Service-oriented architectures involve complex interactions, making failure detection and diagnosis challenging. Traditional static log statements often miss critical variables and lack runtime flexibility, resulting in limited observability. To address this, we propose a bytecode-level variable monitoring approach using the ASM library. Our tool transparently instruments service bytecode, enabling dynamic, source-free monitoring with minimal overhead. We further introduce a fusion model that analyzes bytecode semantics and structure to recommend critical variables at runtime. This enhances service observability and supports more efficient failure diagnosis.

Microservices architecture, known for its advantages in scalability and availability, presents challenges in pinpointing failure causes due to its inherent complexity. Traditional root cause analysis (RCA) methods often lack comprehensive insights into anomaly detection and exhibit shortcomings in finegrained anomaly localization. To address these gaps, we propose RootScan, an innovative framework tailored for fine-grained and interpretable root cause analysis in microservices. Our approach features an event-based observability model that unifies multimodal data (traces, metrics, and logs) into a structured event flow, providing a holistic view of system behavior. Then, we leverage the interpretability of generative networks to detect anomalies from the event flow level to feature level, yielding valuable clues for subsequent root cause analysis. Further, we explore anomaly propagation patterns to precisely trace root causes at fine-grained levels (container, microservice and component). We validate our proposal using an open-source dataset and benchmark microservice environment. Experimental results demonstrate the superiority of our approach, markedly enhancing RCA accuracy and reliability in microservices environments.

Fifth generation new radio vehicle-to-everything (5G-V2X) communication is an emerging technology to support advanced use cases and higher automation levels in Internet of Vehicles. A comprehensive and accurate knowledge of narrow-beam channels plays a crucial role in the utilization of the 5G-V2X technique. This paper focuses on measurement and characterization of vehicle-to-infrastructure (V2I) narrow-beam channel. A flexible narrow-beam channel measurement system using a phased-array antenna is designed, and is employed to perform a series of 3.35 GHz channel measurements with different beam widths in highway primary road and auxiliary road. Based on the collected data, the fading characteristics of V2I narrow-beam channel including path loss, shadow fading and K-factor are extracted, analyzed and then modeled. Then, the V2I narrow-beam channel dispersion in time-frequency-space domain is characterized, and the statistical models of root-mean-square (RMS) delay spread, RMS Doppler spread and RMS angular spread are proposed. In addition, the V2I narrow-beam channel nonstationarity is discussed in terms of the stationarity interval and birth-death process of multipath components, and results of the Markov chain model with parameters like state transition probability matrix and steady-state probability are reported. The results can contribute to the design and evaluation of 5G-V2X technology.

Current medical image synthetic augmentation techniques rely on the intensive use of generative adversarial networks (GANs). However, the nature of GAN architecture leads to heavy computational resources to produce synthetic images and the augmentation process requires multiple stages to complete. To address these challenges, we introduce a novel generative meta curriculum learning method that trains the task-specific model (student) end-to-end with only one additional teacher model. The teacher learns to generate curriculum to feed into the student model for data augmentation and guides the student to improve performance in a meta-learning style. In contrast to the generator and discriminator in GAN, which compete with each other, the teacher and student collaborate to improve the student's performance on the target tasks. Extensive experiments on the histopathology datasets show that leveraging our framework results in significant and consistent improvements in classification performance.

Accurate knowledge of non-stationary narrow-beam channel characteristics is the prerequisite of applying dynamic beamforming technology in high-mobility communication scenarios. This paper proposes a three-dimensional geometry-based stochastic model (GBSM) for non-stationary narrow-beam channels in high-mobility communication scenarios. In the proposed GBSM, the impact of antenna pattern on angular distribution and the birth-death process of clusters in space-time-frequency (STF) domain are considered. Statistical properties of the proposed model are derived, including STF correlation function and multi-link spatial cross-correlation function (CCF), and the corresponding numerical results are analyzed. Moreover, the proposed model is verified by channel measurements in two high-mobility communication scenarios, such as vehicle-to-infrastructure scenario and high-speed railway scenario, in terms of temporal autocorrelation function, frequency correlation function and multi-link spatial CCF. It shows a good agreement between the measurement and model results, which confirms the reliability of the proposed model.

Deep learning networks have demonstrated competitive performance for various tasks on medical images. However, obtaining promising results requires a large amount of annotated data for supervised training, which is labor-intensive. Recently, the increasing interest in transformers has suggested their robust performance on computer vision tasks, including generative adversarial networks (GANs). In this paper, we propose a conditional GAN built on pure transformer-based architectures, named MedViTGAN, to assist in generating synthetic histopathology images for data augmentation in an end-to-end manner. The presented model adopts a conditioned training strategy by incorporating a transformer-based auxiliary classifier to facilitate the discriminative image generation process. We further introduce an adaptive hybrid loss weighting mechanism to balance multiple losses over sources and classes to stabilize the training. Extensive experiments on the histopathology datasets show that leveraging MedViTGAN generated images results in a significant and consistent improvement in classification performance.

Most density-based clustering methods largely rely on how well the underlying density is estimated. However, like clustering, density estimation is also a challenging unsupervised learning problem, especially the determination of the kernel bandwidth. In this paper, we propose a density-based multilayer hierarchical clustering method, called the Deep Nearest Neighbor Descent (D-NND), which can largely alleviate the impact of the density estimation. Unlike previous density-based methods, D-NND learns the underlying density distribution layer by layer and at the same time makes the dataset sparsely and effectively organized into a directed Tree. The experiments on three real-world datasets and several challenging synthetic datasets demonstrate that the proposed method has strong ability to discover the underlying cluster structures and is not very sensitive to the density estimation method, the parameters and the clusters of multiple scales.

To listen to brain activity as a piece of music, we proposed the scale-free brainwave music (SFBM) technology, which could translate the scalp electroencephalogram (EEG) into music notes according to the power law of both EEG and music. In the current study, this methodology was further extended to a musical ensemble of two channels. First, EEG data from two selected channels are translated into musical instrument digital interface (MIDI) sequences, where the EEG parameters modulate the pitch, duration, and volume of each musical note. The phase synchronization index of the two channels is computed by a Hilbert transform. Then the two MIDI sequences are integrated into a chorus according to the phase synchronization index. The EEG with a high synchronization index is represented by more consonant musical intervals, while the low index is expressed by inconsonant musical intervals. The brain ensemble derived from real EEG segments illustrates differences in harmony and pitch distribution during the eyes-closed and eyes-open states. Furthermore, the scale-free phenomena exist in the brainwave ensemble. Therefore, the scale-free brain ensemble modulated by phase synchronization is a new attempt to express the EEG through an auditory and musical way, and it can be used for EEG monitoring and bio-feedback.

Color information plays an important role in better understanding of natural scenes by at least facilitating discriminating boundaries of objects or areas. In this study, we propose a new framework for boundary detection in complex natural scenes based on the color-opponent mechanisms of the visual system. The red-green and blue-yellow color opponent channels in the human visual system are regarded as the building blocks for various color perception tasks such as boundary detection. The proposed framework is a feed forward hierarchical model, which has direct counterpart to the color-opponent mechanisms involved in from the retina to the primary visual cortex (V1). Results show that our simple framework has excellent ability to flexibly capture both the structured chromatic and achromatic boundaries in complex scenes.

The double-opponent color-sensitive cells in the primary visual cortex (V1) of the human visual system (HVS) have long been recognized as the physiological basis of color constancy. We introduce a new color constancy model by imitating the functional properties of the HVS from the retina to the double-opponent cells in V1. The idea behind the model originates from the observation that the color distribution of the responses of double-opponent cells to the input color-biased images coincides well with the light source direction. Then the true illuminant color of a scene is easily estimated by searching for the maxima of the separate RGB channels of the responses of double-opponent cells in the RGB space. Our systematical experimental evaluations on two commonly used image datasets show that the proposed model can produce competitive results in comparison to the complex state-of-the-art approaches, but with a simple implementation and without the need for training.