A modified CM algorithm for blind equalization of MPSK signals

A new blind equalization algorithm for application to wireless communication employing MPSK signals is proposed in this paper.&nbsp; Since the new cost function exploits the amplitude and phase information simultaneously, the proposed algorithm can provide a superior performance than the conventional constant modulus algorithm (CMA) which only use the amplitude knowledge in its cost function.&nbsp; Theoretical analysis and numerical simulations both demonstrate that the steady-state mean square error (MSE) for the proposed algorithm is less than that of the CMA.<br /

A New Reading of Kant's Second Analogy in the Light of Lovejoy's Criticism

In his article “On Kant’s Reply to Hume” (1906), Arthur Lovejoy raises four interconnected objections to Kant’s argument in the Second Analogy. In general, Lovejoy argues that (i) Kant fails to establish that the principle of causality is the basis of the distinction between subjective and objective perceptions of change; (ii) Kant fails to establish that the principle of causality is the basis of the distinction between perceptions of stationary and moving objects; (iii) due to point (i) and (ii), what Kant proves in the Second Analogy has nothing to do with the principle of causality. Therefore, Kant commits the non-sequitur when he concludes that by appealing to the principle of causality, we know a priori that the same kind of antecedent will always be followed by the same kind of consequent; (iv) because of the non-sequitur, Kant fails to respond to Hume’s skepticism about particular causal principles. In this thesis, I defend Kant from Lovejoy’s objections, in the light of which a new interpretation of the Second Analogy will also be provided. I argue that, in contrast to what Lovejoy claims, Kant successfully demonstrates in the Second Analogy that the principle of causality is not only the distinguishing criterion between subjective and objective perceptions of change but is also the distinguishing criterion between perceptions of stationary and moving objects. In addition, the conclusion of the Second Analogy is just a re-statement of what Kant proves, which can be put as a transcendental argument that suggests that the principle of causality is the necessary condition of the possibility of occurrence (experience of objective successions/moving objects), which does not commit any non-sequitur. Consequently, as far as Kant himself is concerned, this transcendental argument is sufficient to respond to Hume’s skepticism concerning the principle of causality (both general and particular)

A new blind signal separation algorithm for instantaneous MIMO system

We address the problem of adaptive blind source separation (BSS) from instantaneous multi-input multi-output (MIMO) channels. In this paper, we propose a new constant modulus (CM)-based algorithm which employ nonlinear function as the de-correlation term. Moreover, it is shown by theoretical analysis that the proposed algorithm has less mean square error (MSE), i.e., better separation performance, in steady state than the cross-correlation and constant modulus algorithm (CC-CMA). Numerical simulations show the effectiveness of the proposed result.<br /

Fast equilibrium reconstruction by deep learning on EAST tokamak

A deep neural network is developed and trained on magnetic measurements (input) and EFIT poloidal magnetic flux (output) on the EAST tokamak. In optimizing the network architecture, we use automatic optimization in searching for the best hyperparameters, which helps the model generalize better. We compare the inner magnetic surfaces and last-closed-flux surfaces (LCFSs) with those from EFIT. We also calculated the normalized internal inductance, which is completely determined by the poloidal magnetic flux and can further reflect the accuracy of the prediction. The time evolution of the internal inductance in full discharges is compared with that provided by EFIT. All of the comparisons show good agreement, demonstrating the accuracy of the machine learning model, which has the high spatial resolution as the off-line EFIT while still meets the time constraint of real-time control

Molecular identification of original plants of Fritillariae cirrhosae bulbus, a Tradtional Chinese Cedicine (TCM) using plant DNA barcoding

Background: DNA barcoding is a widely used tool that enables rapid and accurate identification of species based on standardized DNA regions.Materials and Methods: In this study, potential DNA barcodes, namely three plastid regions (rbcL, trnH-psbA and matK) and one nuclear ribosomal internal transcribed spacer (ITS) were adopted for species identification of original plants of Fritillariae Cirrhosae Bulbus.Results: The rbcL and trnH-psbA regions showed better success rate of PCR amplification and DNA sequencing, as well as superior discriminatory ability. On the contrary, ITS region did not possess effective genetic variation and matK was faced with low success rate of sequencing. Combination of multi-loci sequences could improve identification ability of DNA barcoding. The trnH-psbA + rbcL could discriminate 25% - 100% species based on the Blast, Tree-Building and Distance methods.Conclusion: The potential DNA barcodes could not completely solving species identification of botanic origins of Fritillariae Cirrhosae Bulbus. In future, we should pay more attention to super-barcoding or specific barcode that enhance ability to discriminate the closely related plants.Keywords: Fritillariae Cirrhosae Bulbus, species identification, DNA barcoding, internal transcribed spacer (ITS), traditional Chinese medicine (TCM

HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation

Point-supervised Temporal Action Localization (PSTAL) is an emerging research direction for label-efficient learning. However, current methods mainly focus on optimizing the network either at the snippet-level or the instance-level, neglecting the inherent reliability of point annotations at both levels. In this paper, we propose a Hierarchical Reliability Propagation (HR-Pro) framework, which consists of two reliability-aware stages: Snippet-level Discrimination Learning and Instance-level Completeness Learning, both stages explore the efficient propagation of high-confidence cues in point annotations. For snippet-level learning, we introduce an online-updated memory to store reliable snippet prototypes for each class. We then employ a Reliability-aware Attention Block to capture both intra-video and inter-video dependencies of snippets, resulting in more discriminative and robust snippet representation. For instance-level learning, we propose a point-based proposal generation approach as a means of connecting snippets and instances, which produces high-confidence proposals for further optimization at the instance level. Through multi-level reliability-aware learning, we obtain more reliable confidence scores and more accurate temporal boundaries of predicted proposals. Our HR-Pro achieves state-of-the-art performance on multiple challenging benchmarks, including an impressive average mAP of 60.3% on THUMOS14. Notably, our HR-Pro largely surpasses all previous point-supervised methods, and even outperforms several competitive fully supervised methods. Code will be available at https://github.com/pipixin321/HR-Pro.Comment: 12 pages, 8 figure