STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds.

We investigate how the neural processing in auditory cortex is shaped by the statistics of natural sounds. Hypothesising that auditory cortex (A1) represents the structural primitives out of which sounds are composed, we employ a statistical model to extract such components. The input to the model are cochleagrams which approximate the non-linear transformations a sound undergoes from the outer ear, through the cochlea to the auditory nerve. Cochleagram components do not superimpose linearly, but rather according to a rule which can be approximated using the max function. This is a consequence of the compression inherent in the cochleagram and the sparsity of natural sounds. Furthermore, cochleagrams do not have negative values. Cochleagrams are therefore not matched well by the assumptions of standard linear approaches such as sparse coding or ICA. We therefore consider a new encoding approach for natural sounds, which combines a model of early auditory processing with maximal causes analysis (MCA), a sparse coding model which captures both the non-linear combination rule and non-negativity of the data. An efficient truncated EM algorithm is used to fit the MCA model to cochleagram data. We characterize the generative fields (GFs) inferred by MCA with respect to in vivo neural responses in A1 by applying reverse correlation to estimate spectro-temporal receptive fields (STRFs) implied by the learned GFs. Despite the GFs being non-negative, the STRF estimates are found to contain both positive and negative subfields, where the negative subfields can be attributed to explaining away effects as captured by the applied inference method. A direct comparison with ferret A1 shows many similar forms, and the spectral and temporal modulation tuning of both ferret and model STRFs show similar ranges over the population. In summary, our model represents an alternative to linear approaches for biological auditory encoding while it captures salient data properties and links inhibitory subfields to explaining away effects.

Efficient inference for sparse latent variable models of transcriptional regulation.

MOTIVATION: Regulation of gene expression in prokaryotes involves complex co-regulatory mechanisms involving large numbers of transcriptional regulatory proteins and their target genes. Uncovering these genome-scale interactions constitutes a major bottleneck in systems biology. Sparse latent factor models, assuming activity of transcription factors (TFs) as unobserved, provide a biologically interpretable modelling framework, integrating gene expression and genome-wide binding data, but at the same time pose a hard computational inference problem. Existing probabilistic inference methods for such models rely on subjective filtering and suffer from scalability issues, thus are not well-suited for realistic genome-scale applications. RESULTS: We present a fast Bayesian sparse factor model, which takes input gene expression and binding sites data, either from ChIP-seq experiments or motif predictions, and outputs active TF-gene links as well as latent TF activities. Our method employs an efficient variational Bayes scheme for model inference enabling its application to large datasets which was not feasible with existing MCMC-based inference methods for such models. We validate our method on synthetic data against a similar model in the literature, employing MCMC for inference, and obtain comparable results with a small fraction of the computational time. We also apply our method to large-scale data from Mycobacterium tuberculosis involving ChIP-seq data on 113 TFs and matched gene expression data for 3863 putative target genes. We evaluate our predictions using an independent transcriptomics experiment involving over-expression of TFs. AVAILABILITY AND IMPLEMENTATION: An easy-to-use Jupyter notebook demo of our method with data is available at https://github.com/zhenwendai/SITAR. CONTACT: mudassar.iqbal@manchester.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GP-Select: Accelerating EM Using Adaptive Subspace Preselection.

We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large. The approach is based on iterative latent variable preselection, where we alternate between learning a selection function to reveal the relevant latent variables and using this to obtain a compact approximation of the posterior distribution for EM. This can make inference possible where the number of possible latent states is, for example, exponential in the number of latent variables, whereas an exact approach would be computationally infeasible. We learn the selection function entirely from the observed data and current expectation-maximization state via gaussian process regression. This is in contrast to earlier approaches, where selection functions were manually designed for each problem setting. We show that our approach performs as well as these bespoke selection functions on a wide variety of inference problems. In particular, for the challenging case of a hierarchical model for object localization with occlusion, we achieve results that match a customized state-of-the-art selection method at a far lower computational cost.

Data-Driven Detection of Prominent Objects.

This article deals with the detection of prominent objects in images. As opposed to the standard approaches based on sliding windows, we study a fundamentally different solution by formulating the supervised prediction of a bounding box as an image retrieval task. Indeed, given a global image descriptor, we find the most similar images in an annotated dataset, and transfer the object bounding boxes. We refer to this approach as data-driven detection (DDD). Our key novelty is to design or learn image similarities that explicitly optimize some aspect of the transfer unlike previous work which uses generic representations and unsupervised similarities. In a first variant, we explicitly learn to transfer, by adapting a metric learning approach to work with image and bounding box pairs. Second, we use a representation of images as object probability maps computed from low-level patch classifiers. Experiments show that these two contributions yield in some cases comparable or better results than standard sliding window detectors - despite its conceptual simplicity and run-time efficiency. Our third contribution is an application of prominent object detection, where we improve fine-grained categorization by pre-cropping images with the proposed approach. Finally, we also extend the proposed approach to detect multiple parts of rigid objects.

Autonomous Document Cleaning--A Generative Approach to Reconstruct Strongly Corrupted Scanned Texts.

We study the task of cleaning scanned text documents that are strongly corrupted by dirt such as manual line strokes, spilled ink, etc. We aim at autonomously removing such corruptions from a single letter-size page based only on the information the page contains. Our approach first learns character representations from document patches without supervision. For learning, we use a probabilistic generative model parameterizing pattern features, their planar arrangements and their variances. The model's latent variables describe pattern position and class, and feature occurrences. Model parameters are efficiently inferred using a truncated variational EM approach. Based on the learned representation, a clean document can be recovered by identifying, for each patch, pattern class and position while a quality measure allows for discrimination between character and non-character patterns. For a full Latin alphabet we found that a single page does not contain sufficiently many character examples. However, even if heavily corrupted by dirt, we show that a page containing a lower number of character types can efficiently and autonomously be cleaned solely based on the structural regularity of the characters it contains. In different example applications with different alphabets, we demonstrate and discuss the effectiveness, efficiency and generality of the approach.

Hyper-branched CdTe nanostructures based on the self-assembling of quantum dots and their optical properties.

As the priority of interconnects and active components in nanoscale optical and electronic devices, three-dimensional hyper-branched nanostructures came into focus of research. Recently, a novel crystallization route, named as "nonclassical crystallization," has been reported for three-dimensional nanostructuring. In this process, Quantum dots are used as building blocks for the construction of the whole hyper-branched structures instead of ions or single-molecules in conventional crystallization. The specialty of these nanostructures is the inheritability of pristine quantum dots' physical integrity because of their polycrystalline structures, such as quantum confinement effect and thus the luminescence. Moreover, since a longer diffusion length could exist in polycrystalline nanostructures due to the dramatically decreased distance between pristine quantum dots, the exciton-exciton interaction would be different with well dispersed quantum dots and single crystal nanostructures. This may be a benefit for electron transport in solar cell application. Therefore, it is very necessary to investigate the exciton-exciton interaction in such kind of polycrystalline nanostructures and their optical properites for solar cell application. In this research, we report a novel CdTe hyper-branched nanostructures based on self-assembly of CdTe quantum dots. Each branch shows polycrystalline with pristine quantum dots as the building units. Both steady state and time-resolved spectroscopy were performed to investigate the properties of carrier transport. Steady state optical properties of pristine quantum dots are well inherited by formed structures. While a suppressed multi-exciton recombination rate was observed. This result supports the percolation of carriers through the branches' network.

Hyperbranched CdTe nanostructures via a self-assembly route: optical properties.

In this work, we report a luminescent nanobundle structure formed by a hierarchical self-assembly process of thioglycolic acid (TGA)-capped CdTe quantum dots (QDs). The luminescence intensity of CdTe nanostructures is high enough to get a clear one-photon excitation confocal image. High contrast two-photon excitation confocal images suggest that the nonlinear properties of pristine QDs are well inherited by the formed CdTe nanostructures. The controllability of the structures and inheritance of the optical properties of the building units make the self-assembled nanostructures new generation materials.