J. Li

J. Zhao, J. Li, M. Chen et al.

Functional Data Analysis (FDA) is a statistical domain developed to handle functional data characterized by high dimensionality and complex data structures. Sequential Neural Networks (SNNs) are specialized neural networks capable of processing sequence data, a fundamental aspect of functional data. Despite their great flexibility in modeling functional data, SNNs have been inadequately employed in the FDA community. One notable advantage of SNNs is the ease of implementation, making them accessible to a broad audience beyond academia. Conversely, FDA-based methodologies present challenges, particularly for practitioners outside the field, due to their intricate complexity. In light of this, we propose utilizing SNNs in FDA applications and demonstrate their effectiveness through comparative analyses against popular FDA regression models based on numerical experiments and real-world data analysis. SNN architectures allow us to surpass the limitations of traditional FDA methods, offering scalability, flexibility, and improved analytical performance. Our findings highlight the potential of SNN-based methodologies as powerful tools for data applications involving functional data.

R. Landman, S. Y. Haffert, J. D. Long et al.

Pyramid wavefront sensors (PWFSs) are the preferred choice for current and future extreme adaptive optics (XAO) systems. Almost all instruments use the PWFS in its modulated form to mitigate its limited linearity range. However, this modulation comes at the cost of a reduction in sensitivity, a blindness to petal-piston modes, and a limit to the sensor's ability to operate at high speeds. Therefore, there is strong interest to use the PWFS without modulation, which can be enabled with nonlinear reconstructors. Here, we present the first on-sky demonstration of XAO with an unmodulated PWFS using a nonlinear reconstructor based on convolutional neural networks. We discuss the real-time implementation on the Magellan Adaptive Optics eXtreme (MagAO-X) instrument using the optimized TensorRT framework and show that inference is fast enough to run the control loop at >2 kHz frequencies. Our on-sky results demonstrate a successful closed-loop operation using a model calibrated with internal source data that delivers stable and robust correction under varying conditions. Performance analysis reveals that our smart PWFS achieves nearly the same Strehl ratio as the highly optimized modulated PWFS under favorable conditions on bright stars. Notably, we observe an improvement in performance on a fainter star under the influence of strong winds. These findings confirm the feasibility of using the PWFS in its unmodulated form and highlight its potential for next-generation instruments. Future efforts will focus on achieving even higher control loop frequencies (>3 kHz), optimizing the calibration procedures, and testing its performance on fainter stars, where more gain is expected for the unmodulated PWFS compared to its modulated counterpart.

J. Li, Z. Duan, S. Li et al.

In this paper,an Enhanced Self-Attention (ESA) mechanism has been put forward for robust feature extraction.The proposed ESA is integrated with the recursive gated convolution and self-attention mechanism.In particular, the former is used to capture multi-order feature interaction and the latter is for global feature extraction.In addition, the location of interest that is suitable for inserting the ESA is also worth being explored.In this paper, the ESA is embedded into the encoder layer of the Transformer network for automatic speech recognition (ASR) tasks, and this newly proposed model is named GNCformer. The effectiveness of the GNCformer has been validated using two datasets, that are Aishell-1 and HKUST.Experimental results show that, compared with the Transformer network,0.8%CER,and 1.2%CER improvement for these two mentioned datasets, respectively, can be achieved.It is worth mentioning that only 1.4M additional parameters have been involved in our proposed GNCformer.

J. Li, L. Lyu, X. Liu et al.

The distributed denial of service (DDoS) attack is detrimental to the industrial Internet of things (IIoT) as it triggers severe resource starvation on networked objects. Recent dynamics demonstrate that it is a highly profitable business for attackers using botnets. Current centralized mitigation solutions concentrate on detection and mitigation at a victim's side, paying inadequate attention to hacking costs and the collaboration of defenders. Thus, we propose the federated learning empowered mitigation architecture (FLEAM) to advocate joint defense, incurring a higher hacking expense. FLEAM combines FL and fog computing to reduce mitigation time and improve detection accuracy, enabling defenders to jointly combatting botnets. Our comprehensive evaluations showcase that the attacking expense incurred is 2.5 times higher, the mitigation delay is about 72% lower, and the accuracy is 47% greater on average than classic solutions.

A. Jang Sher, U. Huzaifa, J. Li et al.

In contexts such as teleoperation, robot reprogramming, human-robot-interaction, and neural prosthetics, conveying movement commands to a robotic platform is often a limiting factor. Currently, many applications rely on joint-angle-by-joint-angle prescriptions. This inherently requires a large number of parameters to be specified by the user that scales with the number of degrees of freedom on a platform, creating high bandwidth requirements for interfaces. This paper presents an efficient representation of high-level, spatial commands that specifies many joint angles with relatively few parameters based on a spatial architecture that is judged favorably by human viewers. In particular, a general method for labeling connected platform linkages, generating a databank of user-specified poses, and mapping between high-level spatial commands and specific platform static configurations are presented. Thus, this architecture is ``platform-invariant'' where the same high-level, spatial command can be executed on any platform. This has the advantage that our commands have meaning for human movers as well. In order to achieve this, we draw inspiration from Laban/Bartenieff Movement Studies, an embodied taxonomy for movement description. The architecture is demonstrated through implementation on 26 spatial directions for a Rethink Robotics Baxter, an Aldebaran NAO, and a KUKA youBot. User studies are conducted to validate the claims of the proposed framework.

C. Widanapathirana, J. Li, Y. A. Sekercioglu et al.

We present an automated solution for rapid diagnosis of client device problems in private cloud environments: the Intelligent Automated Client Diagnostic (IACD) system. Clients are diagnosed with the aid of Transmission Control Protocol (TCP) packet traces, by (i) observation of anomalous artifacts occurring as a result of each fault and (ii) subsequent use of the inference capabilities of soft-margin Support Vector Machine (SVM) classifiers. The IACD system features a modular design and is extendible to new faults, with detection capability unaffected by the TCP variant used at the client. Experimental evaluation of the IACD system in a controlled environment demonstrated an overall diagnostic accuracy of 98%.