227 research outputs found
GreenSwirl: Combining traffic signal control and route guidance for reducing traffic congestion
VNC2014 : IEEE Vehicular Networking Conference , Dec 3-5, 2014 , Paderborn, GermanySerious traffic congestion is a major social problem in large cities. Inefficient setting of traffic signal cycles, especially, is one of the main causes of congestion. Green Wave is a method for controlling traffic signals which allows one-way traffic to pass through a series of intersections without being stopped by a red light. Green Wave was tested in several cities around the world, but the results were not satisfactory. Two of the problems with Green Wave are that it still stops the crossing traffic, and it forms congestion in the traffic turning into or out of the crossing streets. To solve these problems, we propose a method of controlling traffic signals, GreenSwirl, in combination with a route guidance method, GreenDrive. GreenSwirl controls traffic signals to enable a smooth flow of traffic through signals times to turn green in succession and through non-stop circular routes through the city. The GreenWave technology is extended thereby. We also use navigation systems to optimize the overall control of the city's traffic. We did a simulation using the traffic simulator SUMO and the road network of Manhattan Island in New York. We confirmed that our method shortens the average travel time by 10%-60%, even when not all cars on the road are equipped to use this system
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Tumor promoter TPA activates Wnt/Ī²-catenin signaling in a casein kinase 1-dependent manner.
The tumor promoter 12-O-tetra-decanoylphorbol-13-acetate (TPA) has been defined by its ability to promote tumorigenesis on carcinogen-initiated mouse skin. Activation of Wnt/Ī²-catenin signaling has a decisive role in mouse skin carcinogenesis, but it remains unclear how TPA activates Wnt/Ī²-catenin signaling in mouse skin carcinogenesis. Here, we found that TPA could enhance Wnt/Ī²-catenin signaling in a casein kinase 1 (CK1) Īµ/Ī“-dependent manner. TPA stabilized CK1Īµ and enhanced its kinase activity. TPA further induced the phosphorylation of LRP6 at Thr1479 and Ser1490 and the formation of a CK1Īµ-LRP6-axin1 complex, leading to an increase in cytosolic Ī²-catenin. Moreover, TPA increased the association of Ī²-catenin with TCF4E in a CK1Īµ/Ī“-dependent way, resulting in the activation of Wnt target genes. Consistently, treatment with a selective CK1Īµ/Ī“ inhibitor SR3029 suppressed TPA-induced skin tumor formation in vivo, probably through blocking Wnt/Ī²-catenin signaling. Taken together, our study has identified a pathway by which TPA activates Wnt/Ī²-catenin signaling
MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation
Pre-trained language models have achieved impressive results in various music
understanding and generation tasks. However, existing pre-training methods for
symbolic melody generation struggle to capture multi-scale, multi-dimensional
structural information in note sequences, due to the domain knowledge
discrepancy between text and music. Moreover, the lack of available large-scale
symbolic melody datasets limits the pre-training improvement. In this paper, we
propose MelodyGLM, a multi-task pre-training framework for generating melodies
with long-term structure. We design the melodic n-gram and long span sampling
strategies to create local and global blank infilling tasks for modeling the
local and global structures in melodies. Specifically, we incorporate pitch
n-grams, rhythm n-grams, and their combined n-grams into the melodic n-gram
blank infilling tasks for modeling the multi-dimensional structures in
melodies. To this end, we have constructed a large-scale symbolic melody
dataset, MelodyNet, containing more than 0.4 million melody pieces. MelodyNet
is utilized for large-scale pre-training and domain-specific n-gram lexicon
construction. Both subjective and objective evaluations demonstrate that
MelodyGLM surpasses the standard and previous pre-training methods. In
particular, subjective evaluations show that, on the melody continuation task,
MelodyGLM gains average improvements of 0.82, 0.87, 0.78, and 0.94 in
consistency, rhythmicity, structure, and overall quality, respectively.
Notably, MelodyGLM nearly matches the quality of human-composed melodies on the
melody inpainting task
Let's Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference
APIs have intricate relations that can be described in text and represented
as knowledge graphs to aid software engineering tasks. Existing relation
extraction methods have limitations, such as limited API text corpus and
affected by the characteristics of the input text.To address these limitations,
we propose utilizing large language models (LLMs) (e.g., GPT-3.5) as a neural
knowledge base for API relation inference. This approach leverages the entire
Web used to pre-train LLMs as a knowledge base and is insensitive to the
context and complexity of input texts. To ensure accurate inference, we design
our analytic flow as an AI Chain with three AI modules: API FQN Parser, API
Knowledge Extractor, and API Relation Decider. The accuracy of the API FQN
parser and API Relation Decider module are 0.81 and 0.83, respectively. Using
the generative capacity of the LLM and our approach's inference capability, we
achieve an average F1 value of 0.76 under the three datasets, significantly
higher than the state-of-the-art method's average F1 value of 0.40. Compared to
CoT-based method, our AI Chain design improves the inference reliability by
67%, and the AI-crowd-intelligence strategy enhances the robustness of our
approach by 26%
Experimental study on working capacity of carbon canister based on Euro VI
In order to study the gasoline working capacity and durability of the carbon canister, the gasoline working capacity test of the carbon canister was conducted under different test conditions. The results showed that the gasoline working capacity of the canister carbon decreased with the increase of fuel vapor loading rate. The fuel vapor volume ratio of the inlet has little effect on the gasoline working capacity. After 300 gasoline working capacity test cycles, the working capacity of butane decreased by about 20%. The fuel vapor adsorption amount in first cycle of each carbon canister is far greater than the desorption amount in first cycle, and also far greater than the adsorption and desorption amount in the subsequent cycles, which indicated that a large amount of fuel vapor occupied the active sites after the first use of the carbon canister and cannot desorb
Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering
While large language models (LLMs) are equipped with longer text input
capabilities than before, they are struggling to seek correct information in
long contexts. The "lost in the middle" problem challenges most LLMs, referring
to the dramatic decline in accuracy when correct information is located in the
middle. To overcome this crucial issue, this paper proposes to enhance the
information searching and reflection ability of LLMs in long contexts via
specially designed tasks called Attention Strengthening Multi-doc QA (ASM QA).
Following these tasks, our model excels in focusing more precisely on the
desired information. Experimental results show substantial improvement in
Multi-doc QA and other benchmarks, superior to state-of-the-art models by 13.7%
absolute gain in shuffled settings, by 21.5% in passage retrieval task. We
release our model, Ziya-Reader to promote related research in the community
Salt Freeze-Thaw Damage Characteristics of Concrete based on Computed Tomography
Freezeāthaw damage and salt erosion are important factors that influence the durability of concrete. In this study, degradation laws of concrete in salt freezeāthaw environment were discussed from the microscopic perspective based on the 3D reconstruction of computed tomography images. A damage model based on concrete aggregate volume and porosity was constructed. Furthermore, the main causes of concrete degradation in the salt freezeāthaw environment were analyzed. Results reveal that, with the increase in salt freezeāthaw cycles, the damage of concrete intensifies gradually, and the uniaxial compressive strength declines steadily. Concrete damages have two causes, namely, changes in concrete porosity and variations in concrete aggregate volume. Damages caused by aggregate volume changes are divided into frost heaving and peeling. In accordance with the constructed damage model, the porosity of concrete materials changes slightly, whereas concrete aggregate volume varies significantly. Aggregate volume changes are the main causes of intensified concrete damages and decreased compressive strength. Research conclusions provide theoretical references to disclosing microscopic damage mechanism of concrete in the salt freezeāthaw environment
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