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

Revisiting the problem of audio-based hit song prediction using convolutional neural networks

Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by exter- nal factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own. Motivated by the recent success of deep learning techniques, we attempt to ex- tend previous work on hit song prediction by jointly learning the audio features and prediction models using deep learning. Specifically, we experiment with a convolutional neural net- work model that takes the primitive mel-spectrogram as the input for feature learning, a more advanced JYnet model that uses an external song dataset for supervised pre-training and auto-tagging, and the combination of these two models. We also consider the inception model to characterize audio infor- mation in different scales. Our experiments suggest that deep structures are indeed more accurate than shallow structures in predicting the popularity of either Chinese or Western Pop songs in Taiwan. We also use the tags predicted by JYnet to gain insights into the result of different models.Comment: To appear in the proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP

Doping and temperature dependence of electron spectrum and quasiparticle dispersion in doped bilayer cuprates

Within the t-t'-J model, the electron spectrum and quasiparticle dispersion in doped bilayer cuprates in the normal state are discussed by considering the bilayer interaction. It is shown that the bilayer interaction splits the electron spectrum of doped bilayer cuprates into the bonding and antibonding components around the $(\pi,0)$ point. The differentiation between the bonding and antibonding components is essential, which leads to two main flat bands around the $(\pi,0)$ point below the Fermi energy. In analogy to the doped single layer cuprates, the lowest energy states in doped bilayer cuprates are located at the $(\pi/2,\pi/2)$ point. Our results also show that the striking behavior of the electronic structure in doped bilayer cuprates is intriguingly related to the bilayer interaction together with strong coupling between the electron quasiparticles and collective magnetic excitations.Comment: 9 pages, 4 figures, updated references, added figures and discussions, accepted for publication in Phys. Rev.

The efficacy of stereotactic body radiation therapy on huge hepatocellular carcinoma unsuitable for other local modalities

BACKGROUND AND AIM: To evaluate the safety and efficacy of Cyberknife stereotactic body radiation therapy (SBRT) and its effect on survival in patients with unresectable huge hepatocellular carcinoma (HCC) unsuitable of other standard treatment option. METHODS: Between 2009 and 2011, 22 patients with unresectable huge HCC (≧10 cm) were treated with SBRT. dose ranged from 26 Gy to 40 Gy in five fractions. Overall survival (OS) and disease-progression free survival (DPFS) were determined by Kaplan-Meier analysis. Tumor response and toxicities were also assessed. RESULTS: After a median follow-up of 11.5 month (range 2–46 months). The objective response rate was achieved in 86.3% (complete response (CR): 22.7% and partial response (PR): 63.6%). The 1-yr. local control rate was 55.56%. The 1-year OS was 50% and median survival was 11 months (range 2–46 months). In univariate analysis, Child-Pugh stage (p = 0.0056) and SBRT dose (p = 0.0017) were significant factors for survival. However, in multivariate analysis, SBRT dose (p = 0.0072) was the most significant factor, while Child-Pugh stage of borderline significance. (p = 0.0514). Acute toxicities were mild and well tolerated. CONCLUSION: This study showed that SBRT can be delivered safely to huge HCC and achieved a substantial tumor regression and survival. The results suggest this technique should be considered a salvage treatment. However, local and regional recurrence remain the major cause of failure. Further studies of combination of SBRT and other treatment modalities may be reasonable

Mechanisms of Compartmentalized Expression of Mrg Class G-Protein-Coupled Sensory Receptors

Mrg class G-protein-coupled receptors (GPCRs) are expressed exclusively in sensory neurons in the trigeminal and dorsal root ganglia. Pharmacological activation of Mrg proteins is capable of modulating sensory neuron activities and elicits nociceptive effects. In this study, we illustrate a control mechanism that allows the Runx1 runt domain transcription factor to generate compartmentalized expression of these sensory GPCRs. Expression of MrgA, MrgB, and MrgC subclasses is confined to an “A/B/C” neuronal compartment that expresses Runx1 transiently (or does not express Runx1), whereas MrgD expression is restricted to a “D” compartment with persistent Runx1 expression. Runx1 is initially required for the expression of all Mrg genes. However, during late development Runx1 becomes a repressor for MrgA/B/C genes. As a result, MrgA/B/C expression persists only in the Runx1^− “A/B/C” compartment. In Δ446 mice, in which Runx1 lacks the C-terminal repression domain, expression of MrgA/B/C genes is dramatically expanded into the Runx1^+ “D” compartment. MrgD expression, however, is resistant to Runx1-mediated repression in the “D” compartment. Therefore, the creation of Runx1+ and Runx1^− compartments, in conjunction with different responses of Mrg genes to Runx1-mediated repression, results in the compartmentalized expression of MrgA/B/C versus MrgD genes. Within the MrgA/B/C compartment, MrgB4-expressing neurons innervate exclusively the hairy skin. Here we found that Smad4, a downstream component of bone morphological protein-mediated signaling, is required selectively for the expression of MrgB4. Our study suggests a new line of evidence that specification of sensory subtypes is established progressively during perinatal and postnatal development

Incidence of type 2 diabetes mellitus in men receiving steroid 5α-reductase inhibitors:population based cohort study

OBJECTIVE: To investigate the incidence of new onset type 2 diabetes mellitus in men receiving steroid 5α-reductase inhibitors (dutasteride or finasteride) for long term treatment of benign prostatic hyperplasia. DESIGN: Population based cohort study. SETTING: UK Clinical Practice Research Datalink (CPRD; 2003-14) and Taiwanese National Health Insurance Research Database (NHIRD; 2002-12). PARTICIPANTS: Men in the CPRD who received dutasteride (n=8231), finasteride (n=30 774), or tamsulosin (n=16 270) were evaluated. Propensity score matching (2:1; dutasteride to finasteride or tamsulosin) produced cohorts of 2090, 3445, and 4018, respectively. In the NHIRD, initial numbers were 1251 (dutasteride), 4194 (finasteride), and 86 263 (tamsulosin), reducing to 1251, 2445, and 2502, respectively, after propensity score matching. MAIN OUTCOMES MEASURE: Incident type 2 diabetes using a Cox proportional hazard model. RESULTS: In the CPRD, 2081 new onset type 2 diabetes events (368 dutasteride, 1207 finasteride, and 506 tamsulosin) were recorded during a mean follow-up time of 5.2 years (SD 3.1 years). The event rate per 10 000 person years was 76.2 (95% confidence interval 68.4 to 84.0) for dutasteride, 76.6 (72.3 to 80.9) for finasteride, and 60.3 (55.1 to 65.5) for tamsulosin. There was a modest increased risk of type 2 diabetes for dutasteride (adjusted hazard ratio 1.32, 95% confidence interval 1.08 to 1.61) and finasteride (1.26, 1.10 to 1.45) compared with tamsulosin. Results for the NHIRD were consistent with the findings for the CPRD (adjusted hazard ratio 1.34, 95% confidence interval 1.17 to 1.54 for dutasteride, and 1.49, 1.38 to 1.61 for finasteride compared with tamsulosin). Propensity score matched analyses showed similar results. CONCLUSIONS: The risk of developing new onset type 2 diabetes appears to be higher in men with benign prostatic hyperplasia exposed to 5α-reductase inhibitors than in men receiving tamsulosin, but did not differ between men receiving dutasteride and those receiving finasteride. Additional monitoring might be required for men starting these drugs, particularly in those with other risk factors for type 2 diabetes