81 research outputs found
Total Phosphorus and Nitrogen Dynamics and Influencing Factors in Dongting Lake Using Landsat Data
Total phosphorus (TP) and total nitrogen (TN) reflect the state of eutrophication. However, traditional point-based water quality monitoring methods are time-consuming and labor-intensive, and insufficient to estimate and assess water quality at a large scale. In this paper, we constructed machine learning models for TP and TN inversion using measured data and satellite imagery band reflectance, and verified it by in situ data. Atmospheric correction was performed on the Landsat Top of Atmosphere (TOP) data by removing the effect of the adjacency effect and correcting differences between Landsat sensors. Then, using the established model, the TP and TN patterns in Dongting Lake with a spatial resolution of 30 m from 1996 to 2021 were derived for the first time. The annual and monthly spatio-temporal variation characteristics of TP and TN in Dongting Lake were investigated in details, and the influences of hydrometeorological elements on water quality variations were analyzed. The results show that the established empirical model can accurately estimate TP with coefficient (R2) ≥ 0.70, root mean square error (RMSE) ≤ 0.057 mg/L, mean relative error (MRE) ≤ 0.23 and TN with R2 ≥ 0.73, RMSE ≤ 0.48 mg/L and MRE ≤ 0.20. From 1996 to 2021, TP in Dongting Lake showed a downward trend and TN showed an upward trend, while the summer value was much higher than the other seasons. Furthermore, the influencing factors on TP and TN variations were investigated and discussed. Between 1996 and 2003, the main contributors to the change of water quality in Dongting Lake were external inputs such as water level and flow. The significant changes in water quantity and sediment characteristics following the operation of the Three Gorges Dam (TGD) in 2003 also had an impact on the water quality in Dongting Lake
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
How to optimally dispatch orders to vehicles and how to tradeoff between
immediate and future returns are fundamental questions for a typical
ride-hailing platform. We model ride-hailing as a large-scale parallel ranking
problem and study the joint decision-making task of order dispatching and fleet
management in online ride-hailing platforms. This task brings unique challenges
in the following four aspects. First, to facilitate a huge number of vehicles
to act and learn efficiently and robustly, we treat each region cell as an
agent and build a multi-agent reinforcement learning framework. Second, to
coordinate the agents from different regions to achieve long-term benefits, we
leverage the geographical hierarchy of the region grids to perform hierarchical
reinforcement learning. Third, to deal with the heterogeneous and variant
action space for joint order dispatching and fleet management, we design the
action as the ranking weight vector to rank and select the specific order or
the fleet management destination in a unified formulation. Fourth, to achieve
the multi-scale ride-hailing platform, we conduct the decision-making process
in a hierarchical way where a multi-head attention mechanism is utilized to
incorporate the impacts of neighbor agents and capture the key agent in each
scale. The whole novel framework is named as CoRide. Extensive experiments
based on multiple cities real-world data as well as analytic synthetic data
demonstrate that CoRide provides superior performance in terms of platform
revenue and user experience in the task of city-wide hybrid order dispatching
and fleet management over strong baselines.Comment: CIKM 201
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank
Learning-to-rank is a core technique in the top-N recommendation task, where
an ideal ranker would be a mapping from an item set to an arrangement (a.k.a.
permutation). Most existing solutions fall in the paradigm of probabilistic
ranking principle (PRP), i.e., first score each item in the candidate set and
then perform a sort operation to generate the top ranking list. However, these
approaches neglect the contextual dependence among candidate items during
individual scoring, and the sort operation is non-differentiable. To bypass the
above issues, we propose Set-To-Arrangement Ranking (STARank), a new framework
directly generates the permutations of the candidate items without the need for
individually scoring and sort operations; and is end-to-end differentiable. As
a result, STARank can operate when only the ground-truth permutations are
accessible without requiring access to the ground-truth relevance scores for
items. For this purpose, STARank first reads the candidate items in the context
of the user browsing history, whose representations are fed into a
Plackett-Luce module to arrange the given items into a list. To effectively
utilize the given ground-truth permutations for supervising STARank, we
leverage the internal consistency property of Plackett-Luce models to derive a
computationally efficient list-wise loss. Experimental comparisons against 9
the state-of-the-art methods on 2 learning-to-rank benchmark datasets and 3
top-N real-world recommendation datasets demonstrate the superiority of STARank
in terms of conventional ranking metrics. Notice that these ranking metrics do
not consider the effects of the contextual dependence among the items in the
list, we design a new family of simulation-based ranking metrics, where
existing metrics can be regarded as special cases. STARank can consistently
achieve better performance in terms of PBM and UBM simulation-based metrics.Comment: CIKM 202
Can Large Language Models Infer Causation from Correlation?
Causal inference is one of the hallmarks of human intelligence. While the
field of CausalNLP has attracted much interest in the recent years, existing
causal inference datasets in NLP primarily rely on discovering causality from
empirical knowledge (e.g., commonsense knowledge). In this work, we propose the
first benchmark dataset to test the pure causal inference skills of large
language models (LLMs). Specifically, we formulate a novel task Corr2Cause,
which takes a set of correlational statements and determines the causal
relationship between the variables. We curate a large-scale dataset of more
than 400K samples, on which we evaluate seventeen existing LLMs. Through our
experiments, we identify a key shortcoming of LLMs in terms of their causal
inference skills, and show that these models achieve almost close to random
performance on the task. This shortcoming is somewhat mitigated when we try to
re-purpose LLMs for this skill via finetuning, but we find that these models
still fail to generalize -- they can only perform causal inference in
in-distribution settings when variable names and textual expressions used in
the queries are similar to those in the training set, but fail in
out-of-distribution settings generated by perturbing these queries. Corr2Cause
is a challenging task for LLMs, and would be helpful in guiding future research
on improving LLMs' pure reasoning skills and generalizability. Our data is at
https://huggingface.co/datasets/causalnlp/corr2cause. Our code is at
https://github.com/causalNLP/corr2cause
Refined Edge Usage of Graph Neural Networks for Edge Prediction
Graph Neural Networks (GNNs), originally proposed for node classification,
have also motivated many recent works on edge prediction (a.k.a., link
prediction). However, existing methods lack elaborate design regarding the
distinctions between two tasks that have been frequently overlooked: (i) edges
only constitute the topology in the node classification task but can be used as
both the topology and the supervisions (i.e., labels) in the edge prediction
task; (ii) the node classification makes prediction over each individual node,
while the edge prediction is determinated by each pair of nodes. To this end,
we propose a novel edge prediction paradigm named Edge-aware Message PassIng
neuRal nEtworks (EMPIRE). Concretely, we first introduce an edge splitting
technique to specify use of each edge where each edge is solely used as either
the topology or the supervision (named as topology edge or supervision edge).
We then develop a new message passing mechanism that generates the messages to
source nodes (through topology edges) being aware of target nodes (through
supervision edges). In order to emphasize the differences between pairs
connected by supervision edges and pairs unconnected, we further weight the
messages to highlight the relative ones that can reflect the differences. In
addition, we design a novel negative node-pair sampling trick that efficiently
samples 'hard' negative instances in the supervision instances, and can
significantly improve the performance. Experimental results verify that the
proposed method can significantly outperform existing state-of-the-art models
regarding the edge prediction task on multiple homogeneous and heterogeneous
graph datasets.Comment: Pre-prin
Lending Interaction Wings to Recommender Systems with Conversational Agents
Recommender systems trained on offline historical user behaviors are
embracing conversational techniques to online query user preference. Unlike
prior conversational recommendation approaches that systemically combine
conversational and recommender parts through a reinforcement learning
framework, we propose CORE, a new offline-training and online-checking paradigm
that bridges a COnversational agent and REcommender systems via a unified
uncertainty minimization framework. It can benefit any recommendation platform
in a plug-and-play style. Here, CORE treats a recommender system as an offline
relevance score estimator to produce an estimated relevance score for each
item; while a conversational agent is regarded as an online relevance score
checker to check these estimated scores in each session. We define uncertainty
as the summation of unchecked relevance scores. In this regard, the
conversational agent acts to minimize uncertainty via querying either
attributes or items. Based on the uncertainty minimization framework, we derive
the expected certainty gain of querying each attribute and item, and develop a
novel online decision tree algorithm to decide what to query at each turn.
Experimental results on 8 industrial datasets show that CORE could be
seamlessly employed on 9 popular recommendation approaches. We further
demonstrate that our conversational agent could communicate as a human if
empowered by a pre-trained large language model.Comment: NeurIPS 202
Facile synthesis of Cu and Cu@Cu-Ni nanocubes and nanowires in hydrophobic solution in the presence of nickel and chloride ions
In this work, we report an example of the facile synthesis of methyl methacrylate/tert-butyl acrylate (MMA/tBA) gradient copolymers (poly(MMA-grad-tBA) using the Cu(0) and conventional ATRP ligands as catalysts in DMF solvent at 25 degrees C. Semi-batch copper(0)-mediated living radical copolymerization technique (Cu(0)-mediated LRP) was used for achieving the chain gradient microstructure of the resulting copolymers. We also compared copolymerizations with two different ATRP ligands at ambient temperature allowing control over the molecular weight and polydispersity with a quarter of catalyst concentration versus a conventional ATRP in dipolar protic solvent (i.e. DMF), while the reaction temperature up to 80 degrees C in a non-polar medium (i.e. toluene) in order to reach the above polymerization efficiency. The addition of a small amount of reducing agent (i.e. hydrazine hydrate) into the reaction system allows the reaction proceeding in the oxygen tolerant system without losing control and decreasing total conversion such as using the reagents without deoxygenating
Quercetin Alleviates Pulmonary Fibrosis in Mice Exposed to Silica by Inhibiting Macrophage Senescence
Quercetin exerts anti-inflammatory, anti-oxidant and other protective effects. Previous studies have shown that senescent cells, such as fibroblasts and type II airway epithelial cells, are strongly implicated in the development of pulmonary fibrosis pathology. However, the role of senescent macrophages during silicosis remains unclear. We investigated the effects of quercetin on macrophage senescence and pulmonary fibrosis, and explored underlying mechanisms. Mice were randomized to six model groups. Vitro model was also established by culturing RAW264.7 macrophages with silica (SiO2). We examined the effects of quercetin on fibrosis, senescence-associated β-galactosidase (SA-β-Gal) activity, and senescence-specific genes (p16, p21, and p53). We showed that quercetin reduced pulmonary fibrosis and inhibited extracellular matrix (ECM) formation. Quercetin also attenuated macrophage senescence induced by SiO2 both in vitro and in vivo. In addition, quercetin significantly decreased the expressions of the senescence-associated secretory phenotype (SASP), including proinflammatory factors (interleukin-1α (Il-1α), Il-6, tumor necrosis factor-α (TNF-α), and transforming growth factor-β1 (TGF-β1)) and matrix metalloproteinases (MMP2, MMP9, and MMP12). In conclusion, quercetin mediated its anti-fibrotic effects by inhibiting macrophage senescence, possibly via SASP
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