A stochastic surrogate model for time-variant reliability analysis of flexible multibody system

The dynamic model of the flexible multibody systems (FMS) is usually the differential equations with time-variant, high nonlinear and strong coupling characteristics. The traditional reliability models are inefficient to solve these problems. And the reliability model is poor in accuracy and computational efficiency. Based on this point, a new stochastic surrogate model for time-variant reliability analysis of FMS is proposed. Combined model order reduction with generalized polynomial chaos, the stochastic surrogate model is established and the statistical characteristics of system responses are obtained. The calculation method of kinematic time-variant reliability is given. Finally, the effectiveness of the method is verified by a rotating flexible beam. The results show that this method has high computational accuracy compared with Monte Carlo method

Design of customised total knee implants with musculoskeletal dynamic simulations

Effects of a customised total knee implant (CTKI) on the contact forces and relative motions of the tibiofemoral and patellofemoral joints have been investigated with computer simulations by applying the patient-specific muscle forces on the lower limb and the joint reaction forces at the ankle and hip joints. Firstly, a method was proposed and realized to create a CTKI based on the geometry of a patient’s knee joint using ANSYS Mechanical APDL. Secondly, a patient-specific musculoskeletal model was built to calculate the muscle forces and joint reaction forces during a squat motion. Finally, a dynamic finite element (FE) model was created in ANSYS incorporating the aforementioned forces and the CTKI to calculate the contact forces and relative motions of the tibiofemoral and patellofemoral joints. In addition, an off-the-shelf symmetric total knee implant (STKI) with cruciate ligaments (CLs) retained was simulated for comparison analysis. Knee joint collateral ligaments with nonlinear properties and pretensions were created in the dynamic FE model. A series of dynamic simulations of a squat motion with different initial laxities of the collateral ligaments were performed on the CTKI model under three treatment scenarios of CLs: both CLs retained, anterior cruciate ligament (ACL) removed and both CLs removed. Results showed that only the CTKI model with both CLs retained resulted in similar femoral external rotation and posterior translation with those of the healthy knees. There were not big differences in the tibiofemoral compressive forces among the three scenarios. All the three tibiofemoral compressive forces showed good agreement with other research results from either in-vivo measurements or simulations. The CTKI has better mobility than the traditional STKI designs. The curvatures of the tibial bearing surfaces have been varied in the transverse and longitudinal directions. Compared with the STKI, the CTKIs could restore patient’s knee function to normal, though the tibiofemoral compressive force observed in CTKIs was larger than that of the STKI in the late 25° of simulated knee flexion angles, which was caused by the large passive knee ligament forces and the larger knee motion ranges. The patella has also been studied and compared between the unresurfaced and resurfaced patellar components. The laxity of patellofemoral ligament was firstly tested on the unresurfaced patellar component. Then, the same dynamic boundary conditions were applied on three different patellar button components. Differences were found in the patellar internal rotation and medial tilt motions between the unresurfaced and resurfaced patellar components. The original patellar button component showed contact between the patellar bone and the femoral component apart from contact between the patellar component and the femoral component. The scaled-up button was able to avoid the contact between the patellar component and the femoral component and reduce the patellar medial translation. However, it resulted in larger patellofemoral force than that of the original and flat patellar components. The patellofemoral forces on the scaled-up patellar component were more fluctuating due to less conformity of the contact surfaces. The scaled-up patellar components were found to have two contact areas on the patellofemoral joint, while the original one had only one contact area

CHEMICAL REACTIVITY OF SIZE-SELECTED SUPPORTED CLUSTERS: COMBINED TPD/XPS STUDIES AND APPARATUS DEVELOPMENT

There has been a sustained research interest in the unique reactivities of ultra-small metal and metal oxide clusters. Numerous studies have highlighted the capability of the cluster deposition method to explore the catalytic reactivity of ultra-small clusters with different sizes, stoichiometries, and substrates. In this thesis, two beam line coupled surface analytical apparatus are introduced. The newly constructed one is an upgraded version of the previous one with better mass selection capability and improved geometries for combined temperature-programmed desorption (TPD) and X-ray photoelectron spectroscopy (XPS) analysis. Two combined TPD and XPS studies, which were performed on the previous apparatus, are presented, including ligation and decomposition of 1,6-hexanedithiol, and decomposition of dimethyl methylphosphonate (DMMP) by size selected copper and copper oxide clusters. Several crucial troubleshooting processes for the new apparatus are discussed, and some preliminary experimental results are presented

DEsignBench: Exploring and Benchmarking DALL-E 3 for Imagining Visual Design

We introduce DEsignBench, a text-to-image (T2I) generation benchmark tailored for visual design scenarios. Recent T2I models like DALL-E 3 and others, have demonstrated remarkable capabilities in generating photorealistic images that align closely with textual inputs. While the allure of creating visually captivating images is undeniable, our emphasis extends beyond mere aesthetic pleasure. We aim to investigate the potential of using these powerful models in authentic design contexts. In pursuit of this goal, we develop DEsignBench, which incorporates test samples designed to assess T2I models on both "design technical capability" and "design application scenario." Each of these two dimensions is supported by a diverse set of specific design categories. We explore DALL-E 3 together with other leading T2I models on DEsignBench, resulting in a comprehensive visual gallery for side-by-side comparisons. For DEsignBench benchmarking, we perform human evaluations on generated images in DEsignBench gallery, against the criteria of image-text alignment, visual aesthetic, and design creativity. Our evaluation also considers other specialized design capabilities, including text rendering, layout composition, color harmony, 3D design, and medium style. In addition to human evaluations, we introduce the first automatic image generation evaluator powered by GPT-4V. This evaluator provides ratings that align well with human judgments, while being easily replicable and cost-efficient. A high-resolution version is available at https://github.com/design-bench/design-bench.github.io/raw/main/designbench.pdf?download=Comment: Project page at https://design-bench.github.io

Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

Despite the promising progress in multi-modal tasks, current large multi-modal models (LMMs) are prone to hallucinating inconsistent descriptions with respect to the associated image and human instructions. This paper addresses this issue by introducing the first large and diverse visual instruction tuning dataset, named Large-scale Robust Visual (LRV)-Instruction. Our dataset comprises 400k visual instructions generated by GPT4, covering 16 vision-and-language tasks with open-ended instructions and answers. Unlike existing studies that primarily focus on positive instruction samples, we design LRV-Instruction to include both positive and negative instructions for more robust visual instruction tuning. Our negative instructions are designed at three semantic levels: (i) Nonexistent Object Manipulation, (ii) Existent Object Manipulation and (iii) Knowledge Manipulation. To efficiently measure the hallucination generated by LMMs, we propose GPT4-Assisted Visual Instruction Evaluation (GAVIE), a stable approach to evaluate visual instruction tuning like human experts. GAVIE does not require human-annotated groundtruth answers and can adapt to diverse instruction formats. We conduct comprehensive experiments to investigate the hallucination of LMMs. Our results demonstrate existing LMMs exhibit significant hallucinations when presented with our negative instructions, particularly Existent Object and Knowledge Manipulation instructions. Moreover, we successfully mitigate hallucination by finetuning MiniGPT4 and mPLUG-Owl on LRV-Instruction while improving performance on several public datasets compared to state-of-the-art methods. Additionally, we observed that a balanced ratio of positive and negative instances in the training data leads to a more robust model.Comment: 40 pages, 32 figures. Under Revie

Mildly Constrained Evaluation Policy for Offline Reinforcement Learning

Offline reinforcement learning (RL) methodologies enforce constraints on the policy to adhere closely to the behavior policy, thereby stabilizing value learning and mitigating the selection of out-of-distribution (OOD) actions during test time. Conventional approaches apply identical constraints for both value learning and test time inference. However, our findings indicate that the constraints suitable for value estimation may in fact be excessively restrictive for action selection during test time. To address this issue, we propose a Mildly Constrained Evaluation Policy (MCEP) for test time inference with a more constrained target policy for value estimation. Since the target policy has been adopted in various prior approaches, MCEP can be seamlessly integrated with them as a plug-in. We instantiate MCEP based on TD3-BC [Fujimoto and Gu, 2021] and AWAC [Nair et al., 2020] algorithms. The empirical results on MuJoCo locomotion tasks show that the MCEP significantly outperforms the target policy and achieves competitive results to state-of-the-art offline RL methods. The codes are open-sourced at https://github.com/egg-west/MCEP.git

The Devil is in the Details: A Deep Dive into the Rabbit Hole of Data Filtering

The quality of pre-training data plays a critical role in the performance of foundation models. Popular foundation models often design their own recipe for data filtering, which makes it hard to analyze and compare different data filtering approaches. DataComp is a new benchmark dedicated to evaluating different methods for data filtering. This paper describes our learning and solution when participating in the DataComp challenge. Our filtering strategy includes three stages: single-modality filtering, cross-modality filtering, and data distribution alignment. We integrate existing methods and propose new solutions, such as computing CLIP score on horizontally flipped images to mitigate the interference of scene text, using vision and language models to retrieve training samples for target downstream tasks, rebalancing the data distribution to improve the efficiency of allocating the computational budget, etc. We slice and dice our design choices, provide in-depth analysis, and discuss open questions. Our approach outperforms the best method from the DataComp paper by over 4% on the average performance of 38 tasks and by over 2% on ImageNet.Comment: 12 pages, 10 figure