Genome-scale metabolic models provide a valuable context for analyzing data from diverse high-throughput experimental techniques. Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. However, these methods make differing assumptions that influence the content and associated predictive capacity of resulting models, such that model content varies more due to methods used than cell types. Here we overcome this problem with a novel framework for inferring the metabolic functions of a cell before model construction. For this, we curated a list of metabolic tasks and developed a framework to infer the activity of these functionalities from transcriptomic data. We protected the data-inferred tasks during the implementation of diverse context-specific model extraction algorithms for 44 cancer cell lines. We show that the protection of data-inferred metabolic tasks decreases the variability of models across extraction methods. Furthermore, resulting models better capture the actual biological variability across cell lines. This study highlights the potential of using biological knowledge, inferred from omics data, to obtain a better consensus between existing extraction algorithms. It further provides guidelines for the development of the next-generation of data contextualization methods.

In this paper, we present a scooter collision avoidance system that can identify red-light runners (RLRs) at intersections. When the RLR behavior is detected, the system would advise the RLR to slow down immediately and warn nearby vehicles on the intersecting road in real time. In particular, we do not consider infrastructure-based solutions such as those utilizing a radar or a camera. This is because, in addition to high implementation costs, collisions can be only avoided at intersections where such infrastructure configurations are deployed. Instead, we advance an on-scooter solution using smartphones carried by scooter riders. Smartphones provide a useful platform that has a high penetration rate, more than sufficient computational power, inertial sensors to reflect the driving behavior, and the communication capability to transmit or receive information from other vehicles. In our system, we utilize a support vector machine and design an RLR classifier for learning and predicting RLR behaviors. The evaluation results show that our system is able to achieve over 70% recognition rates when distinguishing between RLR and non-RLR cases, as compared with approximately 80% recognition rates of the infrastructure-based (and higher cost) solution using a laser range finder (LADAR).

TD-PSOLA is one of the most widely used prosodic modification techniques. However, perceptible distortions are introduced occasionally and how TD-PSOLA affects speech quality has not been fully understood and controlled. In this paper, we present a quality estimation method before performing modification. By exploiting relationship between prosodic modifications and subjective scores, 27 distance measures are proposed and respective performances are given and compared. Extensive search is used to find every possible combination among these measures, and the best correlation between the predicted and subjective scores is 87.6%, which can be obtained by linear regression of 4 proposed distance measures. The proposed method does not require synthesizing target and can be used both in online unit selection and off-line corpus design of TTS systems.

This paper presents a new method for automatically selecting speech segments that are expected to minimize perceptual distortion in synthesis. The method is based on comparison of candidates fully prosody-aligned to each other. Automatic segmentation, pitch marking and PSOLA method work together for prosody alignment. Two distance measures, MFCC and PSQM, are used for comparison because of human perceptual consideration. Experiment shows that the average distortion by using the selected best unit in outside testing is similar to that in training corpus with only few exceptions. The symmetry characteristics and correlation of these two distance measures are also studied and reveal that both are properly symmetric and consistent with each other for most cases.

The present invention provides a new search method to solve the text selection problem more systematically and efficiently based on performance indices and search criteria for the text script generation in the design of corpus-based TTS (text to speech) systems. By controlling a weighting factor the covering rate and hit rate can be increased to improve the robustness of the TTS system. Finally, the scalable and controllable design of the multi-stage search can produce various kinds of text scripts ideally suitable for the requirement of various kinds of corpus-based TTS systems.

This correspondence proposes a new CELP coding method which embeds speech classification in adaptive codebook search. This approach can retain the synthesized speech quality at bit-rates below 4 kb/s. A pitch analyzer is designed to classify each frame by its periodicity, and with a finite-state machine, one of four states is determined. Then the adaptive codebook search scheme is switched according to the state. Simulation results show that higher SEGSNR and lower computation complexity can be achieved, and the pitch contour of the synthesized speech is smoother than that produced by conventional CELP coders.>

A novel spectral coding method, two-dimensional differential line spectra pair coding (2DdLSP), is proposed. Taking advantage of the strong inter-frame, and intra-frame correlation of LSP parameters, a two-dimensional linear prediction technique is used to reduce the variance of the parameters to be quantized. One scalar quantization and two vector quantization schemes are designed to quantize the 2-D prediction residuals. Without further buffering delay, the spectral distortion of 1 dB/sup 2/ can be achieved at 19 b/frame when the frame period is 10 ms. Both within- and out-of-training tests show the robustness of the method to speech data variance.>