Dense Object Grounding in 3D Scenes

Localizing objects in 3D scenes according to the semantics of a given natural language is a fundamental yet important task in the field of multimedia understanding, which benefits various real-world applications such as robotics and autonomous driving. However, the majority of existing 3D object grounding methods are restricted to a single-sentence input describing an individual object, which cannot comprehend and reason more contextualized descriptions of multiple objects in more practical 3D cases. To this end, we introduce a new challenging task, called 3D Dense Object Grounding (3D DOG), to jointly localize multiple objects described in a more complicated paragraph rather than a single sentence. Instead of naively localizing each sentence-guided object independently, we found that dense objects described in the same paragraph are often semantically related and spatially located in a focused region of the 3D scene. To explore such semantic and spatial relationships of densely referred objects for more accurate localization, we propose a novel Stacked Transformer based framework for 3D DOG, named 3DOGSFormer. Specifically, we first devise a contextual query-driven local transformer decoder to generate initial grounding proposals for each target object. Then, we employ a proposal-guided global transformer decoder that exploits the local object features to learn their correlation for further refining initial grounding proposals. Extensive experiments on three challenging benchmarks (Nr3D, Sr3D, and ScanRefer) show that our proposed 3DOGSFormer outperforms state-of-the-art 3D single-object grounding methods and their dense-object variants by significant margins.Comment: ACM MM 202

A Novel ILP Framework for Summarizing Content with High Lexical Variety

Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events. However, there remains a significant need to summarize such content. Examples include the student responses to post-class reflective questions, product reviews, and news articles published by different news agencies related to the same events. High lexical diversity of these documents hinders the system's ability to effectively identify salient content and reduce summary redundancy. In this paper, we overcome this issue by introducing an integer linear programming-based summarization framework. It incorporates a low-rank approximation to the sentence-word co-occurrence matrix to intrinsically group semantically-similar lexical items. We conduct extensive experiments on datasets of student responses, product reviews, and news documents. Our approach compares favorably to a number of extractive baselines as well as a neural abstractive summarization system. The paper finally sheds light on when and why the proposed framework is effective at summarizing content with high lexical variety.Comment: Accepted for publication in the journal of Natural Language Engineering, 201

Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization

Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS. In this paper, we propose a neural approach for Chinese word segmentation which can exploit both lexicon and unlabeled data. Our approach is based on a variant of posterior regularization algorithm, and the unlabeled data and lexicon are incorporated into model training as indirect supervision by regularizing the prediction space of CWS models. Extensive experiments on multiple benchmark datasets in both in-domain and cross-domain scenarios validate the effectiveness of our approach.Comment: 7 pages, 11 figures, accepted by the 2019 World Wide Web Conference (WWW '19

Observation of oscillatory relaxation in the Sn-terminated surface of epitaxial rock-salt SnSe {111}\{111\} topological crystalline insulator

Topological crystalline insulators have been recently predicted and observed in rock-salt structure SnSe $\{111\}$ thin films. Previous studies have suggested that the Se-terminated surface of this thin film with hydrogen passivation, has a reduced surface energy and is thus a preferred configuration. In this paper, synchrotron-based angle-resolved photoemission spectroscopy, along with density functional theory calculations, are used to demonstrate conclusively that a rock-salt SnSe $\{111\}$ thin film epitaxially-grown on \ce{Bi2Se3} has a stable Sn-terminated surface. These observations are supported by low energy electron diffraction (LEED) intensity-voltage measurements and dynamical LEED calculations, which further show that the Sn-terminated SnSe $\{111\}$ thin film has undergone a surface structural relaxation of the interlayer spacing between the Sn and Se atomic planes. In sharp contrast to the Se-terminated counterpart, the observed Dirac surface state in the Sn-terminated SnSe $\{111\}$ thin film is shown to yield a high Fermi velocity, $0.50\times10^6$m/s, which suggests a potential mechanism of engineering the Dirac surface state of topological materials by tuning the surface configuration.Comment: 12 pages, 13 figures, supplementary materials include

Social Media-Based Secondary Distribution of Human Immunodeficiency Virus/Syphilis Self-testing Among Chinese Men Who Have Sex with Men.

BACKGROUND: Social media and secondary distribution (distributing self-testing kits by indexes through their networks) both show strong promise to improve human immunodeficiency virus (HIV) self-testing uptake. We assessed an implementation program in Zhuhai, China, which focused on the secondary distribution of HIV/syphilis self-test kits among men who have sex with men (MSM) via social media. METHODS: Men aged ≥16 years, born biologically male, and ever had sex with another man were recruited as indexes. Banner ads on a social media platform invited the participants to apply for up to 5 self-test kits every 3 months. Index men paid a deposit of US$15/kit refundable upon submitting a photograph of a completed test result via an online submission system. They were informed that they could distribute the kits to others (referred to as "alters"). RESULTS: A total of 371 unique index men applied for 1150 kits (mean age, 28.7 [standard deviation, 6.9] years), of which 1141 test results were returned (99%). Among them, 1099 were valid test results; 810 (74%) were from 331 unique index men, and 289 tests (26%) were from 281 unique alters. Compared to index men, a higher proportion of alters were naive HIV testers (40% vs 21%; P < .001). The total HIV self-test reactivity rate was 3%, with alters having a significantly higher rate than indexes (5% vs 2%; P = .008). A total of 21 people (3%) had a reactive syphilis test result. CONCLUSIONS: Integrating social media with the secondary distribution of self-test kits may hold promise to increase HIV/syphilis testing coverage and case identification among MSM

Investigation on the Temporal Surface Thermal Conditions for Thermal Comfort Researches Inside A Vehicle Cabin Under Summer Season Climate

With the proposes of improving occupant's thermal comfort and reducing the air conditioning power consumption, the present research carried out a comprehensive study on the surface thermal conductions and their influence parameters. A numerical model was built considering the transient conduction, convective and radiation heat transfer inside a vehicle cabin. For more accurate simulation of the radiation heat transfer behaviors, the radiation was considered into two spectral bands (short wave and long wave radiation), and the solar radiation was calculated by two solar fluxes (beam and diffuse solar radiation). An experiment was conducted to validate the numerical approach, showing a good agreement with the surface temperature. The surface thermal conditions were numerically simulated. The results show that the solar radiation is the most important factor in determining the internal surface thermal conditions. Effects of the window glass properties and the car body surface conditions were investigated. The numerical calculation results indicate that reducing the transitivity of window glass can effectively reduce the internal surface temperature. And the reflectivity of the vehicle cabin also has an important influence on the surface temperature, however, it's not so obvious as comparison to the window glass