A Fast Model for the Simulation of External Gear Pumps

External gear pump is an important category of positive displacement fluid machines used to perform the mechanical–hydraulic energy conversions in many fluid power applications. An efficient numerical simulation program is needed to simulate the system in order to provide a direction for design purpose. The model consists of a lumped parameter fluid dynamic model and a model that simulates the radial micro-motions of the gear’s axes of rotation. The system consists of a set of ordinary differential equations related to the conservation on mass of the internal control volumes of the pump, which are given by the tooth space volumes of the gears. Flow connections between the control volumes are introduced, as laminar or turbulent orifices to model the displacing action and the internal leakages of the unit. In order to optimize the numerical solution, the whole system is described in C++ and the ODEs are solved using linear multistep methods. The results of the simulation successfully match the experimental results as well as the predictions of a previously developed simulation tool. The results detail several features of the model, such as its capability of predicting the instantaneous tooth space volume pressure, the micro-motion of the gears and the outlet flow oscillations. The simulation model can be utilized in future research on external gear pump, as well as for design purposes. In particular, with its simulation swiftness the model is particularly suitable for virtual prototyping within numerical optimization procedures

Context Disentangling and Prototype Inheriting for Robust Visual Grounding

Visual grounding (VG) aims to locate a specific target in an image based on a given language query. The discriminative information from context is important for distinguishing the target from other objects, particularly for the targets that have the same category as others. However, most previous methods underestimate such information. Moreover, they are usually designed for the standard scene (without any novel object), which limits their generalization to the open-vocabulary scene. In this paper, we propose a novel framework with context disentangling and prototype inheriting for robust visual grounding to handle both scenes. Specifically, the context disentangling disentangles the referent and context features, which achieves better discrimination between them. The prototype inheriting inherits the prototypes discovered from the disentangled visual features by a prototype bank to fully utilize the seen data, especially for the open-vocabulary scene. The fused features, obtained by leveraging Hadamard product on disentangled linguistic and visual features of prototypes to avoid sharp adjusting the importance between the two types of features, are then attached with a special token and feed to a vision Transformer encoder for bounding box regression. Extensive experiments are conducted on both standard and open-vocabulary scenes. The performance comparisons indicate that our method outperforms the state-of-the-art methods in both scenarios. {The code is available at https://github.com/WayneTomas/TransCP

What Large Language Models Bring to Text-rich VQA?

Text-rich VQA, namely Visual Question Answering based on text recognition in the images, is a cross-modal task that requires both image comprehension and text recognition. In this work, we focus on investigating the advantages and bottlenecks of LLM-based approaches in addressing this problem. To address the above concern, we separate the vision and language modules, where we leverage external OCR models to recognize texts in the image and Large Language Models (LLMs) to answer the question given texts. The whole framework is training-free benefiting from the in-context ability of LLMs. This pipeline achieved superior performance compared to the majority of existing Multimodal Large Language Models (MLLM) on four text-rich VQA datasets. Besides, based on the ablation study, we find that LLM brings stronger comprehension ability and may introduce helpful knowledge for the VQA problem. The bottleneck for LLM to address text-rich VQA problems may primarily lie in visual part. We also combine the OCR module with MLLMs and pleasantly find that the combination of OCR module with MLLM also works. It's worth noting that not all MLLMs can comprehend the OCR information, which provides insights into how to train an MLLM that preserves the abilities of LLM

Orbital redistribution in molecular nanostructures mediated by metal-organic bonds

Dicyanovinyl-quinquethiophene (DCV5T-Me) is a prototype conjugated oligomer for highly efficient organic solar cells. This class of oligothiophenes are built up by an electron-rich donor (D) backbone and terminal electron-deficient acceptor (A) moieties. Here, we investigated its structural and electronic properties when it is adsorbed on a Au(111) surface using low temperature scanning tunneling microscopy/spectroscopy (STM/STS) and atomic force microscopy (AFM). We find that DCV5T-Me self-assembles in extended chains, stabilized by intercalated Au atoms. The effect of metal-ligand hybridization with Au adatoms causes an energetic downshift of the DCV5T-Me lowest unoccupied molecular orbital (LUMO) with respect to the uncoordinated molecules on the surface. The asymmetric coordination of a gold atom to only one molecular end group leads to an asymmetric localization of the LUMO and LUMO+1 states at opposite sides. Using model density functional theory (DFT) calculations, we explain such orbital reshaping as a consequence of linear combinations of the original LUMO and LUMO+1 orbitals, mixed by the attachment of a bridging Au adatom. Our study shows that the alignment of molecular orbitals and their distribution within individual molecules can be modified by contacting them to metal atoms in specific sites

Unsupervised Feature Selection Using Nonnegative Spectral Analysis

In this paper, a new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), is proposed. To exploit the discriminative information in unsupervised scenarios, we perform spectral clustering to learn the cluster labels of the input samples, during which the feature selection is performed simultaneously. The joint learning of the cluster labels and feature selection matrix enables NDFS to select the most discriminative features. To learn more accurate cluster labels, a nonnegative constraint is explicitly imposed to the class indicators. To reduce the redundant or even noisy features, l2,1-norm minimization constraint is added into the objective function, which guarantees the feature selection matrix sparse in rows. Our algorithm exploits the discriminative information and feature correlation simultaneously to select a better feature subset. A simple yet efficient iterative algorithm is designed to optimize the proposed objective function. Experimental results on different real world datasets demonstrate the encouraging performance of our algorithm over the state-of-the-arts