A Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation

A domain adaptation method for urban scene segmentation is proposed in this work. We develop a fully convolutional tri-branch network, where two branches assign pseudo labels to images in the unlabeled target domain while the third branch is trained with supervision based on images in the pseudo-labeled target domain. The re-labeling and re-training processes alternate. With this design, the tri-branch network learns target-specific discriminative representations progressively and, as a result, the cross-domain capability of the segmenter improves. We evaluate the proposed network on large-scale domain adaptation experiments using both synthetic (GTA) and real (Cityscapes) images. It is shown that our solution achieves the state-of-the-art performance and it outperforms previous methods by a significant margin.Comment: Accepted by ICASSP 201

Stability of Monoterpene-Derived α-Hydroxyalkyl-Hydroperoxides in Aqueous Organic Media: Relevance to the Fate of Hydroperoxides in Aerosol Particle Phases

The α-hydroxyalkyl-hydroperoxides [R–(H)C(−OH)(−OOH), α-HH] produced in the ozonolysis of unsaturated organic compounds may contribute to secondary organic aerosol (SOA) aging. α-HHs’ inherent instability, however, hampers their detection and a positive assessment of their actual role. Here we report, for the first time, the rates and products of the decomposition of the α-HHs generated in the ozonolysis of atmospherically important monoterpenes α-pinene (α-P), d-limonene (d-L), γ-terpinene (γ-Tn), and α-terpineol (α-Tp) in water/acetonitrile (W/AN) mixtures. We detect α-HHs and multifunctional decomposition products as chloride adducts by online electrospray ionization mass spectrometry. Experiments involving D₂O and H₂¹⁸O, instead of H₂¹⁶O, and an OH-radical scavenger show that α-HHs decompose into gem-diols + H₂O₂ rather than free radicals. α-HHs decay mono- or biexponentially depending on molecular structure and solvent composition. e-Fold times, τ_(1/e), in water-rich solvent mixtures range from τ_(1/e) = 15–45 min for monoterpene-derived α-HHs to τ_(1/e) > 10³ min for the α-Tp-derived α-HH. All τ_(1/e)’s dramatically increase in <20% (v/v) water. Decay rates of the α-Tp-derived α-HH in pure water increase at lower pH (2.3 ≤ pH ≤ 3.3). The hydroperoxides detected in day-old SOA samples may reflect their increased stability in water-poor media and/or the slow decomposition of α-HHs from functionalized terpenes

Stability of Monoterpene-Derived α-Hydroxyalkyl-Hydroperoxides in Aqueous Organic Media: Relevance to the Fate of Hydroperoxides in Aerosol Particle Phases

The α-hydroxyalkyl-hydroperoxides [R–(H)C(−OH)(−OOH), α-HH] produced in the ozonolysis of unsaturated organic compounds may contribute to secondary organic aerosol (SOA) aging. α-HHs’ inherent instability, however, hampers their detection and a positive assessment of their actual role. Here we report, for the first time, the rates and products of the decomposition of the α-HHs generated in the ozonolysis of atmospherically important monoterpenes α-pinene (α-P), d-limonene (d-L), γ-terpinene (γ-Tn), and α-terpineol (α-Tp) in water/acetonitrile (W/AN) mixtures. We detect α-HHs and multifunctional decomposition products as chloride adducts by online electrospray ionization mass spectrometry. Experiments involving D₂O and H₂¹⁸O, instead of H₂¹⁶O, and an OH-radical scavenger show that α-HHs decompose into gem-diols + H₂O₂ rather than free radicals. α-HHs decay mono- or biexponentially depending on molecular structure and solvent composition. e-Fold times, τ_(1/e), in water-rich solvent mixtures range from τ_(1/e) = 15–45 min for monoterpene-derived α-HHs to τ_(1/e) > 10³ min for the α-Tp-derived α-HH. All τ_(1/e)’s dramatically increase in <20% (v/v) water. Decay rates of the α-Tp-derived α-HH in pure water increase at lower pH (2.3 ≤ pH ≤ 3.3). The hydroperoxides detected in day-old SOA samples may reflect their increased stability in water-poor media and/or the slow decomposition of α-HHs from functionalized terpenes

Bone Mineral Density Reference Standards for Chinese Children Aged 3-18: Cross-Sectional Results of the 2013-2015 China Child and Adolescent Cardiovascular Health (CCACH) Study

Objectives: No nationwide paediatric reference standards for bone mineral density (BMD) are available in China. We aimed to provide sex-specific BMD reference values for Chinese children and adolescents (3-18 years). Methods: Data (10 818 participants aged 3-18 years) were obtained from cross-sectional surveys of the China Child and Adolescent Cardiovascular Health in 2015, which included four municipality cities and three provinces. BMD was measured using Hologic Discovery Dual Energy X-ray Absorptiometry (DXA) scanner. The DXA measures were modelled against age, with height as an independent variable. The LMS statistical method using a curve fitting procedure was used to construct reference smooth cross-sectional centile curves for dependent versus independent variables. Results: Children residing in Northeast China had the highest total body less head (TBLH) BMD while children residing in Shandong Province had the lowest values. Among children, TBLH BMD was higher for boys as compared with girls; but, it increased with age and height in both sexes. Furthermore, TBLH BMD was higher among US children as compared with Chinese children. There was a large difference in BMD for height among children from these two countries. US children had a much higher BMD at each percentile (P) than Chinese children; the largest observed difference was at P50 and P3 and the smallest difference was at P97. Conclusions: This is the first study to present a sex-specific reference dataset for Chinese children aged 3-18 years. The data can help clinicians improve interpretation, assessment and monitoring of densitometry results

Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment

Medical phrase grounding (MPG) aims to locate the most relevant region in a medical image, given a phrase query describing certain medical findings, which is an important task for medical image analysis and radiological diagnosis. However, existing visual grounding methods rely on general visual features for identifying objects in natural images and are not capable of capturing the subtle and specialized features of medical findings, leading to sub-optimal performance in MPG. In this paper, we propose MedRPG, an end-to-end approach for MPG. MedRPG is built on a lightweight vision-language transformer encoder and directly predicts the box coordinates of mentioned medical findings, which can be trained with limited medical data, making it a valuable tool in medical image analysis. To enable MedRPG to locate nuanced medical findings with better region-phrase correspondences, we further propose Tri-attention Context contrastive alignment (TaCo). TaCo seeks context alignment to pull both the features and attention outputs of relevant region-phrase pairs close together while pushing those of irrelevant regions far away. This ensures that the final box prediction depends more on its finding-specific regions and phrases. Experimental results on three MPG datasets demonstrate that our MedRPG outperforms state-of-the-art visual grounding approaches by a large margin. Additionally, the proposed TaCo strategy is effective in enhancing finding localization ability and reducing spurious region-phrase correlations

TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise

Large Language Models (LLMs) exhibit impressive reasoning and data augmentation capabilities in various NLP tasks. However, what about small models? In this work, we propose TeacherLM-7.1B, capable of annotating relevant fundamentals, chain of thought, and common mistakes for most NLP samples, which makes annotation more than just an answer, thus allowing other models to learn "why" instead of just "what". The TeacherLM-7.1B model achieved a zero-shot score of 52.3 on MMLU, surpassing most models with over 100B parameters. Even more remarkable is its data augmentation ability. Based on TeacherLM-7.1B, we augmented 58 NLP datasets and taught various student models with different parameters from OPT and BLOOM series in a multi-task setting. The experimental results indicate that the data augmentation provided by TeacherLM has brought significant benefits. We will release the TeacherLM series of models and augmented datasets as open-source.Comment: 5 figures, 15 page

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Path Planning Strategy for a Manipulator Based on a Heuristically Constructed Network

Collision-free path planning of manipulators is becoming indispensable for space exploration and on-orbit operation. Manipulators in these scenarios are restrained in terms of computing resources and storage, so the path planning method used in such tasks is usually limited in its operating time and the amount of data transmission. In this paper, a heuristically constructed network (HCN) construction strategy is proposed. The HCN construction contains three steps: determining the number of hub configurations and selecting and connecting hub configurations. Considering the connection time and connectivity of HCN, the number of hub configurations is determined first. The selection of hub configurations includes the division of work space and the optimization of the hub configurations. The work space can be divided by considering comprehensively the similarity among the various configurations within the same region, the dissimilarity among all regions, and the correlation among adjacent regions. The hub configurations can be selected by establishing and solving the optimization model. Finally, these hub configurations are connected to obtain the HCN. The simulation indicates that the path points number and the planning time is decreased by 45.5% and 48.4%, respectively, which verify the correctness and effectiveness of the proposed path planning strategy based on the HCN