EAST: An Efficient and Accurate Scene Text Detector

Previous approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging scenarios, even when equipped with deep neural network models, because the overall performance is determined by the interplay of multiple stages and components in the pipelines. In this work, we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes. The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images, eliminating unnecessary intermediate steps (e.g., candidate aggregation and word partitioning), with a single neural network. The simplicity of our pipeline allows concentrating efforts on designing loss functions and neural network architecture. Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500 demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR 2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps at 720p resolution.Comment: Accepted to CVPR 2017, fix equation (3

Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs

Inductive link prediction -- where entities during training and inference stages can be different -- has been shown to be promising for completing continuously evolving knowledge graphs. Existing models of inductive reasoning mainly focus on predicting missing links by learning logical rules. However, many existing approaches do not take into account semantic correlations between relations, which are commonly seen in real-world knowledge graphs. To address this challenge, we propose a novel inductive reasoning approach, namely TACT, which can effectively exploit Topology-Aware CorrelaTions between relations in an entity-independent manner. TACT is inspired by the observation that the semantic correlation between two relations is highly correlated to their topological structure in knowledge graphs. Specifically, we categorize all relation pairs into several topological patterns, and then propose a Relational Correlation Network (RCN) to learn the importance of the different patterns for inductive link prediction. Experiments demonstrate that TACT can effectively model semantic correlations between relations, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the inductive link prediction task.Comment: Accepted to AAAI 202

On the Effectiveness of ASR Representations in Real-world Noisy Speech Emotion Recognition

This paper proposes an efficient attempt to noisy speech emotion recognition (NSER). Conventional NSER approaches have proven effective in mitigating the impact of artificial noise sources, such as white Gaussian noise, but are limited to non-stationary noises in real-world environments due to their complexity and uncertainty. To overcome this limitation, we introduce a new method for NSER by adopting the automatic speech recognition (ASR) model as a noise-robust feature extractor to eliminate non-vocal information in noisy speech. We first obtain intermediate layer information from the ASR model as a feature representation for emotional speech and then apply this representation for the downstream NSER task. Our experimental results show that 1) the proposed method achieves better NSER performance compared with the conventional noise reduction method, 2) outperforms self-supervised learning approaches, and 3) even outperforms text-based approaches using ASR transcription or the ground truth transcription of noisy speech.Comment: Submitted to ICASSP 202

Extending the unified subhalo model to warm dark matter

Using a set of high-resolution N-body simulations, we extend the unified distribution model of cold dark matter (CDM) subhaloes to the warm dark matter(WDM) case. The same model framework combining the unevolved mass function, unevolved radial distribution, and tidal stripping can predict the mass function and spatial distribution of subhaloes in both CDM and WDM simulations. The dependence of the model on the DM particle property is universally parameterized through the half-mode mass of the initial power spectrum. Compared with the CDM model, the WDM model differs most notably in two aspects. 1) In contrast to the power-law form in CDM, the unevolved subhalo mass function for WDM is scale-dependent at the low mass end due to the cut-off in the initial power spectrum. 2) WDM subhaloes are more vulnerable to tidal stripping and disruption due to their lower concentrations at accretion time. Their survival rate is also found to depend on the infall mass. Accounting for these differences, the model predicts a final WDM subhalo mass function that is also proportional to the unevolved subhalo mass function. The radial distribution of WDM subhaloes is predicted to be mass-dependent. For low mass subhaloes, the radial distribution is flatter in the inner halo and steeper in the outer halo compared to the CDM counterpart, due to the scale-dependent unevolved mass function and the enhanced tidal stripping. The code for sampling subhaloes according to our generalized model is available at https://github.com/fhtouma/subgen2 .Comment: 15 pages, 14 figure

MoSculp: Interactive Visualization of Shape and Time

We present a system that allows users to visualize complex human motion via 3D motion sculptures---a representation that conveys the 3D structure swept by a human body as it moves through space. Given an input video, our system computes the motion sculptures and provides a user interface for rendering it in different styles, including the options to insert the sculpture back into the original video, render it in a synthetic scene or physically print it. To provide this end-to-end workflow, we introduce an algorithm that estimates that human's 3D geometry over time from a set of 2D images and develop a 3D-aware image-based rendering approach that embeds the sculpture back into the scene. By automating the process, our system takes motion sculpture creation out of the realm of professional artists, and makes it applicable to a wide range of existing video material. By providing viewers with 3D information, motion sculptures reveal space-time motion information that is difficult to perceive with the naked eye, and allow viewers to interpret how different parts of the object interact over time. We validate the effectiveness of this approach with user studies, finding that our motion sculpture visualizations are significantly more informative about motion than existing stroboscopic and space-time visualization methods.Comment: UIST 2018. Project page: http://mosculp.csail.mit.edu

Facile Synthesis of Nitrogen-doped Porous Carbon for Selective CO2 Capture

AbstractSolid-state post-combustion CO2 sorbents have certain advantages over traditional aqueous amine systems, including reduced regeneration energy since vaporization of liquid water is avoided, tunable pore morphology, and greater chemical variability. We report here an ordered mesoporous nitrogen-doped carbon made by the co- assembly of a modified-pyrrole and triblock copolymer through a soft-templating method, which is facile, economic, and fast compared to the hard-template approach. A high surface area mesoporous carbon was achieved, which is comparable to the silica counterpart. This porous carbon, with a Brunauer–Emmett–Teller (BET) specific surface area of 804.5 m2 g-1, exhibits large CO2 capacities (298K) of 1.0 and 3.1 mmol g-1 at 0.1 and 1bar, respectively, and excellent CO2/N2 selectivity of 51.4. The porous carbon can be fully regenerated solely by inert gas purging without heating. It is stable for multiple adsorption/desorption cycles without reduction in CO2 capacity. These desirable properties render the nitrogen-doped hierarchical porous carbon a promising material for post-combustion CO2 capture

Design and testing of sorbents for CO2 separation of post-combustion and natural gas sweetening applications

In post-combustion processes, sorbents with both high capacity and selectivity are required for reducing the cost of carbon capture. Although physisorbents have the advantage of low energy consumption for regeneration, it remains a challenge to obtain both high capacity and sufficient CO2/N2 selectivity at the same time. A novel N-doped hierarchical carbon has been developed, which exhibits record-high Henry’s law CO2/N2 selectivity among physisorptive carbons while having a high CO2 adsorption capacity. Specifically, the synthesis involves the rational design of a modified pyrrole molecule that can co-assemble with the soft Pluronic template via hydrogen bonding and electrostatic interactions to give rise to mesopores followed by carbonization. The low-temperature carbonization and activation processes allow for the development of ultra-small pores (d2 affinity. Furthermore, the described work provides a strategy to initiate the development of rationally-designed porous conjugated polymer structures and carbon-based materials for various potential applications. In addition to post-combustion capture, natural gas sweetening is another topic of interest. Natural gas, having the lowest carbon intensity compared to coal and petroleum, is projected to increase in production and consumption in the coming decades. However, a drawback associated with natural gas is that it contains considerable amounts of CO2 at the recovery well, making on-site CO2 capture necessary. Solid sorbents are advantageous over traditional amine scrubbing due to their relatively low regeneration energies and non-corrosive nature. However, it remains a challenge to improve the sorbent’s CO2 capacity at elevated pressures relevant to natural gas purification. A series of porous carbons have been developed, which were derived from an intrinsic 3D hierarchical nanostructured polymer hydrogel, with simple and effective tunability over the pore size distribution. The optimized surface area reached a record-high of 4196 m2 g-1 among carbon-based materials. This high surface area along with the abundant micro/narrow mesopores (1.94 cm3 g-1 with d \u3c 4 nm) results in a record-high CO2 capacity (28.3 mmol g-1 at 25 °C and 30 bar) among carbons. This carbon also showed reasonable CO2/CH4 selectivity and excellent cyclability. In addition, this work for the first time combines experimental studies with first-principle molecular simulations for high-pressure CO2 adsorption on porous sorbents. It was found that at elevated pressures, the CO2 density in the adsorbed phase is significantly enhanced in the micro- and narrow mesopores with quantitative values provided for CO2 density. Furthermore, surface nitrogen functionalities have a trivial contribution to the CO2 uptake at high pressures. These findings emphasize the importance of being able to tune a sorbent’s pore size to achieve high CO2 uptake. Thus, the simulation studies help in our understanding of our sorbent’s superior performance as well as provides useful insight into future sorbent development