93 research outputs found
A New Ensemble Learning Framework for 3D Biomedical Image Segmentation
3D image segmentation plays an important role in biomedical image analysis.
Many 2D and 3D deep learning models have achieved state-of-the-art segmentation
performance on 3D biomedical image datasets. Yet, 2D and 3D models have their
own strengths and weaknesses, and by unifying them together, one may be able to
achieve more accurate results. In this paper, we propose a new ensemble
learning framework for 3D biomedical image segmentation that combines the
merits of 2D and 3D models. First, we develop a fully convolutional network
based meta-learner to learn how to improve the results from 2D and 3D models
(base-learners). Then, to minimize over-fitting for our sophisticated
meta-learner, we devise a new training method that uses the results of the
base-learners as multiple versions of "ground truths". Furthermore, since our
new meta-learner training scheme does not depend on manual annotation, it can
utilize abundant unlabeled 3D image data to further improve the model.
Extensive experiments on two public datasets (the HVSMR 2016 Challenge dataset
and the mouse piriform cortex dataset) show that our approach is effective
under fully-supervised, semi-supervised, and transductive settings, and attains
superior performance over state-of-the-art image segmentation methods.Comment: To appear in AAAI-2019. The first three authors contributed equally
to the pape
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis
Time series anomaly detection is a challenging problem due to the complex
temporal dependencies and the limited label data. Although some algorithms
including both traditional and deep models have been proposed, most of them
mainly focus on time-domain modeling, and do not fully utilize the information
in the frequency domain of the time series data. In this paper, we propose a
Time-Frequency analysis based time series Anomaly Detection model, or TFAD for
short, to exploit both time and frequency domains for performance improvement.
Besides, we incorporate time series decomposition and data augmentation
mechanisms in the designed time-frequency architecture to further boost the
abilities of performance and interpretability. Empirical studies on widely used
benchmark datasets show that our approach obtains state-of-the-art performance
in univariate and multivariate time series anomaly detection tasks. Code is
provided at https://github.com/DAMO-DI-ML/CIKM22-TFAD.Comment: Accepted by the ACM International Conference on Information and
Knowledge Management (CIKM 2022
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
Time series anomaly detection is critical for a wide range of applications.
It aims to identify deviant samples from the normal sample distribution in time
series. The most fundamental challenge for this task is to learn a
representation map that enables effective discrimination of anomalies.
Reconstruction-based methods still dominate, but the representation learning
with anomalies might hurt the performance with its large abnormal loss. On the
other hand, contrastive learning aims to find a representation that can clearly
distinguish any instance from the others, which can bring a more natural and
promising representation for time series anomaly detection. In this paper, we
propose DCdetector, a multi-scale dual attention contrastive representation
learning model. DCdetector utilizes a novel dual attention asymmetric design to
create the permutated environment and pure contrastive loss to guide the
learning process, thus learning a permutation invariant representation with
superior discrimination abilities. Extensive experiments show that DCdetector
achieves state-of-the-art results on multiple time series anomaly detection
benchmark datasets. Code is publicly available at
https://github.com/DAMO-DI-ML/KDD2023-DCdetector
DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model
Electrical load forecasting is of great significance for the decision makings
in power systems, such as unit commitment and energy management. In recent
years, various self-supervised neural network-based methods have been applied
to electrical load forecasting to improve forecasting accuracy and capture
uncertainties. However, most current methods are based on Gaussian likelihood
methods, which aim to accurately estimate the distribution expectation under a
given covariate. This kind of approach is difficult to adapt to situations
where temporal data has a distribution shift and outliers. In this paper, we
propose a diffusion-based Seq2seq structure to estimate epistemic uncertainty
and use the robust additive Cauchy distribution to estimate aleatoric
uncertainty. Rather than accurately forecasting conditional expectations, we
demonstrate our method's ability in separating two types of uncertainties and
dealing with the mutant scenarios
LogiCoT: Logical Chain-of-Thought Instruction-Tuning
Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive
chain-of-thought reasoning ability. Recent work on self-instruction tuning,
such as Alpaca, has focused on enhancing the general proficiency of models.
These instructions enable the model to achieve performance comparable to
GPT-3.5 on general tasks like open-domain text generation and paraphrasing.
However, they fall short of helping the model handle complex reasoning tasks.
To bridge the gap, this paper presents LogiCoT, a new instruction-tuning
dataset for Logical Chain-of-Thought reasoning with GPT-4. We elaborate on the
process of harvesting instructions for prompting GPT-4 to generate
chain-of-thought rationales. LogiCoT serves as an instruction set for teaching
models of logical reasoning and elicits general reasoning skills
High-redshift galaxy groups as seen by Athena/WFI
The first massive galaxy groups in the Universe are predicted to have formed
at redshifts well beyond two. Baryonic physics, like stellar and active
galactic nuclei (AGN) feedback in this very active epoch, are expected to have
left a strong imprint on the thermo-dynamic properties of these early galaxy
groups. Therefore, observations of these groups are key to constrain the
relative importance of these physical processes. However, current instruments
are not sensitive enough to detect them easily and characterize their hot gas
content. In this work, we quantify the observing power of the Advanced
Telescope for High ENergy Astrophysics (Athena), the future large X-ray
observatory of the European Space Agency (ESA), for discovering and
characterizing early galaxy groups at high redshifts. We used the SImulation of
X-ray TElescopes (SIXTE) simulator to mimic Athena observations, and a
custom-made wavelet-based algorithm to detect galaxy groups and clusters in the
redshift range . We performed extensive X-ray spectral fitting
in order to characterize their gas temperature and X-ray luminosity. We also
investigate how well Athena will constrain different feedback mechanisms. In
the deep Wide Field Imager (WFI) survey expected to be carried out during part
of Athena's first four years (the nominal mission lifetime) more than 10,000
galaxy groups and clusters at will be discovered. We find that
Athena can detect high-redshift galaxy groups with masses of
and , and almost half of
them will have a gas temperature determined to a precision of . We demonstrate that high-redshift galaxy groups can be detected very
efficiently as extended sources by Athena and that a key parameter determining
the total number of such newly discovered sources is the area on the sky
surveyed by Athena.Comment: 24 pages, 18 figures, accepted for publication in A&
Mechanism Design with Predicted Task Revenue for Bike Sharing Systems
Bike sharing systems have been widely deployed around the world in recent
years. A core problem in such systems is to reposition the bikes so that the
distribution of bike supply is reshaped to better match the dynamic bike
demand. When the bike-sharing company or platform is able to predict the
revenue of each reposition task based on historic data, an additional
constraint is to cap the payment for each task below its predicted revenue. In
this paper, we propose an incentive mechanism called {\em TruPreTar} to
incentivize users to park bicycles at locations desired by the platform toward
rebalancing supply and demand. TruPreTar possesses four important economic and
computational properties such as truthfulness and budget feasibility.
Furthermore, we prove that even when the payment budget is tight, the total
revenue still exceeds or equals the budget. Otherwise, TruPreTar achieves
2-approximation as compared to the optimal (revenue-maximizing) solution, which
is close to the lower bound of at least that we also prove. Using an
industrial dataset obtained from a large bike-sharing company, our experiments
show that TruPreTar is effective in rebalancing bike supply and demand and, as
a result, generates high revenue that outperforms several benchmark mechanisms.Comment: Accepted by AAAI 2020; This is the full version that contains all the
proof
Benchmarks and Custom Package for Electrical Load Forecasting
Load forecasting is of great significance in the power industry as it can
provide a reference for subsequent tasks such as power grid dispatch, thus
bringing huge economic benefits. However, there are many differences between
load forecasting and traditional time series forecasting. On the one hand, load
forecasting aims to minimize the cost of subsequent tasks such as power grid
dispatch, rather than simply pursuing prediction accuracy. On the other hand,
the load is largely influenced by many external factors, such as temperature or
calendar variables. In addition, the scale of predictions (such as
building-level loads and aggregated-level loads) can also significantly impact
the predicted results. In this paper, we provide a comprehensive load
forecasting archive, which includes load domain-specific feature engineering to
help forecasting models better model load data. In addition, different from the
traditional loss function which only aims for accuracy, we also provide a
method to customize the loss function based on the forecasting error,
integrating it into our forecasting framework. Based on this, we conducted
extensive experiments on load data at different levels, providing a reference
for researchers to compare different load forecasting models
Tissue-Engineered Trachea Consisting of Electrospun Patterned sc-PLA/GO-g-IL Fibrous Membranes with Antibacterial Property and 3D-Printed Skeletons with Elasticity
In this study, a tissue-engineered trachea, consisting of multilevel structural electrospun polylactide (PLA) membranes enveloping 3D-printed thermoplastic polyurethane (TPU) skeletons, was developed to create a mechanically robust, antibacterial and bioresorbable graft for the tracheal reconstruction. The study design incorporated two distinct uses of stereocomplex PLA: patterned electrospun fibers to enhance tissue integration compared to the random layered fibers, meanwhile possessing good antibacterial property; and 3D-printed TPU scaffold with elasticity to provide external support and protection. Herein, ionic liquid (IL)-functioned graphene oxide (GO) was synthesized and presented enhanced mechanical and hydrophilicity properties. More interesting, antibacterial activity of the GO-g-IL modified PLA membranes were proved by Escherichia coli and Staphylococcus aureus, showing superior antibacterial effect compared to single GO or IL. The synergistic antibacterial effect could be related to that GO break cytomembrane of bacteria by its extremely sharp edges, while IL works by electrostatic interaction between its cationic structures and electronegative phosphate groups of bacteria membranes, leading to the loss of cell electrolyte and cell death. Hence, after L929 fibroblast cells were seeded on patterned fibrous membranes with phenotypic shape, further effective cell infiltration, cell proliferation and attachment were observed. In addition, the tissue-engineered trachea scaffolds were implanted into rabbit models. The in vivo result confirmed that the scaffolds with patterned membranes manifested favorable biocompatibility and promoted tissue regeneration
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