3,351 research outputs found
Chlorophyll-a Algorithms for Oligotrophic Oceans: A Novel Approach Based on Three-Band Reflectance Difference
A new empirical algorithm is proposed to estimate surface chlorophyll-a concentrations (Chl) in the global ocean for Chl less than or equal to 0.25 milligrams per cubic meters (approximately 77% of the global ocean area). The algorithm is based on a color index (CI), defined as the difference between remote sensing reflectance (R(sub rs), sr(sup -1) in the green and a reference formed linearly between R(sub rs) in the blue and red. For low Chl waters, in situ data showed a tighter (and therefore better) relationship between CI and Chl than between traditional band-ratios and Chl, which was further validated using global data collected concurrently by ship-borne and SeaWiFS satellite instruments. Model simulations showed that for low Chl waters, compared with the band-ratio algorithm, the CI-based algorithm (CIA) was more tolerant to changes in chlorophyll-specific backscattering coefficient, and performed similarly for different relative contributions of non-phytoplankton absorption. Simulations using existing atmospheric correction approaches further demonstrated that the CIA was much less sensitive than band-ratio algorithms to various errors induced by instrument noise and imperfect atmospheric correction (including sun glint and whitecap corrections). Image and time-series analyses of SeaWiFS and MODIS/Aqua data also showed improved performance in terms of reduced image noise, more coherent spatial and temporal patterns, and consistency between the two sensors. The reduction in noise and other errors is particularly useful to improve the detection of various ocean features such as eddies. Preliminary tests over MERIS and CZCS data indicate that the new approach should be generally applicable to all existing and future ocean color instruments
Efficient Heterogeneous Graph Learning via Random Projection
Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep
learning on heterogeneous graphs. Typical HGNNs require repetitive message
passing during training, limiting efficiency for large-scale real-world graphs.
Recent pre-computation-based HGNNs use one-time message passing to transform a
heterogeneous graph into regular-shaped tensors, enabling efficient mini-batch
training. Existing pre-computation-based HGNNs can be mainly categorized into
two styles, which differ in how much information loss is allowed and
efficiency. We propose a hybrid pre-computation-based HGNN, named Random
Projection Heterogeneous Graph Neural Network (RpHGNN), which combines the
benefits of one style's efficiency with the low information loss of the other
style. To achieve efficiency, the main framework of RpHGNN consists of
propagate-then-update iterations, where we introduce a Random Projection
Squashing step to ensure that complexity increases only linearly. To achieve
low information loss, we introduce a Relation-wise Neighbor Collection
component with an Even-odd Propagation Scheme, which aims to collect
information from neighbors in a finer-grained way. Experimental results
indicate that our approach achieves state-of-the-art results on seven small and
large benchmark datasets while also being 230% faster compared to the most
effective baseline. Surprisingly, our approach not only surpasses
pre-processing-based baselines but also outperforms end-to-end methods.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation
Controllable text generation is a challenging and meaningful field in natural
language generation (NLG). Especially, poetry generation is a typical one with
well-defined and strict conditions for text generation which is an ideal
playground for the assessment of current methodologies. While prior works
succeeded in controlling either semantic or metrical aspects of poetry
generation, simultaneously addressing both remains a challenge. In this paper,
we pioneer the use of the Diffusion model for generating sonnets and Chinese
SongCi poetry to tackle such challenges. In terms of semantics, our
PoetryDiffusion model, built upon the Diffusion model, generates entire
sentences or poetry by comprehensively considering the entirety of sentence
information. This approach enhances semantic expression, distinguishing it from
autoregressive and large language models (LLMs). For metrical control, the
separation feature of diffusion generation and its constraint control module
enable us to flexibly incorporate a novel metrical controller to manipulate and
evaluate metrics (format and rhythm). The denoising process in PoetryDiffusion
allows for gradual enhancement of semantics and flexible integration of the
metrical controller which can calculate and impose penalties on states that
stray significantly from the target control distribution. Experimental results
on two datasets demonstrate that our model outperforms existing models in
automatic evaluation of semantic, metrical, and overall performance as well as
human evaluation.Comment: 9 Page
Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System
Dialogue systems and large language models (LLMs) have gained considerable
attention. However, the direct utilization of LLMs as task-oriented dialogue
(TOD) models has been found to underperform compared to smaller task-specific
models. Nonetheless, it is crucial to acknowledge the significant potential of
LLMs and explore improved approaches for leveraging their impressive abilities.
Motivated by the goal of leveraging LLMs, we propose an alternative approach
called User-Guided Response Optimization (UGRO) to combine it with a smaller
TOD model. This approach uses LLM as annotation-free user simulator to assess
dialogue responses, combining them with smaller fine-tuned end-to-end TOD
models. By utilizing the satisfaction feedback generated by LLMs, UGRO further
optimizes the supervised fine-tuned TOD model. Specifically, the TOD model
takes the dialogue history as input and, with the assistance of the user
simulator's feedback, generates high-satisfaction responses that meet the
user's requirements. Through empirical experiments on two TOD benchmarks, we
validate the effectiveness of our method. The results demonstrate that our
approach outperforms previous state-of-the-art (SOTA) results.Comment: Accepted by CIKM 202
Radiative transfer modeling of phytoplankton fluorescence quenching processes
We report the first radiative transfer model that is able to simulate phytoplankton fluorescence with both photochemical and non-photochemical quenching included. The fluorescence source term in the inelastic radiative transfer equation is proportional to both the quantum yield and scalar irradiance at excitation wavelengths. The photochemical and nonphotochemical quenching processes change the quantum yield based on the photosynthetic active radiation. A sensitivity study was performed to demonstrate the dependence of the fluorescence signal on chlorophyll a concentration, aerosol optical depths and solar zenith angles. This work enables us to better model the phytoplankton fluorescence, which can be used in the design of new space-based sensors that can provide sufficient sensitivity to detect the phytoplankton fluorescence signal. It could also lead to more accurate remote sensing algorithms for the study of phytoplankton physiology
DREADD: A Chemogenetic GPCR Signaling Platform
Recently, we created a family of engineered G protein-coupled receptors (GPCRs) called DREADD (designer receptors exclusively activated by designer drugs) which can precisely control three major GPCR signaling pathways (Gq, Gi, and Gs). DREADD technology has been successfully applied in a variety of in vivo studies to control GPCR signaling, and here we describe recent advances of DREADD technology and discuss its potential application in drug discovery, gene therapy, and tissue engineering
- …