Gongsun Longzi’s “form”: Minimal word meaning

Inspired by Gongsun Longzi’s “form-naming” idea about word meaning, this paper argues that 1) the internal lexicon contains only the list of word-meaning pairs, with no additional information either as part of word meaning or as a structural level above it; 2) the meaning of word is a minimal C-Form, the identifying conceptual meaning that individuates a concept; 3) C-Form is the interface between word meaning and concept meaning; and 4) a sentence has a minimal semantic content, consisting of the minimal meanings of the words composing it, which is propositional and truth-evaluable, and contextual elements contribute nothing to the meaning of language expressions. This paper adheres to semantic minimalism, believing meanwhile that meaning holism helps in semantics inquiry, since reflection on language meaning differs from language meaning itself. 

Efficient Frozen Gaussian Sampling Algorithms for Nonadiabatic Quantum Dynamics at Metal Surfaces

In this article, we propose a Frozen Gaussian Sampling (FGS) algorithm for simulating nonadiabatic quantum dynamics at metal surfaces with a continuous spectrum. This method consists of a Monte-Carlo algorithm for sampling the initial wave packets on the phase space and a surface-hopping type stochastic time propagation scheme for the wave packets. We prove that to reach a certain accuracy threshold, the sample size required is independent of both the semiclassical parameter $\varepsilon$ and the number of metal orbitals $N$, which makes it one of the most promising methods to study the nonadiabatic dynamics. The algorithm and its convergence properties are also validated numerically. Furthermore, we carry out numerical experiments including exploring the nuclei dynamics, electron transfer and finite-temperature effects, and demonstrate that our method captures the physics which can not be captured by classical surface hopping trajectories.Comment: 41 pages, 10 figure

Dynamic MDETR: A Dynamic Multimodal Transformer Decoder for Visual Grounding

Multimodal transformer exhibits high capacity and flexibility to align image and text for visual grounding. However, the existing encoder-only grounding framework (e.g., TransVG) suffers from heavy computation due to the self-attention operation with quadratic time complexity. To address this issue, we present a new multimodal transformer architecture, coined as Dynamic Mutilmodal DETR (Dynamic MDETR), by decoupling the whole grounding process into encoding and decoding phases. The key observation is that there exists high spatial redundancy in images. Thus, we devise a new dynamic multimodal transformer decoder by exploiting this sparsity prior to speed up the visual grounding process. Specifically, our dynamic decoder is composed of a 2D adaptive sampling module and a text guided decoding module. The sampling module aims to select these informative patches by predicting the offsets with respect to a reference point, while the decoding module works for extracting the grounded object information by performing cross attention between image features and text features. These two modules are stacked alternatively to gradually bridge the modality gap and iteratively refine the reference point of grounded object, eventually realizing the objective of visual grounding. Extensive experiments on five benchmarks demonstrate that our proposed Dynamic MDETR achieves competitive trade-offs between computation and accuracy. Notably, using only 9% feature points in the decoder, we can reduce ~44% GFLOPs of the multimodal transformer, but still get higher accuracy than the encoder-only counterpart. In addition, to verify its generalization ability and scale up our Dynamic MDETR, we build the first one-stage CLIP empowered visual grounding framework, and achieve the state-of-the-art performance on these benchmarks.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) in October 202

Anomalous phase transition of layered lepidocrocite titania nanosheets to anatase and rutile

In this study, phase transformations from lepidocrocite titania (L-TiO2) nanosheets to rutile (R-TiO2) and anatase (A-TiO2) have been systematically investigated as a function of the preparation conditions, such as pH and freeze-drying, and as a function of the temperature treatment. We have found that the transformation of (L-TiO2) into rutile takes place upon freeze-drying treatment. We report that temperature determined the final phase structure in the transition phase of the L-TiO2 nanosheets into TiO2 nanoparticles, while the pH determined the final morphology and particle size. On the basis of the experimental results, two different transition pathways of dissolution–recrystallization and topologically rolling transition have been proposed. Our results give a full map of phase transition and morphology evolution of L-TiO2 to R-TiO2/A-TiO2 that can provide guideline to new materials design, especially for photocatalysts

A Supramolecular Strategy to Assemble Multifunctional Viral Nanoparticles

Using a one-pot approach driven by the supramolecular interaction between β-cyclodextrin and adamantyl moieties, multifunctional viral nanoparticles can be facilely formulated for biomedical applications

MGMAE: Motion Guided Masking for Video Masked Autoencoding

Masked autoencoding has shown excellent performance on self-supervised video representation learning. Temporal redundancy has led to a high masking ratio and customized masking strategy in VideoMAE. In this paper, we aim to further improve the performance of video masked autoencoding by introducing a motion guided masking strategy. Our key insight is that motion is a general and unique prior in video, which should be taken into account during masked pre-training. Our motion guided masking explicitly incorporates motion information to build temporal consistent masking volume. Based on this masking volume, we can track the unmasked tokens in time and sample a set of temporal consistent cubes from videos. These temporal aligned unmasked tokens will further relieve the information leakage issue in time and encourage the MGMAE to learn more useful structure information. We implement our MGMAE with an online efficient optical flow estimator and backward masking map warping strategy. We perform experiments on the datasets of Something-Something V2 and Kinetics-400, demonstrating the superior performance of our MGMAE to the original VideoMAE. In addition, we provide the visualization analysis to illustrate that our MGMAE can sample temporal consistent cubes in a motion-adaptive manner for more effective video pre-training.Comment: ICCV 2023 camera-ready versio