Critical phenomena in gravitational collapse of Husain-Martinez-Nunez scalar field

We construct analytical models to study the critical phenomena in gravitational collapse of the Husain-Martinez-Nunez massless scalar field. We first use the cut-and-paste technique to match the conformally flat solution ($c=0$ ) onto an outgoing Vaidya solution. To guarantee the continuity of the metric and the extrinsic curvature, we prove that the two solutions must be joined at a null hypersurface and the metric function in Vaidya spacetime must satisfy some constraints. We find that the mass of the black hole in the resulting spacetime takes the form $M\propto (p-p^*)^\gamma$, where the critical exponent $\gamma$ is equal to $0.5$. For the case $c\neq 0$, we show that the scalar field must be joined onto two pieces of Vaidya spacetimes to avoid a naked singularity. We also derive the power-law mass formula with $\gamma=0.5$. Compared with previous analytical models constructed from a different scalar field with continuous self-similarity, we obtain the same value of $\gamma$. However, we show that the solution with $c\neq 0$ is not self-similar. Therefore, we provide a rare example that a scalar field without self-similarity also possesses the features of critical collapse.Comment: 14 pages, 6 figure

Exploration or Exploitation？The Impact of Empowering Leadership on Ambidextrous Innovations for Medium-Sized Enterprises in China: The Moderating Role of Managerial Ties

The study focuses on the impact of empowering leadership on the ambidextrous innovation of SMEs in emerging economies. There are two types of definitions for ambidextrous innovation on the strategic perspective, balance and trade-off. In this article, we only discuss trade-off for the ambidextrous innovation of enterprises. On the one hand, we studied the influence mechanism of the empowering leadership behavior of SMEs on the ambidextrous innovation of SMEs in emerging economies, with the sample in China. On the other hand, the paper also reveals the role of managerial ties of leaders on the empowering leadership and ambidextrous innovation. Through the questionnaire survey and data analysis, we concluded that the leader’s managerial ties could moderate the relationship between the empowering leadership and the SMEs' ambidextrous innovation willingness. The conclusions of this study provide a reference for the further development and reform of SMEs in China and other emerging economies. Keywords: SME; Empowering leadership; Ambidextrous innovation; Managerial Ties DOI: 10.7176/EJBM/11-16-05 Publication date:June 30th 201

Towards the TopMost: A Topic Modeling System Toolkit

Topic models have been proposed for decades with various applications and recently refreshed by the neural variational inference. However, these topic models adopt totally distinct dataset, implementation, and evaluation settings, which hinders their quick utilization and fair comparisons. This greatly hinders the research progress of topic models. To address these issues, in this paper we propose a Topic Modeling System Toolkit (TopMost). Compared to existing toolkits, TopMost stands out by covering a wider range of topic modeling scenarios including complete lifecycles with dataset pre-processing, model training, testing, and evaluations. The highly cohesive and decoupled modular design of TopMost enables quick utilization, fair comparisons, and flexible extensions of different topic models. This can facilitate the research and applications of topic models. Our code, tutorials, and documentation are available at https://github.com/bobxwu/topmost

Effect of sleeve length on deformation properties of grouted splices

Provedeno je ispitivanje dvanaest injektiranih spojeva armaturnih šipki da bi se utvrdila njihova deformacijska svojstva pod vlačnim opterećenjem. Promjenljivi parametri tijekom ispitivanja bili su promjer i vrsta spojnih šipaka te veličina cjevastih spojeva. Izrađeni su, provjereni i korišteni odgovarajući modeli bazirani na metodi konačnih elemenata kako bi se odredio utjecaj dužine spoja na deformacijska svojstva injektiranih spojeva. Dužina spoja znatno je utjecala na način sloma i na deformacijska svojstva spojeva. Predložena je metoda poboljšanja deformacijskih svojstava određene vrste spoja primjenom kritične dužine spoja i načina loma šipke.Tests on 12 grouted splices were performed to investigate their deformation properties under tensile loading. Test variables included diameter and type of spliced bars and the size of splicing sleeves. Finite element method based models of the splices were built, verified and used to investigate the effect of sleeve length on the deformation properties of the grouted splices. The sleeve length significantly affected the failure modes and further affected the splices’ deformation properties. A recommendation was proposed to maximize deformation properties of a specific splice by using a critical sleeve length associated with failure mode of bar fracture

NOC: High-Quality Neural Object Cloning with 3D Lifting of Segment Anything

With the development of the neural field, reconstructing the 3D model of a target object from multi-view inputs has recently attracted increasing attention from the community. Existing methods normally learn a neural field for the whole scene, while it is still under-explored how to reconstruct a certain object indicated by users on-the-fly. Considering the Segment Anything Model (SAM) has shown effectiveness in segmenting any 2D images, in this paper, we propose Neural Object Cloning (NOC), a novel high-quality 3D object reconstruction method, which leverages the benefits of both neural field and SAM from two aspects. Firstly, to separate the target object from the scene, we propose a novel strategy to lift the multi-view 2D segmentation masks of SAM into a unified 3D variation field. The 3D variation field is then projected into 2D space and generates the new prompts for SAM. This process is iterative until convergence to separate the target object from the scene. Then, apart from 2D masks, we further lift the 2D features of the SAM encoder into a 3D SAM field in order to improve the reconstruction quality of the target object. NOC lifts the 2D masks and features of SAM into the 3D neural field for high-quality target object reconstruction. We conduct detailed experiments on several benchmark datasets to demonstrate the advantages of our method. The code will be released

Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions

Modern Review Helpfulness Prediction systems are dependent upon multiple modalities, typically texts and images. Unfortunately, those contemporary approaches pay scarce attention to polish representations of cross-modal relations and tend to suffer from inferior optimization. This might cause harm to model's predictions in numerous cases. To overcome the aforementioned issues, we propose Multimodal Contrastive Learning for Multimodal Review Helpfulness Prediction (MRHP) problem, concentrating on mutual information between input modalities to explicitly elaborate cross-modal relations. In addition, we introduce Adaptive Weighting scheme for our contrastive learning approach in order to increase flexibility in optimization. Lastly, we propose Multimodal Interaction module to address the unalignment nature of multimodal data, thereby assisting the model in producing more reasonable multimodal representations. Experimental results show that our method outperforms prior baselines and achieves state-of-the-art results on two publicly available benchmark datasets for MRHP problem.Comment: Accepted to the main EMNLP 2022 conferenc

Trojan Horse nanotheranostics with dual transformability and multifunctionality for highly effective cancer treatment.

Nanotheranostics with integrated diagnostic and therapeutic functions show exciting potentials towards precision nanomedicine. However, targeted delivery of nanotheranostics is hindered by several biological barriers. Here, we report the development of a dual size/charge- transformable, Trojan-Horse nanoparticle (pPhD NP) for delivery of ultra-small, full active pharmaceutical ingredients (API) nanotheranostics with integrated dual-modal imaging and trimodal therapeutic functions. pPhD NPs exhibit ideal size and charge for drug transportation. In tumour microenvironment, pPhD NPs responsively transform to full API nanotheranostics with ultra-small size and higher surface charge, which dramatically facilitate the tumour penetration and cell internalisation. pPhD NPs enable visualisation of biodistribution by near-infrared fluorescence imaging, tumour accumulation and therapeutic effect by magnetic resonance imaging. Moreover, the synergistic photothermal-, photodynamic- and chemo-therapies achieve a 100% complete cure rate on both subcutaneous and orthotopic oral cancer models. This nanoplatform with powerful delivery efficiency and versatile theranostic functions shows enormous potentials to improve cancer treatment

DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding

Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query. Recent advances employ the attention mechanism to learn the relations between video moments and the text query. However, naive attention might not be able to appropriately capture such relations, resulting in ineffective distributions where target video moments are difficult to separate from the remaining ones. To resolve the issue, we propose an energy-based model framework to explicitly learn moment-query distributions. Moreover, we propose DemaFormer, a novel Transformer-based architecture that utilizes exponential moving average with a learnable damping factor to effectively encode moment-query inputs. Comprehensive experiments on four public temporal language grounding datasets showcase the superiority of our methods over the state-of-the-art baselines.Comment: Accepted at EMNLP 2023 (Findings

Topic Modeling as Multi-Objective Contrastive Optimization

Recent representation learning approaches enhance neural topic models by optimizing the weighted linear combination of the evidence lower bound (ELBO) of the log-likelihood and the contrastive learning objective that contrasts pairs of input documents. However, document-level contrastive learning might capture low-level mutual information, such as word ratio, which disturbs topic modeling. Moreover, there is a potential conflict between the ELBO loss that memorizes input details for better reconstruction quality, and the contrastive loss which attempts to learn topic representations that generalize among input documents. To address these issues, we first introduce a novel contrastive learning method oriented towards sets of topic vectors to capture useful semantics that are shared among a set of input documents. Secondly, we explicitly cast contrastive topic modeling as a gradient-based multi-objective optimization problem, with the goal of achieving a Pareto stationary solution that balances the trade-off between the ELBO and the contrastive objective. Extensive experiments demonstrate that our framework consistently produces higher-performing neural topic models in terms of topic coherence, topic diversity, and downstream performance.Comment: Accepted at ICLR 2024 (poster