Is the large-scale structure traced by the BOSS LOWZ galaxies consistent with Planck\textit{Planck}?

Recently, several studies reported a significant discrepancy between the clustering and lensing of the Baryon Oscillation Spectroscopic Survey (BOSS) galaxies in the $\textit{Planck}$ cosmology. We construct a simple yet powerful model based on the linear theory to assess whether this discrepancy points toward deviations from $\textit{Planck}$. Focusing on scales $10<R<30$ $h^{-1}\mathrm{Mpc}$, we model the amplitudes of clustering and lensing of BOSS LOWZ galaxies using three parameters: galaxy bias $b_\mathrm{g}$, galaxy-matter cross-correlation coefficient $r_\mathrm{gm}$, and $A$, defined as the ratio between the true and $\textit{Planck}$ values of $\sigma_8$. Using the cross-correlation matrix as a diagnostic, we detect systematic uncertainties that drive spurious correlations among the low-mass galaxies. After building a clean LOWZ sample with $r_\mathrm{gm}\sim1$, we derive a joint constraint of $b_\mathrm{g}$ and $A$ from clustering+lensing, yielding $b_\mathrm{g}=2.47_{-0.30}^{+0.36}$ and $A=0.81_{-0.09}^{+0.10}$, i.e., a $2\sigma$ tension with $\textit{Planck}$. However, due to the strong degeneracy between $b_\mathrm{g}$ and $A$, systematic uncertainties in $b_\mathrm{g}$ could masquerade as a tension with $A=1$. To ascertain this possibility, we develop a new method to measure $b_\mathrm{g}$ from the cluster-galaxy cross-correlation and cluster weak lensing using an overlapping cluster sample. By applying the independent bias measurement ($b_\mathrm{g}=1.76\pm0.22$) as a prior, we successfully break the degeneracy and derive stringent constraints of $b_\mathrm{g}=2.02_{-0.15}^{+0.16}$ and $A=0.96\pm0.07$. Therefore, our result suggests that the large-scale clustering and lensing of LOWZ galaxies are consistent with $\textit{Planck}$, while the different bias estimates may be related to some observational systematics in the target selection.Comment: 8 pages, 5 figures, comments welcome

Robust fault estimation for stochastic Takagi-Sugeno fuzzy systems

Nowadays, industrial plants are calling for high-performance fault diagnosis techniques to meet stringent requirements on system availability and safety in the event of component failures. This paper deals with robust fault estimation problems for stochastic nonlinear systems subject to faults and unknown inputs relying on Takagi-Sugeno fuzzy models. Augmented approach jointly with unknown input observers for stochastic Takagi-Sugeno models is exploited here, which allows one to estimate both considered faults and full system states robustly. The considered unknown inputs can be either completely decoupled or partially decoupled by observers. For the un-decoupled part of unknown inputs, which still influence error dynamics, stochastic input-to-state stability properties are applied to take nonzero inputs into account and sufficient conditions are achieved to guarantee bounded estimation errors under bounded unknown inputs. Linear matrix inequalities are employed to compute gain matrices of the observer, leading to stochastic input-to-state-stable error dynamics and optimization of the estimation performances against un-decoupled unknown inputs. Finally, simulation on wind turbine benchmark model is applied to validate the performances of the suggested fault reconstruction methodologies

Tensile behaviour of pitting corroded steel bars:Laboratory investigation and probabilistic-based analysis

Localised corrosion in steel bars has been a long-standing issue in the durability of reinforced concrete structures, but a comprehensive scheme for the analysis of pitting corroded steel bars, especially with respect to the deformation capacity, is not currently available. In this study, the morphological characteristics of 27 pitting steel bars were captured using a 3D scanner. The measured data were used to establish the probability distribution model of the cross-sectional areas of the corroded bars. Uniaxial tensile tests were conducted, and the evolving deformation field of the corroded bars was recorded through Digital Image Correlation (DIC). Based on the 3D reconstructed model and DIC results, an analytical method for evaluating the mechanical properties of pitting steel bars was developed and validated. The results show that the two-component Gaussian mixture distribution model outperforms conventional unimodal distribution models. Comparison of the analytical results with experimental data demonstrates that the proposed procedure is capable of predicting not only the ultimate strength but also the gauge length-dependent ultimate strain of corroded bars. Additionally, there exists a strengthening effect in the ultimate stress at the critical sections and this effect should not be ignored for accurate predictions.</p

Material Removal Optimization Strategy of 3D Block Cutting Based on Geometric Computation Method

During the material removal stage in stone rough processing, milling type has been widely explored, which, however, may cause time and material consumption, as well as substantial stress for the environment. To improve the material removal rate and waste reuse rate in the rough processing stage for three-dimensional stone products with a special shape, in this paper, circular saw disc cutting is explored to cut a convex polyhedron out of a blank box, which approaches a target product. Unlike milling optimization, this problem cannot be well solved by mathematical methods, which have to be solved by geometrical methods instead. An automatic block cutting strategy is proposed intuitively by considering a series of geometrical optimization approaches for the first time. To obtain a big removal block, constructing cutting planes based on convex vertices is uniquely proposed. Specifically, the removal vertices (the maximum thickness of material removal) are searched based on the octree algorithm, and the cutting plane is constructed based on this thickness to guarantee a relatively big removal block. Moreover, to minimize the cutting time, the geometrical characteristics of the intersecting convex polygon of the cutting plane with the convex polyhedron are analyzed, accompanied by the constraints of the guillotine cutting mode. The optimization algorithm determining the cutting path is presented with a feed direction accompanied by the shortest cutting stroke, which confirms the shortest cutting time. From the big removal block and shortest cutting time, the suboptimal solution of the average material removal rate (the ratio of material removal volume to cutting time) is generated. Finally, the simulation is carried out on a blank box to approach a bounding sphere both on MATLAB and the Vericut platform. In this case study, for the removal of 85% of material with 19 cuts, the proposed cutting strategy achieves five times higher the average material removal rate than that of one higher milling capacity case

Not so different after all? An event-related potential study on item and source memory for object-scene pairs in German and Chinese young adults

In recent years, several cross-cultural studies reported that Westerners focus more on central aspects of a scene (e.g., an object) relative to peripheral aspects (e.g., the background), whereas Easterners more evenly allocate attention to central and peripheral aspects. In memory tasks, Easterners exhibit worse recognition for the central object when peripheral aspects are changed, whereas Westerners are less affected by peripheral changes. However, most of these studies rely on hit rates without correcting for response bias, whereas studies accounting for response bias failed to replicate cultural differences in memory tasks. In this event-related potential (ERP) study, we investigated item and source memory for semantically unrelated object-scene pairs in German and Chinese young adults using memory measures corrected for response bias (i.e., the discrimination index Pr). Both groups completed study-test cycles with either item memory tests or source memory tests. In item memory blocks, participants completed an old/new recognition test for the central object. Source memory blocks entailed an associative recognition test for the association between object and background. Item and source memory were better for intact than for recombined pairs. However, as verified with frequentist and Bayesian analyzes, this context effect was not modulated by culture. The ERP results revealed an old/new effect for the item memory task in both groups which was again not modulated by culture. Our findings suggest that cultural differences in young adults do not manifest in intentional memory tasks probing memory for object-scene pairs without semantic relations when using bias-corrected memory measures

The HIF-1 Hypoxia-Inducible Factor Modulates Lifespan in C. elegans

During normal development or during disease, animal cells experience hypoxic (low oxygen) conditions, and the hypoxia-inducible factor (HIF) transcription factors implement most of the critical changes in gene expression that enable animals to adapt to this stress. Here, we examine the roles of HIF-1 in post-mitotic aging. We examined the effects of HIF-1 over-expression and of hif-1 loss-of-function mutations on longevity in C. elegans, a powerful genetic system in which adult somatic cells are post-mitotic. We constructed transgenic lines that expressed varying levels of HIF-1 protein and discovered a positive correlation between HIF-1 expression levels and lifespan. The data further showed that HIF-1 acted in parallel to the SKN-1/NRF and DAF-16/FOXO transcription factors to promote longevity. HIF-1 over-expression also conferred increased resistance to heat and oxidative stress. We isolated and characterized additional hif-1 mutations, and we found that each of 3 loss-of-function mutations conferred increased longevity in normal lab culture conditions, but, unlike HIF-1 over-expression, a hif-1 deletion mutation did not extend the lifespan of daf-16 or skn-1 mutants. We conclude that HIF-1 over-expression and hif-1 loss-of-function mutations promote longevity by different pathways. These data establish HIF-1 as one of the key stress-responsive transcription factors that modulate longevity in C. elegans and advance our understanding of the regulatory networks that link oxygen homeostasis and aging

Cross-Subject Data Splitting for Brain-to-Text Decoding

Recent major milestones have successfully decoded non-invasive brain signals (e.g. functional Magnetic Resonance Imaging (fMRI) and electroencephalogram (EEG)) into natural language. Despite the progress in model design, how to split the datasets for training, validating, and testing still remains a matter of debate. Most of the prior researches applied subject-specific data splitting, where the decoding model is trained and evaluated per subject. Such splitting method poses challenges to the utilization efficiency of dataset as well as the generalization of models. In this study, we propose a cross-subject data splitting criterion for brain-to-text decoding on various types of cognitive dataset (fMRI, EEG), aiming to maximize dataset utilization and improve model generalization. We undertake a comprehensive analysis on existing cross-subject data splitting strategies and prove that all these methods suffer from data leakage, namely the leakage of test data to training set, which significantly leads to overfitting and overestimation of decoding models. The proposed cross-subject splitting method successfully addresses the data leakage problem and we re-evaluate some SOTA brain-to-text decoding models as baselines for further research