Contextual-based Image Inpainting: Infer, Match, and Translate

We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to generate visually coherent completion given a high-resolution image with missing components. In order to overcome the difficulty to directly learn the distribution of high-dimensional image data, we divide the task into inference and translation as two separate steps and model each step with a deep neural network. We also use simple heuristics to guide the propagation of local textures from the boundary to the hole. We show that, by using such techniques, inpainting reduces to the problem of learning two image-feature translation functions in much smaller space and hence easier to train. We evaluate our method on several public datasets and show that we generate results of better visual quality than previous state-of-the-art methods.Comment: ECCV 2018 camera read

Studies of multiple stellar systems - IV. The triple-lined spectroscopic system Gliese 644

We present a radial-velocity study of the triple-lined system Gliese 644 and derive spectroscopic elements for the inner and outer orbits with periods of 2.9655 and 627 days. We also utilize old visual data, as well as modern speckle and adaptive optics observations, to derive a new astrometric solution for the outer orbit. These two orbits together allow us to derive masses for each of the three components in the system: M_A = 0.410 +/- 0.028 (6.9%), M_Ba = 0.336 +/- 0.016 (4.7%), and $M_Bb = 0.304 +/- 0.014 (4.7%) M_solar. We suggest that the relative inclination of the two orbits is very small. Our individual masses and spectroscopic light ratios for the three M stars in the Gliese 644 system provide three points for the mass-luminosity relation near the bottom of the Main Sequence, where the relation is poorly determined. These three points agree well with theoretical models for solar metallicity and an age of 5 Gyr. Our radial velocities for Gliese 643 and vB 8, two common-proper-motion companions of Gliese 644, support the interpretation that all five M stars are moving together in a physically bound group. We discuss possible scenarios for the formation and evolution of this configuration, such as the formation of all five stars in a sequence of fragmentation events leading directly to the hierarchical configuration now observed, versus formation in a small N cluster with subsequent dynamical evolution into the present hierarchical configuration.Comment: 17 pages, 9 figures, Accepted for publication in MNRA

In Brain Multi-Photon Imaging of Vaterite Drug Delivery Cargoes loaded with Carbon Dots

Biocompatible fluorescent agents, such as phenylenediamine carbon dots (CDs), are key contributors to the theragnostic paradigm, enabling real-time in vivo imaging of drug delivery cargoes. This study explores the optical properties of these CDs, demonstrating their potential for two-photon fluorescence imaging in brain vessels. Using an open aperture z-scan technique, we measured the wavelength-dependent nonlinear absorption cross-section of the CDs, achieving a peak value near 50 GM. This suggests the potential use of phenylenediamine CDs for efficient multiphoton excitation in the 775 - 895 nm spectral range. Mesoporous vaterite nanoparticles were loaded with fluorescent CDs to examine the possibility of a simultaneous imaging and drug delivery platform. Efficient one and two-photon imaging of the CD-vaterite composites, uptaken by macrophage and genetically engineered C6-Glioma cells, was demonstrated. For an in vivo scenario, vaterite nanoparticles loaded with CDs were directly injected into the brain of a living mouse, and their flow was monitored in real-time within the blood vessels. The facile synthesis of phenylenediamine carbon dots, their significant nonlinear responses, and biological compatibility show a viable route for implementing drug tracking and sensing platforms in living systems

Necessary and sufficient conditions of solution uniqueness in ℓ1\ell_1 minimization

This paper shows that the solutions to various convex $\ell_1$ minimization problems are \emph{unique} if and only if a common set of conditions are satisfied. This result applies broadly to the basis pursuit model, basis pursuit denoising model, Lasso model, as well as other $\ell_1$ models that either minimize $f(Ax-b)$ or impose the constraint $f(Ax-b)\leq\sigma$, where $f$ is a strictly convex function. For these models, this paper proves that, given a solution $x^*$ and defining I=\supp(x^*) and s=\sign(x^*_I), $x^*$ is the unique solution if and only if $A_I$ has full column rank and there exists $y$ such that $A_I^Ty=s$ and $|a_i^Ty|_\infty<1$ for $i\not\in I$. This condition is previously known to be sufficient for the basis pursuit model to have a unique solution supported on $I$. Indeed, it is also necessary, and applies to a variety of other $\ell_1$ models. The paper also discusses ways to recognize unique solutions and verify the uniqueness conditions numerically.Comment: 6 pages; revised version; submitte

Fully Automatic Expression-Invariant Face Correspondence

We consider the problem of computing accurate point-to-point correspondences among a set of human face scans with varying expressions. Our fully automatic approach does not require any manually placed markers on the scan. Instead, the approach learns the locations of a set of landmarks present in a database and uses this knowledge to automatically predict the locations of these landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan. To accurately fit the expression of the template to the expression of the scan, we use as template a blendshape model. Our algorithm was tested on a database of human faces of different ethnic groups with strongly varying expressions. Experimental results show that the obtained point-to-point correspondence is both highly accurate and consistent for most of the tested 3D face models

Weak pairwise correlations imply strongly correlated network states in a neural population

Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher order interactions among large groups of elements play an important role. In the vertebrate retina, we show that weak correlations between pairs of neurons coexist with strongly collective behavior in the responses of ten or more neurons. Surprisingly, we find that this collective behavior is described quantitatively by models that capture the observed pairwise correlations but assume no higher order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behavior. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons.Comment: Full account of work presented at the conference on Computational and Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah (http://cosyne.org

Stimulus-dependent maximum entropy models of neural population codes

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. To be able to infer a model for this distribution from large-scale neural recordings, we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. The model is able to capture the single-cell response properties as well as the correlations in neural spiking due to shared stimulus and due to effective neuron-to-neuron connections. Here we show that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. As a result, the SDME model gives a more accurate account of single cell responses and in particular outperforms uncoupled models in reproducing the distributions of codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like surprise and information transmission in a neural population.Comment: 11 pages, 7 figure

End-to-end Interpretable Learning of Non-blind Image Deblurring

Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic splitting method with Richardson fixed-point iterations for its least-squares updates and a proximal operator for the auxiliary variable updates. We propose to precondition the Richardson solver using approximate inverse filters of the (known) blur and natural image prior kernels. Using convolutions instead of a generic linear preconditioner allows extremely efficient parameter sharing across the image, and leads to significant gains in accuracy and/or speed compared to classical FFT and conjugate-gradient methods. More importantly, the proposed architecture is easily adapted to learning both the preconditioner and the proximal operator using CNN embeddings. This yields a simple and efficient algorithm for non-blind image deblurring which is fully interpretable, can be learned end to end, and whose accuracy matches or exceeds the state of the art, quite significantly, in the non-uniform case.Comment: Accepted at ECCV2020 (poster

Frame Theory for Signal Processing in Psychoacoustics

This review chapter aims to strengthen the link between frame theory and signal processing tasks in psychoacoustics. On the one side, the basic concepts of frame theory are presented and some proofs are provided to explain those concepts in some detail. The goal is to reveal to hearing scientists how this mathematical theory could be relevant for their research. In particular, we focus on frame theory in a filter bank approach, which is probably the most relevant view-point for audio signal processing. On the other side, basic psychoacoustic concepts are presented to stimulate mathematicians to apply their knowledge in this field

H-Ras Nanocluster Stability Regulates the Magnitude of MAPK Signal Output

H-Ras is a binary switch that is activated by multiple co-factors and triggers several key cellular pathways one of which is MAPK. The specificity and magnitude of downstream activation is achieved by the spatio-temporal organization of the active H-Ras in the plasma membrane. Upon activation, the GTP bound H-Ras binds to Galectin-1 (Gal-1) and becomes transiently immobilized in short-lived nanoclusters on the plasma membrane from which the signal is propagated to Raf. In the current study we show that stabilizing the H-Ras-Gal-1 interaction, using bimolecular fluorescence complementation (BiFC), leads to prolonged immobilization of H-Ras.GTP in the plasma membrane which was measured by fluorescence recovery after photobleaching (FRAP), and increased signal out-put to the MAPK module. EM measurements of Raf recruitment to the H-Ras.GTP nanoclusters demonstrated that the enhanced signaling observed in the BiFC stabilized H-Ras.GTP nanocluster was attributed to increased H-Ras immobilization rather than to an increase in Raf recruitment. Taken together these data demonstrate that the magnitude of the signal output from a GTP-bound H-Ras nanocluster is proportional to its stability