Scene Graph Generation by Iterative Message Passing

Understanding a visual scene goes beyond recognizing individual objects in isolation. Relationships between objects also constitute rich semantic information about the scene. In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image. We propose a novel end-to-end model that generates such structured scene representation from an input image. The model solves the scene graph inference problem using standard RNNs and learns to iteratively improves its predictions via message passing. Our joint inference model can take advantage of contextual cues to make better predictions on objects and their relationships. The experiments show that our model significantly outperforms previous methods for generating scene graphs using Visual Genome dataset and inferring support relations with NYU Depth v2 dataset.Comment: CVPR 201

Weakly supervised 3D Reconstruction with Adversarial Constraint

Supervised 3D reconstruction has witnessed a significant progress through the use of deep neural networks. However, this increase in performance requires large scale annotations of 2D/3D data. In this paper, we explore inexpensive 2D supervision as an alternative for expensive 3D CAD annotation. Specifically, we use foreground masks as weak supervision through a raytrace pooling layer that enables perspective projection and backpropagation. Additionally, since the 3D reconstruction from masks is an ill posed problem, we propose to constrain the 3D reconstruction to the manifold of unlabeled realistic 3D shapes that match mask observations. We demonstrate that learning a log-barrier solution to this constrained optimization problem resembles the GAN objective, enabling the use of existing tools for training GANs. We evaluate and analyze the manifold constrained reconstruction on various datasets for single and multi-view reconstruction of both synthetic and real images

SEGCloud: Semantic Segmentation of 3D Point Clouds

3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent works leverage the capabilities of Neural Networks (NNs), but are limited to coarse voxel predictions and do not explicitly enforce global consistency. We present SEGCloud, an end-to-end framework to obtain 3D point-level segmentation that combines the advantages of NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields (FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation. Then the FC-CRF enforces global consistency and provides fine-grained semantics on the points. We implement the latter as a differentiable Recurrent NN to allow joint optimization. We evaluate the framework on two indoor and two outdoor 3D datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance comparable or superior to the state-of-the-art on all datasets.Comment: Accepted as a spotlight at the International Conference of 3D Vision (3DV 2017

Integrated TiO2 resonators for visible photonics

We demonstrate waveguide-coupled titanium dioxide (TiO2) racetrack resonators with loaded quality factors of 2x10^4 for the visible wavelengths. The structures were fabricated in sputtered TiO2 thin films on oxidized silicon substrates using standard top-down nanofabrication techniques, and passively probed in transmission measurements using a tunable red laser. Devices based on this material could serve as integrated optical elements as well as passive platforms for coupling to visible quantum emitters.Comment: 4 pages, 3 figure

A computational framework to emulate the human perspective in flow cytometric data analysis

Background: In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not hold true in real-life samples, which can have populations of arbitrary shapes and considerable inter-sample variation. <p/>Results: To address this, we developed a new framework flowScape for emulating certain key aspects of the human perspective in analyzing flow data, which we implemented in multiple steps. First, flowScape begins with creating a mathematically rigorous map of the high-dimensional flow data landscape based on dense and sparse regions defined by relative concentrations of events around modes. In the second step, these modal clusters are connected with a global hierarchical structure. This representation allows flowScape to perform ridgeline analysis for both traversing the landscape and isolating cell populations at different levels of resolution. Finally, we extended manual gating with a new capacity for constructing templates that can identify target populations in terms of their relative parameters, as opposed to the more commonly used absolute or physical parameters. This allows flowScape to apply such templates in batch mode for detecting the corresponding populations in a flexible, sample-specific manner. We also demonstrated different applications of our framework to flow data analysis and show its superiority over other analytical methods. <p/>Conclusions: The human perspective, built on top of intuition and experience, is a very important component of flow cytometric data analysis. By emulating some of its approaches and extending these with automation and rigor, flowScape provides a flexible and robust framework for computational cytomics

Secukinumab for psoriatic arthritis:comparative effectiveness versus licensed biologics/apremilast: a network meta-analysis

Aim: A network meta-analysis using randomized controlled trial data compared psoriatic arthritis (PsA) outcomes (American College of Rheumatology [ACR], Psoriasis Area Severity Index [PASI] and Psoriatic Arthritis Response Criteria [PsARC] response rates) at 12–16 weeks for secukinumab, adalimumab, apremilast, certolizumab, etanercept, golimumab, infliximab and ustekinumab. Patients & methods: Trials were identified by systematic review. Separate networks were developed for the full-study populations, biologic-naive patients and biologic-experienced patients. Results: In the full populations, secukinumab, adalimumab, golimumab and infliximab demonstrated the highest ACR response rates. Secukinumab and infliximab demonstrated the highest PASI response rates, and infliximab and etanercept demonstrated the highest PsARC response rates. Conclusion: In the full populations, secukinumab demonstrated good efficacy across all outcomes. All treatments for active PsA included in this comprehensive network meta-analysis demonstrated superiority to placebo

Development of novel 4‐arylpyridin‐2‐one and 6‐arylpyrimidin‐4‐one positive allosteric modulators of the M1 muscarinic acetylcholine receptor

This study investigated the structure‐activity relationships of 4‐phenylpyridin‐2‐one and 6‐phenylpyrimidin‐4‐one muscarinic M1 acetylcholine receptor (M1 mAChRs) positive allosteric modulators (PAMs). The presented series focuses on modifications to the core and top motif of the reported leads, MIPS1650 (1) and MIPS1780 (2). Profiling of our novel analogues showed that these modifications result in more nuanced effects on the allosteric properties compared to our previous compounds with alterations to the biaryl pendant. Further pharmacological characterisation of the selected compounds in radioligand binding, IP1 accumulation and β‐arrestin 2 recruitment assays demonstrated that despite primarily acting as affinity modulators, the PAMs displayed different pharmacological properties across the two cellular assays. The novel PAM 7f is a potential lead candidate for further development of peripherally‐restricted M1 PAMs, due to its lower blood‐brain‐barrier (BBB) permeability and improved exposure in the periphery compared to lead 2