An improved spline-local mean decomposition and its application to vibration analysis of rotating machinery with rub-impact fault

A troublesome problem in application of local mean decomposition (LMD) is that the moving averaging process is time-consuming and inaccurate in processing the mechanical vibration signals. An improved spline-LMD (SLMD) method is proposed to solve this problem. The proposed method uses the cubic spline interpolation to compute the upper and lower envelopes of a signal, and then the local mean and envelope estimate functions can be derived using the envelopes. Meanwhile, a signal extending approach based on self-adaptive waveform matching technique is applied to extend the raw signal and overcome the boundary distortion resulting from the process of computing the upper and lower envelopes. Subsequently, this paper compares SLMD with LMD in four aspects through a simulative signal. The comparative results illustrate that SLMD consumes less computation time and produces more accurate decomposed results than LMD. In the experimental part, SLMD and LMD are respectively applied to analyze the vibration signals resulting from a rotor-bearing system with rub-impact fault. The results show that SLMD can more efficiently and accurately extract the important fault features, which demonstrates that SLMD performs better than LMD in analyzing the mechanical vibration signals

Chinese Spelling Correction as Rephrasing Language Model

This paper studies Chinese Spelling Correction (CSC), which aims to detect and correct potential spelling errors in a given sentence. Current state-of-the-art methods regard CSC as a sequence tagging task and fine-tune BERT-based models on sentence pairs. However, we note a critical flaw in the process of tagging one character to another, that the correction is excessively conditioned on the error. This is opposite from human mindset, where individuals rephrase the complete sentence based on its semantics, rather than solely on the error patterns memorized before. Such a counter-intuitive learning process results in the bottleneck of generalizability and transferability of machine spelling correction. To address this, we propose $Rephrasing Language Modeling$ (ReLM), where the model is trained to rephrase the entire sentence by infilling additional slots, instead of character-to-character tagging. This novel training paradigm achieves the new state-of-the-art results across fine-tuned and zero-shot CSC benchmarks, outperforming previous counterparts by a large margin. Our method also learns transferable language representation when CSC is jointly trained with other tasks

E(2)-Equivariant Graph Planning for Navigation

Learning for robot navigation presents a critical and challenging task. The scarcity and costliness of real-world datasets necessitate efficient learning approaches. In this letter, we exploit Euclidean symmetry in planning for 2D navigation, which originates from Euclidean transformations between reference frames and enables parameter sharing. To address the challenges of unstructured environments, we formulate the navigation problem as planning on a geometric graph and develop an equivariant message passing network to perform value iteration. Furthermore, to handle multi-camera input, we propose a learnable equivariant layer to lift features to a desired space. We conduct comprehensive evaluations across five diverse tasks encompassing structured and unstructured environments, along with maps of known and unknown, given point goals or semantic goals. Our experiments confirm the substantial benefits on training efficiency, stability, and generalization

Integrating Symmetry into Differentiable Planning with Steerable Convolutions

We study how group symmetry helps improve data efficiency and generalization for end-to-end differentiable planning algorithms when symmetry appears in decision-making tasks. Motivated by equivariant convolution networks, we treat the path planning problem as \textit{signals} over grids. We show that value iteration in this case is a linear equivariant operator, which is a (steerable) convolution. This extends Value Iteration Networks (VINs) on using convolutional networks for path planning with additional rotation and reflection symmetry. Our implementation is based on VINs and uses steerable convolution networks to incorporate symmetry. The experiments are performed on four tasks: 2D navigation, visual navigation, and 2 degrees of freedom (2DOFs) configuration space and workspace manipulation. Our symmetric planning algorithms improve training efficiency and generalization by large margins compared to non-equivariant counterparts, VIN and GPPN.Comment: Restructured main text and appendix. Renamed from "Integrating Symmetry into Differentiable Planning

(2E,6E)-2,6-Bis(2-fluoro-5-meth­oxy­benzyl­idene)cyclo­hexan-1-one

The title compound, C22H20F2O3, a derivative of curcumin, crystallized with two independent mol­ecules in the asymmetric unit. The mean planes of the two 2-fluoro-5-meth­oxy­phenyl groups are aligned at 24.88 (11)° in one mol­ecule and 24.19 (15)° in the other. The dihedral angles between the mean plane of the penta-1,4-dien-3-one group and those of the two 2-fluoro-5-meth­oxy­phenyl rings are 51.16 (11) and 49.16 (10)° in the first mol­ecule, and 45.69 (15) and 54.00 (14)° in the second. The mol­ecules adopt E configurations about the central olefinic bonds

The Advantage of PET and CT Integration in Examination of Lung Tumors

Purpose. To evaluate the diagnosis value of integrated positron emission tomography and computed tomography (PET/CT) with lung masses, this study emphasized the correlation between tumor size and maximum standardized uptake value (SUVmax) in selected regions of interest (ROI) of lung masses. Material and Methods. A retrospective analysis was performed on 85 patients with solid pulmonary lesions, all verified by pathology. The morphology, edge (speculated margins and lobule), size, density of pulmonary masses, and on-chest CT images were reviewed. The SUVmax in ROI of pulmonary masses was calculated. Results. Among the 85 patients with lung masses, 59 patients presented with pulmonary malignant neoplasm and 26 patients with benign lesions. The sensitivity, specificity, and accuracy were 89.8%, 61.5%, 81.2%, respectively, for PET measurement only, 88.1%, 65.4%, 81.2% for CT only, and 96.6%, 80.8%, 91.8% for PET/CT. The size of pulmonary malignant neoplasm in the 59 patients was apparently correlated with the ROI's SUVmax (r=0.617, P<.001). However, the size of pulmonary benign mass in the 26 patients was not correlated with the SUVmax. Conclusion. PET/CT is of greater value in characterization of lung masses than PET and CT performed separately. The examination of lung tumor can be further specified by the correlation between the size of pulmonary malignant neoplasm and the ROI's SUVmax

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction

Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in drug discovery. However, most DL based QSAR models are restricted to limited labeled data to achieve better performance, and also are sensitive to model scale and hyper-parameters. In this paper, we propose Uni-QSAR, a powerful Auto-ML tool for molecule property prediction tasks. Uni-QSAR combines molecular representation learning (MRL) of 1D sequential tokens, 2D topology graphs, and 3D conformers with pretraining models to leverage rich representation from large-scale unlabeled data. Without any manual fine-tuning or model selection, Uni-QSAR outperforms SOTA in 21/22 tasks of the Therapeutic Data Commons (TDC) benchmark under designed parallel workflow, with an average performance improvement of 6.09\%. Furthermore, we demonstrate the practical usefulness of Uni-QSAR in drug discovery domains

Void Lensing in Cubic Galileon Gravity

Weak lensing studies via cosmic voids are a promising probe of Modified Gravity (MG). Excess surface mass density (ESD) is widely used as a lensing statistic in weak lensing research. In this paper, we use the ray-tracing method to study the ESD around voids in simulations based on Cubic Galileon (CG) gravity. With the compilation of N-body simulation and ray-tracing method, changes in structure formation and deflection angle resulting from MG can both be considered, making the extraction of lensing signals more realistic. We find good agreements between the measurement and theoretical prediction of ESD for CG gravity. Meanwhile, the lensing signals are much less affected by the change of the deflection angle than the change of the structure formation, indicating a good approximation of regarding ESD (statistics) as the projection of 3D dark matter density field. Finally, we demonstrate that it is impossible to distinguish CG and General Relativity in our simulation, however, in the next-generation survey, thanks to the large survey area and the increased galaxy number density, detecting the differences between these two models is possible. The methodology employed in this paper that combines N-body simulation and ray-tracing method can be a robust way to measure the lensing signals from simulations based on the MGs, and especially on that which significantly modifies the deflection angle.Comment: 14 pages, 9 figure