Learning to Associate Words and Images Using a Large-scale Graph

We develop an approach for unsupervised learning of associations between co-occurring perceptual events using a large graph. We applied this approach to successfully solve the image captcha of China's railroad system. The approach is based on the principle of suspicious coincidence. In this particular problem, a user is presented with a deformed picture of a Chinese phrase and eight low-resolution images. They must quickly select the relevant images in order to purchase their train tickets. This problem presents several challenges: (1) the teaching labels for both the Chinese phrases and the images were not available for supervised learning, (2) no pre-trained deep convolutional neural networks are available for recognizing these Chinese phrases or the presented images, and (3) each captcha must be solved within a few seconds. We collected 2.6 million captchas, with 2.6 million deformed Chinese phrases and over 21 million images. From these data, we constructed an association graph, composed of over 6 million vertices, and linked these vertices based on co-occurrence information and feature similarity between pairs of images. We then trained a deep convolutional neural network to learn a projection of the Chinese phrases onto a 230-dimensional latent space. Using label propagation, we computed the likelihood of each of the eight images conditioned on the latent space projection of the deformed phrase for each captcha. The resulting system solved captchas with 77% accuracy in 2 seconds on average. Our work, in answering this practical challenge, illustrates the power of this class of unsupervised association learning techniques, which may be related to the brain's general strategy for associating language stimuli with visual objects on the principle of suspicious coincidence.Comment: 8 pages, 7 figures, 14th Conference on Computer and Robot Vision 201

A Mechanism for the Inhibition of Neural Progenitor Cell Proliferation by Cocaine

Investigating the mechanism of cocaine's effect on fetal brain development, Chun-Ting Lee and colleagues find that down-regulation of cyclin A by a cocaine metabolite inhibits neural proliferation

Examples of the complex architecture of the human transcriptome revealed by RACE and high-density tiling arrays

Recently, we mapped the sites of transcription across ∼30% of the human genome and elucidated the structures of several hundred novel transcripts. In this report, we describe a novel combination of techniques including the rapid amplification of cDNA ends (RACE) and tiling array technologies that was used to further characterize transcripts in the human transcriptome. This technical approach allows for several important pieces of information to be gathered about each array-detected transcribed region, including strand of origin, start and termination positions, and the exonic structures of spliced and unspliced coding and noncoding RNAs. In this report, the structures of transcripts from 14 transcribed loci, representing both known genes and unannotated transcripts taken from the several hundred randomly selected unannotated transcripts described in our previous work are represented as examples of the complex organization of the human transcriptome. As a consequence of this complexity, it is not unusual that a single base pair can be part of an intricate network of multiple isoforms of overlapping sense and antisense transcripts, the majority of which are unannotated. Some of these transcripts follow the canonical splicing rules, whereas others combine the exons of different genes or represent other types of noncanonical transcripts. These results have important implications concerning the correlation of genotypes to phenotypes, the regulation of complex interlaced transcriptional patterns, and the definition of a gene