G2T: A simple but versatile framework for topic modeling based on pretrained language model and community detection

It has been reported that clustering-based topic models, which cluster high-quality sentence embeddings with an appropriate word selection method, can generate better topics than generative probabilistic topic models. However, these approaches suffer from the inability to select appropriate parameters and incomplete models that overlook the quantitative relation between words with topics and topics with text. To solve these issues, we propose graph to topic (G2T), a simple but effective framework for topic modelling. The framework is composed of four modules. First, document representation is acquired using pretrained language models. Second, a semantic graph is constructed according to the similarity between document representations. Third, communities in document semantic graphs are identified, and the relationship between topics and documents is quantified accordingly. Fourth, the word--topic distribution is computed based on a variant of TFIDF. Automatic evaluation suggests that G2T achieved state-of-the-art performance on both English and Chinese documents with different lengths. Human judgements demonstrate that G2T can produce topics with better interpretability and coverage than baselines. In addition, G2T can not only determine the topic number automatically but also give the probabilistic distribution of words in topics and topics in documents. Finally, G2T is publicly available, and the distillation experiments provide instruction on how it works

Adaptive Fuzzy Finite-Time Singular Perturbation Control for Flexible Joint Manipulators With State Constraints

An adaptive fuzzy finite-time singular perturbation control is proposed for flexible joint manipulators with state constraints. First, the flexible joint manipulator system is decoupled into a rigid subsystem and a fast subsystem through singular perturbation technique. Second, a finite-time controller is introduced to improve the response speed of the rigid subsystem so that it can converge within a finite time. And then, all the rigid subsystem states are confined within the scope of the constraint by the barrier Lyapunov function. Third, the model’s uncertainties and unknown external disturbances are handled by adaptive fuzzy technique. Finally, the effectiveness of the new control scheme is illustrated by the simulation

Fuzzy Observer-based Command Filtered Adaptive Control of Flexible Joint Robots with Time-varying Output Constraints

Flexible joint robots (FJR) systems are used in many aspects of actual production due to its high compliance, low energy consumption, human-computer interaction safety and other characteristics. A fuzzy observer-based command filtered adaptive control method is applied to make FJR systems with time-varying output constraints (TVOC) and model uncertainties operate safely in a complex environment in this brief. Chiefly, a fuzzy observer is developed to estimate the link's angle velocity and motor angle velocity of the FJR. Next, by combining time-varying barrier Lyapunov function (TVBLF) with fuzzy logic systems, the uncertainties of the FJR model are approximated without violating the TVOC. Besides, the command filtered method with error compensation signal resolves the issue of 'explosion of complexity' and removes the impacts of filtering errors. The stability of the FJR system is verified by Lyapunov stability theory. Simulation shows that the devised approach can insure the TVOC, the validity of the observer and position tracking accuracy of the system.</p

MicroRNA-542 suppressed the proliferation of human glioma cells by targeting talin-2 (TLN2)

Purpose: To investigate the effect of miR-542 in the development of human glioma. Methods: The expressions of miR-542 and TLN2 in glioma cells and normal human astrocytes were determined using qRT-PCR, while MTT and colony formation assays were used to determine cell proliferation. Western blotting was used to determine protein expression. Results: It was revealed that miR-542 was significantly downregulated in glioma cells. Overexpression of miR-542 inhibited the proliferation and clonogenicity of glioma cells via induction of apoptosis. The percentage of apoptotic U87 cells increased from 5.32 in control to 26.76 upon miR-542 overexpression. Moreover, TLN2 was identified as the functional regulatory target of miR542 in glioma. The expression of TLN2 was markedly upregulated in human glioma cells. However, overexpression of miR-542 suppressed TLN2 expression. Silencing of TLN2 mimicked the tumor-suppressive effects of miR-542 in glioma cells, but this effect was blocked by TLN2 over-expression. Conclusion: These results suggest that miR-542 exerted glioma-suppressive effect, with TLN2 as its functional regulatory target. Keywords: Glioma; Proliferation; Micro-RNA; Tumorigenesis; MiR-542; Apoptosis; Prognosis; talin-2; Oncogen