178 research outputs found
Optimization and Analysis of Wireless Powered Multi-antenna Cooperative Systems
In this paper, we consider a three-node cooperative wireless powered
communication system consisting of a multi-antenna hybrid access point (H-AP)
and a single-antenna relay and a single-antenna user. The energy constrained
relay and user first harvest energy in the downlink and then the relay assists
the user using the harvested power for information transmission in the uplink.
The optimal energy beamforming vector and the time split between harvest and
cooperation are investigated. To reduce the computational complexity,
suboptimal designs are also studied, where closed-form expressions are derived
for the energy beamforming vector and the time split. For comparison purposes,
we also present a detailed performance analysis in terms of the achievable
outage probability and the average throughput of an intuitive energy
beamforming scheme, where the H-AP directs all the energy towards the user. The
findings of the paper suggest that implementing multiple antennas at the H-AP
can significantly improve the system performance, and the closed-form
suboptimal energy beamforming vector and time split yields near optimal
performance. Also, for the intuitive beamforming scheme, a diversity order of
(N+1)/2 can be achieved, where N is the number of antennas at the H-AP
PiRL: Participant-Invariant Representation Learning for Healthcare
Due to individual heterogeneity, performance gaps are observed between
generic (one-size-fits-all) models and person-specific models in data-driven
health applications. However, in real-world applications, generic models are
usually more favorable due to new-user-adaptation issues and system
complexities, etc. To improve the performance of the generic model, we propose
a representation learning framework that learns participant-invariant
representations, named PiRL. The proposed framework utilizes maximum mean
discrepancy (MMD) loss and domain-adversarial training to encourage the model
to learn participant-invariant representations. Further, a triplet loss, which
constrains the model for inter-class alignment of the representations, is
utilized to optimize the learned representations for downstream health
applications. We evaluated our frameworks on two public datasets related to
physical and mental health, for detecting sleep apnea and stress, respectively.
As preliminary results, we found the proposed approach shows around a 5%
increase in accuracy compared to the baseline
A Survey on Backdoor Attack and Defense in Natural Language Processing
Deep learning is becoming increasingly popular in real-life applications,
especially in natural language processing (NLP). Users often choose training
outsourcing or adopt third-party data and models due to data and computation
resources being limited. In such a situation, training data and models are
exposed to the public. As a result, attackers can manipulate the training
process to inject some triggers into the model, which is called backdoor
attack. Backdoor attack is quite stealthy and difficult to be detected because
it has little inferior influence on the model's performance for the clean
samples. To get a precise grasp and understanding of this problem, in this
paper, we conduct a comprehensive review of backdoor attacks and defenses in
the field of NLP. Besides, we summarize benchmark datasets and point out the
open issues to design credible systems to defend against backdoor attacks.Comment: 12 pages, QRS202
Bayesian Criterion for Re-randomization
Re-randomization has gained popularity as a tool for experiment-based causal
inference due to its superior covariate balance and statistical efficiency
compared to classic randomized experiments. However, the basic re-randomization
method, known as ReM, and many of its extensions have been deemed sub-optimal
as they fail to prioritize covariates that are more strongly associated with
potential outcomes. To address this limitation and design more efficient
re-randomization procedures, a more precise quantification of covariate
heterogeneity and its impact on the causal effect estimator is in a great
appeal. This work fills in this gap with a Bayesian criterion for
re-randomization and a series of novel re-randomization procedures derived
under such a criterion. Both theoretical analyses and numerical studies show
that the proposed re-randomization procedures under the Bayesian criterion
outperform existing ReM-based procedures significantly in effectively balancing
covariates and precisely estimating the unknown causal effect
Early detection of gastric cancer via high-resolution terahertz imaging system
Terahertz (THz) wave has demonstrated a good prospect in recent years, but the resolution is still one of the problems that restrict the application of THz technology in medical imaging. Paraffin-embedded samples are mostly used in THz medical imaging studies, which are thicker and significantly different from the current gold standard slice pathological examination in sample preparation. In addition, THz absorption in different layers of normal and cancerous tissues also remains to be further explored. In this study, we constructed a high-resolution THz imaging system to scan non-tumorous adjacent tissue slices and gastric cancer (GC) tissue slices. In this system, a THz quantum cascade laser emitted a pulsed 3 THz signal and the transmitted THz wave was received by a THz detector implemented in a 65 nm CMOS process. The slice thickness was only 20 μm, which was close to that of the medical pathology examination. We successfully found THz transmittance differences between different layers of normal gastric tissues based on THz images, and the resolution could reach 60 μm for the first time. The results indicated that submucosa had a lower THz transmittance than that of mucosa and muscular layer in non-tumorous adjacent tissue. However, in GC tissue, THz transmittance of mucosa and submucosa was similar, caused by the decreased transmittance of mucosa, where the cancer occurs. Therefore, we suppose that the similar terahertz transmittance between gastric mucosa and submucosa may indicate the appearance of cancerization. The images obtained from our THz imaging system were clearer than those observed with naked eyes, and can be directly compared with microscopic images. This is the first application of THz imaging technology to identify non-tumorous adjacent tissue and GC tissue based on the difference in THz wave absorption between different layers in the tissue. Our present work not only demonstrated the potential of THz imaging to promote early diagnosis of GC, but also suggested a new direction for the identification of normal and cancerous tissues by analyzing differences in THz transmittance between different layers of tissue
Mean Field Game-based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems
Data collection and processing timely is crucial for mobile crowd integrated
sensing, communication, and computation~(ISCC) systems with various
applications such as smart home and connected cars, which requires numerous
integrated sensing and communication~(ISAC) devices to sense the targets and
offload the data to the base station~(BS) for further processing. However, as
the number of ISAC devices growing, there exists intensive interactions among
ISAC devices in the processes of data collection and processing since they
share the common network resources. In this paper, we consider the environment
sensing problem in the large-scale mobile crowd ISCC systems and propose an
efficient waveform precoding design algorithm based on the mean field
game~(MFG). Specifically, to handle the complex interactions among large-scale
ISAC devices, we first utilize the MFG method to transform the influence from
other ISAC devices into the mean field term and derive the
Fokker-Planck-Kolmogorov equation, which model the evolution of the system
state. Then, we derive the cost function based on the mean field term and
reformulate the waveform precoding design problem. Next, we utilize the G-prox
primal-dual hybrid gradient algorithm to solve the reformulated problem and
analyze the computational complexity of the proposed algorithm. Finally,
simulation results demonstrate that the proposed algorithm can solve the
interactions among large-scale ISAC devices effectively in the ISCC process. In
addition, compared with other baselines, the proposed waveform precoding design
algorithm has advantages in improving communication performance and reducing
cost function.Comment: 13 pages,9 figure
Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning
Despite the success of fully-supervised human skeleton sequence modeling,
utilizing self-supervised pre-training for skeleton sequence representation
learning has been an active field because acquiring task-specific skeleton
annotations at large scales is difficult. Recent studies focus on learning
video-level temporal and discriminative information using contrastive learning,
but overlook the hierarchical spatial-temporal nature of human skeletons.
Different from such superficial supervision at the video level, we propose a
self-supervised hierarchical pre-training scheme incorporated into a
hierarchical Transformer-based skeleton sequence encoder (Hi-TRS), to
explicitly capture spatial, short-term, and long-term temporal dependencies at
frame, clip, and video levels, respectively. To evaluate the proposed
self-supervised pre-training scheme with Hi-TRS, we conduct extensive
experiments covering three skeleton-based downstream tasks including action
recognition, action detection, and motion prediction. Under both supervised and
semi-supervised evaluation protocols, our method achieves the state-of-the-art
performance. Additionally, we demonstrate that the prior knowledge learned by
our model in the pre-training stage has strong transfer capability for
different downstream tasks.Comment: Accepted to ECCV 202
Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images
Top-down instance segmentation framework has shown its superiority in object
detection compared to the bottom-up framework. While it is efficient in
addressing over-segmentation, top-down instance segmentation suffers from
over-crop problem. However, a complete segmentation mask is crucial for
biological image analysis as it delivers important morphological properties
such as shapes and volumes. In this paper, we propose a region proposal
rectification (RPR) module to address this challenging incomplete segmentation
problem. In particular, we offer a progressive ROIAlign module to introduce
neighbor information into a series of ROIs gradually. The ROI features are fed
into an attentive feed-forward network (FFN) for proposal box regression. With
additional neighbor information, the proposed RPR module shows significant
improvement in correction of region proposal locations and thereby exhibits
favorable instance segmentation performances on three biological image datasets
compared to state-of-the-art baseline methods. Experimental results demonstrate
that the proposed RPR module is effective in both anchor-based and anchor-free
top-down instance segmentation approaches, suggesting the proposed method can
be applied to general top-down instance segmentation of biological images. Code
is available
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