MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment

Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed of multiple instruments/tracks with their own temporal dynamics, but collectively they unfold over time interdependently. Lastly, musical notes are often grouped into chords, arpeggios or melodies in polyphonic music, and thereby introducing a chronological ordering of notes is not naturally suitable. In this paper, we propose three models for symbolic multi-track music generation under the framework of generative adversarial networks (GANs). The three models, which differ in the underlying assumptions and accordingly the network architectures, are referred to as the jamming model, the composer model and the hybrid model. We trained the proposed models on a dataset of over one hundred thousand bars of rock music and applied them to generate piano-rolls of five tracks: bass, drums, guitar, piano and strings. A few intra-track and inter-track objective metrics are also proposed to evaluate the generative results, in addition to a subjective user study. We show that our models can generate coherent music of four bars right from scratch (i.e. without human inputs). We also extend our models to human-AI cooperative music generation: given a specific track composed by human, we can generate four additional tracks to accompany it. All code, the dataset and the rendered audio samples are available at https://salu133445.github.io/musegan/ .Comment: to appear at AAAI 201

"An Econometric Analysis of SARS and Avian Flu on International Tourist Arrivals to Asia"

This paper compares the impacts of SARS and human deaths arising from Avian Flu on international tourist arrivals to Asia. The effects of SARS and human deaths from Avian Flu will be compared directly according to human deaths. The nature of the short run and long run relationship is examined empirically by estimating a static line fixed effect model and a difference transformation dynamic model, respectively. Empirical results from the static fixed effect and difference transformation dynamic models are consistent, and indicate that both the short run and long run SARS effect have a more significant impact on international tourist arrivals than does Avian Flu. In addition, the effects of deaths arising from both SARS and Avian Flu suggest that SARS is more important to international tourist arrivals than is Avian Flu. Thus, while Avian Flu is here to stay, its effect is currently not as significant as that of SARS.

Automatic Key Posture Selection for Human Behavior Analysis

[[abstract]]A novel human posture analysis framework that can perform automatic key posture selection and template matching for human behavior analysis is proposed. The entropy measurement, which is commonly adopted as an important feature to describe the degree of disorder in thermodynamics, is used as an underlying feature for identifying key postures. First, we use cumulative entropy change as an indicator to select an appropriate set of key postures from a human behavior video sequence and then conduct a cross entropy check to remove redundant key postures. With the key postures detected and stored as human posture templates, the degree of similarity between a query posture and a database template is evaluated using a modified Hausdorff distance measure. The experiment results show that the proposed system is highly efficient and powerful[[fileno]]2030144030013[[department]]電機工程學

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

Genomic sequence of temperate phage Smp131 of Stenotrophomonas maltophilia that has similar prophages in xanthomonads

Stenotrophomonas maltophilia is a ubiquitous Gram-negative bacterium previously named as Xanthomonas maltophilia. This organism is an important nosocomial pathogen associated with infections in immunocompromised patients. Clinical isolates of S. maltophilia are mostly resistant to multiple antibiotics and treatment of its infections is becoming problematic. Several virulent bacteriophages, but not temperate phage, of S. maltophilia have been characterized

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

Literature Review on Hospital Costs for Patients Undergoing Colectomy

Objective: This study aims to identify the range of direct hospital costs associated with a minimally invasive or open colectomy procedure across different countries. Poster presented at 2016 ISPOR conference in Washington DC.https://jdc.jefferson.edu/jcphposters/1009/thumbnail.jp

Literature Review on Hospital Costs for Patients Undergoing Hysterectomy

Objective: This study aims to identify the range of direct hospital costs associated with a minimally invasive or abdonimal hysterectomy procedure across different countries. Poster presented at 2016 ISPOR conference in Washington DC.https://jdc.jefferson.edu/jcphposters/1010/thumbnail.jp

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

七家灣拆壩後之河道演變模式

This study focused on channel responses one and a half years after dam removal in the Chijiawan Creek and proposed a channel evolution model based on analyses of hydrology, morphology, and images. Channel adjustment is highly influenced by the distance between the dam and the headcut erosion. We defined nine and six stages of the channel evolution model for the upstream and downstream reach, respectively, according to the cross sections 48 m upstream and 30 m downstream from the dam. It took a couple of minutes to reach stage B (main channel migration) and one year or so to reach stage E3 (widening and continued incision). As Chijiawan Creek has not reached the quasi-equilibrium state, stage F’, we suggest that the establishment of a long-term channel evolution model is critical for in-situ monitoring.為探究七家灣溪一號壩拆壩後達到準平衡階段之河道演變模式，本研究蒐集水文、地形與影像資料，分析拆壩後一年半之河道演變情形，做為建立長期河道演變模式之基礎。七家灣溪之河道調整程度和距壩遠近與溯源侵蝕有關。本研究根據壩上游48 m 處與下游30 m 處斷面，分別定義上下游九個與六個河道演變階段。在時間尺度上，上游河道進入階段B(主河道調整) 僅需數分鐘、進入階段E3(河道拓寬並持續下切) 需1~2 年、而準平衡階段F’尚未達到，因此以此研究所建立之河道演變模式為基礎，持續監測未來七家灣溪達到準平衡階段之過程有其必要