Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data

It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not difficult to collect a large set of audio data for each user, it is difficult to transcribe it. However, it is now possible to automatically discover acoustic tokens from unlabeled personal data in an unsupervised way. We therefore propose a multi-task deep learning framework called a phoneme-token deep neural network (PTDNN), jointly trained from unsupervised acoustic tokens discovered from unlabeled data and very limited transcribed data for personalized acoustic modeling. We term this scenario "weakly supervised". The underlying intuition is that the high degree of similarity between the HMM states of acoustic token models and phoneme models may help them learn from each other in this multi-task learning framework. Initial experiments performed over a personalized audio data set recorded from Facebook posts demonstrated that very good improvements can be achieved in both frame accuracy and word accuracy over popularly-considered baselines such as fDLR, speaker code and lightly supervised adaptation. This approach complements existing speaker adaptation approaches and can be used jointly with such techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201

Micro- and Nano-Structuring of Materials via Ultrashort Pulsed Laser Ablation

Laser material processing has been demonstrated as an effective means for machining almost every solid material. The quality of laser machining depends on the processing parameters that dictate material ablation mechanisms. The understanding of the complex physics associated with ultrashort pulsed laser (USPL) material interaction and ablation has advanced significantly owing to a great many theoretical and experimental studies in the past 20 years. To date, USPLs have been considered as a novel tool for micro- and nano-machining of bulk or thin film materials and for internal modification of transparent materials via multi-photon absorption in a tiny focal volume. Moreover, USPL material processing is now gaining interest in other applications, such as in sensors, electronics and medical device industries

Formal Record of Mecopus hopei Rosenschöld, 1838 (Curculionidae: Conoderinae) in Taiwan

Abstract: Mecopus hopei Rosenschöld, 1838 is a common species in Taiwan. However, it has been frequently misspelled in Taiwanese records, leading to its omission from regional catalogs. In this study, we formally record M. hopei in Taiwan and provide dorsal habitus photos and barcode sequences to improve species identification

Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning

Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions. The primary difficulty in designing effective learning models to solve such problems lies in how to bridge the gap between the source and target distributions. In this paper, we provide comprehensive analysis of feature learning algorithms used in conjunction with linear classifiers for domain adaptation. Our analysis shows that in order to achieve good adaptation performance, the second moments of the source domain distribution and target domain distribution should be similar. Based on our new analysis, a novel extremely easy feature learning algorithm for domain adaptation is proposed. Furthermore, our algorithm is extended by leveraging multiple layers, leading to a deep linear model. We evaluate the effectiveness of the proposed algorithms in terms of domain adaptation tasks on the Amazon review dataset and the spam dataset from the ECML/PKDD 2006 discovery challenge.Comment: ijca

Is Contract Farming More Profitable and Efficient Than Non-Contract Farming-A Survey Study of Rice Farms In Taiwan

Trade liberalization and globalization has modernized the food retail sector in Taiwan, affecting consumers, producers and trade patterns. These changes have placed significant pressures on farmers and processors including more stringent quality control and product varieties. The government has launched a rice production-marketing contract program in 2005 to assist rice farmers and the agro-business sector to work together as partners. The minimum scale for each contract is 50 hectares of adjacent rice paddies with 50 participants including rice farmers, seedling providers, millers and marketing agents. In order to evaluate the outcome of this program, a survey is conducted in the summer of 2005 after the first (spring) crop is harvested. Information of price and value of output and major variable and fixed inputs are collected along with characteristics of the farmers and farms. The survey results show that the average revenue of a contract farm is about 11 percent higher than an average non-contract farm. The per hectare cost of production in a contract farm is about 13 percent lower and as a result the average profit margin under contract is more than 50 percent above those without contract. A swtiching regression profit frontier model is adopted to further investigate their efficiency performance. The result indicates that an average contract farms is 20 percent more efficient than an average non-contract farm in a comparable operating environment. The result also suggests that although contract farming has potential to improve the profit of smallholders, it is not a sufficient condition for such improvement.Land Economics/Use,

The Association between Virus Prevalence and Intercolonial Aggression Levels in the Yellow Crazy Ant, Anoplolepis Gracilipes (Jerdon).

The recent discovery of multiple viruses in ants, along with the widespread infection of their hosts across geographic ranges, provides an excellent opportunity to test whether viral prevalence in the field is associated with the complexity of social interactions in the ant population. In this study, we examined whether the association exists between the field prevalence of a virus and the intercolonial aggression of its ant host, using the yellow crazy ant (Anoplolepis gracilipes) and its natural viral pathogen (TR44839 virus) as a model system. We delimitated the colony boundary and composition of A. gracilipes in a total of 12 study sites in Japan (Okinawa), Taiwan, and Malaysia (Penang), through intercolonial aggression assay. The spatial distribution and prevalence level of the virus was then mapped for each site. The virus occurred at a high prevalence in the surveyed colonies of Okinawa and Taiwan (100% infection rate across all sites), whereas virus prevalence was variable (30%-100%) or none (0%) at the sites in Penang. Coincidentally, colonies in Okinawa and Taiwan displayed a weak intercolonial boundary, as aggression between colonies is generally low or moderate. Contrastingly, sites in Penang were found to harbor a high proportion of mutually aggressive colonies, a pattern potentially indicative of complex colony composition. Our statistical analyses further confirmed the observed correlation, implying that intercolonial interactions likely contribute as one of the effective facilitators of/barriers to virus prevalence in the field population of this ant species

The Effects Between Numerical Tabulations And Graphs Of Financial Information On The Judgment Of Investors

Due to developments in information markets and advancements in information technology, and with the rapidity of information flow on the Internet, it is vital to increase the level of information transparency. Disclosure methods of financial information have presently become an important topic of discussion. By using numerical tables, non-distorted graphs or distorted graphs of financial information, this research discusses whether financial information display types indeed influence investors’ judgments and decisions. We investigate and analyze the use of graph disclosure in Taiwan and use experiment design methods to test the effect of investors’ judgment by comparing different display types of financial information. Our results find graphs are used to display comparative than numerical financial information, showing how this can influence investors’ awareness and judgments and use of graphs can be used to manipulate impressions (impression management)

Reconstruction of human protein interolog network using evolutionary conserved network

<p>Abstract</p> <p>Background</p> <p>The recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. Specifically, the availability of information about experimental protein-protein interactions and other protein features on the Internet enables human protein-protein interactions to be computationally predicted from co-evolution events (interolog). This study also considers other protein interaction features, including sub-cellular localization, tissue-specificity, the cell-cycle stage and domain-domain combination. Computational methods need to be developed to integrate these heterogeneous biological data to facilitate the maximum accuracy of the human protein interaction prediction.</p> <p>Results</p> <p>This study proposes a relative conservation score by finding maximal quasi-cliques in protein interaction networks, and considering other interaction features to formulate a scoring method. The scoring method can be adopted to discover which protein pairs are the most likely to interact among multiple protein pairs. The predicted human protein-protein interactions associated with confidence scores are derived from six eukaryotic organisms – rat, mouse, fly, worm, thale cress and baker's yeast.</p> <p>Conclusion</p> <p>Evaluation results of the proposed method using functional keyword and Gene Ontology (GO) annotations indicate that some confidence is justified in the accuracy of the predicted interactions. Comparisons among existing methods also reveal that the proposed method predicts human protein-protein interactions more accurately than other interolog-based methods.</p