Unpaired Image Captioning via Scene Graph Alignments

Most of current image captioning models heavily rely on paired image-caption datasets. However, getting large scale image-caption paired data is labor-intensive and time-consuming. In this paper, we present a scene graph-based approach for unpaired image captioning. Our framework comprises an image scene graph generator, a sentence scene graph generator, a scene graph encoder, and a sentence decoder. Specifically, we first train the scene graph encoder and the sentence decoder on the text modality. To align the scene graphs between images and sentences, we propose an unsupervised feature alignment method that maps the scene graph features from the image to the sentence modality. Experimental results show that our proposed model can generate quite promising results without using any image-caption training pairs, outperforming existing methods by a wide margin.Comment: Accepted in ICCV 201

Carbon burial in the mid-latitude fjords of Scotland

This project was supported by funding from the Scottish Blue Carbon Forum and BBSRC/NERC (ref. BB/M026620/01).Fjord sediments are recognized global hotspots for the burial of organic carbon (OC) and as an integral part of the global carbon (C) cycle. Relative to their spatial extent, more OC is trapped and stored in the sediments of fjords than any other marine sedimentary environment. Until recently, our understanding of the rate at which OC accumulates and is buried in mid-latitude fjord sediments was poor, as these systems have largely been overlooked in favour of their high latitude counterparts. In this study, we quantify and explore the drivers of OC burial in the mid-latitude fjords of Scotland. By examining fifteen sediment cores from ten fjords, it is estimated that on average 57.1 ± 10.9 g C m−2 yr−1 accumulates in the sediments of Scottish fjords, exceeding observed OC burial in other vegetated fjord systems. When combined with an understanding of the spatial heterogeneity of the fjord sediments, it is estimated that Scottish fjords bury 84,000 t of OC annually, which is equivalent to the whole North Sea sedimentary system, despite the area of the latter being approximately 190 times larger. These findings highlight that mid-latitude fjords play a more significant role in global carbon cycling than previously thought, providing highly effective burial and storage of OC in fjord sediments.PostprintPeer reviewe

Carbon accumulation and storage across contrasting saltmarshes of Scotland

This research was finically supported by the Scottish Blue Carbon Forum and the Natural Environment Research Council funded Carbon Storage in Intertidal Environments (C-SIDE) project (grant NE/R010846/1).Saltmarshes are acknowledged to be “carbon hotspots” due to their capacity to trap and store large quantities of carbon (C) within their soils and potentially have the ability to regulated climate over different timescales. In-turn governments and international organizations are now recognizing the need to include these intertidal ecosystems in national and global C accounting. Yet, in many regions, estimates of organic carbon (OC) storage and the rate at which OC is buried in saltmarsh soils either do not exist or at not at the scale necessary for inclusion in national C budgets. Here we bring together tools from across the geosciences to investigate the quantity of OC held within the soil and above/belowground biomass alongside estimates of the rate at which OC accumulates and the source of the OC within the soils of four contrasting Scottish saltmarshes. Using radiometric dating techniques it is estimated that OC accumulates at a rate of between 29.1 and 198.1 g C m⁻² yr⁻¹ across the different study sites. In contrast, the source of the OC varies little across the sites with 73%–99% of the OC within the saltmarsh soil originating from terrestrial/in situ sources; marine-derived OC plays a minor role in the development of the saltmarsh OC stocks. Using average values derived from the four sites it is possible to make first-order estimates of saltmarsh OC stocks and accumulation rates for all Scotland's 240 mapped saltmarshes. It is estimated that across Scotland saltmarsh habitat stores 1.15 ± 0.21 Mt OC which is supplemented by an additional 4385 ± 481 tonnes of OC each year.Publisher PDFPeer reviewe

Di-μ-hydroxido-bis­[aqua­trichlorido­tin(IV)] diethyl ether disolvate

The title compound, [Sn2Cl6(OH)2(H2O)2]·2C4H10O, consists of a centrosymmetric molecule and two additional solvent molecules and has an infinite two-dimensional network extending parallel to (101). The Sn atom is six-coordinate with a distorted octa­hedral geometry. Additional O—H⋯O hydrogen bonding leads to stabilization of the crystal structure

Neural Point Process for Learning Spatiotemporal Event Dynamics

Learning the dynamics of spatiotemporal events is a fundamental problem. Neural point processes enhance the expressivity of point process models with deep neural networks. However, most existing methods only consider temporal dynamics without spatial modeling. We propose Deep Spatiotemporal Point Process (\ours{}), a deep dynamics model that integrates spatiotemporal point processes. Our method is flexible, efficient, and can accurately forecast irregularly sampled events over space and time. The key construction of our approach is the nonparametric space-time intensity function, governed by a latent process. The intensity function enjoys closed form integration for the density. The latent process captures the uncertainty of the event sequence. We use amortized variational inference to infer the latent process with deep networks. Using synthetic datasets, we validate our model can accurately learn the true intensity function. On real-world benchmark datasets, our model demonstrates superior performance over state-of-the-art baselines. Our code and data can be found at the https://github.com/Rose-STL-Lab/DeepSTPP

μ-3-Thienylmalonato-κ2 O 1:O 3-bis­[triphenyl­tin(IV)]

The title compound, [Sn2(C6H5)6(C7H4O4S)], contains two molecules with similar conformations in the asymmetric unit. In each mol­ecule, the Sn atoms adopt a distorted tetra­hedral geometry arising from three C atoms of three phenyl rings and one O atom from the bridging 3-thienylmalonato ligand. The mol­ecules lie about inversion centers with the ligands facing each other, with C⋯O distances of 3.417 (10) and 3.475 (10) Å

Use of lead-210 as a novel tracer for lead (Pb) sources in plants

Lead (Pb) released from anthropogenic sources and stored in environmental repositories can be a potential source for secondary pollution. Here we develop a novel approach for tracking Pb from atmospheric deposition and other sources in the environment using fallout 210Pb as a tracer, and apply the method to samples collected from Richmond Park, London, the UK. The mean ratio of 210Pb to total Pb in atmospheric depositions collected from a site adjacent to the park during August–October 2012 was 96 Bq mg−1, while the ratio in surface soils from the park was typically an order of magnitude lower. The difference between these values made it possible to trace the source of Pb in the plants. The 210Pb/Pb ratios in plants varied from 0 to 34 Bq mg−1 indicating different levels of Pb absorption from the atmosphere. The ratio in mosses had an average value of 22 Bq mg−1. This suggests that only around 20% of the Pb they contain was from direct atmospheric deposition, revealing possible limitations in the use of terrestrial mosses for monitoring atmospheric pollution. As well as tracking sources, variations in the 210Pb/Pb ratio can also reveal ways in which Pb is transferred within plants

Net atmospheric mercury deposition to Svalbard : estimates from lacustrine sediments

Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Atmospheric Environment 59 (2012): 509-513, doi:10.1016/j.atmosenv.2012.05.048.In this study we used lake sediments, which faithfully record Hg inputs, to derive estimates of net atmospheric Hg deposition to Svalbard, Norwegian Arctic. With the exception of one site affected by local pollution, the study lakes show twofold to fivefold increases in sedimentary Hg accumulation since 1850, likely due to long-range atmospheric transport and deposition of anthropogenic Hg. Sedimentary Hg accumulation in these lakes is a linear function of the ratio of catchment area to lake area, and we used this relationship to model net atmospheric Hg flux: preindustrial and modern estimates are 2.5±3.3 μg/m2/y and 7.0±3.0 μg/m2/y, respectively. The modern estimate, by comparison with data for Hg wet deposition, indicates that atmospheric mercury depletion events (AMDEs) or other dry deposition processes contribute approximately half (range 0-70%) of the net flux. Hg from AMDEs may be moving in significant quantities into aquatic ecosystems, where it is a concern because of contamination of aquatic food webs.Funding was provided by an NSERC Discovery Grant (Drevnick) and the Norges forskningsråd (grant number 107745/730)

Time-Space Relationship Analysis Model on the Bus Driving Characteristics of Different Drivers Based on the Traffic Performance Index System

With the extensive application of the concept of green traffic, the relationship between the driving characteristics of different drivers and energy consumption and traffic performance conditions, etc. is gradually becoming a research hotspot. Based on bus status data recorded by travel data recorders with a vehicle-mounted satellite positioning function and in view of external bus behaviours and driver’s performance, a bus driving characteristic model of drivers is established. A time-space analysis model of the driving characteristics of different drivers based on traffic performance index is also established through fuzzy association rules and a type-2 fuzzy set prediction algorithm. Test results show that the prediction algorithm can accurately describe the time-space relationship between the traffic congestion index and bus driving characteristic model and achieve relatively high prediction accuracy. The problem of the lagging release of traffic performance index caused by massive calculation for floating vehicle data can be effectively solved through this algorithm, which can serve as an important reference for analyzing traffic performance conditions, as well as the energy conservation and emission reduction of buses