Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss

We devise a cascade GAN approach to generate talking face video, which is robust to different face shapes, view angles, facial characteristics, and noisy audio conditions. Instead of learning a direct mapping from audio to video frames, we propose first to transfer audio to high-level structure, i.e., the facial landmarks, and then to generate video frames conditioned on the landmarks. Compared to a direct audio-to-image approach, our cascade approach avoids fitting spurious correlations between audiovisual signals that are irrelevant to the speech content. We, humans, are sensitive to temporal discontinuities and subtle artifacts in video. To avoid those pixel jittering problems and to enforce the network to focus on audiovisual-correlated regions, we propose a novel dynamically adjustable pixel-wise loss with an attention mechanism. Furthermore, to generate a sharper image with well-synchronized facial movements, we propose a novel regression-based discriminator structure, which considers sequence-level information along with frame-level information. Thoughtful experiments on several datasets and real-world samples demonstrate significantly better results obtained by our method than the state-of-the-art methods in both quantitative and qualitative comparisons

The power spectrum from the angular distribution of galaxies in the CFHTLS-Wide fields at redshift ~0.7

We measure the real-space galaxy power spectrum on large scales at redshifts 0.5 to 1.2 using optical colour-selected samples from the CFHT Legacy Survey. With the redshift distributions measured with a preliminary ~14000 spectroscopic redshifts from the VIMOS Public Extragalactic Redshift Survey (VIPERS), we deproject the angular distribution and directly estimate the three-dimensional power spectrum. We use a maximum likelihood estimator that is optimal for a Gaussian random field giving well-defined window functions and error estimates. This measurement presents an initial look at the large-scale structure field probed by the VIPERS survey. We measure the galaxy bias of the VIPERS-like sample to be b_g=1.38 +- 0.05 (sigma_8=0.8) on scales k<0.2h/mpc averaged over 0.5<z<1.2. We further investigate three photometric redshift slices, and marginalising over the bias factors while keeping other LCDM parameters fixed, we find the matter density Omega_m=0.30+-0.06.Comment: Minor changes to match journal versio

Competing Sound Sources Reveal Spatial Effects in Cortical Processing

Neurons in the avian auditory forebrain show strong sensitivity to the spatial configuration of two competing sources, even though there is only weak spatial dependence for any single source

Meiosis-Specific Loading of the Centromere-Specific Histone CENH3 in Arabidopsis thaliana

Centromere behavior is specialized in meiosis I, so that sister chromatids of homologous chromosomes are pulled toward the same side of the spindle (through kinetochore mono-orientation) and chromosome number is reduced. Factors required for mono-orientation have been identified in yeast. However, comparatively little is known about how meiotic centromere behavior is specialized in animals and plants that typically have large tandem repeat centromeres. Kinetochores are nucleated by the centromere-specific histone CENH3. Unlike conventional histone H3s, CENH3 is rapidly evolving, particularly in its N-terminal tail domain. Here we describe chimeric variants of CENH3 with alterations in the N-terminal tail that are specifically defective in meiosis. Arabidopsis thaliana cenh3 mutants expressing a GFP-tagged chimeric protein containing the H3 N-terminal tail and the CENH3 C-terminus (termed GFP-tailswap) are sterile because of random meiotic chromosome segregation. These defects result from the specific depletion of GFP-tailswap protein from meiotic kinetochores, which contrasts with its normal localization in mitotic cells. Loss of the GFP-tailswap CENH3 variant in meiosis affects recruitment of the essential kinetochore protein MIS12. Our findings suggest that CENH3 loading dynamics might be regulated differently in mitosis and meiosis. As further support for our hypothesis, we show that GFP-tailswap protein is recruited back to centromeres in a subset of pollen grains in GFP-tailswap once they resume haploid mitosis. Meiotic recruitment of the GFP-tailswap CENH3 variant is not restored by removal of the meiosis-specific cohesin subunit REC8. Our results reveal the existence of a specialized loading pathway for CENH3 during meiosis that is likely to involve the hypervariable N-terminal tail. Meiosis-specific CENH3 dynamics may play a role in modulating meiotic centromere behavior

Improving spatial localization in MEG inverse imaging by leveraging intersubject anatomical differences

Modern neuroimaging techniques enable non-invasive observation of ongoing neural processing, with magnetoencephalography (MEG) in particular providing direct measurement of neural activity with millisecond time resolution. However, accurately mapping measured MEG sensor readings onto the underlying source neural structures remains an active area of research. This so-called inverse problem is ill posed, and poses a challenge for source estimation that is often cited as a drawback limiting MEG data interpretation. However, anatomically constrained MEG localization estimates may be more accurate than commonly believed. Here we hypothesize that, by combining anatomically constrained inverse estimates across subjects, the spatial uncertainty of MEG source localization can be mitigated. Specifically, we argue that differences in subject brain geometry yield differences in point-spread functions, resulting in improved spatial localization across subjects. To test this, we use standard methods to combine subject anatomical MRI scans with coregistration information to obtain an accurate forward (physical) solution, modeling the MEG sensor data resulting from brain activity originating from different cortical locations. Using a linear minimum-norm inverse to localize this brain activity, we demonstrate that a substantial increase in the spatial accuracy of MEG source localization can result from combining data from subjects with differing brain geometry. This improvement may be enabled by an increase in the amount of available spatial information in MEG data as measurements from different subjects are combined. This approach becomes more important in the face of practical issues of coregistration errors and potential noise sources, where we observe even larger improvements in localization when combining data across subjects. Finally, we use a simple auditory N100(m) localization task to show how this effect can influence localization using a recorded neural dataset