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

Space Net Optimization

Most metaheuristic algorithms rely on a few searched solutions to guide later searches during the convergence process for a simple reason: the limited computing resource of a computer makes it impossible to retain all the searched solutions. This also reveals that each search of most metaheuristic algorithms is just like a ballpark guess. To help address this issue, we present a novel metaheuristic algorithm called space net optimization (SNO). It is equipped with a new mechanism called space net; thus, making it possible for a metaheuristic algorithm to use most information provided by all searched solutions to depict the landscape of the solution space. With the space net, a metaheuristic algorithm is kind of like having a ``vision'' on the solution space. Simulation results show that SNO outperforms all the other metaheuristic algorithms compared in this study for a set of well-known single objective bound constrained problems in most cases.Comment: 12 pages, 6 figure

San-Huang-Xie-Xin-Tang Protects against Activated Microglia- and 6-OHDA-Induced Toxicity in Neuronal SH-SY5Y Cells

San-Huang-Xie-Xin-Tang (SHXT), composed of Coptidis rhizoma, Scutellariae radix and Rhei rhizoma, is a traditional Chinese herbal medicine used to treat gastritis, gastric bleeding and peptic ulcers. This study investigated the neuroprotective effects of SHXT on microglia-mediated neurotoxicity using co-cultured lipopolysaccharide (LPS)-activated microglia-like BV-2 cells with neuroblastoma SH-SY5Y cells. Effects of SHXT on 6-hydroxydopamine (6-OHDA)-induced neurotoxicity were also examined in SH-SY5Y cells. Results indicated SHXT inhibited LPS-induced inflammation of BV-2 cells by downregulation of iNOS, NO, COX-2, PGE2, gp91phox, iROS, TNF-α, IL-1β, inhibition of IκBα degradation and upregulation of HO-1. In addition, SHXT increased cell viability and down regulated nNOS, COX-2 and gp91phox of SH-SY5Y cells co-cultured with LPS activated BV-2 cells. SHXT treatment increased cell viability and mitochondria membrane potential (MMP), decreased expression of nNOS, COX-2, gp91phox and iROS, and inhibited IκBα degradation in 6-OHDA-treated SH-SY5Y cells. SHXT also attenuated LPS activated BV-2 cells- and 6-OHDA-induced cell death in differentiated SH-SY5Y cells with db-cAMP. Furthermore, SHXT-inhibited nuclear translocation of p65 subunit of NF-κB in LPS treated BV-2 cells and 6-OHDA treated SH-SY5Y cells. In conclusion, SHXT showed protection from activated microglia- and 6-OHDA-induced neurotoxicity by attenuating inflammation and oxidative stress

An Analysis of ROI of Taiwan’s Stock Market: A Case Study in Light of the Chinese Tradition of Store in Winter

There is a Chinese saying that goes “plough in spring, hoe in summer, harvest in autumn, and store in winter”, which reflects the traditional farming practice of Taiwanese in response to the change of seasons and the ancient annual work-rest pattern of Chinese farmers. This lifestyle of Chinese, however, might be different from that of foreigners. In light of this, this study is carried out based on “Are there any regular variations in the Taiwan stock market: a case study of Taiwan stock exchange capitalization weighted stock Index (TAIEX) ”, a study by Yang and Yang (2015), in order to determine whether this Chinese idea has rendered Taiwan’s stock market any underlying characteristic which is different from other countries’ stock markets in terms of investment activities. The results do reveal a regular variation pattern of Taiwan’s stock market. In the study, the seasonal change of traditional Chinese farming work-rest schedule is investigated in conjunction with the seasonal variation of Taiwan’s stock market. The results reveal that the ROI of Taiwan’s stock market tends to be most significant in winter, i.e. there is a Winter Effect. The study also tries to determine whether this effect fits the January Effect in foreign countries. The results suggest the existence of a December Effect in ROI of Taiwan’s stock market

Shape-controlled single-crystal growth of InP at low temperatures down to 220 °C.

III-V compound semiconductors are widely used for electronic and optoelectronic applications. However, interfacing III-Vs with other materials has been fundamentally limited by the high growth temperatures and lattice-match requirements of traditional deposition processes. Recently, we developed the templated liquid-phase (TLP) crystal growth method for enabling direct growth of shape-controlled single-crystal III-Vs on amorphous substrates. Although in theory, the lowest temperature for TLP growth is that of the melting point of the group III metal (e.g., 156.6 °C for indium), previous experiments required a minimum growth temperature of 500 °C, thus being incompatible with many application-specific substrates. Here, we demonstrate low-temperature TLP (LT-TLP) growth of single-crystalline InP patterns at substrate temperatures down to 220 °C by first activating the precursor, thus enabling the direct growth of InP even on low thermal budget substrates such as plastics and indium-tin-oxide (ITO)-coated glass. Importantly, the material exhibits high electron mobilities and good optoelectronic properties as demonstrated by the fabrication of high-performance transistors and light-emitting devices. Furthermore, this work may enable integration of III-Vs with silicon complementary metal-oxide-semiconductor (CMOS) processing for monolithic 3D integrated circuits and/or back-end electronics

AVATAR: Robust Voice Search Engine Leveraging Autoregressive Document Retrieval and Contrastive Learning

Voice, as input, has progressively become popular on mobiles and seems to transcend almost entirely text input. Through voice, the voice search (VS) system can provide a more natural way to meet user's information needs. However, errors from the automatic speech recognition (ASR) system can be catastrophic to the VS system. Building on the recent advanced lightweight autoregressive retrieval model, which has the potential to be deployed on mobiles, leading to a more secure and personal VS assistant. This paper presents a novel study of VS leveraging autoregressive retrieval and tackles the crucial problems facing VS, viz. the performance drop caused by ASR noise, via data augmentations and contrastive learning, showing how explicit and implicit modeling the noise patterns can alleviate the problems. A series of experiments conducted on the Open-Domain Question Answering (ODSQA) confirm our approach's effectiveness and robustness in relation to some strong baseline systems

KINEMATICS ANALYSIS OF THE UPPER EXTREMITY DURING THE TWOHANDED BACKHAND DRIVE VOLLEY FOR FEMALE TENNIS PLAYERS

The purpose of this study was to discuss the motion characteristics of the arms in the two-handed backhand drive volley. Five elite female tennis players participated in this study, their two-handed backhand drive volley strokes were analysed, and all participants are right handed. Motion Analysis System with 10 Eagle Digital inferred high speed cameras at 200Hz were used for this study. The results show a similar elbow and wrist speed strategy in x-axis between two-handed ground stroke and drive volley, our study also found that the rear arm dominates the stroke and mainly provide the topspin that is required for the skill of the drive volley. In order to create better stroke efficiency, the right elbow reached peak velocity first, followed by the right wrist before racket impact with the ball

dbPTM: an information repository of protein post-translational modification

dbPTM is a database that compiles information on protein post-translational modifications (PTMs), such as the catalytic sites, solvent accessibility of amino acid residues, protein secondary and tertiary structures, protein domains and protein variations. The database includes all of the experimentally validated PTM sites from Swiss-Prot, PhosphoELM and O-GLYCBASE. Only a small fraction of Swiss-Prot proteins are annotated with experimentally verified PTM. Although the Swiss-Prot provides rich information about the PTM, other structural properties and functional information of proteins are also essential for elucidating protein mechanisms. The dbPTM systematically identifies three major types of protein PTM (phosphorylation, glycosylation and sulfation) sites against Swiss-Prot proteins by refining our previously developed prediction tool, KinasePhos (). Solvent accessibility and secondary structure of residues are also computationally predicted and are mapped to the PTM sites. The resource is now freely available at