Polarization of kilonova emission from a black hole-neutron star merger

A multi-messenger, black hole (BH) - neutron star (NS) merger event still remains to be detected. The tidal (dynamical) ejecta from such an event, thought to produce a kinonova, is concentrated in the equatorial plane and occupies only part of the whole azimuthal angle. In addition, recent simulations suggest that the outflow or wind from the post-merger remnant disk, presumably anisotropic, can be a major ejecta component responsible for a kilonova. For any ejecta whose photosphere shape deviates from the spherical symmetry, the electron scattering at the photosphere causes a net polarization in the kilonova light. Recent observational and theoretical polarization studies have been focused to the NS-NS merger kilonova AT2017gfo. We extend those work to the case of a BH-NS merger kilonova. We show that the degree of polarization at the first $\sim 1$ hr can be up to $\sim$ 3\% if a small amount ($10^{-4} M_{\odot}$) of free neutrons have survived in the fastest component of the dynamical ejecta, whose beta-decay causes a precursor in the kilonova light. The polarization degree can be $\sim$ 0.6\% if free neutrons survived in the fastest component of the disk wind. Future polarization detection of a kilonova will constrain the morphology and composition of the dominant ejecta component, therefore help to identify the nature of the merger.Comment: 10 pages, 5 figures. Accepted for publication in Ap

Semantic Object Parsing with Local-Global Long Short-Term Memory

Semantic object parsing is a fundamental task for understanding objects in detail in computer vision community, where incorporating multi-level contextual information is critical for achieving such fine-grained pixel-level recognition. Prior methods often leverage the contextual information through post-processing predicted confidence maps. In this work, we propose a novel deep Local-Global Long Short-Term Memory (LG-LSTM) architecture to seamlessly incorporate short-distance and long-distance spatial dependencies into the feature learning over all pixel positions. In each LG-LSTM layer, local guidance from neighboring positions and global guidance from the whole image are imposed on each position to better exploit complex local and global contextual information. Individual LSTMs for distinct spatial dimensions are also utilized to intrinsically capture various spatial layouts of semantic parts in the images, yielding distinct hidden and memory cells of each position for each dimension. In our parsing approach, several LG-LSTM layers are stacked and appended to the intermediate convolutional layers to directly enhance visual features, allowing network parameters to be learned in an end-to-end way. The long chains of sequential computation by stacked LG-LSTM layers also enable each pixel to sense a much larger region for inference benefiting from the memorization of previous dependencies in all positions along all dimensions. Comprehensive evaluations on three public datasets well demonstrate the significant superiority of our LG-LSTM over other state-of-the-art methods.Comment: 10 page

Interpretable Structure-Evolving LSTM

This paper develops a general framework for learning interpretable data representation via Long Short-Term Memory (LSTM) recurrent neural networks over hierarchal graph structures. Instead of learning LSTM models over the pre-fixed structures, we propose to further learn the intermediate interpretable multi-level graph structures in a progressive and stochastic way from data during the LSTM network optimization. We thus call this model the structure-evolving LSTM. In particular, starting with an initial element-level graph representation where each node is a small data element, the structure-evolving LSTM gradually evolves the multi-level graph representations by stochastically merging the graph nodes with high compatibilities along the stacked LSTM layers. In each LSTM layer, we estimate the compatibility of two connected nodes from their corresponding LSTM gate outputs, which is used to generate a merging probability. The candidate graph structures are accordingly generated where the nodes are grouped into cliques with their merging probabilities. We then produce the new graph structure with a Metropolis-Hasting algorithm, which alleviates the risk of getting stuck in local optimums by stochastic sampling with an acceptance probability. Once a graph structure is accepted, a higher-level graph is then constructed by taking the partitioned cliques as its nodes. During the evolving process, representation becomes more abstracted in higher-levels where redundant information is filtered out, allowing more efficient propagation of long-range data dependencies. We evaluate the effectiveness of structure-evolving LSTM in the application of semantic object parsing and demonstrate its advantage over state-of-the-art LSTM models on standard benchmarks.Comment: To appear in CVPR 2017 as a spotlight pape

1-[5-(3,4-Dichlorophenyl)-3-(2-naphthyl)-4,5-dihydropyrazol-1-yl]ethanone

In the title compound, C21H16Cl2N2O, the central pyrazoline ring makes dihedral angles of 90.1 (3) and 7.8 (3)°, with the pendant benzene ring and naphthalene ring system, respectively. In the crystal structure, weak C—H⋯O inter­actions lead to chains of mol­ecules

Reversible Recursive Instance-level Object Segmentation

In this work, we propose a novel Reversible Recursive Instance-level Object Segmentation (R2-IOS) framework to address the challenging instance-level object segmentation task. R2-IOS consists of a reversible proposal refinement sub-network that predicts bounding box offsets for refining the object proposal locations, and an instance-level segmentation sub-network that generates the foreground mask of the dominant object instance in each proposal. By being recursive, R2-IOS iteratively optimizes the two sub-networks during joint training, in which the refined object proposals and improved segmentation predictions are alternately fed into each other to progressively increase the network capabilities. By being reversible, the proposal refinement sub-network adaptively determines an optimal number of refinement iterations required for each proposal during both training and testing. Furthermore, to handle multiple overlapped instances within a proposal, an instance-aware denoising autoencoder is introduced into the segmentation sub-network to distinguish the dominant object from other distracting instances. Extensive experiments on the challenging PASCAL VOC 2012 benchmark well demonstrate the superiority of R2-IOS over other state-of-the-art methods. In particular, the $\text{AP}^r$ over $20$ classes at $0.5$ IoU achieves $66.7\%$, which significantly outperforms the results of $58.7\%$ by PFN~\cite{PFN} and $46.3\%$ by~\cite{liu2015multi}.Comment: 9 page

Efficient Semantic-based Content Search in P2P Network

Most existing Peer-to-Peer (P2P) systems support only title-based searches and are limited in functionality when compared to today’s search engines. In this paper, we present the design of a distributed P2P information sharing system that supports semantic-based content searches of relevant documents. First, we propose a general and extensible framework for searching similar documents in P2P network. The framework is based on the novel concept of Hierarchical Summary Structure. Second, based on the framework, we develop our efficient document searching system, by effectively summarizing and maintaining all documents within the network with different granularity. Finally, an experimental study is conducted on a real P2P prototype, and a large-scale network is further simulated. The results show the effectiveness, efficiency and scalability of the proposed system.Singapore-MIT Alliance (SMA