75 research outputs found
Development Of Structural-functional Integrated Energy Storage Concrete With Innovative Macro-encapsulated PCM By Hollow Steel Ball
Phase change materials (PCMs) have great potential for applications in energy efficient buildings. In this study, an innovative method of macro-encapsulation of PCM using hollow steel balls (HSB) was developed and the thermal and mechanical performance of PCM-HSB concrete was examined. The macro-encapsulation system (PCM-HSB) was attached with a metal clamp (c) for better mechanical interlocking with the mortar matrix. The latent heat of PCM-HSB-c that can be acquired is approximately 153.1 J/g, which can be considered to rank highly among PCM composites. According to the self-designed thermal performance evaluation, the PCM–HSB-c concrete panel is capable of reducing and deferring the peak indoor temperature. The indoor temperature of the room model using PCM-HSB-c panels was significantly lower than the ones with normal concrete panels by a range of 3–6%. Furthermore, the test room using a higher PCM-HSB-c content demonstrated a greater ability to maintain a lower indoor room temperature for a longer period of time during heating cycles. In consideration of the mechanical properties, thermal performance and other aspects of cost factors, 50% and 75% PCM-HSB-c replacement levels are recommended in producing concrete
Model and Data Agreement for Learning with Noisy Labels
Learning with noisy labels is a vital topic for practical deep learning as
models should be robust to noisy open-world datasets in the wild. The
state-of-the-art noisy label learning approach JoCoR fails when faced with a
large ratio of noisy labels. Moreover, selecting small-loss samples can also
cause error accumulation as once the noisy samples are mistakenly selected as
small-loss samples, they are more likely to be selected again. In this paper,
we try to deal with error accumulation in noisy label learning from both model
and data perspectives. We introduce mean point ensemble to utilize a more
robust loss function and more information from unselected samples to reduce
error accumulation from the model perspective. Furthermore, as the flip images
have the same semantic meaning as the original images, we select small-loss
samples according to the loss values of flip images instead of the original
ones to reduce error accumulation from the data perspective. Extensive
experiments on CIFAR-10, CIFAR-100, and large-scale Clothing1M show that our
method outperforms state-of-the-art noisy label learning methods with different
levels of label noise. Our method can also be seamlessly combined with other
noisy label learning methods to further improve their performance and
generalize well to other tasks. The code is available in
https://github.com/zyh-uaiaaaa/MDA-noisy-label-learning.Comment: Accepted by AAAI2023 Worksho
Variational Counterfactual Prediction under Runtime Domain Corruption
To date, various neural methods have been proposed for causal effect
estimation based on observational data, where a default assumption is the same
distribution and availability of variables at both training and inference
(i.e., runtime) stages. However, distribution shift (i.e., domain shift) could
happen during runtime, and bigger challenges arise from the impaired
accessibility of variables. This is commonly caused by increasing privacy and
ethical concerns, which can make arbitrary variables unavailable in the entire
runtime data and imputation impractical. We term the co-occurrence of domain
shift and inaccessible variables runtime domain corruption, which seriously
impairs the generalizability of a trained counterfactual predictor. To counter
runtime domain corruption, we subsume counterfactual prediction under the
notion of domain adaptation. Specifically, we upper-bound the error w.r.t. the
target domain (i.e., runtime covariates) by the sum of source domain error and
inter-domain distribution distance. In addition, we build an adversarially
unified variational causal effect model, named VEGAN, with a novel two-stage
adversarial domain adaptation scheme to reduce the latent distribution
disparity between treated and control groups first, and between training and
runtime variables afterwards. We demonstrate that VEGAN outperforms other
state-of-the-art baselines on individual-level treatment effect estimation in
the presence of runtime domain corruption on benchmark datasets
Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention
Although face recognition has made impressive progress in recent years, we
ignore the racial bias of the recognition system when we pursue a high level of
accuracy. Previous work found that for different races, face recognition
networks focus on different facial regions, and the sensitive regions of
darker-skinned people are much smaller. Based on this discovery, we propose a
new de-bias method based on gradient attention, called Gradient Attention
Balance Network (GABN). Specifically, we use the gradient attention map (GAM)
of the face recognition network to track the sensitive facial regions and make
the GAMs of different races tend to be consistent through adversarial learning.
This method mitigates the bias by making the network focus on similar facial
regions. In addition, we also use masks to erase the Top-N sensitive facial
regions, forcing the network to allocate its attention to a larger facial
region. This method expands the sensitive region of darker-skinned people and
further reduces the gap between GAM of darker-skinned people and GAM of
Caucasians. Extensive experiments show that GABN successfully mitigates racial
bias in face recognition and learns more balanced performance for people of
different races.Comment: Accepted by CVPR 2023 worksho
Label-free Node Classification on Graphs with Large Language Models (LLMS)
In recent years, there have been remarkable advancements in node
classification achieved by Graph Neural Networks (GNNs). However, they
necessitate abundant high-quality labels to ensure promising performance. In
contrast, Large Language Models (LLMs) exhibit impressive zero-shot proficiency
on text-attributed graphs. Yet, they face challenges in efficiently processing
structural data and suffer from high inference costs. In light of these
observations, this work introduces a label-free node classification on graphs
with LLMs pipeline, LLM-GNN. It amalgamates the strengths of both GNNs and LLMs
while mitigating their limitations. Specifically, LLMs are leveraged to
annotate a small portion of nodes and then GNNs are trained on LLMs'
annotations to make predictions for the remaining large portion of nodes. The
implementation of LLM-GNN faces a unique challenge: how can we actively select
nodes for LLMs to annotate and consequently enhance the GNN training? How can
we leverage LLMs to obtain annotations of high quality, representativeness, and
diversity, thereby enhancing GNN performance with less cost? To tackle this
challenge, we develop an annotation quality heuristic and leverage the
confidence scores derived from LLMs to advanced node selection. Comprehensive
experimental results validate the effectiveness of LLM-GNN. In particular,
LLM-GNN can achieve an accuracy of 74.9% on a vast-scale dataset \products with
a cost less than 1 dollar.Comment: The code will be available soon via
https://github.com/CurryTang/LLMGN
Single-Cell Multimodal Prediction via Transformers
The recent development of multimodal single-cell technology has made the
possibility of acquiring multiple omics data from individual cells, thereby
enabling a deeper understanding of cellular states and dynamics. Nevertheless,
the proliferation of multimodal single-cell data also introduces tremendous
challenges in modeling the complex interactions among different modalities. The
recently advanced methods focus on constructing static interaction graphs and
applying graph neural networks (GNNs) to learn from multimodal data. However,
such static graphs can be suboptimal as they do not take advantage of the
downstream task information; meanwhile GNNs also have some inherent limitations
when deeply stacking GNN layers. To tackle these issues, in this work, we
investigate how to leverage transformers for multimodal single-cell data in an
end-to-end manner while exploiting downstream task information. In particular,
we propose a scMoFormer framework which can readily incorporate external domain
knowledge and model the interactions within each modality and cross modalities.
Extensive experiments demonstrate that scMoFormer achieves superior performance
on various benchmark datasets. Remarkably, scMoFormer won a Kaggle silver medal
with the rank of 24/1221 (Top 2%) without ensemble in a NeurIPS 2022
competition. Our implementation is publicly available at Github.Comment: CIKM 202
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
Learning on Graphs has attracted immense attention due to its wide real-world
applications. The most popular pipeline for learning on graphs with textual
node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes
shallow text embedding as initial node representations, which has limitations
in general knowledge and profound semantic understanding. In recent years,
Large Language Models (LLMs) have been proven to possess extensive common
knowledge and powerful semantic comprehension abilities that have
revolutionized existing workflows to handle text data. In this paper, we aim to
explore the potential of LLMs in graph machine learning, especially the node
classification task, and investigate two possible pipelines: LLMs-as-Enhancers
and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text
attributes with their massive knowledge and then generate predictions through
GNNs. The latter attempts to directly employ LLMs as standalone predictors. We
conduct comprehensive and systematical studies on these two pipelines under
various settings. From comprehensive empirical results, we make original
observations and find new insights that open new possibilities and suggest
promising directions to leverage LLMs for learning on graphs.Comment: fix some minor typos and error
Deep Learning in Single-Cell Analysis
Single-cell technologies are revolutionizing the entire field of biology. The
large volumes of data generated by single-cell technologies are
high-dimensional, sparse, heterogeneous, and have complicated dependency
structures, making analyses using conventional machine learning approaches
challenging and impractical. In tackling these challenges, deep learning often
demonstrates superior performance compared to traditional machine learning
methods. In this work, we give a comprehensive survey on deep learning in
single-cell analysis. We first introduce background on single-cell technologies
and their development, as well as fundamental concepts of deep learning
including the most popular deep architectures. We present an overview of the
single-cell analytic pipeline pursued in research applications while noting
divergences due to data sources or specific applications. We then review seven
popular tasks spanning through different stages of the single-cell analysis
pipeline, including multimodal integration, imputation, clustering, spatial
domain identification, cell-type deconvolution, cell segmentation, and
cell-type annotation. Under each task, we describe the most recent developments
in classical and deep learning methods and discuss their advantages and
disadvantages. Deep learning tools and benchmark datasets are also summarized
for each task. Finally, we discuss the future directions and the most recent
challenges. This survey will serve as a reference for biologists and computer
scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi
Electro-Acupuncture Stimulation (EAS) Control by Imaginary Movement with Feedback Based on Electroencephalograph (EEG) Sensoring
Electro-acupuncture stimulation (EAS) technique applies the electrical nerve stimulation therapy on traditional acupuncture points to restore the muscle tension. The rapid promotion and development of brain-computer interface (BCI) technology makes the thought-control of EAS possible. This paper designed a new BCI-control-EAS (BCICEAS) system by using event-related desynchronization (ERD) of EEG signal evoked by imaginary movement. The change of EEG signal was observed in the training and stimulation experiments with visual feedback. The Fisher parameters were extracted from feature frequency bands of EEG and classified into EAS control commands by Mahalanobis distance (MD) classifier. A feedback training technique was employed to correlate the enhancement of relevant EEG feature through a visual interface with a virtual liquid column, whose height varied along with EEG power spectral feature. According to the statistics analysis of 12 subjects, experimental results revealed the effective improvement of feedback training on signal feature and reliable control of EAS. It is expected that the proposed control method can explore a new way for EAS system design and help people who sufferers with severe movement dysfunction
- …