AccoMontage-3: Full-Band Accompaniment Arrangement via Sequential Style Transfer and Multi-Track Function Prior

We propose AccoMontage-3, a symbolic music automation system capable of generating multi-track, full-band accompaniment based on the input of a lead melody with chords (i.e., a lead sheet). The system contains three modular components, each modelling a vital aspect of full-band composition. The first component is a piano arranger that generates piano accompaniment for the lead sheet by transferring texture styles to the chords using latent chord-texture disentanglement and heuristic retrieval of texture donors. The second component orchestrates the piano accompaniment score into full-band arrangement according to the orchestration style encoded by individual track functions. The third component, which connects the previous two, is a prior model characterizing the global structure of orchestration style over the whole piece of music. From end to end, the system learns to generate full-band accompaniment in a self-supervised fashion, applying style transfer at two levels of polyphonic composition: texture and orchestration. Experiments show that our system outperforms the baselines significantly, and the modular design offers effective controls in a musically meaningful way

Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls

We propose Polyffusion, a diffusion model that generates polyphonic music scores by regarding music as image-like piano roll representations. The model is capable of controllable music generation with two paradigms: internal control and external control. Internal control refers to the process in which users pre-define a part of the music and then let the model infill the rest, similar to the task of masked music generation (or music inpainting). External control conditions the model with external yet related information, such as chord, texture, or other features, via the cross-attention mechanism. We show that by using internal and external controls, Polyffusion unifies a wide range of music creation tasks, including melody generation given accompaniment, accompaniment generation given melody, arbitrary music segment inpainting, and music arrangement given chords or textures. Experimental results show that our model significantly outperforms existing Transformer and sampling-based baselines, and using pre-trained disentangled representations as external conditions yields more effective controls.Comment: In Proceedings of the 24th Conference of the International Society for Music Information Retrieval (ISMIR 2023), Milan, Ital

Electrospun Nanofibers for Neural Tissue Engineering

Biodegradable nanofibers produced by electrospinning represent a new class of promising scaffolds to support nerve regeneration. We begin with a brief discussion on electrospinning of nanofibers and methods for controlling the structure, porosity, and alignment of the electrospun nanofibers. The methods include control of the nanoscale morphology and microscale alignment for the nanofibers, as well as the fabrication of macroscale, three-dimensional tubular structures. We then highlight recent studies that utilize electrospun nanofibers to manipulate biological processes relevant to nervous tissue regeneration, including stem cell differentiation, guidance of neurite extension, and peripheral nerve injury treatments. The main objective of this feature article is to provide valuable insights into methods for investigating the mechanisms of neurite growth on novel nanofibrous scaffolds and optimization of the nanofiber scaffolds and conduits for repairing peripheral nerve injuries

Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation

Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion. However, the uncertainty quantification of the restored HSI has not been addressed to date. Accurate uncertainty quantification of the denoised HSI facilitates to applications such as multi-source or multi-scale data fusion, data assimilation, and product uncertainty quantification, since these applications require an accurate approach to describe the statistical distributions of the input data. Therefore, we propose a prior-free closed-form element-wise uncertainty quantification method for LRMA-based HSI restoration. Our closed-form algorithm overcomes the difficulty of the HSI patch mixing problem caused by the sliding-window strategy used in the conventional LRMA process. The proposed approach only requires the uncertainty of the observed HSI and provides the uncertainty result relatively rapidly and with similar computational complexity as the LRMA technique. We conduct extensive experiments to validate the estimation accuracy of the proposed closed-form uncertainty approach. The method is robust to at least 10% random impulse noise at the cost of 10-20% of additional processing time compared to the LRMA. The experiments indicate that the proposed closed-form uncertainty quantification method is more applicable to real-world applications than the baseline Monte Carlo test, which is computationally expensive. The code is available in the attachment and will be released after the acceptance of this paper.Comment: Accepted for publication by IEEE Transactions on Geoscience and Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing (TGRS

Nanofiber Scaffolds with Gradations in Mineral Content for Mimicking the Tendon-to-Bone Insertion Site

We have demonstrated a simple and versatile method for generating a continuously graded, bonelike calcium phosphate coating on a nonwoven mat of electrospun nanofibers. A linear gradient in calcium phosphate content could be achieved across the surface of the nanofiber mat. The gradient had functional consequences with regard to stiffness and biological activity. Specifically, the gradient in mineral content resulted in a gradient in the stiffness of the scaffold and further influenced the activity of mouse preosteoblast MC3T3 cells. This new class of nanofiberbased scaffolds can potentially be employed for repairing the tendon-to-bone insertion site via a tissue engineering approach

Neurite Outgrowth on Nanofiber Scaffolds with Different Orders, Structures, and Surface Properties

Electrospun nanofibers can be readily assembled into various types of scaffolds for applications in neural tissue engineering. The objective of this study is to examine and understand the unique patterns of neurite outgrowth from primary dorsal root ganglia (DRG) cultured on scaffolds of electrospun nanofibers having different orders, structures, and surface properties. We found that the neurites extended radially outward from the DRG main body without specific directionality when cultured on a nonwoven mat of randomly oriented nanofibers. In contrast, the neurites preferentially extended along the long axis of fiber when cultured on a parallel array of aligned nanofibers. When seeded at the border between regions of aligned and random nanofibers, the same DRG simultaneously expressed aligned and random neurite fields in response to the underlying nanofibers. When cultured on a double-layered scaffold where the nanofibers in each layer were aligned along a different direction, the neurites were found to be dependent on the fiber density in both layers. This biaxial pattern clearly demonstrates that neurite outgrowth can be influenced by nanofibers in different layers of a scaffold, rather than the topmost layer only. Taken together, these results will provide valuable information pertaining to the design of nanofiber scaffolds for neuroregenerative applications, as well as the effects of topology on neurite outgrowth, growth cone guidance, and axonal regeneration

Radially Aligned, Electrospun Nanofibers as Dural Substitutes for Wound Closure and Tissue Regeneration Applications

This paper reports the fabrication of scaffolds consisting of radially aligned poly(ε-caprolactone) nanofibers by utilizing a collector composed of a central point electrode and a peripheral ring electrode. This novel class of scaffolds was able to present nanoscale topographic cues to cultured cells, directing and enhancing their migration from the periphery to the center. We also established that such scaffolds could induce faster cellular migration and population than nonwoven mats consisting of random nanofibers. Dural fibroblast cells cultured on these two types of scaffolds were found to express type I collagen, the main extracellular matrix component in dural mater. The type I collagen exhibited a high degree of organization on the scaffolds of radially aligned fibers and a haphazard distribution on the scaffolds of random fibers. Taken together, the scaffolds based on radially aligned, electrospun nanofibers show great potential as artificial dural substitutes and may be particularly useful as biomedical patches or grafts to induce wound closure and/or tissue regeneration