The Development of Guzheng Tuning

The guzheng is a musical instrument that has existed for about 2500 years, since the period between 770 and 476 BCE. The tuning of the guzheng is the foundation that influences how its music performed. It plays a decisive role not only in musical creation, but also in musical skills and applications. It also affects the sustainability of the guzheng\u27s art. By analyzing the traditions and the developments of the tuning of the guzheng, this thesis will inform the reader about how the various tunings of the guzheng have influenced its development. In addition, this thesis will deeply explore the causes that made the guzheng develop and spread from generation to generation, and search for the reasons for its continuing influence on Chinese culture

A Smartphone-Based Prototype System for Incident/Work Zone Management Driven by Crowd-Sourced Data

This project develops a smartphone-based prototype system that supplements the 511 system to improve its dynamic traffic routing service to state highway users under non-recurrent congestion. This system will save considerable time to provide crucial traffic information and en-route assistance to travelers for them to avoid being trapped in traffic congestion due to accidents, work zones, hazards, or special events. It also creates a feedback loop between travelers and responsible agencies that enable the state to effectively collect, fuse, and analyze crowd-sourced data for next-gen transportation planning and management. This project can result in substantial economic savings (e.g. less traffic congestion, reduced fuel wastage and emissions) and safety benefits for the freight industry and society due to better dissemination of real-time traffic information by highway users. Such benefits will increase significantly in future with the expected increase in freight traffic on the network. The proposed system also has the flexibility to be integrated with various transportation management modules to assist state agencies to improve transportation services and daily operations

ICMRec: Item Cluster-Wise Multi-Objective Optimization for Unbiased Recommendation

The traditional observed data used to train the recommender model suffers from severe bias issues (e.g., exposure bias, popularity bias). Interactions of a small fraction of head items account for almost the whole training data. The normal training paradigm from such biased data tends to repetitively generate recommendations from the head items, which further exacerbates the biases and affects the exploration of potentially interesting items from the niche set. In this work, distinct from existing methods, we innovatively explore the central theme of unbiased recommendation from an item cluster-wise multi-objective optimization perspective. Aiming to balance the learning on various item clusters that differ in popularity during the training process, we characterize the recommendation task as an item cluster-wise multi-objective optimization problem. To this end, we propose a model-agnostic framework namely Item Cluster-Wise Multi-Objective Recommendation (ICMRec) for unbiased recommendation. In detail, we define our item cluster-wise optimization target that the recommender model should balance all item clusters that differ in popularity. Thus we set the model learning on each item cluster as a unique optimization objective. To achieve this goal, we first explore items' popularity levels from a novel causal reasoning perspective. Then, we devise popularity discrepancy-based bisecting clustering to separate the discriminated item clusters. Next, we adaptively find the overall harmonious gradient direction for multiple item cluster-wise optimization objectives from a Pareto-efficient solver. Finally, in the prediction stage, we perform counterfactual inference to further eliminate the impact of user conformity. Extensive experimental results demonstrate the superiorities of ICMRec on overall recommendation performance and biases elimination. Codes will be open-source upon acceptance

Electrocardiogram Baseline Wander Suppression Based on the Combination of Morphological and Wavelet Transformation Based Filtering

One of the major noise components in electrocardiogram (ECG) is the baseline wander (BW). Effective methods for suppressing BW include the wavelet-based (WT) and the mathematical morphological filtering-based (MMF)algorithms. However, the T waveform distortions introduced by the WTand the rectangular/trapezoidal distortions introduced by MMF degrade the quality of the output signal. Hence, in this study, we introduce a method by combining the MMF and WTto overcome the shortcomings of both existing methods. To demonstrate the effectiveness of the proposed method, artificial ECG signals containing a clinicalBW are used for numerical simulation, and we also create a realistic model of baseline wander to compare the proposed method with other state-of-the-art methods commonly used in the literature. /e results show that the BW suppression effect of the proposed method is better than that of the others. Also, the new method is capable of preserving the outline of the BW and avoiding waveform distortions caused by the morphology filter, thereby obtaining an enhanced quality of ECG