The neocortex contains excitatory neurons and inhibitory interneurons. Clones of neocortical excitatory neurons originating from the same progenitor cell are spatially organized and contribute to the formation of functional microcircuits. In contrast, relatively little is known about the production and organization of neocortical inhibitory interneurons. We found that neocortical inhibitory interneurons were produced as spatially organized clonal units in the developing ventral telencephalon. Furthermore, clonally related interneurons did not randomly disperse but formed spatially isolated clusters in the neocortex. Individual clonal clusters consisting of interneurons expressing the same or distinct neurochemical markers exhibited clear vertical or horizontal organization. These results suggest that the lineage relationship plays a pivotal role in the organization of inhibitory interneurons in the neocortex.

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We present an end-to-end system for multi-speaker emotional speech synthesis. In particular, our system learns emotion classes from just two speakers then generalizes these classes to other speakers from whom no emotional data was seen. We address the problem by integrating disentangled, fine-grained prosody features with global, sentence-level emotion embedding. These fine-grained features learn to represent local prosodic variations disentangled from speaker, tone and global emotion label. Compared to systems that model emotions at sentence level only, our method achieves higher ratings in naturalness and expressiveness, while retaining comparable speaker similarity ratings.

When digital forgery is introduced in a digital images, in order to create a convincing match, it always includes operation of scaling, rotation or skewing, which procedures typically require resampling and interpolation step. However, the resampling detection is always severely affected by the blocking artifacts introduced by JPEG compression. In order to solve this problem, in this paper an effective method has been proposed to distinguish the periodicity introduced by resampling and JPEG compression. At first, the probability map of an image was obtained by using the expectation-maximization algorithm. Then, it was Fourier-transformed and matched with affinetransform templates and a scaled JPEG template to detect whether the image was resampled, JPEG-compressed or both of them. Experimental results are provided to show the given method is dependable and effective in different kinds of digital tampering when resampling and JPEG compression occurred. Moreover, compared to the existed methods, better results are obtained by us.

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