Based on Intel's Many Integrated Core (MIC) architecture, Intel Xeon Phi is one of the few truly many-core CPUs -featuring around 60 fairly powerful cores, two levels of caches, and graphic memory, all interconnected by a very fast ring. Given its promised ease-of-use and high performance, we took Xeon Phi out for a test drive. In this paper, we present this experience at two different levels: (1) the microbenchmark level, where we stress "each nut and bolt" of Phi in the lab, and (2) the application level, where we study Phi's performance response in a real-life environment. At the microbenchmarking level, we show the high performance of five components of the architecture, focusing on their maximum achieved performance and the prerequisites to achieve it. Next, we choose a medical imaging application (Leukocyte Tracking) as a case study. We observed that it is rather easy to get functional code and start benchmarking, but the first performance numbers can be far from satisfying. Our experience indicates that a simple data structure and massive parallelism are critical for Xeon Phi to perform well. When compiler-driven parallelization and/or vectorization fails, programming Xeon Phi for performance can become very challenging.

Audio scene classification, the problem of predicting class labels of audio scenes, has drawn lots of attention during the last several years. However, it remains challenging and falls short of accuracy and efficiency. Recently, Convolutional Neural Network (CNN)-based methods have achieved better performance with comparison to the traditional methods. Nevertheless, conventional single channel CNN may fail to consider the fact that additional cues may be embedded in the multi-channel recordings. In this paper, we explore the use of Multi-channel CNN for the classification task, which aims to extract features from different channels in an end-to-end manner. We conduct the evaluation compared with the conventional CNN and traditional Gaussian Mixture Model-based methods. Moreover, to improve the classification accuracy further, this paper explores the using of mixup method. In brief, mixup trains the neural network on linear combinations of pairs of the representation of audio scene examples and their labels. By employing the mixup approach for data augmentation, the novel model can provide higher prediction accuracy and robustness in contrast with previous models, while the generalization error can also be reduced on the evaluation data.

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Astrophysical techniques have pioneered the discovery of neutrino mass properties. Current cosmological observations give an upper bound on neutrino masses by attempting to disentangle the small neutrino contribution from the sum of all matter using precise theoretical models. We discover the differential neutrino condensation effect in our TianNu N -body simulation. Neutrino masses can be inferred using this effect by comparing galaxy properties in regions of the universe with different neutrino relative abundance (i.e. the local neutrino to cold dark matter density ratio). In "neutrino-rich" regions, more neutrinos can be captured by massive halos compared to "neutrino-poor" regions. This effect differentially skews the halo mass function and opens up the path to independent neutrino mass measurements in current or future galaxy surveys.Neutrinos are elusive elementary particles whose fundamental properties are incredibly difficult to measure. 40 years after their first direct detection 1, 2 , flavour oscillation experiments [3][4][5] confirmed that at least two neutrino types are massive and placed a lower bound on the sum of their mass:. This discovery has a profound impact on our understanding of the early Universe, where neutrinos are produced in great numbers. First in a relativistic state, they contribute to the radiation energy density, thereby modulating the matter-to-radiation ratio in a way that depends on their mass. This leaves an imprint on the Cosmic Microwave Background 1 arXiv:1609.08968v1 [astro-ph.CO]

Whale vocal calls contain valuable information and abundant characteristics that are important for classification of whale sub-populations and related biological research. In this study, an effective data-driven approach based on pre-trained Convolutional Neural Networks (CNN) using multi-scale waveforms and time-frequency feature representations is developed in order to perform the classification of whale calls from a large open-source dataset recorded by sensors carried by whales. Specifically, the classification is carried out through a transfer learning approach by using pre-trained state-of-the-art CNN models in the field of computer vision. 1D raw waveforms and 2D log-mel features of the whale-call data are respectively used as the input of CNN models. For raw waveform input, windows are applied to capture multiple sketches of a whale-call clip at different time scales and stack the features from different sketches for classification. When using the log-mel features, the delta and delta-delta features are also calculated to produce a 3-channel feature representation for analysis. In the training, a 4-fold cross-validation technique is employed to reduce the overfitting effect, while the Mix-up technique is also applied to implement data augmentation in order to further improve the system performance. The results show that the proposed method can improve the accuracies by more than 20% in percentage for the classification into 16 whale pods compared with the baseline method using groups of 2D shape descriptors of spectrograms and the Fisher discriminant scores on the same dataset. Moreover, it is shown that classifications based on log-mel features have higher accuracies than those based directly on raw waveforms. The phylogeny graph is also produced to significantly illustrate the relationships among the whale sub-populations.