Supervised and Unsupervised Transfer Learning for Question Answering

Although transfer learning has been shown to be successful for tasks like object and speech recognition, its applicability to question answering (QA) has yet to be well-studied. In this paper, we conduct extensive experiments to investigate the transferability of knowledge learned from a source QA dataset to a target dataset using two QA models. The performance of both models on a TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson et al., 2013) is significantly improved via a simple transfer learning technique from MovieQA (Tapaswi et al., 2016). In particular, one of the models achieves the state-of-the-art on all target datasets; for the TOEFL listening comprehension test, it outperforms the previous best model by 7%. Finally, we show that transfer learning is helpful even in unsupervised scenarios when correct answers for target QA dataset examples are not available.Comment: To appear in NAACL HLT 2018 (long paper

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

Discovering Mixtures of Structural Causal Models from Time Series Data

In fields such as finance, climate science, and neuroscience, inferring causal relationships from time series data poses a formidable challenge. While contemporary techniques can handle nonlinear relationships between variables and flexible noise distributions, they rely on the simplifying assumption that data originates from the same underlying causal model. In this work, we relax this assumption and perform causal discovery from time series data originating from mixtures of different causal models. We infer both the underlying structural causal models and the posterior probability for each sample belonging to a specific mixture component. Our approach employs an end-to-end training process that maximizes an evidence-lower bound for data likelihood. Through extensive experimentation on both synthetic and real-world datasets, we demonstrate that our method surpasses state-of-the-art benchmarks in causal discovery tasks, particularly when the data emanates from diverse underlying causal graphs. Theoretically, we prove the identifiability of such a model under some mild assumptions

Beating standard quantum limit via two-axis magnetic susceptibility measurement

We report a metrology scheme which measures magnetic susceptibility of an atomic spin ensemble along the $x$ and $z$ direction and produces parameter estimation with precision beating the standard quantum limit. The atomic ensemble is initialized via one-axis spin squeezing with optimized squeezing time and parameter $\phi$ to be estimated is assumed as uniformly distributed between 0 and $2\pi$. One estimation of $\phi$ can be produced with every two magnetic susceptibility data measured along the two axis respectively, which has imprecision scaling $(1.43\pm{}0.02)/N^{0.687\pm0.003}$ with respect to the number N of atomic spins. The measurement scheme is easy to implement and thus one step towards practical application of quantum metrology.Comment: 4 pages, 2 figures, comments are most welcom

Incorporating significant amino acid pairs to identify O-linked glycosylation sites on transmembrane proteins and non-transmembrane proteins

<p>Abstract</p> <p>Background</p> <p>While occurring enzymatically in biological systems, O-linked glycosylation affects protein folding, localization and trafficking, protein solubility, antigenicity, biological activity, as well as cell-cell interactions on membrane proteins. Catalytic enzymes involve glycotransferases, sugar-transferring enzymes and glycosidases which trim specific monosaccharides from precursors to form intermediate structures. Due to the difficulty of experimental identification, several works have used computational methods to identify glycosylation sites.</p> <p>Results</p> <p>By investigating glycosylated sites that contain various motifs between Transmembrane (TM) and non-Transmembrane (non-TM) proteins, this work presents a novel method, GlycoRBF, that implements radial basis function (RBF) networks with significant amino acid pairs (SAAPs) for identifying O-linked glycosylated serine and threonine on TM proteins and non-TM proteins. Additionally, a membrane topology is considered for reducing the false positives on glycosylated TM proteins. Based on an evaluation using five-fold cross-validation, the consideration of a membrane topology can reduce 31.4% of the false positives when identifying O-linked glycosylation sites on TM proteins. Via an independent test, GlycoRBF outperforms previous O-linked glycosylation site prediction schemes.</p> <p>Conclusion</p> <p>A case study of Cyclic AMP-dependent transcription factor ATF-6 alpha was presented to demonstrate the effectiveness of GlycoRBF. Web-based GlycoRBF, which can be accessed at <url>http://GlycoRBF.bioinfo.tw</url>, can identify O-linked glycosylated serine and threonine effectively and efficiently. Moreover, the structural topology of Transmembrane (TM) proteins with glycosylation sites is provided to users. The stand-alone version of GlycoRBF is also available for high throughput data analysis.</p

Stability analysis and dynamic equilibrium of a Kuroshio generator system

Global resources for conventional energy are currently being exhausted, and several countries worldwide are attempting to develop renewable energy. Current generator systems are a subject of ocean power research. This paper proposes a novel design of a Kuroshio generator system (KGS) that is suitable for the maritime environment of Taiwan (i.e., an average flow velocity of the Kuroshio Current is 1.45 m/s and the flow can be accelerated on Keelung Sill with a depth of 50-250 m). The KGS combined a reliable cable design and simple anchor system at sea and was not affected by motion changes of rotation axes in yaw and roll by way of an appropriate rudder design. An intuitive simulation method applied using MapleSim software was used to create a rigid KGS model. Different modeling frameworks for varied cable design and joint positions were adjusted to meet system requirements. An intuitive simulation method applied using MapleSim software was used to create a rigid KGS model. Different modeling frameworks for varied cable design and joint positions were adjusted to meet system requirements. The stability analysis was performed to determine dynamic equilibrium and motion behavior of the KGS and the combined cable design. The optimal spring stiffness and damper coefficient of polyester fibers were set as 5×105 N/m and 3×105 N∙s/m in the simulation, respectively. Furthermore, to achieve the torque equilibrium in pitch motion of the KGS, an optimal joint position that was relative to the leading infraedge of the outer duct was set at 2.2 m along the negative surge axis according to their responses in the simulation. Finally, the force and torque generated by the hydrodynamic effect in the KGS and the estimated specifications of a direct-drive permanent magnet generator equipped with an external rotor were imported into the simulation. Consequently, the motion ranges of translation axes in surge and heave were converged within 0.5 m, and the estimated output power in the KGS exceeded 54.8 kW

The Number of Alternative Products and the Information about it on the Online Shop

As the Internet can aggregate and distribute a great amount of information to users, providing numerous products for consumers has been recognized as a major advantage of electronic commerce. Causing by the problem of information overload, however, consumers facing many alternatives on the online shop may feel hard to decide which one they prefer. Based on the theory of decision style and prospect theory, this study explores if too many products sold on the online shop will reduce consumers’ subjective status toward their buying decision. A 3×3 between subjects experiment was conducted and showed that the buyers’ decision style, the quantity of alternative products and the information about it will affect consumers’ subjective status. These results suggest that we should consider the role of electronic intermediaries more carefully, and further examine the theory of information overload and the need for information literacy to prepare for the future