Technological Regimes and Firm Survival: Evidence across Sectors and over Time

In addition to the usual variables representing firm- and industry-specific features that impact the firm’s survival, this paper uses three R&D related variables to reflect two Schumpeterian technological regimes: creative destruction (the entrepreneurial regime) and creative accumulation (the routinized regime). After controlling for age, size, entry barriers, capital intensity, the profit margin, the concentration ratio, the profit-cost ratio and entry rates, the empirical results confirm the theoretical relationship between technological regimes and the survival rate of new firms: new firms are more likely to survive under the entrepreneurial regime. Moreover, this effect is larger within the younger cohorts of firms than within the older ones.Firm Survival, Technological Regimes, Taiwanese Manufacturing

The Effects of Language Difference on Operational Performance and Satisfaction with B2B E-Marketplace Interface

This study integrated the user interface and information content of the business-to-business (B2B) electronic marketplace (e-marketplace) with language to analyze whether language differences affect the definition of good interface design and the information content that should be provided via an e-marketplace. An experimental design was adopted for collecting data from tasks, and then the Questionnaire for User Interface Satisfaction (QUIS) was used to ascertain how satisfied subjects were with regard to using the B2B e-marketplace interfaces. Study results showed that the language, the e-marketplace interface the subject used, and a combination of the two predict a person’s operational performance and satisfaction with a B2B e-marketplace. This study’s results provide a better understanding of whether B2B e-marketplace service providers should develop interfaces based on specific languages

Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusion

Bird-eye-view (BEV) based methods have made great progress recently in multi-view 3D detection task. Comparing with BEV based methods, sparse based methods lag behind in performance, but still have lots of non-negligible merits. To push sparse 3D detection further, in this work, we introduce a novel method, named Sparse4D, which does the iterative refinement of anchor boxes via sparsely sampling and fusing spatial-temporal features. (1) Sparse 4D Sampling: for each 3D anchor, we assign multiple 4D keypoints, which are then projected to multi-view/scale/timestamp image features to sample corresponding features; (2) Hierarchy Feature Fusion: we hierarchically fuse sampled features of different view/scale, different timestamp and different keypoints to generate high-quality instance feature. In this way, Sparse4D can efficiently and effectively achieve 3D detection without relying on dense view transformation nor global attention, and is more friendly to edge devices deployment. Furthermore, we introduce an instance-level depth reweight module to alleviate the ill-posed issue in 3D-to-2D projection. In experiment, our method outperforms all sparse based methods and most BEV based methods on detection task in the nuScenes dataset

Sparse4D v3: Advancing End-to-End 3D Detection and Tracking

In autonomous driving perception systems, 3D detection and tracking are the two fundamental tasks. This paper delves deeper into this field, building upon the Sparse4D framework. We introduce two auxiliary training tasks (Temporal Instance Denoising and Quality Estimation) and propose decoupled attention to make structural improvements, leading to significant enhancements in detection performance. Additionally, we extend the detector into a tracker using a straightforward approach that assigns instance ID during inference, further highlighting the advantages of query-based algorithms. Extensive experiments conducted on the nuScenes benchmark validate the effectiveness of the proposed improvements. With ResNet50 as the backbone, we witnessed enhancements of 3.0\%, 2.2\%, and 7.6\% in mAP, NDS, and AMOTA, achieving 46.9\%, 56.1\%, and 49.0\%, respectively. Our best model achieved 71.9\% NDS and 67.7\% AMOTA on the nuScenes test set. Code will be released at \url{https://github.com/linxuewu/Sparse4D}

General approach of causal mediation analysis with causally ordered multiple mediators and survival outcome

Causal mediation analysis with multiple mediators (causal multi-mediation analysis) is critical in understanding why an intervention works, especially in medical research. Deriving the path-specific effects (PSEs) of exposure on the outcome through a certain set of mediators can detail the causal mechanism of interest. However, the existing models of causal multi-mediation analysis are usually restricted to partial decomposition, which can only evaluate the cumulative effect of several paths. Moreover, the general form of PSEs for an arbitrary number of mediators has not been proposed. In this study, we provide a generalized definition of PSE for partial decomposition (partPSE) and for complete decomposition, which are extended to the survival outcome. We apply the interventional analogues of PSE (iPSE) for complete decomposition to address the difficulty of non-identifiability. Based on Aalen’s additive hazards model and Cox’s proportional hazards model, we derive the generalized analytic forms and illustrate asymptotic property for both iPSEs and partPSEs for survival outcome. The simulation is conducted to evaluate the performance of estimation in several scenarios. We apply the new methodology to investigate the mechanism of methylation signals on mortality mediated through the expression of three nested genes among lung cancer patients