Sub-workflow parallel implementation of aerosol optical depth retrieval from MODIS data case on a Grid platform

Aerosol Optical Depth (AOD) is an significant parameter of aerosol optical properties. Operational production of AOD datasets over long time series, large-scale coverage puts on a severe challenge to computing technologies due to both the complexity of retrieval algorithm and the huge data amounts. The Grid computing solution-Remote Sensing Service Node (RSSN) was constructed as a high-throughput platform for remote sensing applications. Taking the sub-workflow level characteristics of some remote sensing retrieval applications into consideration, a sub-workflow parallel implementation for the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor data was taken on the RSSN, and an initial experiment result proved that the subworkflow parallel could further reduce the runtime of data parallel solutions commonly used

Quality assurance plan for China collection 2.0 aerosol datasets

The inversion of atmospheric aerosol optical depth (AOD) using satellite data has always been a challenge topic in atmospheric research. In order to solve the aerosol retrieval problem over bright land surface, the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm has been developed based on the synergetic using of the MODIS data of TERRA and AQUA satellites [1, 2]. In this paper we describe, in details, the quality assessment or quality assurance (QA) plan for AOD products derived using the SRAP algorithm. The pixel-based QA plan is to give a QA flag to every step of the process in the AOD retrieval. The quality assessment procedures include three common aspects: 1) input data resource flags, 2) retrieval processing flags, 3) product quality flags [3]. Besides, all AOD products are assigned a QA ‘confidence’ flag (QAC) that represents the aggregation of all the individual QA flags. This QAC value ranges from 3 to 0, with QA = 3 indicating the retrievals of highest confidence and QA = 2/QA = 1 progressively lower confidence [4], and 0 means ‘bad’ quality. These QA (QAC) flags indicate how the particular retrieval process should be considered. It is also used as a filter for expected quantitative value of the retrieval, or to provide weighting for aggregating/averaging computations [5]. All of the QA flags are stored as a “bit flag” scientific dataset array in which QA flags of each step are stored in particular bit positions

Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines. The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors. Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multi-answer setting. Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning. Our code and models can be found at https://github.com/LuJunru/TranCLR.Comment: Findings of EMNLP 202

Dynamic gain and frequency comb formation in exceptional-point lasers

Exceptional points (EPs)--singularities in the parameter space of non-Hermitian systems where two nearby eigenmodes coalesce--feature unique properties with applications for microcavity lasers such as sensitivity enhancement and chiral emission. Present EP lasers operate with static populations in the gain medium. Here, we show theoretically that a laser operating sufficiently close to an EP will spontaneously induce a multi-spectral multi-modal instability that creates an oscillating population inversion and generates a frequency comb. The comb formation is enhanced by the non-orthogonality of modes via the Petermann factor. Such an "EP comb" features an ultra-compact size and a widely tunable repetition rate, without requiring external modulators or a continuous-wave pump. We develop an exact ab initio dynamic solution of the space-dependent Maxwell-Bloch equations, describing all steady-state properties of the EP comb. We illustrate this phenomenon in a realistic parity-time-symmetric 5-{\mu}m-long AlGaAs cavity and validate our prediction with finite-difference time-domain simulations. This work reveals the rich physics that connect non-Hermitian degeneracies and the nonlinear dynamics of gain media to fundamentally alter the laser behavior

Extracting event temporal relations via hyperbolic geometry

Detecting events and their evolution through time is a crucial task in natural language understanding. Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. However, embeddings in the Euclidean space cannot capture richer asymmetric relations such as event temporal relations. We thus propose to embed events into hyperbolic spaces, which are intrinsically oriented at modeling hierarchical structures. We introduce two approaches to encode events and their temporal relations in hyperbolic spaces. One approach leverages hyperbolic embeddings to directly infer event relations through simple geometrical operations. In the second one, we devise an end-to-end architecture composed of hyperbolic neural units tailored for the temporal relation extraction task. Thorough experimental assessments on widely used datasets have shown the benefits of revisiting the tasks on a different geometrical space, resulting in state-of-the-art performance on several standard metrics. Finally, the ablation study and several qualitative analyses highlighted the rich event semantics implicitly encoded into hyperbolic spaces

China collection 2.1: Aerosol Optical Depth dataset for mainland China at 1km resolution

A wide range of data products have been published since the operation of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's TERRA and AQUA satellites. Based on DarkTarget and DeepBlue method, NASA has published Aerosol Optical Depth (AOD) products Collection 6.0 with spatial resolution of 3km. Although validated globally, regional and systematic errors are still found in the MODIS-retrieved AOD products. This is especially remarkable for bright heterogeneous land surface, such as mainland China. In order to solve the aerosol retrieval problem over heterogeneous bright land surface, the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) has been developed based on the synergetic use of the MODIS data of TERRA and AQUA satellites. Using the SRAP algorithm, we produced AOD dataset-China Collection 2.1 at 1km spatial resolution, dated from August 2002 to 2012. We compared the China Collection 2.1 AOD datasets for 2010 with AERONET data. From those 2460 collocations, representing mutually cloud-free conditions, we find that 62% of China Collection 2.1 AOD values comparing with AERONET-observed values within an expected error envelop of 20% and 55% within an expected error envelop of 15%. Compared with MODIS Level 2 aerosol products, China Collection 2.1 AOD datasets have a more complete coverage with fewer data gaps over the study region

One-stop interventional procedure for bicuspid aortic stenosis in a patient with coexisting aortic coarctation: a case report

IntroductionCoarctation of the aorta (CoA) is usually diagnosed and corrected early in life. Most untreated patients with CoA usually die before 50 years of age. Adult patients with concomitant CoA and severe bicuspid aortic stenosis are relatively rare and present complex management challenges without standard guidelines.Case summaryA 63-year-old female patient with uncontrolled hypertension was admitted due to chest pain and dyspnea upon exertion (NYHA grades III). Echocardiogram showed a severely calcified and stenotic bicuspid aortic valve (BAV). A severe stenotic calcified eccentric aortic coarctation 20 mm distal to the left subclavian artery (LSA) was discovered by computed tomography (CT) angiography. Following consultation with the cardiac team and patient willingness, we performed a one-stop interventional procedure to repair both defects. First, a cheatham-platinum (CP) stent was implanted via the right femoral access, immediately distal to the LSA. Due to the markedly twisted and angled descending aortic arch, we chose to perform transcatheter aortic valve replacement (TAVR) via the left common carotid artery. The patient was discharged and followed up for 1 year without symptoms.DiscussionAlthough surgery is still the main treatment for these diseases, it is not suitable for high-risk surgical patient. Transcatheter intervention for patients with severe aortic stenosis complicated with CoA simultaneously is rarely reported. The success of this procedure depends on the patient's vascular condition, the skills of the heart team, and the availability of the technical platform.ConclusionOur case report demonstrates the feasibility and efficacy of a one-stop interventional procedure in an adult patient with concurrent severely calcified BAV and CoA via two different vascular approaches. Transcatheter intervention, in contrast to traditional surgical approaches or two-stop interventional procedures, as a minimally invasive and novel method, offers a wider range of therapeutic methods for such diseases

Event-centric question answering via contrastive learning and invertible event transformation

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines. The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors. Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multi-answer setting. Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning. Our code and models can be found at https://github.com/LuJunru/TranCLR

CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion

The dual-encoder has become the de facto architecture for dense retrieval. Typically, it computes the latent representations of the query and document independently, thus failing to fully capture the interactions between the query and document. To alleviate this, recent research has focused on obtaining query-informed document representations. During training, it expands the document with a real query, but during inference, it replaces the real query with a generated one. This inconsistency between training and inference causes the dense retrieval model to prioritize query information while disregarding the document when computing the document representation. Consequently, it performs even worse than the vanilla dense retrieval model because its performance heavily relies on the relevance between the generated queries and the real query.In this paper, we propose a curriculum sampling strategy that utilizes pseudo queries during training and progressively enhances the relevance between the generated query and the real query. By doing so, the retrieval model learns to extend its attention from the document alone to both the document and query, resulting in high-quality query-informed document representations. Experimental results on both in-domain and out-of-domain datasets demonstrate that our approach outperforms previous dense retrieval models.Comment: Accetpted to EMNLP 202