i.e., HIGT) for WSI analysis. With Graph Neural Network and Transformer as the building commons, HIGT can learn both short-range local information and long-range global representation of the WSI pyramids. Considering that the information from different resolutions is complementary and can benefit each other during the learning process, we further design a novel Bidirectional Interaction block to establish communication between different levels within the WSI pyramids. Finally, we aggregate both coarse-grained and fine-grained features learned from different levels together for slide-level prediction. We evaluate our methods on two public WSI datasets from TCGA projects, i.e., kidney carcinoma (KICA) and esophageal carcinoma (ESCA). Experimental results show that our HIGT outperforms both hierarchical and non-hierarchical state-of-the-art methods on both tumor subtyping and staging tasks.
Information and Communication Technology (ICT) is being provided to the variety of end-users demands, thereby providing a better and improved management of services is crucial. Therefore, Service Level Agreements (SLAs) are essential and play a key role to manage the provided services among the network entities. This survey identifies the state of the art covering concepts, approaches and open problems of the SLAs establishment, deployment and management. This paper is organised in a way that the reader can access a variety of proposed SLA methods and models addressed and provides an overview of the SLA actors and elements. It also describes SLAs' characteristics and objectives. SLAs' existing methodologies are explained and categorised followed by the Service Quality Categories (SQD) and Quality-Based Service Descriptions (QSD). SLA modelling and architectures are discussed, and open research problems and future research directions are introduced. The establishment of a reliable, safe and QoE-aware computer networking needs a group of services that goes beyond pure networking services. Therefore, within the paper this broader set of services are taken into consideration and for each Service Level Objective (SLO) the related services domains will be indicated. The purpose of this survey is to identify existing research gaps in utilising SLA elements to develop a generic methodology, considering all quality parameters beyond the Quality of Service (QoS) and what must or can be taken into account to define, establish and deploy an SLA. This study is still an active research on how to specify and develop an SLA to achieve the win-win agreements among all actors.
Continual Learning has been challenging, especially when dealing with unsupervised scenarios such as Unsupervised Online General Continual Learning (UOGCL), where the learning agent has no prior knowledge of class boundaries or task change information. While previous research has focused on reducing forgetting in supervised setups, recent studies have shown that self-supervised learners are more resilient to forgetting. This paper proposes a novel approach that enhances memory usage for contrastive learning in UOGCL by defining and using stream-dependent data augmentations together with some implementation tricks. Our proposed method is simple yet effective, achieves state-of-the-art results compared to other unsupervised approaches in all considered setups, and reduces the gap between supervised and unsupervised continual learning. Our domain-aware augmentation procedure can be adapted to other replay-based methods, making it a promising strategy for continual learning.
Denoising diffusion probabilistic models (DDPMs) have shown promising performance for speech synthesis. However, a large number of iterative steps are required to achieve high sample quality, which restricts the inference speed. Maintaining sample quality while increasing sampling speed has become a challenging task. In this paper, we propose a "Co"nsistency "Mo"del-based "Speech" synthesis method, CoMoSpeech, which achieve speech synthesis through a single diffusion sampling step while achieving high audio quality. The consistency constraint is applied to distill a consistency model from a well-designed diffusion-based teacher model, which ultimately yields superior performances in the distilled CoMoSpeech. Our experiments show that by generating audio recordings by a single sampling step, the CoMoSpeech achieves an inference speed more than 150 times faster than real-time on a single NVIDIA A100 GPU, which is comparable to FastSpeech2, making diffusion-sampling based speech synthesis truly practical. Meanwhile, objective and subjective evaluations on text-to-speech and singing voice synthesis show that the proposed teacher models yield the best audio quality, and the one-step sampling based CoMoSpeech achieves the best inference speed with better or comparable audio quality to other conventional multi-step diffusion model baselines. Audio samples are available at //comospeech.github.io/.
Recently, Mutual Information (MI) has attracted attention in bounding the generalization error of Deep Neural Networks (DNNs). However, it is intractable to accurately estimate the MI in DNNs, thus most previous works have to relax the MI bound, which in turn weakens the information theoretic explanation for generalization. To address the limitation, this paper introduces a probabilistic representation of DNNs for accurately estimating the MI. Leveraging the proposed MI estimator, we validate the information theoretic explanation for generalization, and derive a tighter generalization bound than the state-of-the-art relaxations.
Weakly-Supervised Object Detection (WSOD) and Localization (WSOL), i.e., detecting multiple and single instances with bounding boxes in an image using image-level labels, are long-standing and challenging tasks in the CV community. With the success of deep neural networks in object detection, both WSOD and WSOL have received unprecedented attention. Hundreds of WSOD and WSOL methods and numerous techniques have been proposed in the deep learning era. To this end, in this paper, we consider WSOL is a sub-task of WSOD and provide a comprehensive survey of the recent achievements of WSOD. Specifically, we firstly describe the formulation and setting of the WSOD, including the background, challenges, basic framework. Meanwhile, we summarize and analyze all advanced techniques and training tricks for improving detection performance. Then, we introduce the widely-used datasets and evaluation metrics of WSOD. Lastly, we discuss the future directions of WSOD. We believe that these summaries can help pave a way for future research on WSOD and WSOL.
Graph Neural Networks (GNN) has demonstrated the superior performance in many challenging applications, including the few-shot learning tasks. Despite its powerful capacity to learn and generalize from few samples, GNN usually suffers from severe over-fitting and over-smoothing as the model becomes deep, which limit the model scalability. In this work, we propose a novel Attentive GNN to tackle these challenges, by incorporating a triple-attention mechanism, \ie node self-attention, neighborhood attention, and layer memory attention. We explain why the proposed attentive modules can improve GNN for few-shot learning with theoretical analysis and illustrations. Extensive experiments show that the proposed Attentive GNN outperforms the state-of-the-art GNN-based methods for few-shot learning over the mini-ImageNet and Tiered-ImageNet datasets, with both inductive and transductive settings.
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