A Single Multi-Task Deep Neural Network with a Multi-Scale Feature Aggregation Mechanism for Manipulation Relationship Reasoning in Robotic Grasping

Grasping specific objects in complex and irregularly stacked scenes is still challenging for robotics. Because the robot is not only required to identify the object's grasping posture but also needs to reason the manipulation relationship between the objects. In this paper, we propose a manipulation relationship reasoning network with a multi-scale feature aggregation (MSFA) mechanism for robot grasping tasks. MSFA aggregates high-level semantic information and low-level spatial information in a cross-scale connection way to improve the generalization ability of the model. Furthermore, to improve the accuracy, we propose to use intersection features with rich location priors for manipulation relationship reasoning. Experiments are validated in VMRD datasets and real environments, respectively. The experimental results demonstrate that our proposed method can accurately predict the manipulation relationship between objects in the scene of multi-object stacking. Compared with previous methods, it significantly improves reasoning speed and accuracy

An Accelerated Stochastic ADMM for Nonconvex and Nonsmooth Finite-Sum Optimization

The nonconvex and nonsmooth finite-sum optimization problem with linear constraint has attracted much attention in the fields of artificial intelligence, computer, and mathematics, due to its wide applications in machine learning and the lack of efficient algorithms with convincing convergence theories. A popular approach to solve it is the stochastic Alternating Direction Method of Multipliers (ADMM), but most stochastic ADMM-type methods focus on convex models. In addition, the variance reduction (VR) and acceleration techniques are useful tools in the development of stochastic methods due to their simplicity and practicability in providing acceleration characteristics of various machine learning models. However, it remains unclear whether accelerated SVRG-ADMM algorithm (ASVRG-ADMM), which extends SVRG-ADMM by incorporating momentum techniques, exhibits a comparable acceleration characteristic or convergence rate in the nonconvex setting. To fill this gap, we consider a general nonconvex nonsmooth optimization problem and study the convergence of ASVRG-ADMM. By utilizing a well-defined potential energy function, we establish its sublinear convergence rate $O(1/T)$, where $T$ denotes the iteration number. Furthermore, under the additional Kurdyka-Lojasiewicz (KL) property which is less stringent than the frequently used conditions for showcasing linear convergence rates, such as strong convexity, we show that the ASVRG-ADMM sequence has a finite length and converges to a stationary solution with a linear convergence rate. Several experiments on solving the graph-guided fused lasso problem and regularized logistic regression problem validate that the proposed ASVRG-ADMM performs better than the state-of-the-art methods.Comment: 40 Pages, 8 figure

Anomaly Detection by Adapting a pre-trained Vision Language Model

Recently, large vision and language models have shown their success when adapting them to many downstream tasks. In this paper, we present a unified framework named CLIP-ADA for Anomaly Detection by Adapting a pre-trained CLIP model. To this end, we make two important improvements: 1) To acquire unified anomaly detection across industrial images of multiple categories, we introduce the learnable prompt and propose to associate it with abnormal patterns through self-supervised learning. 2) To fully exploit the representation power of CLIP, we introduce an anomaly region refinement strategy to refine the localization quality. During testing, the anomalies are localized by directly calculating the similarity between the representation of the learnable prompt and the image. Comprehensive experiments demonstrate the superiority of our framework, e.g., we achieve the state-of-the-art 97.5/55.6 and 89.3/33.1 on MVTec-AD and VisA for anomaly detection and localization. In addition, the proposed method also achieves encouraging performance with marginal training data, which is more challenging

Deep Instance Segmentation with Automotive Radar Detection Points

Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points. With the development of automotive radar technologies in recent years, instance segmentation becomes possible by using automotive radar. Its data contain contexts such as radar cross section and micro-Doppler effects, and sometimes can provide detection when the field of view is obscured. The outcome from instance segmentation could be potentially used as the input of trackers for tracking targets. The existing methods often utilize a clustering based classification framework, which fits the need of real-time processing but has limited performance due to minimum information provided by sparse radar detection points. In this paper, we propose an efficient method based on clustering of estimated semantic information to achieve instance segmentation for the sparse radar detection points. In addition, we show that the performance of the proposed approach can be further enhanced by incorporating the visual multi-layer perceptron. The effectiveness of the proposed method is verified by experimental results on the popular RadarScenes dataset, achieving 89.53% mCov and 86.97% mAP0.5, which is the best comparing to other approaches in the literature. More significantly, the proposed algorithm consumes memory around 1MB, and the inference time is less than 40ms. These two criteria ensure the practicality of the proposed method in real-world system

Assessing r2SCAN meta-GGA functional for structural parameters, cohesive energy, mechanical modulus and thermophysical properties of 3d, 4d and 5d transition metals

The recent development of the accurate and efficient semilocal density functionals on the third rung of Jacob's ladder of density functional theory such as the revised regularized strongly constrained and appropriately normed (r2SCAN) density functional could enable the rapid and highly reliable prediction of the elasticity and temperature dependence of thermophysical parameters of refractory elements and their intermetallic compounds using quasi-harmonic approximation (QHA). Here, we present a comparative evaluation of the equilibrium cell volumes, cohesive energy, mechanical moduli, and thermophysical properties (Debye temperature and thermal expansion coefficient) for 22 transition metals using semilocal density functionals, including local density approximation (LDA), the Perdew-Burke-Ernzerhof (PBE) and PBEsol generalized gradient approximations (GGA), and the r2SCAN meta-GGA. PBEsol and r2SCAN deliver the same level of accuracies for structural, mechanical and thermophysical properties. Otherwise, PBE and r2SCAN perform better than LDA and PBEsol for calculating cohesive energies of transition metals. Among the tested density functionals, r2SCAN provides an overall well-balanced performance for reliably computing the cell volumes, cohesive energies, mechanical properties, and thermophysical properties of various 3d, 4d, and 5d transition metals using QHA. Therefore, we recommend that r2SCAN could be employed as a workhorse method to evaluate the thermophysical properties of transition metal compounds and alloys in the high throughput workflows

LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting

Road traffic forecasting plays a critical role in smart city initiatives and has experienced significant advancements thanks to the power of deep learning in capturing non-linear patterns of traffic data. However, the promising results achieved on current public datasets may not be applicable to practical scenarios due to limitations within these datasets. First, the limited sizes of them may not reflect the real-world scale of traffic networks. Second, the temporal coverage of these datasets is typically short, posing hurdles in studying long-term patterns and acquiring sufficient samples for training deep models. Third, these datasets often lack adequate metadata for sensors, which compromises the reliability and interpretability of the data. To mitigate these limitations, we introduce the LargeST benchmark dataset. It encompasses a total number of 8,600 sensors in California with a 5-year time coverage and includes comprehensive metadata. Using LargeST, we perform in-depth data analysis to extract data insights, benchmark well-known baselines in terms of their performance and efficiency, and identify challenges as well as opportunities for future research. We release the datasets and baseline implementations at: https://github.com/liuxu77/LargeST

Experimental study on permeability evolution of slender coal pillar of entry driven along goaf

Under the condition of roadway driving along goaf, slender coal pillar is affected by multiple mining-induced disturbances, and the permeability of coal and rock mass affected by mining will change due to the development and compaction of mining fractures and primary fractures. Determining the evolution of slender coal pillar permeability at different mining stages is the theoretical basis for the prevention and control of gas water disasters in adjacent goaf at the same layer. Taking the mining with slender coal gate pillar of the Carboniferous extra thick coal seam in Datong Mining Area as the engineering background, the distribution characteristics of the stress field for the slender coal gate pillar of the coal seam in different mining stages are comprehensively determined by the methods of geostress testing and numerical simulation, which provides a basis for the determination of the stress path for experimental research. The DJG - Ⅱ triaxial loading coal rock seepage testing equipment was used to conduct experimental research on the evolution of coal pillar permeability in different mining stages. The research results are as follows: The quantitative influence relationship between permeability and stress of slender coal gate pillar in different mining stages is established. The overall performance is that the permeability decreases with the increase of axial stress, and the permeability increases with the decrease of axial pressure in unloading stage; It reveals the evolution of stress strain permeability of the coal pillar in different mining stages. When loading and unloading in the first and second stages, the deformation of coal sample is still in the elastic deformation stage, and the change amplitude and rate of permeability are relatively gentle. In the third mining-impacted stage, the irreversible plastic failure of the specimen made the permeability increase sharply, and the rate of increase was also significantly greater than the first two mining stages. The permeability of slender coal pillar increased by 324.389 times compared with the initial permeability. In this stage, the slender coal pillar was damaged and lost its gas water barrier performance. It was clear that the 6 m small coal pillar was not damaged in the first two mining stages of the super thick coal seam gob side entry project. The research results can provide reference or theoretical support for the study of permeability evolution characteristics of slender coal pillar in different mining stages, and the prevention and control of gas water disasters in adjacent goaf under the condition of gob side entry mining in hard roof extra thick coal seams

Association between urinary arsenic species and vitamin D deficiency: a cross-sectional study in Chinese pregnant women

BackgroundAn increasing number of studies suggest that environmental pollution may increase the risk of vitamin D deficiency (VDD). However, less is known about arsenic (As) exposure and VDD, particularly in Chinese pregnant women.ObjectivesThis study examines the correlations of different urinary As species with serum 25 (OH) D and VDD prevalence.MethodsWe measured urinary arsenite (As3+), arsenate (As5+), monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA) levels and serum 25(OH)D2, 25(OH)D3, 25(OH) D levels in 391 pregnant women in Tianjin, China. The diagnosis of VDD was based on 25(OH) D serum levels. Linear relationship, Logistic regression, and Bayesian kernel machine regression (BKMR) were used to examine the associations between urinary As species and VDD.ResultsOf the 391 pregnant women, 60 received a diagnosis of VDD. Baseline information showed significant differences in As3+, DMA, and tAs distribution between pregnant women with and without VDD. Logistic regression showed that As3+ was significantly and positively correlated with VDD (OR: 4.65, 95% CI: 1.79, 13.32). Meanwhile, there was a marginally significant positive correlation between tAs and VDD (OR: 4.27, 95% CI: 1.01, 19.59). BKMR revealed positive correlations between As3+, MMA and VDD. However, negative correlations were found between As5+, DMA and VDD.ConclusionAccording to our study, there were positive correlations between iAs, especially As3+, MMA and VDD, but negative correlations between other As species and VDD. Further studies are needed to determine the mechanisms that exist between different As species and VDD

Research on the characteristics of CO2-water interface and the law of dissolution and mass transfer under the condition of carbon sequestration in goaf

As an important negative carbon technology to solve the carbon emission problem in the coal industry, the CO2 sequestration in the mine goaf has a wide application prospect in the secondary utilization of waste resources in the goaf and the capture and storage of CO2. In this study, the influence of different temperatures and pressures, formation water salinity and cationic solution type on the interfacial tension (IFT) of CO2-formation water system was investigated by using in-situ interfacial tension meter. The gas-liquid interface diffusion effect of CO2 injection into water-bearing coal rock mass was clarified. The equation of state (SAFT-LJ equation of state) based on statistical association theory combined with the Lanner-Jones potential energy model and density gradient theory (DGT) were combined to predict the theoretical value of IFT. Using a self-developed geological sequestration and geochemical reaction simulation experimental platform, various exploratory experiments were conducted to investigate the solubility of CO2 under the same conditions. The characteristics of CO2 solubility variation in the reservoir environment of the goaf were obtained, and the corresponding theoretical values of CO2 solubility were calculated using the D-S model. The experimental results show that when the ambient temperature is constant, the reservoir pressure in the goaf is linearly negatively correlated with the IFT value. As the reservoir temperature increases, the IFT value increases correspondingly, but the change range is small. Under constant temperature and pressure conditions, there is a positive correlation between salinity and IFT value. Within the scope of this experiment, low pressure, high temperature, and high salinity promote an increase in the IFT value. The IFT values between CO2-salt solutions show an increasing trend with the increasing valence of cations (K+ < Na+ < Ca2+ < Mg2+). The pressure of the depleted reservoir is positively correlated with the CO2 solubility. When the temperature is 25 °C and under conditions of pure water, as the pressure increases from 0.5 MPa to 2.5 MPa, the corresponding CO2 solubility increases from 0.1627 mol/kg to 0.7141 mol/kg. The CO2 solubility decreases with the increases of temperature and salinity. Under the same concentration, monovalent cation solutions (NaCl, KCl) can dissolve more CO2 than divalent cation solutions (CaCl2, MgCl2). The free phase CO2 injected into the goaf overcomes the interfacial tension and breaks the geochemical balance of the goaf strata through diffusion and dissolution mass transfer. By clarifying the influence of reservoir temperature and pressure conditions and goaf water environment on IFT value and CO2 solubility, the gas-liquid interface effect and dissolution mass transfer mechanism of CO2-formation water are clarified, so as to provide a theoretical basis for the safety and evaluation of CO2 sequestration in the closed mine goaf

Emerging Potential of Exosomes on Adipogenic Differentiation of Mesenchymal Stem Cells

The mesenchymal stem cells have multidirectional differentiation potential and can differentiate into adipocytes, osteoblasts, cartilage tissue, muscle cells and so on. The adipogenic differentiation of mesenchymal stem cells is of great significance for the construction of tissue-engineered fat and the treatment of soft tissue defects. Exosomes are nanoscale vesicles secreted by cells and widely exist in body fluids. They are mainly involved in cell communication processes and transferring cargo contents to recipient cells. In addition, exosomes can also promote tissue and organ regeneration. Recent studies have shown that various exosomes can influence the adipogenic differentiation of stem cells. In this review, the effects of exosomes on stem cell differentiation, especially on adipogenic differentiation, will be discussed, and the mechanisms and conclusions will be drawn. The main purpose of studying the role of these exosomes is to understand more comprehensively the influencing factors existing in the process of stem cell differentiation into adipocytes and provide a new idea in adipose tissue engineering research