Machine Learning Enabled Prediction of Mechanical Properties of Tungsten Disulfide Monolayer

One of two-dimensional transition metal dichalcogenide materials, tungsten disulfide (WS2), has aroused much research interest, and its mechanical properties play an important role in a practical application. Here the mechanical properties of h-WS2 and t-WS2 monolayers in the armchair and zigzag directions are evaluated by utilizing the molecular dynamics (MD) simulations and machine learning (ML) technique. We mainly focus on the effects of chirality, system size, temperature, strain rate, and random vacancy defect on mechanical properties, including fracture strain, fracture strength, and Young’s modulus. We find that the mechanical properties of h-WS2 surpass those of t-WS2 due to the different coordination spheres of the transition metal atoms. It can also be observed that the fracture strain, fracture strength, and Young’s modulus decrease when temperature and vacancy defect ratio are enhanced. The random forest (RF) supervised ML algorithm is employed to model the correlations between different impact factors and target outputs. A total number of 3600 MD simulations are performed to generate the training and testing dataset for the ML model. The mechanical properties of WS2 (i.e., target outputs) can be predicted using the trained model with the knowledge of different input features, such as WS2 type, chirality, temperature, strain rate, and defect ratio. The mean square errors of ML predictions for the mechanical properties are orders of magnitude smaller than the actual values of each property, indicating good training results of the RF model

Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh Reconstruction

As it is hard to calibrate single-view RGB images in the wild, existing 3D human mesh reconstruction (3DHMR) methods either use a constant large focal length or estimate one based on the background environment context, which can not tackle the problem of the torso, limb, hand or face distortion caused by perspective camera projection when the camera is close to the human body. The naive focal length assumptions can harm this task with the incorrectly formulated projection matrices. To solve this, we propose Zolly, the first 3DHMR method focusing on perspective-distorted images. Our approach begins with analysing the reason for perspective distortion, which we find is mainly caused by the relative location of the human body to the camera center. We propose a new camera model and a novel 2D representation, termed distortion image, which describes the 2D dense distortion scale of the human body. We then estimate the distance from distortion scale features rather than environment context features. Afterwards, we integrate the distortion feature with image features to reconstruct the body mesh. To formulate the correct projection matrix and locate the human body position, we simultaneously use perspective and weak-perspective projection loss. Since existing datasets could not handle this task, we propose the first synthetic dataset PDHuman and extend two real-world datasets tailored for this task, all containing perspective-distorted human images. Extensive experiments show that Zolly outperforms existing state-of-the-art methods on both perspective-distorted datasets and the standard benchmark (3DPW)

Environmental capacity and fluxes of land-sourced pollutants around the Leizhou Peninsula in the summer

Although the water environment has certain self-purification capability, the natural balance is disrupted, leading to water quality deterioration when the discharge load of wastewater exceeds a certain threshold. This problem implies the urgency of evaluating marine environmental capacity as a necessary parameter for marine sustainable development of marine ecosystems. Through principal component analysis (PCA), clustering, and other methods, we analyzed the average concentration and fluxes of land-sourced pollutants and determined the pollution level around the Leizhou Peninsula. Combined with the Delft3D hydrodynamic numerical model, tidal hydrodynamic forces and pollutants migration and diffusion were calculated. Based on in-situ measured data, the model was validated. The sharing rate method was used to calculate the marine environmental capacity in Zhanjiang Bay and analyzed their impact on seawater eutrophication. The results showed that: (1) The average concentrations of chemical oxygen demand (COD), ammonia nitrogen (NH4 +), total nitrogen (TN), and total phosphorus (TP) around Leizhou Peninsula were 22.56 mg/L, 0.69 mg/L, 6.69 mg/L, and 0.69 mg/L, respectively. (2) Six areas (Area A-F) can be divided into, based on the discharge of land-sourced pollutants into the sea area. According to the results of PCA, clustering, and other methods, the average concentration and fluxes of land-sourced pollutants in Area B (i.e. Zhanjiang Bay) were very high. (3) The environmental capacity of Zhanjiang Bay was calculated through Delft3D numerical simulation, and it was found that the COD and TN environmental capacity of 6 sewage outlets exceeded the standard, while the TP environmental capacity of 3 sewage outlets exceeded the standard. (4) According to the statistical research result, most of the Zhanjiang Bay waters has been restricted by nitrogen for over a decade. Therefore, we speculate that although TN environmental capacity exceeds the standard, its impact on eutrophication in Zhanjiang Bay is still limited to a certain extent

Femtosecond Laser Filamentation in Atmospheric Turbulence

The effects of turbulence intensity and turbulence region on the distribution of femtosecond laser filaments are experimentally elaborated. Through the ultrasonic signals emitted by the filaments, and it is observed that increasing turbulence intensity and expanding turbulence active region cause an increase in the start position of the filament, and a decrease in filament length, which can be well explained by the theoretical calculation. It is also observed that the random perturbation of the air refractive index caused by atmospheric turbulence expanded the spot size of the filament. Additionally, when turbulence intensity reaches , multiple filaments are formed. Furthermore, the standard deviation of the transverse displacement of filament is found to be proportional to the square root of turbulent structure constant under the experimental turbulence parameters in this paper. These results contribute to the study of femtosecond laser propagation mechanisms in complex atmospheric turbulence conditionsComment: 9 pages, 4 figure

Towards a Learner-Centered Explainable AI: Lessons from the learning sciences

In this short paper, we argue for a refocusing of XAI around human learning goals. Drawing upon approaches and theories from the learning sciences, we propose a framework for the learner-centered design and evaluation of XAI systems. We illustrate our framework through an ongoing case study in the context of AI-augmented social work.Comment: 7 pages, 2 figure

SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation

Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications. Despite encouraging progress, current state-of-the-art methods still depend largely on a confined set of training datasets. In this work, we investigate scaling up EHPS towards the first generalist foundation model (dubbed SMPLer-X), with up to ViT-Huge as the backbone and training with up to 4.5M instances from diverse data sources. With big data and the large model, SMPLer-X exhibits strong performance across diverse test benchmarks and excellent transferability to even unseen environments. 1) For the data scaling, we perform a systematic investigation on 32 EHPS datasets, including a wide range of scenarios that a model trained on any single dataset cannot handle. More importantly, capitalizing on insights obtained from the extensive benchmarking process, we optimize our training scheme and select datasets that lead to a significant leap in EHPS capabilities. 2) For the model scaling, we take advantage of vision transformers to study the scaling law of model sizes in EHPS. Moreover, our finetuning strategy turn SMPLer-X into specialist models, allowing them to achieve further performance boosts. Notably, our foundation model SMPLer-X consistently delivers state-of-the-art results on seven benchmarks such as AGORA (107.2 mm NMVE), UBody (57.4 mm PVE), EgoBody (63.6 mm PVE), and EHF (62.3 mm PVE without finetuning). Homepage: https://caizhongang.github.io/projects/SMPLer-X/Comment: Homepage: https://caizhongang.github.io/projects/SMPLer-X