Synthesis, characterization and ethylene polymerization behaviour of binuclear nickel halides bearing 4,5,9,10-tetra(arylimino)pyrenylidenes

Pyrene-4,5,9,10-tetraone was prepared via the oxidation of pyrene, and reacted with various anilines to afford a series of 4,5,9,10-tetra(arylimino)pyrenylidene derivatives (L1–L4). The tetraimino-pyrene compounds L1 and L2 were reacted with two equivalents of (DME)NiBr₂ in CH₂Cl₂ to afford the corresponding dinickel bromide complexes (Ni1 and Ni2). The organic compounds were fully characterized, whilst the bi-metallic complexes were characterized by FT-IR spectra and elemental analysis. The molecular structures of representative organic and nickel compounds were confirmed by single-crystal X-ray diffraction studies. These nickel complexes exhibited high activities towards ethylene polymerization in the presence of either MAO or Me₂AlCl, maintaining a high activity over a prolonged period (longer than previously reported dinickel complex pre-catalysts). The polyethylene obtained was characterized by GPC, DSC and FT-IR spectroscopy and was found to possess branched features

Delving StyleGAN Inversion for Image Editing: A Foundation Latent Space Viewpoint

GAN inversion and editing via StyleGAN maps an input image into the embedding spaces ($\mathcal{W}$, $\mathcal{W^+}$, and $\mathcal{F}$) to simultaneously maintain image fidelity and meaningful manipulation. From latent space $\mathcal{W}$ to extended latent space $\mathcal{W^+}$ to feature space $\mathcal{F}$ in StyleGAN, the editability of GAN inversion decreases while its reconstruction quality increases. Recent GAN inversion methods typically explore $\mathcal{W^+}$ and $\mathcal{F}$ rather than $\mathcal{W}$ to improve reconstruction fidelity while maintaining editability. As $\mathcal{W^+}$ and $\mathcal{F}$ are derived from $\mathcal{W}$ that is essentially the foundation latent space of StyleGAN, these GAN inversion methods focusing on $\mathcal{W^+}$ and $\mathcal{F}$ spaces could be improved by stepping back to $\mathcal{W}$. In this work, we propose to first obtain the precise latent code in foundation latent space $\mathcal{W}$. We introduce contrastive learning to align $\mathcal{W}$ and the image space for precise latent code discovery. %The obtaining process is by using contrastive learning to align $\mathcal{W}$ and the image space. Then, we leverage a cross-attention encoder to transform the obtained latent code in $\mathcal{W}$ into $\mathcal{W^+}$ and $\mathcal{F}$, accordingly. Our experiments show that our exploration of the foundation latent space $\mathcal{W}$ improves the representation ability of latent codes in $\mathcal{W^+}$ and features in $\mathcal{F}$, which yields state-of-the-art reconstruction fidelity and editability results on the standard benchmarks. Project page: \url{https://github.com/KumapowerLIU/CLCAE}

Array Signal Processing for Synthetic Aperture Radar (SAR)

Synthetic aperture radar (SAR) is a kind of imaging radar that can produce high resolution images of targets and terrain on the ground. At present, most of SAR processing algorithms are based on matched filtering. This method is easy to implement and can produce stable results. However, It also has some limitations. This approach must obey the Nyquist sampling theorem and the resolution depends on bandwidth of pulses. This means that the matched filter approach must be based on a large amount of raw data but the performance is limited. With the development of radar imaging, it is difficult for the matched filtering approach to meet the requirement of high resolution SAR images. In this thesis, a new processing method based on the least squares (LS) beamforming is utilized in the processing of SAR raw data. The model of SAR simulates a virtual linear array. The processing of SAR data can also be seen as a process of beamforming. The 1- D azimuth direction echo data is processed using the beamforming method. Simulation results based on the least squares design method are compared with the matched filtering method and the conventional beamforming method with different windows

Geometric Error Identification for 6DoF Robotic Manipulator Calibration to Improve Absolute Positioning Accuracy

As robotic manipulators become extensively incorporated in various modern industries, there is a growing list of applications for human-to-robot interaction and robot-to-robot collaboration, which requires strong performance on the absolute positioning accuracy of the robot. The lack of accuracy could come from extreme operating environments, manufacturing and assembly errors, dynamic influence from gear compliance and backlash, etc. This thesis tackles the accuracy issue from two aspects: tighter mechanical tolerances and a closer matching kinematics model with the actual robot. For these purposes, according to the geometry of a pneumatically driven six DoF manipulator, a 6D parametric kinematics model is firstly derived. The proposed model is highly flexible in terms of introducing, anywhere in each linkage of the manipulator, any number of virtual mechanical tolerance points that lump effects of dimension and orientation deviations caused by mechanical tolerances. Therefore, concerned mechanical tolerances can be added to the model and studied through Fuzzy arithmetic to analyze their influence on the TCP position. Meanwhile, geometric errors are also the primary source of discrepancies between the nominal model and real hardware. The model can include translational and rotational error parameters that need identification to quantify the effects from the geometric errors at the locations of the virtual mechanical tolerance points. For an effective identification, dependent error parameters are systematically eliminated using QR decomposition. Once the model reduction is completed, the nonlinear least-squares optimization problem using the Gauss-Newton line search method is formulated to identify the remaining independent error parameters. The identification process is eventually verified on the experimental manipulator. In a nutshell, the thesis presents 1) a tolerance analysis tool that offers insights for potential targeted manufacturing improvements to decrease the dominant tolerances, and 2) a capable parameter identification process that rectifies the nominal kinematics model to agree with the hardware.M.S

Continual Learning with Dirichlet Generative-based Rehearsal

Recent advancements in data-driven task-oriented dialogue systems (ToDs) struggle with incremental learning due to computational constraints and time-consuming issues. Continual Learning (CL) attempts to solve this by avoiding intensive pre-training, but it faces the problem of catastrophic forgetting (CF). While generative-based rehearsal CL methods have made significant strides, generating pseudo samples that accurately reflect the underlying task-specific distribution is still a challenge. In this paper, we present Dirichlet Continual Learning (DCL), a novel generative-based rehearsal strategy for CL. Unlike the traditionally used Gaussian latent variable in the Conditional Variational Autoencoder (CVAE), DCL leverages the flexibility and versatility of the Dirichlet distribution to model the latent prior variable. This enables it to efficiently capture sentence-level features of previous tasks and effectively guide the generation of pseudo samples. In addition, we introduce Jensen-Shannon Knowledge Distillation (JSKD), a robust logit-based knowledge distillation method that enhances knowledge transfer during pseudo sample generation. Our experiments confirm the efficacy of our approach in both intent detection and slot-filling tasks, outperforming state-of-the-art methods

Advanced Bionic Attachment Equipment Inspired by the Attachment Performance of Aquatic Organisms: A Review

In nature, aquatic organisms have evolved various attachment systems, and their attachment ability has become a specific and mysterious survival skill for them. Therefore, it is significant to study and use their unique attachment surfaces and outstanding attachment characteristics for reference and develop new attachment equipment with excellent performance. Based on this, in this review, the unique non-smooth surface morphologies of their suction cups are classified and the key roles of these special surface morphologies in the attachment process are introduced in detail. The recent research on the attachment capacity of aquatic suction cups and other related attachment studies are described. Emphatically, the research progress of advanced bionic attachment equipment and technology in recent years, including attachment robots, flexible grasping manipulators, suction cup accessories, micro-suction cup patches, etc., is summarized. Finally, the existing problems and challenges in the field of biomimetic attachment are analyzed, and the focus and direction of biomimetic attachment research in the future are pointed out