94 research outputs found
Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases
A critical challenge in constructing a natural language interface to database
(NLIDB) is bridging the semantic gap between a natural language query (NLQ) and
the underlying data. Two specific ways this challenge exhibits itself is
through keyword mapping and join path inference. Keyword mapping is the task of
mapping individual keywords in the original NLQ to database elements (such as
relations, attributes or values). It is challenging due to the ambiguity in
mapping the user's mental model and diction to the schema definition and
contents of the underlying database. Join path inference is the process of
selecting the relations and join conditions in the FROM clause of the final SQL
query, and is difficult because NLIDB users lack the knowledge of the database
schema or SQL and therefore cannot explicitly specify the intermediate tables
and joins needed to construct a final SQL query. In this paper, we propose
leveraging information from the SQL query log of a database to enhance the
performance of existing NLIDBs with respect to these challenges. We present a
system Templar that can be used to augment existing NLIDBs. Our extensive
experimental evaluation demonstrates the effectiveness of our approach, leading
up to 138% improvement in top-1 accuracy in existing NLIDBs by leveraging SQL
query log information.Comment: Accepted to IEEE International Conference on Data Engineering (ICDE)
201
DEEP CONVECTIVE TRANSPORT AND WET SCAVENGING IN DIFFERENT CONVECTIVE REGIMES DURING THE DC3 FIELD CAMPAIGN
Deep convective transport of surface moisture and pollution from the planetary boundary layer to the upper troposphere and lower stratosphere affects the radiation budget and climate. Firstly, I analyzed the deep convective transport through cloud-resolved simulations of three different convective regimes from the 2012 Deep Convective Clouds and Chemistry (DC3) field campaign: an airmass thunderstorm, a supercell storm, and a mesoscale convective system (MCS). Analysis of vertical flux divergence shows that deep convective transport in the supercell case is the strongest per unit area, while transport of boundary layer insoluble trace gases is relatively weak in the MCS due to the injection of clean air into the mid-troposphere by a strong rear inflow jet. Additionally, forward and backward trajectories are used to determine the source of the upper-level detrained air.
My second focus is using of cloud parameterized Weather Research and Forecasting model coupled with chemistry (WRF-Chem) simulations to analyze the subgrid deep convective transport in the supercell case and MCS case. Based on the precipitation results, the best WRF simulation of these storms was obtained with use of the Grell-Freitas (GF) convective scheme. The default subgrid convective transport scheme was replaced with a scheme to compute convective transport within the GF subgrid cumulus parameterization, which resulted in improved transport simulations. The results demonstrate the importance of having subgrid convective transport consistent with the convective parameterization in regional models. Moreover, the subgrid scale convective transport played a more significant role in the supercell case than the MCS case.
I evaluated the model-simulated subgrid wet scavenging of soluble trace gases (such as HNO3, CH2O, CH3OOH, H2O2, and SO2) in the supercell case, and improved subgrid wet scavenging by determining appropriate ice retention factors, and by adjusting the conversion rate of cloud water to rain water. The introduction of the ice retention factors greatly improved the model simulation of less soluble species (e.g. decreased the CH2O simulation error by 12 % and decreased the CH3OOH simulation error by 63%). Finally, I conducted a > 24-hour long simulation to examine downwind ozone production and its sensitivity to the ice retention factors
Small but Mighty: New Benchmarks for Split and Rephrase
Split and Rephrase is a text simplification task of rewriting a complex
sentence into simpler ones. As a relatively new task, it is paramount to ensure
the soundness of its evaluation benchmark and metric. We find that the widely
used benchmark dataset universally contains easily exploitable syntactic cues
caused by its automatic generation process. Taking advantage of such cues, we
show that even a simple rule-based model can perform on par with the
state-of-the-art model. To remedy such limitations, we collect and release two
crowdsourced benchmark datasets. We not only make sure that they contain
significantly more diverse syntax, but also carefully control for their quality
according to a well-defined set of criteria. While no satisfactory automatic
metric exists, we apply fine-grained manual evaluation based on these criteria
using crowdsourcing, showing that our datasets better represent the task and
are significantly more challenging for the models.Comment: In EMNLP 202
IMD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation
Recent progresses on self-supervised 3D human action representation learning
are largely attributed to contrastive learning. However, in conventional
contrastive frameworks, the rich complementarity between different skeleton
modalities remains under-explored. Moreover, optimized with distinguishing
self-augmented samples, models struggle with numerous similar positive
instances in the case of limited action categories. In this work, we tackle the
aforementioned problems by introducing a general Inter- and Intra-modal Mutual
Distillation (IMD) framework. In IMD, we first re-formulate the
cross-modal interaction as a Cross-modal Mutual Distillation (CMD) process.
Different from existing distillation solutions that transfer the knowledge of a
pre-trained and fixed teacher to the student, in CMD, the knowledge is
continuously updated and bidirectionally distilled between modalities during
pre-training. To alleviate the interference of similar samples and exploit
their underlying contexts, we further design the Intra-modal Mutual
Distillation (IMD) strategy, In IMD, the Dynamic Neighbors Aggregation (DNA)
mechanism is first introduced, where an additional cluster-level discrimination
branch is instantiated in each modality. It adaptively aggregates
highly-correlated neighboring features, forming local cluster-level
contrasting. Mutual distillation is then performed between the two branches for
cross-level knowledge exchange. Extensive experiments on three datasets show
that our approach sets a series of new records.Comment: submitted to IJCV. arXiv admin note: substantial text overlap with
arXiv:2208.1244
Masked Motion Predictors are Strong 3D Action Representation Learners
In 3D human action recognition, limited supervised data makes it challenging
to fully tap into the modeling potential of powerful networks such as
transformers. As a result, researchers have been actively investigating
effective self-supervised pre-training strategies. In this work, we show that
instead of following the prevalent pretext task to perform masked
self-component reconstruction in human joints, explicit contextual motion
modeling is key to the success of learning effective feature representation for
3D action recognition. Formally, we propose the Masked Motion Prediction (MAMP)
framework. To be specific, the proposed MAMP takes as input the masked
spatio-temporal skeleton sequence and predicts the corresponding temporal
motion of the masked human joints. Considering the high temporal redundancy of
the skeleton sequence, in our MAMP, the motion information also acts as an
empirical semantic richness prior that guide the masking process, promoting
better attention to semantically rich temporal regions. Extensive experiments
on NTU-60, NTU-120, and PKU-MMD datasets show that the proposed MAMP
pre-training substantially improves the performance of the adopted vanilla
transformer, achieving state-of-the-art results without bells and whistles. The
source code of our MAMP is available at https://github.com/maoyunyao/MAMP.Comment: To appear in ICCV 202
Pronounced Increases in Nitrogen Emissions and Deposition Due to the Historic 2020 Wildfires in the Western U.S.
Wildfire outbreaks can lead to extreme biomass burning (BB) emissions of both oxidized (e.g., nitrogen oxides; NOx= NO+NO2) and reduced form(e.g., ammonia; NH3) nitrogen (N) compounds. High N emissions aremajor concerns for air quality, atmospheric deposition, and consequential human and ecosystemhealth impacts. In this study, we use both satellite-based observations and modeling results to quantify the contribution of BB to the total emissions, and approximate the impact on total N deposition in the western U.S. Our results show that during the 2020 wildfire season of August–October, BB contributes significantly to the total emissions, with a satellite-derived fraction of NH3 to the total reactiveN emissions (median~40%) in the range of aircraft observations. During the peak of the western August Complex Fires in September, BB contributed to~55%(for the contiguous U.S.) and~83%(for thewestern U.S.) of the monthly total NOx and NH3 emissions. Overall, there is good model performance of the George Mason University- Wildfire Forecasting System(GMU-WFS) used in this work. The extreme BB emissions lead to significant contributions to the total N deposition for different ecosystems in California, with an average August – October 2020 relative increase of~78%(from7.1 to 12.6 kg ha−1 year−1) in deposition rate tomajor vegetation types (mixed forests+grasslands/ shrublands/savanna) compared to the GMU-WFS simulations without BB emissions. For mixed forest types only, the average N deposition rate increases (from 6.2 to 16.9 kg ha−1 year−1) are even larger at ~173%. Such large N deposition due to extreme BB emissions are much (~6-12 times) larger than low-end critical load thresholds for major vegetation types (e.g., forests at 1.5-3 kg ha−1 year−1), and thus may result in adverse N deposition effects across larger areas of lichen communities found in California\u27s mixed conifer forests
FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge
Detecting factual errors in textual information, whether generated by large
language models (LLM) or curated by humans, is crucial for making informed
decisions. LLMs' inability to attribute their claims to external knowledge and
their tendency to hallucinate makes it difficult to rely on their responses.
Humans, too, are prone to factual errors in their writing. Since manual
detection and correction of factual errors is labor-intensive, developing an
automatic approach can greatly reduce human effort. We present FLEEK, a
prototype tool that automatically extracts factual claims from text, gathers
evidence from external knowledge sources, evaluates the factuality of each
claim, and suggests revisions for identified errors using the collected
evidence. Initial empirical evaluation on fact error detection (77-85\% F1)
shows the potential of FLEEK. A video demo of FLEEK can be found at
https://youtu.be/NapJFUlkPdQ.Comment: EMNLP 2023 (Demonstration Track
- …