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

Study of singly heavy baryon lifetimes

We study the inclusive decay widths of singly heavy baryons with the improved bag model in which the unwanted center-of-mass motion is removed. Additional insight is gained by comparing the charmed and bottom baryons. We discuss the running of the baryon matrix elements and compare the results with the non-relativistic quark model (NRQM). While the calculated two-quark operator elements are compatible with the literature, those of the four-quark ones deviate largely. In particular, the heavy quark limit holds reasonably well in the bag model for four-quark operator matrix elements but is badly broken in the NRQM. We predict $1-\tau(\Omega_b)/ \tau(\Lambda_b^0) = (8.34\pm2.22)\%$ in accordance with the current experimental value of $(11.5^{+12.2}_{-11.6})\%$ and compatible with $(13.2\pm 4.7)\%$ obtained in the NRQM. We find an excellent agreement between theory and experiment for the lifetimes of bottom baryons. We confirm that $\Omega_c^0$ could live longer than $\Lambda_c^+$ after the dimension-7 four-quark operators are taken into account. We recommend to measure some semileptonic inclusive branching fractions in the forthcoming experiments to discern different approaches. For example, we obtain ${\cal BF} (\Xi_c^+ \to X e^+ \nu_e) = (8.57\pm 0.49)\%$ and ${\cal BF} (\Omega_c^0 \to X e^+ \nu_e) = (1.88\pm 1.69)\%$ in sharp contrast to ${\cal BF} (\Xi_c^+ \to X e^+ \nu_e) = (12.74^{+2.54}_{-2.45})\%$ and ${\cal BF} (\Omega_c^0 \to X e^+ \nu_e) = (7.59^{+2.49}_{-2.24})\%$ found in the NRQM.Comment: Accepted by JHEP, 39 pages, 4 figure

CCATMos: Convolutional Context-aware Transformer Network for Non-intrusive Speech Quality Assessment

Speech quality assessment has been a critical component in many voice communication related applications such as telephony and online conferencing. Traditional intrusive speech quality assessment requires the clean reference of the degraded utterance to provide an accurate quality measurement. This requirement limits the usability of these methods in real-world scenarios. On the other hand, non-intrusive subjective measurement is the ``golden standard" in evaluating speech quality as human listeners can intrinsically evaluate the quality of any degraded speech with ease. In this paper, we propose a novel end-to-end model structure called Convolutional Context-Aware Transformer (CCAT) network to predict the mean opinion score (MOS) of human raters. We evaluate our model on three MOS-annotated datasets spanning multiple languages and distortion types and submit our results to the ConferencingSpeech 2022 Challenge. Our experiments show that CCAT provides promising MOS predictions compared to current state-of-art non-intrusive speech assessment models with average Pearson correlation coefficient (PCC) increasing from 0.530 to 0.697 and average RMSE decreasing from 0.768 to 0.570 compared to the baseline model on the challenge evaluation test set

Tumour burden score for hepatocellular carcinoma: Is it an authentic prognostic marker?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163375/2/bjs11927.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163375/1/bjs11927_am.pd

Characterization of two Runx1-dependent nociceptor differentiation programs necessary for inflammatory versus neuropathic pain

BACKGROUND: The cellular and molecular programs that control specific types of pain are poorly understood. We reported previously that the runt domain transcription factor Runx1 is initially expressed in most nociceptors and controls sensory neuron phenotypes necessary for inflammatory and neuropathic pain. RESULTS: Here we show that expression of Runx1-dependent ion channels and receptors is distributed into two nociceptor populations that are distinguished by persistent or transient Runx1 expression. Conditional mutation of Runx1 at perinatal stages leads to preferential impairment of Runx1-persistent nociceptors and a selective defect in inflammatory pain. Conversely, constitutive Runx1 expression in Runx1-transient nociceptors leads to an impairment of Runx1-transient nociceptors and a selective deficit in neuropathic pain. Notably, the subdivision of Runx1-persistent and Runx1-transient nociceptors does not follow the classical nociceptor subdivision into IB4+ nonpeptidergic and IB4- peptidergic populations. CONCLUSION: Altogether, we have uncovered two distinct Runx1-dependent nociceptor differentiation programs that are permissive for inflammatory versus neuropathic pain. These studies lend support to a transcription factor-based distinction of neuronal classes necessary for inflammatory versus neuropathic pain

A New Business Model of Electronic Commerce with Innovative Strategies

There are a lot of problems that make the business of electronic stores very difficult, especially for those firms that lack the required expertise and resources for running an electronic business. This study proposes a new business model of electronic commerce (EC), which aims to tackle those problems and help enterprises run electronic stores well. This model applies the franchise system of chain store, a very successful modern business model, to the management of electronic stores to take advantage of the chain’s competitive power by integrating individual affiliate sites as a whole. There are eight components in the model. Implementation strategies of the model, which are quite different from those generic strategies commonly used in implementing business models, are also proposed. The feasibility of the model and its implementation strategies were validated using the Nominal Group Technique (NGT), the case study, and the questionnaire survey approaches. Finally, practical implications for applying the model are discussed, and directions for further study are also suggested

TCN AA: A Wi Fi based Temporal Convolution Network for Human to Human Interaction Recognition with Augmentation and Attention

The utilization of Wi-Fi-based human activity recognition (HAR) has gained considerable interest in recent times, primarily owing to its applications in various domains such as healthcare for monitoring breath and heart rate, security, elderly care, and others. These Wi-Fi-based methods exhibit several advantages over conventional state-of-the-art techniques that rely on cameras and sensors, including lower costs and ease of deployment. However, a significant challenge associated with Wi-Fi-based HAR is the significant decline in performance when the scene or subject changes. To mitigate this issue, it is imperative to train the model using an extensive dataset. In recent studies, the utilization of CNN-based models or sequence-to-sequence models such as LSTM, GRU, or Transformer has become prevalent. While sequence-to-sequence models can be more precise, they are also more computationally intensive and require a larger amount of training data. To tackle these limitations, we propose a novel approach that leverages a temporal convolution network with augmentations and attention, referred to as TCN-AA. Our proposed method is computationally efficient and exhibits improved accuracy even when the data size is increased threefold through our augmentation techniques. Our experiments on a publicly available dataset indicate that our approach outperforms existing state-of-the-art methods, with a final accuracy of 99.42%.Comment: Published to IEEE Internet of things Journal but haven't been accepted yet (under review

Identification of overexpressed cytokines as serum biomarkers of hepatitis C virus-induced liver fibrosis using bead-based flexible multiple analyte profiling

Hepatic inflammation is the stimulator to activate hepatic stellate cells (HSCs) and triggers fibrogenesis. Cytokines are produced during liver inflammation and maybe considered as liver fibrosis biomarker. The aim of this study was to investigate whether cytokines can be used as reliable biomarkers of liver fibrosis using flexible multi-analyte profiling (xMAP). A total of 61 chronic hepatitis C patients with different severity of liver fibrosis were enrolled. Liver biopsy was used as standard to assess the severity of fibrosis according to METAVIR classification. Afterward, 15 samples from healthy controls were analyzed and totally 50 cytokines were screened using flexible multi-analyte profiling to discover differential biomarkers. Finally, levels of protein expressions of individual stages of liver fibrosis were measured. In histological examination, the necroinflammatory score (histology activity index, HAI) was increased from F1 to F4 stage in hepatitis C virus (HCV) infected patients, indicating that inflammation was accompanied with the progression of liver fibrosis. Using flexible multi-analyte profiling, four serum cytokines, including IFN-α2 (p=0.023), GRO-α (p=0.013), SCF (p=0.047) and SDF-1α p=0.024), were identified under antibody specific recognition and elevated with HAI score. This study reveals the relationship between cytokines and liver fibrosis, and demonstrated that IFN-α2, GRO-α, SCF and SDF-1 α may be used as biomarkers to predict liver fibrosis. The overexpressed cytokines may play a role in the progression of liver fibrosis and deserves further investigation.Keywords: Cytokine, flexible multi-analyte profiling, hepatitis C virus, liver fibrosisAfrican Journal of Biotechnology Vol. 11(29), pp. 7535-7541, 29 April, 201