Fine-grained classification based on multi-scale pyramid convolution networks

The large intra-class variance and small inter-class variance are the key factor affecting fine-grained image classification. Recently, some algorithms have been more accurate and efficient. However, these methods ignore the multi-scale information of the network, resulting in insufficient ability to capture subtle changes. To solve this problem, a weakly supervised fine-grained classification network based on multi-scale pyramid is proposed in this paper. It uses pyramid convolution kernel to replace ordinary convolution kernel in residual network, which can expand the receptive field of the convolution kernel and use complementary information of different scales. Meanwhile, the weakly supervised data augmentation network (WS-DAN) is used to prevent over fitting and improve the performance of the model. In addition, a new attention module, which includes spatial attention and channel attention, is introduced to pay more attention to the object part in the image. The comprehensive experiments are carried out on three public benchmarks. It shows that the proposed method can extract subtle feature and achieve classification effectively.

Finding Home in the Harmonic Diaspora

Chapter 14 listens to Lusi Fonsi and Daddy Yankee’s “Despacito,” which reinvigorated the market for Latin pop and mainstreamed the sound of reggaetón for a new generation, hybridizing new and classic forms of Puerto Rican music into an international smash hit. The song’s success is due in part to a more subtle feature: its use of a classic chord progression called the Axis, which has the unique property of being able to generate two tonal homes at once. It is impossible to say whether “Despacito” is in a major key or a minor key, a pleasantly disorienting effect that also sets to music the migratory identities of its composers.

Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors

Because of the limitations of the traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm is proposed. First, the radiation source signal was preprocessed, and a Hilbert transform was performed to obtain the instantaneous amplitude of the signal. Then, the improved fractal box-counting dimension of the signal instantaneous amplitude was extracted as the first eigenvector. At the same time, the improved fractal box-counting dimension of the signal without the Hilbert transform was extracted as the second eigenvector. Finally, the dual improved fractal box-counting dimension eigenvectors formed the multi-dimensional eigenvectors as signal subtle features, which were used for radiation source signal recognition by the grey relation algorithm. The experimental results show that, compared with the traditional fractal box-counting dimension algorithm and the single improved fractal box-counting dimension algorithm, the proposed dual improved fractal box-counting dimension algorithm can better extract the signal subtle distribution characteristics under different reconstruction phase space, and has a better recognition effect with good real-time performance.

Injuries to the shoulder

The vast majority of shoulder complaints are due to soft tissue lesions, and rotator cuff disorders represent the largest diagnostic category of these. Many shoulder complaints are multifactorial in origin, and articular and extra-articular disorders can coexist. Instability also plays a major role; the shoulder is the most mobile joint of the body, achieving this mobility at the expense of its stability. Loss of the fine balance between optimal mobility of the joint and its stability is a common, albeit frequently subtle, feature of shoulder complaints....