Business Value of Enterprise Micro-Blogs: Empirical Study from weibo.com in Sina

The increasing use of micro-blogs as marketing tools has increased the research attention on the usage and performance of enterprise micro-blogs. Based on research on information system (IS) usage and the resource-based view (RBV) theory, this study develops a model to measure the business value of enterprise micro-blogs. The model consists of metrics on micro-blog usage, micro-blog operational performance, firm capability, and performance. Questionnaires were distributed to firms that use micro-blogs. This study collects 317 valid responses for empirical analysis. The result suggests that the extent of micro-blog usage improves the operational performance of enterprise micro-blogs directly and indirectly by increasing firm capability. The operational performance of enterprise micro-blogs significantly affects firm performance. This study reveals the mechanism of business value generation of enterprise micro-blogs and extends the stream of research that combines IS usage and the RBV theory

On the Robustness of Safe Reinforcement Learning under Observational Perturbations

Safe reinforcement learning (RL) trains a policy to maximize the task reward while satisfying safety constraints. While prior works focus on the performance optimality, we find that the optimal solutions of many safe RL problems are not robust and safe against carefully designed observational perturbations. We formally analyze the unique properties of designing effective state adversarial attackers in the safe RL setting. We show that baseline adversarial attack techniques for standard RL tasks are not always effective for safe RL and proposed two new approaches - one maximizes the cost and the other maximizes the reward. One interesting and counter-intuitive finding is that the maximum reward attack is strong, as it can both induce unsafe behaviors and make the attack stealthy by maintaining the reward. We further propose a more effective adversarial training framework for safe RL and evaluate it via comprehensive experiments. This paper provides a pioneer work to investigate the safety and robustness of RL under observational attacks for future safe RL studies.Comment: 30 pages, 4 figures, 8 table

Constrained Decision Transformer for Offline Safe Reinforcement Learning

Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment. We aim to tackle a more challenging problem: learning a safe policy from an offline dataset. We study the offline safe RL problem from a novel multi-objective optimization perspective and propose the $\epsilon$-reducible concept to characterize problem difficulties. The inherent trade-offs between safety and task performance inspire us to propose the constrained decision transformer (CDT) approach, which can dynamically adjust the trade-offs during deployment. Extensive experiments show the advantages of the proposed method in learning an adaptive, safe, robust, and high-reward policy. CDT outperforms its variants and strong offline safe RL baselines by a large margin with the same hyperparameters across all tasks, while keeping the zero-shot adaptation capability to different constraint thresholds, making our approach more suitable for real-world RL under constraints.Comment: 15 pages, 7 figure

Technical Report for Argoverse Challenges on Unified Sensor-based Detection, Tracking, and Forecasting

This report presents our Le3DE2E solution for unified sensor-based detection, tracking, and forecasting in Argoverse Challenges at CVPR 2023 Workshop on Autonomous Driving (WAD). We propose a unified network that incorporates three tasks, including detection, tracking, and forecasting. This solution adopts a strong Bird's Eye View (BEV) encoder with spatial and temporal fusion and generates unified representations for multi-tasks. The solution was tested in the Argoverse 2 sensor dataset to evaluate the detection, tracking, and forecasting of 26 object categories. We achieved 1st place in Detection, Tracking, and Forecasting on the E2E Forecasting track in Argoverse Challenges at CVPR 2023 WAD

Biaxial Tensile Strain Enhances Electron Mobility of Monolayer Transition Metal Dichalcogenides

Strain engineering can modulate the material properties of two-dimensional (2D) semiconductors for electronic and optoelectronic applications. Recent theory and experiments have found that uniaxial tensile strain can improve the electron mobility of monolayer MoS$_2$, a 2D semiconductor, but the effects of biaxial strain on charge transport are not well-understood in 2D semiconductors. Here, we use biaxial tensile strain on flexible substrates to probe the electron mobility in monolayer WS$_2$ and MoS$_2$ transistors. This approach experimentally achieves ~2x higher on-state current and mobility with ~0.3% applied biaxial strain in WS$_2$, the highest mobility improvement at the lowest strain reported to date. We also examine the mechanisms behind this improvement through density functional theory simulations, concluding that the enhancement is primarily due to reduced intervalley electron-phonon scattering. These results underscore the role of strain engineering 2D semiconductors for flexible electronics, sensors, integrated circuits, and other optoelectronic applications.Comment: Corrected titl

Metallic vanadium disulfide nanosheets as a platform material for multifunctional electrode applications

Nano-thick metallic transition metal dichalcogenides such as VS$_{2}$ are essential building blocks for constructing next-generation electronic and energy-storage applications, as well as for exploring unique physical issues associated with the dimensionality effect. However, such 2D layered materials have yet to be achieved through either mechanical exfoliation or bottom-up synthesis. Herein, we report a facile chemical vapor deposition route for direct production of crystalline VS$_{2}$ nanosheets with sub-10 nm thicknesses and domain sizes of tens of micrometers. The obtained nanosheets feature spontaneous superlattice periodicities and excellent electrical conductivities (~3$\times$10$^{3}$ S cm$^{-1}$), which has enabled a variety of applications such as contact electrodes for monolayer MoS$_{2}$ with contact resistances of ~1/4 to that of Ni/Au metals, and as supercapacitor electrodes in aqueous electrolytes showing specific capacitances as high as 8.6$\times$10$^{2}$ F g$^{-1}$. This work provides fresh insights into the delicate structure-property relationship and the broad application prospects of such metallic 2D materials.Comment: 23 pages, 5 figue