Taming Diffusion Models for Music-driven Conducting Motion Generation

Generating the motion of orchestral conductors from a given piece of symphony music is a challenging task since it requires a model to learn semantic music features and capture the underlying distribution of real conducting motion. Prior works have applied Generative Adversarial Networks (GAN) to this task, but the promising diffusion model, which recently showed its advantages in terms of both training stability and output quality, has not been exploited in this context. This paper presents Diffusion-Conductor, a novel DDIM-based approach for music-driven conducting motion generation, which integrates the diffusion model to a two-stage learning framework. We further propose a random masking strategy to improve the feature robustness, and use a pair of geometric loss functions to impose additional regularizations and increase motion diversity. We also design several novel metrics, including Frechet Gesture Distance (FGD) and Beat Consistency Score (BC) for a more comprehensive evaluation of the generated motion. Experimental results demonstrate the advantages of our model.Comment: Accepted by AAAI 2023 Summer Symposiu

Services Liberalization and Export Diversity: Theory and Evidence from Chinese Firms

During the last decades, we observe a liberalization trend in the services sector globally. Using the Chinese exporting firm data, this paper studies how multi-product firms adjust their export strategies in response to the services trade liberalization across export destination countries. Our study finds a highly significant positive relation between the services trade liberalization in the destination countries and each firm's export diversify, which is measured as the product scope, the Herfindahl-Hirschman style index, or the value skewness across varieties,export product switch. Our empirical analysis further finds that firms increase the relatedness of their exporting varieties towards the OECD countries, but reduce it towards the non-OECD countries. With a conventional multi-product firm model, we explore the mechanisms behind all our empirical findings

Services Liberalization and Export Diversity: Theory and Evidence from Chinese Firms

During the last decades, we observe a liberalization trend in the services sector globally. Using the Chinese exporting firm data, this paper studies how multi-product firms adjust their export strategies in response to the services trade liberalization across export destination countries. Our study finds a highly significant positive relation between the services trade liberalization in the destination countries and each firm's export diversify, which is measured as the product scope, the Herfindahl-Hirschman style index, or the value skewness across varieties,export product switch. Our empirical analysis further finds that firms increase the relatedness of their exporting varieties towards the OECD countries, but reduce it towards the non-OECD countries. With a conventional multi-product firm model, we explore the mechanisms behind all our empirical findings

Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning

Diffusion models have demonstrated highly-expressive generative capabilities in vision and NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are also powerful in modeling complex policies or trajectories in offline datasets. However, these works have been limited to single-task settings where a generalist agent capable of addressing multi-task predicaments is absent. In this paper, we aim to investigate the effectiveness of a single diffusion model in modeling large-scale multi-task offline data, which can be challenging due to diverse and multimodal data distribution. Specifically, we propose Multi-Task Diffusion Model (\textsc{MTDiff}), a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis in multi-task offline settings. \textsc{MTDiff} leverages vast amounts of knowledge available in multi-task data and performs implicit knowledge sharing among tasks. For generative planning, we find \textsc{MTDiff} outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D. For data synthesis, \textsc{MTDiff} generates high-quality data for testing tasks given a single demonstration as a prompt, which enhances the low-quality datasets for even unseen tasks.Comment: 21 page

Services Liberalization and Export Diversity: Theory and Evidence from Chinese Firms

During the last decades, we observe a liberalization trend in the services sector globally. Using the Chinese exporting firm data, this paper studies how multi-product firms adjust their export strategies in response to the services trade liberalization across export destination countries. Our study finds a highly significant positive relation between the services trade liberalization in the destination countries and each firm's export diversify, which is measured as the product scope, the Herfindahl-Hirschman style index, or the value skewness across varieties,export product switch. Our empirical analysis further finds that firms increase the relatedness of their exporting varieties towards the OECD countries, but reduce it towards the non-OECD countries. With a conventional multi-product firm model, we explore the mechanisms behind all our empirical findings

Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications

Value-added Tax Reform and Services Exports: Evidence from China

In 2012, a sales tax was replaced in China by a value-added tax (VAT). The effect of this change on services exports is evaluated in this paper. VAT reform was introduced across provinces and service sectors at different times, so we can identify the impacts of VAT reform on firms’ export behavior by utilizing a difference-in-difference-in-difference (DDD) estimation methodology. We find that VAT reform significantly increases service exports, in both intensive and extensive margins. The export enhancing effects are larger for non-state-owned enterprises, and for firms of larger scale and higher productivity levels. VAT reform alleviates tax magnification and double taxation, and effectively promotes the competitiveness of China’s services exports

Wet Modal Analyses of Various Length Coaxial Sump Pump Rotors with Acoustic-Solid Coupling

The dynamic characteristics of the rotor components were determined using a first-order modal model of the rotor components for various sump pump shaft lengths for actual working environments. By employing ANSYS-Workbench software, this paper uses a fluid-solid coupling analysis to calculate the reaction forces of the fluid on the rotor with results, which is then used in dry and wet modal analyses of the rotor parts to calculate the vibration modal characteristics with and without prestresses. The differences between the wet and dry modal characteristics were compared and investigated by ANSYS. The results show that increasing the sump pump shaft length reduces the first-order natural frequency of the prestressed rotor components. The structure also experiences stress stiffening, which is more obvious in the high-order modes. The natural frequency of the rotor in the wet mode is about 16% less than that in the dry mode for the various shaft lengths due to the added mass of the water on the surface which reduces the natural frequency. In the wet modal analysis, when the structure is in a different fluid medium, the influence of its modal distribution will also change, this is because the additional mass produced by the fluid medium of different density on the structure surface is different. Thus, the wet modal analysis of the rotor is important for more accurate dynamic analyses