Bioinspired Coordinated Path Following for Vessels with Speed Saturation Based on Virtual Leader

This paper investigates the coordinated path following of multiple marine vessels with speed saturation. Based on virtual leader strategy, the authors show how the neural dynamic model and passivity-based techniques are brought together to yield a distributed control strategy. The desired path following is achieved by means of a virtual dynamic leader, whose controller is designed based on the biological neural shunting model. Utilizing the characteristic of bounded and smooth output of neural dynamic model, the tracking error jump is avoided and speed saturation problem is solved in straight path. Meanwhile, the coordinated path following of multiple vessels with a desired spatial formation is achieved through defining the formation reference point. The consensus of formation reference point is realized by using the synchronization controller based on passivity. Finally, simulation results validate the effectiveness of the proposed coordinated algorithm

Genome-wide identification and molecular evolution of NAC gene family in Dendrobium nobile

NAC transcription factors are an important genes that regulate plant growth and development, and can regulate functions such as fruit ripening in plants. Based on genome data of Dendrobium nobile, the NAC gene family was identified and analyzed by bioinformatics methods. In this study, we identified 85 NAC genes in Dendrobium nobile genome, and systematically analyzed the NAC gene family. We found that they were distributed unevenly in the nineteen chromosomes. The amino acid length of D. nobile NAC gene family (DnoNACs) ranged from 80 to 1065, molecular weight ranged from 22.17 to 119.02 kD, and isoelectric point ranged from 4.61~9.26. Its promoter region contains multiple stress responsive elements, including light responsive, gibberellin-responsive, abscisic acid responsiveness, MeJA-responsiveness and drought-inducibility elements. Phylogenetic analysis indicates that the D. nobile NAC gene family is most closely related to Dendrobium catenatum and Dendrobium chrysotoxum. Analysis of SSR loci indicates that the fraction of mononucleotide repeats was the largest, as was the frequency of A/T. Non-coding RNA analysis showed that these 85 NAC genes contain 397 miRNAs. The collinearity analysis shows that 9 collinear locis were found on the chromosomes of D. nobile with Arabidopsis thaliana, and 75 collinear locis with D.chrysotoxum. QRT-PCR experiment under different salt concentration and temperature conditions verified the response mechanism of DnoNAC gene family under stress conditions. Most DnoNAC genes are sensitive to salt stress and temperature stress. The results of this study provide a reference for further understanding the function of NAC gene in D. nobile

Finite-Time Tracking Control for a Class of MIMO Nonlinear Systems with Unknown Asymmetric Saturations

This paper addresses the problem of finite-time tracking control for multiple-input and multiple-output (MIMO) nonlinear systems with asymmetric saturations. A systematic approach is proposed to eliminate the effects of unmeasured external disturbances and unknown asymmetric saturations. In the proposed control strategy, a terminal sliding mode disturbance observer is provided to estimate the augmented disturbance (which contains the unknown asymmetric input saturation and external disturbance). The approximation error of the augmented disturbance can converge to zero in a fixed finite-time interval. Furthermore, a novel finite-time tracking control algorithm is developed to guarantee fast convergence of the tracking error. Compared with the existing results on finite-time tracking control, the chattering problem and the input saturation problem can be solved in a unified framework. Several simulations are given to demonstrate the effectiveness of the proposed approach

1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Data

This paper details our participation in the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) workshop @ EMNLP 2022, where we take part in Subtask 1 of Shared Task 3. We approach the given task of event causality detection by proposing a self-training pipeline that follows a teacher-student classifier method. More specifically, we initially train a teacher model on the true, original task data, and use that teacher model to self-label data to be used in the training of a separate student model for the final task prediction. We test how restricting the number of positive or negative self-labeled examples in the self-training process affects classification performance. Our final results show that using self-training produces a comprehensive performance improvement across all models and self-labeled training sets tested within the task of event causality sequence classification. On top of that, we find that self-training performance did not diminish even when restricting either positive/negative examples used in training. Our code is be publicly available at https://github.com/Gzhang-umich/1CademyTeamOfCASE.Comment: Paper from CASE workshop at EMNLP 202

Robust Coordinated Formation for Multiple Surface Vessels Based on Backstepping Sliding Mode Control

We investigate the problem of coordinated formation control for multiple surface vessels in the presence of unknown external disturbances. In order to realize the leaderless coordinated formation and achieve the robustness against unknown external disturbances, a new robust coordinated formation control algorithm based on backstepping sliding mode control is proposed. The proposed coordinated control algorithm is achieved by defining a new switched function using the combination of position tracking error and cross-coupling error. Particularly, the cross-coupling error is defined using velocity tracking error and velocity synchronization error so as to be applicable for sliding mode controller design. Furthermore, the adaptive control law is proposed to estimate unknown disturbances for each vessel. The globally asymptotically stability is proved using the Lyapunov direct method. Finally, the effectiveness of the proposed coordinated formation control algorithm is demonstrated by corresponding simulations

Design and Synthesis Functional Selective Catalytic Reduction Catalyst for NOx Removal

AbstractMeOx-Cu-SSZ-13 (Me=Mn, Ce) was synthesized by physically mixing mental oxide and ion-exchanged zeolite. The composite catalyst showed highly efficient for the NOx removal using NH3-SCR method. And the NO conversion is 98% for MnOx-CeO2/Cu-SSZ-13 at 150°C and 97% for MnOx/Cu-SSZ-13 at 175°C. Meanwhile, the N2 selectivity remains more than 98%. The catalysts are characterized by using XRD and SEM. The XRD patterns show that all samples are highly crystallized and without impurities. The SEM demonstrates all samples have uniform crystal size. Composite catalyst especially combined with Cu-SSZ-13 has considerable potential as a catalyst in the area of NOx conversion

Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance

As large language models (LLMs) are continuously being developed, their evaluation becomes increasingly important yet challenging. This work proposes Chain-of-Thought Hub, an open-source evaluation suite on the multi-step reasoning capabilities of large language models. We are interested in this setting for two reasons: (1) from the behavior of GPT and PaLM model family, we observe that complex reasoning is likely to be a key differentiator between weaker and stronger LLMs; (2) we envisage large language models to become the next-generation computational platform and foster an ecosystem of LLM-based new applications, this naturally requires the foundation models to perform complex tasks that often involve the composition of linguistic and logical operations. Our approach is to compile a suite of challenging reasoning benchmarks to track the progress of LLMs. Our current results show that: (1) model scale clearly correlates with reasoning capabilities; (2) As of May 2023, Claude-v1.3 and PaLM-2 are the only two models that are comparable with GPT-4, while open-sourced models still lag behind; (3) LLaMA-65B performs closely to code-davinci-002, indicating that with successful further development such as reinforcement learning from human feedback (RLHF), it has great potential to be close to GPT-3.5-Turbo. Our results also suggest that for the open-source efforts to catch up, the community may focus more on building better base models and exploring RLHF.Comment: Preprint. Code at https://github.com/FranxYao/chain-of-thought-hu