68 research outputs found
The Process of Firm’s Entry, Survival and Growth: A Conceptual and Empirical Analysis
This study aims at providing a better understanding of the process of entrepreneurial activities. By reviewing recent literature on start-ups, it establishes the micro foundations of firm’s entry and exit, etc., and characterizes the features of founder, firm and regional context. Statistical data of start-ups in 2005 and their performance in the following three years are drawn from Statistics Sweden, to allow empirical examination of the theoretical findings. This study suggests that the motivations for entrepreneurial activities are entrepreneurs’ expectations on their characteristics and abilities; and the process of entrepreneurial activities consists of different phases and stages. For entrepreneurs the empirical findings exhibit the irrelevance of financial support, and the negative impacts of partnership. Policy-makers are advised to pay specific attention to regional environment for promoting business performance
A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents
The burgeoning fields of robot learning and embodied AI have triggered an
increasing demand for large quantities of data. However, collecting sufficient
unbiased data from the target domain remains a challenge due to costly data
collection processes and stringent safety requirements. Consequently,
researchers often resort to data from easily accessible source domains, such as
simulation and laboratory environments, for cost-effective data acquisition and
rapid model iteration. Nevertheless, the environments and embodiments of these
source domains can be quite different from their target domain counterparts,
underscoring the need for effective cross-domain policy transfer approaches. In
this paper, we conduct a systematic review of existing cross-domain policy
transfer methods. Through a nuanced categorization of domain gaps, we
encapsulate the overarching insights and design considerations of each problem
setting. We also provide a high-level discussion about the key methodologies
used in cross-domain policy transfer problems. Lastly, we summarize the open
challenges that lie beyond the capabilities of current paradigms and discuss
potential future directions in this field
Reconfigurable Intelligent Surface-Assisted Secret Key Generation in Spatially Correlated Channels
Reconfigurable intelligent surface (RIS) is a disruptive technology to
enhance the performance of physical-layer key generation (PKG) thanks to its
ability to smartly customize the radio environments. Existing RIS-assisted PKG
methods are mainly based on the idealistic assumption of an independent and
identically distributed (i.i.d.) channel model at both the base station (BS)
and the RIS. However, the i.i.d. model is inaccurate for a typical RIS in an
isotropic scattering environment and neglecting the existence of channel
spatial correlation would possibly degrade the PKG performance. In this paper,
we establish a general spatially correlated channel model and propose a new
channel probing framework based on the transmit and the reflective beamforming.
We derive a closed-form key generation rate (KGR) expression and formulate an
optimization problem, which is solved by using the low-complexity Block
Successive Upper-bound Minimization (BSUM) with Mirror-Prox method. Simulation
results show that compared to the existing methods based on the i.i.d. fading
model, our proposed method achieves about dB transmit power gain when the
spacing between two neighboring RIS elements is a quarter of the wavelength.
Also, the KGR increases significantly with the number of RIS elements while
that increases marginally with the number of BS antennas.Comment: arXiv admin note: text overlap with arXiv:2207.1175
Multi-Scale Expressions of One Optimal State Regulated by Dopamine in the Prefrontal Cortex
The prefrontal cortex (PFC), which plays key roles in many higher cognitive processes, is a hierarchical system consisting of multi-scale organizations. Optimizing the working state at each scale is essential for PFC's information processing. Typical optimal working states at different scales have been separately reported, including the dopamine-mediated inverted-U profile of the working memory (WM) at the system level, critical dynamics at the network level, and detailed balance of excitatory and inhibitory currents (E/I balance) at the cellular level. However, it remains unclear whether these states are scale-specific expressions of the same optimal state and, if so, what is the underlying mechanism for its regulation traversing across scales. Here, by studying a neural network model, we show that the optimal performance of WM co-occurs with the critical dynamics at the network level and the E/I balance at the level of individual neurons, suggesting the existence of a unified, multi-scale optimal state for the PFC. Importantly, such a state could be modulated by dopamine at the synaptic level through a series of U or inverted-U profiles. These results suggest that seemingly different optimal states for specific scales are multi-scale expressions of one condition regulated by dopamine. Our work suggests a cross-scale perspective to understand the PFC function and its modulation
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments
Deep learning-based physical-layer secret key generation (PKG) has been used
to overcome the imperfect uplink/downlink channel reciprocity in frequency
division duplexing (FDD) orthogonal frequency division multiplexing (OFDM)
systems. However, existing efforts have focused on key generation for users in
a specific environment where the training samples and test samples obey the
same distribution, which is unrealistic for real world applications. This paper
formulates the PKG problem in multiple environments as a learning-based problem
by learning the knowledge such as data and models from known environments to
generate keys quickly and efficiently in multiple new environments.
Specifically, we propose deep transfer learning (DTL) and meta-learning-based
channel feature mapping algorithms for key generation. The two algorithms use
different training methods to pre-train the model in the known environments,
and then quickly adapt and deploy the model to new environments. Simulation
results show that compared with the methods without adaptation, the DTL and
meta-learning algorithms both can improve the performance of generated keys. In
addition, the complexity analysis shows that the meta-learning algorithm can
achieve better performance than the DTL algorithm with less time, lower CPU and
GPU resources
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