90 research outputs found
Intelligent Perception Control System of Railway Level Crossing Gate Based on TRIZ Theory
TRIZ theory is an innovative method to analyse problems and solve them, which is widely used in many fields. In this paper, TRIZ theory is used to improve the design of railway crossing guardrail system. The use of nine-screen analysis, functional analysis, cause-effect chain analysis and other tools to analyse the problem of poor manual control effect in the railway crossing guardrail system, the use of technical contradictions, physical contradictions and other tools to improve the system design, effectively reduce the possibility of danger when cars and pedestrians cross railway crossings, improve the traffic safety and traffic order of the railway level crossing, and reduce the work burden of railway crossing caretakers
Effect of Narcissistic Personality on Entrepreneurial Intention Among College Students: Mediation Role of Entrepreneurial Self-Efficacy
Exploring the factors influencing entrepreneurial intention is crucial to entrepreneurial practice and education. For a comprehensive understanding of the influence of narcissistic personality on entrepreneurial intention, this study analyzed the relationship between narcissistic personality, entrepreneurial self-efficacy, and entrepreneurial intention in college students sampled from three higher vocational colleges in Beijing, China. A total of 252 valid questionnaires were collected. The results show that the narcissistic personality of the college students has a significant positive effect on entrepreneurial intention and entrepreneurial self-efficacy. Entrepreneurial self-efficacy of the college students has a significant positive effect on entrepreneurial intention and plays a partial mediation role in the relationship between narcissistic personality and entrepreneurial intention. Thus, the study results provide some reference for further improving entrepreneurial practice and education
Corrigendum: Effect of narcissistic personality on entrepreneurial intention among college students: mediation role of entrepreneurial self-efficacy
Gate-controlled reversible rectifying behaviour in tunnel contacted atomically-thin MoS transistor
Atomically-thin 2D semiconducting materials integrated into van der Waals
heterostructures have enabled architectures that hold great promise for next
generation nanoelectronics. However, challenges still remain to enable their
full acceptance as compliant materials for integration in logic devices. Two
key-components to master are the barriers at metal/semiconductor interfaces and
the mobility of the semiconducting channel, which endow the building-blocks of
diode and field effect transistor. Here, we have devised a reverted
stacking technique to intercalate a wrinkle-free h-BN tunnel layer between
MoS channel and contacting electrodes. Vertical tunnelling of electrons
therefore makes it possible to suppress the Schottky barriers and Fermi level
pinning, leading to homogeneous gate-control of the channel chemical potential
across the bandgap edges. The observed unprecedented features of ambipolar
to diode, which can be reversibly gate tuned, paves the way for
future logic applications and high performance switches based on atomically
thin semiconducting channel.Comment: 23 pages, 5 main figures + 9 SI figure
kNN-CLIP: retrieval enables training-free segmentation on continually expanding large vocabularies
Rapid advancements in continual segmentation have yet to bridge the gap of scaling to large continually expanding vocabularies under compute-constrained scenarios. We discover that traditional continual training leads to catastrophic forgetting under compute constraints, unable to outperform zero-shot segmentation methods. We introduce a novel strategy for semantic and panoptic segmentation with zero forgetting, capable of adapting to continually growing vocabularies without the need for retraining or large memory costs. Our training-free approach, kNN-CLIP, leverages a database of instance embeddings to enable open-vocabulary segmentation approaches to continually expand their vocabulary on any given domain with a single-pass through data, while only storing embeddings minimizing both compute and memory costs. This method achieves state-of-the-art mIoU performance across large-vocabulary semantic and panoptic segmentation datasets. We hope kNN-CLIP represents a step forward in enabling more efficient and adaptable continual segmentation, paving the way for advances in real-world large-vocabulary continual segmentation methods
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional
sparse graph-structured data into low-dimensional dense vectors, which is a
fundamental task that has been widely studied in a range of fields, including
machine learning and data mining. Classic graph embedding methods follow the
basic idea that the embedding vectors of interconnected nodes in the graph can
still maintain a relatively close distance, thereby preserving the structural
information between the nodes in the graph. However, this is sub-optimal due
to: (i) traditional methods have limited model capacity which limits the
learning performance; (ii) existing techniques typically rely on unsupervised
learning strategies and fail to couple with the latest learning paradigms;
(iii) representation learning and downstream tasks are dependent on each other
which should be jointly enhanced. With the remarkable success of deep learning,
deep graph representation learning has shown great potential and advantages
over shallow (traditional) methods, there exist a large number of deep graph
representation learning techniques have been proposed in the past decade,
especially graph neural networks. In this survey, we conduct a comprehensive
survey on current deep graph representation learning algorithms by proposing a
new taxonomy of existing state-of-the-art literature. Specifically, we
systematically summarize the essential components of graph representation
learning and categorize existing approaches by the ways of graph neural network
architectures and the most recent advanced learning paradigms. Moreover, this
survey also provides the practical and promising applications of deep graph
representation learning. Last but not least, we state new perspectives and
suggest challenging directions which deserve further investigations in the
future
Skywork: A More Open Bilingual Foundation Model
In this technical report, we present Skywork-13B, a family of large language
models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both
English and Chinese texts. This bilingual foundation model is the most
extensively trained and openly published LLMs of comparable size to date. We
introduce a two-stage training methodology using a segmented corpus, targeting
general purpose training and then domain-specific enhancement training,
respectively. We show that our model not only excels on popular benchmarks, but
also achieves \emph{state of the art} performance in Chinese language modeling
on diverse domains. Furthermore, we propose a novel leakage detection method,
demonstrating that test data contamination is a pressing issue warranting
further investigation by the LLM community. To spur future research, we release
Skywork-13B along with checkpoints obtained during intermediate stages of the
training process. We are also releasing part of our SkyPile corpus, a
collection of over 150 billion tokens of web text, which is the largest high
quality open Chinese pre-training corpus to date. We hope Skywork-13B and our
open corpus will serve as a valuable open-source resource to democratize access
to high-quality LLMs
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