547 research outputs found
The Relevance BetweenIntangible Assets and Accounting Earnings Quality in Chinese High-Tech Enterprises
Much attention has been paid to Chinese listed companies, especially high technology enterprises. This paper follows 765 high technology enterprises in Shanghai and Shenzhen as sample, positively analyzes sample companiesâ intangible assets influence on accounting earnings quality. Study finds that high technology enterprises gross margin is significantly and positively correlated with intangible assets, and negatively correlated with the operating profit margin. This paper gives more explanation based on the findings, and put forward the corresponding suggestions
How do credit ratings affect corporate investment efficiency?
This study examines the impact of credit ratings on the efficiency of firms' investments. Using a large sample of US firms, we find a positive relationship between the existence of credit ratings and investment efficiency. The crossâsectional analyses show the positive relationship is more pronounced for firms with greater information asymmetry and weaker corporate governance. Our results are robust to different methods to address potential endogeneity concerns, alternative measures of key variables, and the inclusion of additional control variables. Overall, the findings support the notion that credit rating agencies enhance information transparency and external monitoring, thereby allowing rated firms to promote investment efficiency. The findings contribute to our understanding of the significant role played by credit rating agencies in shaping firms' investment behaviour and efficiency
Bionic Collapsible Wings in Aquatic-aerial Robot
The concept of aerial-aquatic robots has emerged as an innovative solution
that can operate both in the air and underwater. Previous research on the
design of such robots has been mainly focused on mature technologies such as
fixed-wing and multi-rotor aircraft. Flying fish, a unique aerial-aquatic
animal that can both swim in water and glide over the sea surface, has not been
fully explored as a bionic robot model, especially regarding its motion
patterns with the collapsible pectoral fins. To verify the contribution of the
collapsible wings to the flying fish motion pattern, we have designed a novel
bio-robot with collapsible wings inspired by the flying fish. The bionic
prototype has been successfully designed and fabricated, incorporating
collapsible wings with soft hydraulic actuators, an innovative application of
soft actuators to a micro aquatic-aerial robot. We have analyzed and built a
precise model of dynamics for control, and tested both the soft hydraulic
actuators and detailed aerodynamic coefficients. To further verify the
feasibility of collapsible wings, we conducted simulations in different
situations such as discharge angles, the area of collapsible wings, and the
advantages of using ground effect. The results confirm the control of the
collapsible wings and demonstrate the unique multi-modal motion pattern between
water and air. Overall, our research represents the study of the collapsible
wings in aquatic-aerial robots and significant contributes to the development
of aquatic-aerial robots. The using of the collapsible wings must a
contribution to the future aquatic-aerial robot
SYNLOCO: Synthesizing Central Pattern Generator and Reinforcement Learning for Quadruped Locomotion
The Central Pattern Generator (CPG) is adept at generating rhythmic gait
patterns characterized by consistent timing and adequate foot clearance. Yet,
its open-loop configuration often compromises the system's control performance
in response to environmental variations. On the other hand, Reinforcement
Learning (RL), celebrated for its model-free properties, has gained significant
traction in robotics due to its inherent adaptability and robustness. However,
initiating traditional RL approaches from the ground up presents computational
challenges and a heightened risk of converging to suboptimal local minima. In
this paper, we propose an innovative quadruped locomotion framework, SYNLOCO,
by synthesizing CPG and RL that can ingeniously integrate the strengths of both
methods, enabling the development of a locomotion controller that is both
stable and natural. Furthermore, we introduce a set of performance-driven
reward metrics that augment the learning of locomotion control. To optimize the
learning trajectory of SYNLOCO, a two-phased training strategy is presented.
Our empirical evaluation, conducted on a Unitree GO1 robot under varied
conditions--including distinct velocities, terrains, and payload
capacities--showcases SYNLOCO's ability to produce consistent and clear-footed
gaits across diverse scenarios. The developed controller exhibits resilience
against substantial parameter variations, underscoring its potential for robust
real-world applications.Comment: 7 Page
Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations
While state-of-the-art NLP models have demonstrated excellent performance for
aspect based sentiment analysis (ABSA), substantial evidence has been presented
on their lack of robustness. This is especially manifested as significant
degradation in performance when faced with out-of-distribution data. Recent
solutions that rely on counterfactually augmented datasets show promising
results, but they are inherently limited because of the lack of access to
explicit causal structure. In this paper, we present an alternative approach
that relies on non-counterfactual data augmentation. Our proposal instead
relies on using noisy, cost-efficient data augmentations that preserve
semantics associated with the target aspect. Our approach then relies on
modelling invariances between different versions of the data to improve
robustness. A comprehensive suite of experiments shows that our proposal
significantly improves upon strong pre-trained baselines on both standard and
robustness-specific datasets. Our approach further establishes a new
state-of-the-art on the ABSA robustness benchmark and transfers well across
domains.Comment: 10pages,1 figure,10 table
Meta-DSP: A Meta-Learning Approach for Data-Driven Nonlinear Compensation in High-Speed Optical Fiber Systems
Non-linear effects in long-haul, high-speed optical fiber systems
significantly hinder channel capacity. While the Digital Backward Propagation
algorithm (DBP) with adaptive filter (ADF) can mitigate these effects, it
suffers from an overwhelming computational complexity. Recent solutions have
incorporated deep neural networks in a data-driven strategy to alleviate this
complexity in the DBP model. However, these models are often limited to a
specific symbol rate and channel number, necessitating retraining for different
settings, their performance declines significantly under high-speed and
high-power conditions. We introduce Meta-DSP, a novel data-driven nonlinear
compensation model based on meta-learning that processes multi-modal data
across diverse transmission rates, power levels, and channel numbers. This not
only enhances signal quality but also substantially reduces the complexity of
the nonlinear processing algorithm. Our model delivers a 0.7 dB increase in the
Q-factor over Electronic Dispersion Compensation (EDC), and compared to DBP, it
curtails computational complexity by a factor of ten while retaining comparable
performance. From the perspective of the entire signal processing system, the
core idea of Meta-DSP can be employed in any segment of the overall
communication system to enhance the model's scalability and generalization
performance. Our research substantiates Meta-DSP's proficiency in addressing
the critical parameters defining optical communication networks
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