der(1;7)(q10;p10).We observed that MDS patients with der(1;7) (q10;p10) present male predominance and have a better outcome than the -7/del(7q) group (Supplementary Table 4).Our findings revealed that the mutation spectrum of patients with der(1;7)(q10; p10) was different from that of MDS with -7/del(7q).We demonstrate for the first time, to our knowledge, that der(1;7) (q10;p10) is associated with a high frequency of mutations in RUNX1.Further studies are needed to clarify whether and how mutations of RUNX1 contribute to the pathogenesis of this subtype.
We describe the development of our speech-to-text transcription systems for the 2015 Multi-Genre Broadcast (MGB) challenge. Key features of the systems are: a segmentation system based on deep neural networks (DNNs); the use of HTK 3.5 for building DNN-based hybrid and tandem acoustic models and the use of these models in a joint decoding framework; techniques for adaptation of DNN based acoustic models including parameterised activation function adaptation; alternative acoustic models built using Kaldi; and recurrent neural network language models (RNNLMs) and RNNLM adaptation. The same language models were used with both HTK and Kaldi acoustic models and various combined systems built. The final systems had the lowest error rates on the evaluation data.
Traffic congestion monitoring is a long-term concern in urban areas. However, due to the complex structure of urban road networks and large amounts of traffic data, it is necessary to find an efficient way to identify traffic congestion in urban areas. In the big data era, more and more researchers are using traffic data to model traffic road networks and to identify traffic dynamics. Through the grid mapping method, this paper proposes an efficient abstraction approach to simplify the structure of a road network and then to identify urban traffic congestion. Based on the probe vehicle trajectory data, the intersection nodes between trajectories and grid boundaries are clustered through the method of density-based spatial clustering of applications with noise (DBSCAN). Then, a new traffic performance index is established by the principal component analysis (PCA) method based on the traffic characteristics in the node network. With the case study in Beijing, the proposed method effectively identifies urban traffic congestion in spatial and temporal dimensions. The proposed method is map-independent because it is only based on the probe vehicle data without a digital map. The method is highly efficient for a large urban road network in practice because all the calculations are basic operations based on the cells. Moreover, the proposed method can distinguish the expressway and the frontage roads. The mean absolute error (MAE) is about 10 km/h and the root-mean-square error (RMSE) is lower than 14 km/h. This method is expected to provide valuable spatiotemporal information for traffic engineers and managerial personnel to identify and relieve the traffic congestion problem.
This paper presents the results of an interferometric study of the shock/boundary-layer interaction at a compression corner. The boundary layer upstream of the corner is a non-equilibrium state having been disturbed by injection through a porous surface. As well as being relevant to the design of transpiration-cooled surfaces, the results show many similarities to the interaction in an adverse pressure gradient. The changes in the nature of the interaction as the approaching profile is distorted is fully explored and the results should provide a useful test case for computational fluid mechanics. The results are also used to evaluate the accuracy of interferometric techniques.
In sensor networks many efforts have been made on barrier coverage. Most of them rely on the assumption that sensors are randomly or manually deployed around the region of interest. It is obvious that the random deployment wastes many redundant sensors without contribution on the barrier formation. Moreover, in most real scenarios, it is difficult to deploy sensors manually due to the region usually in large scale or in danger. Hence, this paper studies the problem that using mobile sensors to form barrier surrounding the region automatically. The fundamental objective is to take full advantage of the limited number of mobile sensors to form the barrier coverage with the highest detection capability. The challenge is that the sensors only have local information. A fully distributed algorithm based on virtual force and convex analysis is developed for the objective to relocate the sensors from the original positions to uniformly distribute on the convex hull of the region. Simulation results verify the validity of our proposed cooperative scheme.
Selecting an appropriate intensity of epidemic prevention and control measures is of vital significance to promoting the two-way dynamic coordination of epidemic prevention and control and economic development. In order to balance epidemic control and economic development and suggest scientific and reasonable traffic control measures, this paper proposes a SEIQR model considering population migration and the propagation characteristics of the exposed and the asymptomatic, based on the data of COVID-19 cases, Baidu Migration, and the tourist economy. Further, the factor traffic control intensity is included in the model. After determining the functional relationship between the control intensity and the number of tourists and the cumulative number of confirmed cases, the NSGA-II algorithm is employed to perform multi-objective optimization with consideration of the requirements for epidemic prevention and control and for economic development to get an appropriate traffic control intensity and suggest scientific traffic control measures. With Xi’an City as an example. The results show that the Pearson correlation coefficient between the predicted data of this improved model and the actual data is 0.996, the R-square in the regression analysis is 0.993, with a significance level of below 0.001, suggesting that the predicted data of the model are more accurate. With the continuous rise of traffic control intensity in different simulation scenarios, the cumulative number of cases decreases by a significant amplitude. While balancing the requirements for epidemic prevention and control and for tourist economy development, the model works out the control intensity to be 0.68, under which some traffic control measures are suggested. The model presented in this paper can be used to analyze the impacts of different traffic control intensities on epidemic transmission. The research results in this paper reveal the traffic control measures balancing the requirements for epidemic prevention and control and for economic development.
In recent years recurrent neural network language models (RNNLMs) have been successfully applied to a range of tasks including speech recognition. However, an important issue that limits the quantity of data used, and their possible application areas, is the computational cost in training. A signi??cant part of this cost is associated with the softmax function at the output layer, as this requires a normalization term to be explicitly calculated. This impacts both the training and testing speed, especially when a large output vocabulary is used. To address this problem, noise contrastive estimation (NCE) is explored in RNNLM training. NCE does not require the above normalization during both training and testing. It is insensitive to the output layer size. On a large vocabulary conversational telephone speech recognition task, a doubling in training speed on a GPU and a 56 times speed up in test time evaluation on a CPU were obtained.
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