Patch-based Progressive 3D Point Set Upsampling

We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we progressively train a cascade of patch-based upsampling networks on different levels of detail end-to-end. We propose a series of architectural design contributions that lead to a substantial performance boost. The effect of each technical contribution is demonstrated in an ablation study. Qualitative and quantitative experiments show that our method significantly outperforms the state-of-the-art learning-based and optimazation-based approaches, both in terms of handling low-resolution inputs and revealing high-fidelity details.Comment: accepted to cvpr2019, code available at https://github.com/yifita/P3

ECRG4 regulates neutrophil recruitment and CD44 expression during the inflammatory response to injury.

The complex molecular microenvironment of the wound bed regulates the duration and degree of inflammation in the wound repair process, while its dysregulation leads to impaired healing. Understanding factors controlling this response provides therapeutic targets for inflammatory disease. Esophageal cancer-related gene 4 (ECRG4) is a candidate chemokine that is highly expressed on leukocytes. We used ECRG4 knockout (KO) mice to establish that the absence of ECRG4 leads to defective neutrophil recruitment with a delay in wound healing. An in vitro human promyelocyte model identified an ECRG4-mediated suppression of the hyaluronic acid receptor, CD44, a key receptor mediating inflammation resolution. In ECRG4 KO mouse leukocytes, there was an increase in CD44 expression, consistent with a model in which ECRG4 negatively regulates CD44 levels. Therefore, we propose a previously unidentified mechanism in which ECRG4 regulates early neutrophil recruitment and subsequent CD44-mediated resolution of inflammation

Uniquely human CHRFAM7A gene increases the hematopoietic stem cell reservoir in mice and amplifies their inflammatory response.

A subset of genes in the human genome are uniquely human and not found in other species. One example is CHRFAM7A, a dominant-negative inhibitor of the antiinflammatory α7 nicotinic acetylcholine receptor (α7nAChR/CHRNA7) that is also a neurotransmitter receptor linked to cognitive function, mental health, and neurodegenerative disease. Here we show that CHRFAM7A blocks ligand binding to both mouse and human α7nAChR, and hypothesized that CHRFAM7A-transgenic mice would allow us to study its biological significance in a tractable animal model of human inflammatory disease, namely SIRS, the systemic inflammatory response syndrome that accompanies severe injury and sepsis. We found that CHRFAM7A increased the hematopoietic stem cell (HSC) reservoir in bone marrow and biased HSC differentiation to the monocyte lineage in vitro. We also observed that while the HSC reservoir was depleted in SIRS, HSCs were spared in CHRFAM7A-transgenic mice and that these mice also had increased immune cell mobilization, myeloid cell differentiation, and a shift to inflammatory monocytes from granulocytes in their inflamed lungs. Together, the findings point to a pathophysiological inflammatory consequence to the emergence of CHRFAM7A in the human genome. To this end, it is interesting to speculate that human genes like CHRFAM7A can account for discrepancies between the effectiveness of drugs like α7nAChR agonists in animal models and human clinical trials for inflammatory and neurodegenerative disease. The findings also support the hypothesis that uniquely human genes may be contributing to underrecognized human-specific differences in resiliency/susceptibility to complications of injury, infection, and inflammation, not to mention the onset of neurodegenerative disease

Shock Geometry and Spectral Breaks in Large SEP Events

Solar energetic particle (SEP) events are traditionally classified as "impulsive" or "gradual." It is now widely accepted that in gradual SEP events, particles are accelerated at coronal mass ejection-driven (CME-driven) shocks. In many of these large SEP events, particle spectra exhibit double power law or exponential rollover features, with the break energy or rollover energy ordered as (Q/A)^α, with Q being the ion charge in e and A the ion mass in units of proton mass m_p . This Q/A dependence of the spectral breaks provides an opportunity to study the underlying acceleration mechanism. In this paper, we examine how the Q/A dependence may depend on shock geometry. Using the nonlinear guiding center theory, we show that α ~ 1/5 for a quasi-perpendicular shock. Such a weak Q/A dependence is in contrast to the quasi-parallel shock case where α can reach 2. This difference in α reflects the difference of the underlying parallel and perpendicular diffusion coefficients κ_(||) and κ ⊥. We also examine the Q/A dependence of the break energy for the most general oblique shock case. Our analysis offers a possible way to remotely examine the geometry of a CME-driven shock when it is close to the Sun, where the acceleration of particle to high energies occurs

Nonlinear reduced models for state and parameter estimation

State estimation aims at approximately reconstructing the solution $u$ to a parametrized partial differential equation from $m$ linear measurements, when the parameter vector $y$ is unknown. Fast numerical recovery methods have been proposed based on reduced models which are linear spaces of moderate dimension $n$ which are tailored to approximate the solution manifold $\mathcal{M}$ where the solution sits. These methods can be viewed as deterministic counterparts to Bayesian estimation approaches, and are proved to be optimal when the prior is expressed by approximability of the solution with respect to the reduced model. However, they are inherently limited by their linear nature, which bounds from below their best possible performance by the Kolmogorov width $d_m(\mathcal{M})$ of the solution manifold. In this paper we propose to break this barrier by using simple nonlinear reduced models that consist of a finite union of linear spaces $V_k$, each having dimension at most $m$ and leading to different estimators $u_k^*$. A model selection mechanism based on minimizing the PDE residual over the parameter space is used to select from this collection the final estimator $u^*$. Our analysis shows that $u^*$ meets optimal recovery benchmarks that are inherent to the solution manifold and not tied to its Kolmogorov width. The residual minimization procedure is computationally simple in the relevant case of affine parameter dependence in the PDE. In addition, it results in an estimator $y^*$ for the unknown parameter vector. In this setting, we also discuss an alternating minimization (coordinate descent) algorithm for joint state and parameter estimation, that potentially improves the quality of both estimators

Extrachromosomal circles of satellite repeats and 5S ribosomal DNA in human cells

<p>Abstract</p> <p>Background</p> <p>Extrachomosomal circular DNA (eccDNA) is ubiquitous in eukaryotic organisms and was detected in every organism tested, including in humans. A two-dimensional gel electrophoresis facilitates the detection of eccDNA in preparations of genomic DNA. Using this technique we have previously demonstrated that most of eccDNA consists of exact multiples of chromosomal tandemly repeated DNA, including both coding genes and satellite DNA.</p> <p>Results</p> <p>Here we report the occurrence of eccDNA in every tested human cell line. It has heterogeneous mass ranging from less than 2 kb to over 20 kb. We describe eccDNA homologous to human alpha satellite and the <it>Sst</it>I mega satellite. Moreover, we show, for the first time, circular multimers of the human 5S ribosomal DNA (rDNA), similar to previous findings in <it>Drosophila </it>and plants. We further demonstrate structures that correspond to intermediates of rolling circle replication, which emerge from the circular multimers of 5S rDNA and <it>Sst</it>I satellite.</p> <p>Conclusions</p> <p>These findings, and previous reports, support the general notion that every chromosomal tandem repeat is prone to generate eccDNA in eukryoric organisms including humans. They suggest the possible involvement of eccDNA in the length variability observed in arrays of tandem repeats. The implications of eccDNA on genome biology may include mechanisms of centromere evolution, concerted evolution and homogenization of tandem repeats and genomic plasticity.</p

Verification of Neural Networks Local Differential Classification Privacy

Neural networks are susceptible to privacy attacks. To date, no verifier can reason about the privacy of individuals participating in the training set. We propose a new privacy property, called local differential classification privacy (LDCP), extending local robustness to a differential privacy setting suitable for black-box classifiers. Given a neighborhood of inputs, a classifier is LDCP if it classifies all inputs the same regardless of whether it is trained with the full dataset or whether any single entry is omitted. A naive algorithm is highly impractical because it involves training a very large number of networks and verifying local robustness of the given neighborhood separately for every network. We propose Sphynx, an algorithm that computes an abstraction of all networks, with a high probability, from a small set of networks, and verifies LDCP directly on the abstract network. The challenge is twofold: network parameters do not adhere to a known distribution probability, making it difficult to predict an abstraction, and predicting too large abstraction harms the verification. Our key idea is to transform the parameters into a distribution given by KDE, allowing to keep the over-approximation error small. To verify LDCP, we extend a MILP verifier to analyze an abstract network. Experimental results show that by training only 7% of the networks, Sphynx predicts an abstract network obtaining 93% verification accuracy and reducing the analysis time by $1.7\cdot10^4$x