Human Pose Estimation using Global and Local Normalization

In this paper, we address the problem of estimating the positions of human joints, i.e., articulated pose estimation. Recent state-of-the-art solutions model two key issues, joint detection and spatial configuration refinement, together using convolutional neural networks. Our work mainly focuses on spatial configuration refinement by reducing variations of human poses statistically, which is motivated by the observation that the scattered distribution of the relative locations of joints e.g., the left wrist is distributed nearly uniformly in a circular area around the left shoulder) makes the learning of convolutional spatial models hard. We present a two-stage normalization scheme, human body normalization and limb normalization, to make the distribution of the relative joint locations compact, resulting in easier learning of convolutional spatial models and more accurate pose estimation. In addition, our empirical results show that incorporating multi-scale supervision and multi-scale fusion into the joint detection network is beneficial. Experiment results demonstrate that our method consistently outperforms state-of-the-art methods on the benchmarks.Comment: ICCV201

The Mechanism of the Origination of Auto-allopolyploidy and Aneuploidy in Higher Plants Based on the Cases of Iris and Triticeae.

Cytomixis is a natural process of chromatin exchange among cells. In Iris confusa and I. japonica, the synchronized cytomixis takes place between PMC\u27s during a stage just before meiosis. This process produces euploid and aneuploid offspring. The chromosome number of a fertile diploid plant is 30 (2n). Most accessions of I. confusa and I. japonica are sterile aneuploids. The chromosome numbers are varied, ranging 2n = 28 to 60. In Triticeae cytomixis plays an important role in spontaneous chromosome doubling or redoubling, resulting in the origin of auto-allopolyploidy and aneuploidy. We have obtained amphidiploid plants by spontaneous chromosome doubling. These plants indicate indirectly that cytomixis takes place in the macrosporocytes, giving rise to high level auto-allopolyploid Triticeae species

The Study on N Genome of Leymus Species

Leymus Hochst. is a perennial genus of Triticeae. All species in Leymus have the genomes NX. The genome N is from the genus Psathyrostachys. Two Psathyrostachys species, diploid P. huashanica Keng ex Kuo and P. juncea (Fische.) Nevski (2n= 14), were hybridized with allotetraploid, Leymus secalinus (Georgi.) Tzvelev and L. multicaulis (Kar. & Kir.) Tzvelev. Meiotic behavior of the synthetic hybrids was studied. The chromosome pairings indicated that one L. secalinus genome and one L. multicaulis genome were closely homologous with both P. huashanica and P. juncea genomes. The data of genomic analysis in the hybrids of P. huashanica crossed with L. secalinus and L. multicaulis are so similar to those in the hybrids of P. juncea crossed with L. secalinus and L. multicaulis, there is no significant difference between them. Both P. huashanica and P. juncea are possible donors of the N genome of L. secalinus and L. multicaulis

Utilizing Multiple Inputs Autoregressive Models for Bearing Remaining Useful Life Prediction

Accurate prediction of the Remaining Useful Life (RUL) of rolling bearings is crucial in industrial production, yet existing models often struggle with limited generalization capabilities due to their inability to fully process all vibration signal patterns. We introduce a novel multi-input autoregressive model to address this challenge in RUL prediction for bearings. Our approach uniquely integrates vibration signals with previously predicted Health Indicator (HI) values, employing feature fusion to output current window HI values. Through autoregressive iterations, the model attains a global receptive field, effectively overcoming the limitations in generalization. Furthermore, we innovatively incorporate a segmentation method and multiple training iterations to mitigate error accumulation in autoregressive models. Empirical evaluation on the PMH2012 dataset demonstrates that our model, compared to other backbone networks using similar autoregressive approaches, achieves significantly lower Root Mean Square Error (RMSE) and Score. Notably, it outperforms traditional autoregressive models that use label values as inputs and non-autoregressive networks, showing superior generalization abilities with a marked lead in RMSE and Score metrics

Utilizing VQ-VAE for End-to-End Health Indicator Generation in Predicting Rolling Bearing RUL

The prediction of the remaining useful life (RUL) of rolling bearings is a pivotal issue in industrial production. A crucial approach to tackling this issue involves transforming vibration signals into health indicators (HI) to aid model training. This paper presents an end-to-end HI construction method, vector quantised variational autoencoder (VQ-VAE), which addresses the need for dimensionality reduction of latent variables in traditional unsupervised learning methods such as autoencoder. Moreover, concerning the inadequacy of traditional statistical metrics in reflecting curve fluctuations accurately, two novel statistical metrics, mean absolute distance (MAD) and mean variance (MV), are introduced. These metrics accurately depict the fluctuation patterns in the curves, thereby indicating the model's accuracy in discerning similar features. On the PMH2012 dataset, methods employing VQ-VAE for label construction achieved lower values for MAD and MV. Furthermore, the ASTCN prediction model trained with VQ-VAE labels demonstrated commendable performance, attaining the lowest values for MAD and MV.Comment: 17 figure

Preparation of resistant sweet potato starch by steam explosion technology using response surface methodology

Purpose: To obtain the optimal conditions and analyze the structure, gelatinization, and digestion characteristics of resistant sweet potato starch prepared by steam explosion (SE) technology.Methods: A response surface method was used to investigate the effects of explosion pressure, pressure-holding time and autoclaving time on digestion resistance of sweet potato starch. The resulting resistant sweet potato starch was identified by Fourier transform infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), and for in vitro starch digestion rate.Results: The optimum preparation conditions for resistant sweet potato starch were explosion pressure, 2.1 MPa; pressure-holding time, 56 s; and autoclaving time, 26 min. Under these conditions, digestion resistance of sweet potato starch of up to 37.73 ± 0.86 % was obtained. Infra-red spectra indicate that no new chemical groups appeared in the structure of the resistant starch. Furthermore, a gelatinisation induced endothermic peak was observed in the DSC thermogram of potato starch at about 160 °C. The in vitro digestion data showed that the in vitro digestion rate had undergone a significant decrease.Conclusion: Sweet potato starch treated by SE and autoclaving has lower digestibility and therefore, can potentially be used in food or medicine for diabetic patients.Keywords: Resistant sweet potato starch, Steam explosion, Digestion resistance, Starch digestion rate, Response surface methodolog

Effect of steam explosion pre-treatment on molecular structure of sweet potato starch

Purpose: To examine the effect of steam-explosion (SE) strength on the molecular structure of sweet potato starch.Methods: Sweet potato starch was pre-treated using SE method. The effects of SE pressure and pressure-holding time on the molecular structure of the sweet potato starch were investigated by gel chromatography (GPC), infrared spectroscopy, and grading analysis.Results: The molecular weight (MW) of the starch pre-treated by SE technology decreased with increasing explosion pressure and pressure-holding time; however, the individual MW of amylopectin and amylose declined from 439,834 and 6578 to 238,603 and 4845, respectively. Furthermore, the peak area ratio (obtained by GPC) of amylopectin decreased from 84.39 to 65.16 % while that of amylose increased from 15.61 to 34.84 %. No new absorption peaks were found in the infrared spectra of sweet potato starch following SE pre-treatment. Crystallization index and median diameter of sweet potato starch increased from 1.661 to 1.959 and from 13.73 μm to 76.36 μm, respectively, with rising pressure and pressure-holding time, following SE pre-treatment.Conclusion: SE pre-treatment effectively degrades the degree of polymerisation of molecular chains in sweet potato starch and enhances the degree of crystallinity thereof. SE method is an approach for the production of sweet potato starch with high-level anti-digestion characteristics.Keywords: Sweet potato starch, Steam-explosion, Molecular weight, Degree of crystallinity, Particle diamete