359 research outputs found
Graph Analysis Using a GPU-based Parallel Algorithm: Quantum Clustering
The article introduces a new method for applying Quantum Clustering to graph
structures. Quantum Clustering (QC) is a novel density-based unsupervised
learning method that determines cluster centers by constructing a potential
function. In this method, we use the Graph Gradient Descent algorithm to find
the centers of clusters. GPU parallelization is utilized for computing
potential values. We also conducted experiments on five widely used datasets
and evaluated using four indicators. The results show superior performance of
the method. Finally, we discuss the influence of on the experimental
results
Content Adaptive NN-Based In-Loop Filter for VVC
The most recent video coding standard VVC contains five in-loop filters to reduce compression artifacts that come from the common drawbacks of block-based hybrid compression framework. However, those traditional in-loop filters are insufficient to deal with the complicated compression artifacts. The emergence of Neural Networks (NNs) has brought significant advancements in the realm of image and video processing, offering a promising avenue for improving video compression. Many prior studies in this domain have focused on training models on large datasets to achieve generalization, rather than catering to specific content characteristics. In this work, we introduced a content-adaptive in-loop filter for Versatile Video Coding (VVC) working with other in-loop filters. The content adaptation is achieved by over-fitting a pre-trained model at the encoder side on the test data. To reduce the bitrate overhead, the Neural Network Compression and Representation (NNR) standard has been introduced which focuses on compressing NNs efficiently. Furthermore, rather than over-fitting all parameters within the NN model, we introduce a set of learnable parameters known as multipliers, which serve to further reduce the bitrate overhead. The proposed model takes auxiliary information including Boundary Strength (BS) and Quantization parameter (QP) as input. Additionally, we have conducted a comprehensive series of experiments to identify the optimal combination of hyperparameters for this approach. The results indicate coding gains of -2.07% (Y), -5.54% (Cb), -1.95% (Cr) Bjøntegaard Delta rate (BD-rate) for Class B and -1.34% (Y), -1.88% (Cb), -0.52% (Cr) Bjøntegaard Delta rate (BD-rate) for Class D with respect to the Peak Signal-to-Noise Ration (PSNR) on top of the Versatile Video Coding (VVC) Test Model (VVC) 12.0 with NN-based Video Coding (NNVC) 5.0, in Random Access (RA) configuration
Stock Market Simulation
In this Interactive Qualifying Project (IQP), the group conducted a 14-week stock market simulation using three different trading strategies: technical, swing, and position trading. The team researched the fundamentals of the stock market and the basics of trading using tools and resources gathered from the Internet. Each member managed a portfolio using one trading strategy with an initial $500,000 to invest. Trading decisions were supported by market analysis techniques and results were exchanged in weekly conventions. The project gave the team members a valuable beginning stock trading experience and helped them to gain a better knowledge and understanding of the stock market. This IQP has built a strong foundation for potential investment in the future
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