Improving Automatic Jazz Melody Generation by Transfer Learning Techniques

In this paper, we tackle the problem of transfer learning for Jazz automatic generation. Jazz is one of representative types of music, but the lack of Jazz data in the MIDI format hinders the construction of a generative model for Jazz. Transfer learning is an approach aiming to solve the problem of data insufficiency, so as to transfer the common feature from one domain to another. In view of its success in other machine learning problems, we investigate whether, and how much, it can help improve automatic music generation for under-resourced musical genres. Specifically, we use a recurrent variational autoencoder as the generative model, and use a genre-unspecified dataset as the source dataset and a Jazz-only dataset as the target dataset. Two transfer learning methods are evaluated using six levels of source-to-target data ratios. The first method is to train the model on the source dataset, and then fine-tune the resulting model parameters on the target dataset. The second method is to train the model on both the source and target datasets at the same time, but add genre labels to the latent vectors and use a genre classifier to improve Jazz generation. The evaluation results show that the second method seems to perform better overall, but it cannot take full advantage of the genre-unspecified dataset.Comment: 8 pages, Accepted to APSIPA ASC(Asia-Pacific Signal and Information Processing Association Annual Summit and Conference ) 201

Transformer-based Image Compression with Variable Image Quality Objectives

This paper presents a Transformer-based image compression system that allows for a variable image quality objective according to the user's preference. Optimizing a learned codec for different quality objectives leads to reconstructed images with varying visual characteristics. Our method provides the user with the flexibility to choose a trade-off between two image quality objectives using a single, shared model. Motivated by the success of prompt-tuning techniques, we introduce prompt tokens to condition our Transformer-based autoencoder. These prompt tokens are generated adaptively based on the user's preference and input image through learning a prompt generation network. Extensive experiments on commonly used quality metrics demonstrate the effectiveness of our method in adapting the encoding and/or decoding processes to a variable quality objective. While offering the additional flexibility, our proposed method performs comparably to the single-objective methods in terms of rate-distortion performance

Transformer-based Variable-rate Image Compression with Region-of-interest Control

This paper proposes a transformer-based learned image compression system. It is capable of achieving variable-rate compression with a single model while supporting the region-of-interest (ROI) functionality. Inspired by prompt tuning, we introduce prompt generation networks to condition the transformer-based autoencoder of compression. Our prompt generation networks generate content-adaptive tokens according to the input image, an ROI mask, and a rate parameter. The separation of the ROI mask and the rate parameter allows an intuitive way to achieve variable-rate and ROI coding simultaneously. Extensive experiments validate the effectiveness of our proposed method and confirm its superiority over the other competing methods.Comment: Accepted to IEEE ICIP 202

Device Integrity of Drug-eluting Depot Stent for Smart Drug Delivery

Atherosclerosis, or hardening of the arteries, is a condition in which plaque, made of cholesterol, fatty substances, cellular waste products, calcium, and fibrin, builds up inside the arteries. A metallic stent is a small mesh tube that is used to treat these narrowed arteries such as coronary artery diseases. The drug-eluting stent has a metallic stent platform coated with drug-polymer mix and has been shown to be superior to its metallic stent counterpart in reducing restenosis. In the past few years, a novel variation of the drug-eluting stent with micro-sized drug reservoirs (depot stent) has been introduced to the market. It allows smart programmable drug delivery with spatial/temporal control and has potential advantages over conventional stents. The drug-polymer mix compound can be altered from one reservoir to the next, allowing a highly-controlled release of different medications. For example, this depot stent concept can be applied in the renal indication for potential treatment of both renal artery stenosis (upstream) and its associated kidney diseases (downstream) simultaneously. However, the creation of such drug reservoirs on the stent struts inevitably compromises its mechanical integrity. In this study, the effects of these drug reservoirs on stent key clinical attributes were systematically investigated. We developed finite element models to predict the mechanical integrity of a balloon-expandable stent at various stages of its function life such as manufacturing and acute deployment, as well as the stent radial strength and chronic fatigue life. Simulation results show that (1) creating drug reservoirs on a stent strut could impact the stent fatigue resistance to certain degrees; (2) drug reservoirs on the high stress concentration regions led to much greater loss in all key clinical attributes than reservoirs on other locations; (3) reservoir shape change resulted in little differences in all key clinical attributes; and (4) for the same drug loading capacity, larger and fewer reservoirs yielded higher fatigue safety factor. These results can help future stent designers to achieve the optimal balance of stent mechanical integrity and smart drug delivery, thereby opening up a wide variety of new opportunities for disease treatments. We also proposed an optimized depot stent with tripled drug capacity and acceptable marginal trade-off in key clinical attributes when compared to the current drug-eluting stents. This depot stent prototype was manufactured for the demonstration of our design concept

TransTIC: Transferring Transformer-based Image Compression from Human Visualization to Machine Perception

This work aims for transferring a Transformer-based image compression codec from human vision to machine perception without fine-tuning the codec. We propose a transferable Transformer-based image compression framework, termed TransTIC. Inspired by visual prompt tuning, we propose an instance-specific prompt generator to inject instance-specific prompts to the encoder and task-specific prompts to the decoder. Extensive experiments show that our proposed method is capable of transferring the codec to various machine tasks and outshining the competing methods significantly. To our best knowledge, this work is the first attempt to utilize prompting on the low-level image compression task