105 research outputs found
Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing
Given significant air pollution problems, air quality index (AQI) monitoring
has recently received increasing attention. In this paper, we design a mobile
AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS,
to efficiently build fine-grained AQI maps in realtime. Specifically, we first
propose the Gaussian plume model on basis of the neural network (GPM-NN), to
physically characterize the particle dispersion in the air. Based on GPM-NN, we
propose a battery efficient and adaptive monitoring algorithm to monitor AQI at
the selected locations and construct an accurate AQI map with the sensed data.
The proposed adaptive monitoring algorithm is evaluated in two typical
scenarios, a two-dimensional open space like a roadside park, and a
three-dimensional space like a courtyard inside a building. Experimental
results demonstrate that our system can provide higher prediction accuracy of
AQI with GPM-NN than other existing models, while greatly reducing the power
consumption with the adaptive monitoring algorithm
Game Theoretic Approaches to Massive Data Processing in Wireless Networks
Wireless communication networks are becoming highly virtualized with
two-layer hierarchies, in which controllers at the upper layer with tasks to
achieve can ask a large number of agents at the lower layer to help realize
computation, storage, and transmission functions. Through offloading data
processing to the agents, the controllers can accomplish otherwise prohibitive
big data processing. Incentive mechanisms are needed for the agents to perform
the controllers' tasks in order to satisfy the corresponding objectives of
controllers and agents. In this article, a hierarchical game framework with
fast convergence and scalability is proposed to meet the demand for real-time
processing for such situations. Possible future research directions in this
emerging area are also discussed
Grid Jigsaw Representation with CLIP: A New Perspective on Image Clustering
Unsupervised representation learning for image clustering is essential in
computer vision. Although the advancement of visual models has improved image
clustering with efficient visual representations, challenges still remain.
Firstly, these features often lack the ability to represent the internal
structure of images, hindering the accurate clustering of visually similar
images. Secondly, the existing features tend to lack finer-grained semantic
labels, limiting the ability to capture nuanced differences and similarities
between images.
In this paper, we first introduce Jigsaw based strategy method for image
clustering called Grid Jigsaw Representation (GJR) with systematic exposition
from pixel to feature in discrepancy against human and computer. We emphasize
that this algorithm, which mimics human jigsaw puzzle, can effectively improve
the model to distinguish the spatial feature between different samples and
enhance the clustering ability. GJR modules are appended to a variety of deep
convolutional networks and tested with significant improvements on a wide range
of benchmark datasets including CIFAR-10, CIFAR-100/20, STL-10, ImageNet-10 and
ImageNetDog-15.
On the other hand, convergence efficiency is always an important challenge
for unsupervised image clustering. Recently, pretrained representation learning
has made great progress and released models can extract mature visual
representations. It is obvious that use the pretrained model as feature
extractor can speed up the convergence of clustering where our aim is to
provide new perspective in image clustering with reasonable resource
application and provide new baseline. Further, we innovate pretrain-based Grid
Jigsaw Representation (pGJR) with improvement by GJR. The experiment results
show the effectiveness on the clustering task with respect to the ACC, NMI and
ARI three metrics and super fast convergence speed
CDR: Conservative Doubly Robust Learning for Debiased Recommendation
In recommendation systems (RS), user behavior data is observational rather
than experimental, resulting in widespread bias in the data. Consequently,
tackling bias has emerged as a major challenge in the field of recommendation
systems. Recently, Doubly Robust Learning (DR) has gained significant attention
due to its remarkable performance and robust properties. However, our
experimental findings indicate that existing DR methods are severely impacted
by the presence of so-called Poisonous Imputation, where the imputation
significantly deviates from the truth and becomes counterproductive.
To address this issue, this work proposes Conservative Doubly Robust strategy
(CDR) which filters imputations by scrutinizing their mean and variance.
Theoretical analyses show that CDR offers reduced variance and improved tail
bounds.In addition, our experimental investigations illustrate that CDR
significantly enhances performance and can indeed reduce the frequency of
poisonous imputation
Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements
With the rapid proliferation of textual data, predicting long texts has
emerged as a significant challenge in the domain of natural language
processing. Traditional text prediction methods encounter substantial
difficulties when grappling with long texts, primarily due to the presence of
redundant and irrelevant information, which impedes the model's capacity to
capture pivotal insights from the text. To address this issue, we introduce a
novel approach to long-text classification and prediction. Initially, we employ
embedding techniques to condense the long texts, aiming to diminish the
redundancy therein. Subsequently,the Bidirectional Encoder Representations from
Transformers (BERT) embedding method is utilized for text classification
training. Experimental outcomes indicate that our method realizes considerable
performance enhancements in classifying long texts of Preferential Trade
Agreements. Furthermore, the condensation of text through embedding methods not
only augments prediction accuracy but also substantially reduces computational
complexity. Overall, this paper presents a strategy for long-text prediction,
offering a valuable reference for researchers and engineers in the natural
language processing sphere.Comment: AI4TS Workshop@AAAI 2024 accepted publicatio
Structural Knowledge Informed Continual Multivariate Time Series Forecasting
Recent studies in multivariate time series (MTS) forecasting reveal that
explicitly modeling the hidden dependencies among different time series can
yield promising forecasting performance and reliable explanations. However,
modeling variable dependencies remains underexplored when MTS is continuously
accumulated under different regimes (stages). Due to the potential distribution
and dependency disparities, the underlying model may encounter the catastrophic
forgetting problem, i.e., it is challenging to memorize and infer different
types of variable dependencies across different regimes while maintaining
forecasting performance. To address this issue, we propose a novel Structural
Knowledge Informed Continual Learning (SKI-CL) framework to perform MTS
forecasting within a continual learning paradigm, which leverages structural
knowledge to steer the forecasting model toward identifying and adapting to
different regimes, and selects representative MTS samples from each regime for
memory replay. Specifically, we develop a forecasting model based on graph
structure learning, where a consistency regularization scheme is imposed
between the learned variable dependencies and the structural knowledge while
optimizing the forecasting objective over the MTS data. As such, MTS
representations learned in each regime are associated with distinct structural
knowledge, which helps the model memorize a variety of conceivable scenarios
and results in accurate forecasts in the continual learning context. Meanwhile,
we develop a representation-matching memory replay scheme that maximizes the
temporal coverage of MTS data to efficiently preserve the underlying temporal
dynamics and dependency structures of each regime. Thorough empirical studies
on synthetic and real-world benchmarks validate SKI-CL's efficacy and
advantages over the state-of-the-art for continual MTS forecasting tasks
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