63 research outputs found
A Spatio-Temporal Graph Convolutional Network for Gesture Recognition from High-Density Electromyography
Accurate hand gesture prediction is crucial for effective upper-limb
prosthetic limbs control. As the high flexibility and multiple degrees of
freedom exhibited by human hands, there has been a growing interest in
integrating deep networks with high-density surface electromyography (HD-sEMG)
grids to enhance gesture recognition capabilities. However, many existing
methods fall short in fully exploit the specific spatial topology and temporal
dependencies present in HD-sEMG data. Additionally, these studies are often
limited number of gestures and lack generality. Hence, this study introduces a
novel gesture recognition method, named STGCN-GR, which leverages
spatio-temporal graph convolution networks for HD-sEMG-based human-machine
interfaces. Firstly, we construct muscle networks based on functional
connectivity between channels, creating a graph representation of HD-sEMG
recordings. Subsequently, a temporal convolution module is applied to capture
the temporal dependences in the HD-sEMG series and a spatial graph convolution
module is employed to effectively learn the intrinsic spatial topology
information among distinct HD-sEMG channels. We evaluate our proposed model on
a public HD-sEMG dataset comprising a substantial number of gestures (i.e.,
65). Our results demonstrate the remarkable capability of the STGCN-GR method,
achieving an impressive accuracy of 91.07% in predicting gestures, which
surpasses state-of-the-art deep learning methods applied to the same dataset
Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation
In this paper, we study the task of synthetic-to-real domain generalized
semantic segmentation, which aims to learn a model that is robust to unseen
real-world scenes using only synthetic data. The large domain shift between
synthetic and real-world data, including the limited source environmental
variations and the large distribution gap between synthetic and real-world
data, significantly hinders the model performance on unseen real-world scenes.
In this work, we propose the Style-HAllucinated Dual consistEncy learning
(SHADE) framework to handle such domain shift. Specifically, SHADE is
constructed based on two consistency constraints, Style Consistency (SC) and
Retrospection Consistency (RC). SC enriches the source situations and
encourages the model to learn consistent representation across
style-diversified samples. RC leverages real-world knowledge to prevent the
model from overfitting to synthetic data and thus largely keeps the
representation consistent between the synthetic and real-world models.
Furthermore, we present a novel style hallucination module (SHM) to generate
style-diversified samples that are essential to consistency learning. SHM
selects basis styles from the source distribution, enabling the model to
dynamically generate diverse and realistic samples during training. Experiments
show that our SHADE yields significant improvement and outperforms
state-of-the-art methods by 5.07% and 8.35% on the average mIoU of three
real-world datasets on single- and multi-source settings respectively
Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization
Domain shift widely exists in the visual world, while modern deep neural
networks commonly suffer from severe performance degradation under domain shift
due to the poor generalization ability, which limits the real-world
applications. The domain shift mainly lies in the limited source environmental
variations and the large distribution gap between source and unseen target
data. To this end, we propose a unified framework, Style-HAllucinated Dual
consistEncy learning (SHADE), to handle such domain shift in various visual
tasks. Specifically, SHADE is constructed based on two consistency constraints,
Style Consistency (SC) and Retrospection Consistency (RC). SC enriches the
source situations and encourages the model to learn consistent representation
across style-diversified samples. RC leverages general visual knowledge to
prevent the model from overfitting to source data and thus largely keeps the
representation consistent between the source and general visual models.
Furthermore, we present a novel style hallucination module (SHM) to generate
style-diversified samples that are essential to consistency learning. SHM
selects basis styles from the source distribution, enabling the model to
dynamically generate diverse and realistic samples during training. Extensive
experiments demonstrate that our versatile SHADE can significantly enhance the
generalization in various visual recognition tasks, including image
classification, semantic segmentation and object detection, with different
models, i.e., ConvNets and Transformer.Comment: Accepted by IJCV. Journal extension of arXiv:2204.02548. Code is
available at https://github.com/HeliosZhao/SHADE-VisualD
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TTSVD: an efficient sparse decision making model with two-way trust recommendation in the AI enabled IoT systems
The convergence of AI and IoT enables data to be quickly explored and turned into vital decisions, and however, there are still some challenging issues to be further addressed. For example, lacking of enough data in AI-based decision making (so called Sparse Decision Making, SDM) will decrease the efficiency
dramatically, or even disable the intelligent IoT networks. Taking the intelligent IoT networks as the network infrastructure, the recommendation systems have been facing such SDM problems. A naive solution is to introduce so-called trust information. However, trust information also maybe face the difficulty of sparse trust evidence (a.k.a sparse trust problem). In our work, an accurate sparse decision making model with two-way trust recommendation in the AI enabled IoT systems is proposed by us, named TT-SVD. Our model incorporates both trust information and rating information more completely, which can efficiently alleviate the above mentioned sparse trust problem and therefore be able to solve the cold start and data sparsity problems. Specifically, we first consider the two-fold trust influences from both trustees and trustors, which can be represented by a factor called trust propensity. To this end, we propose a dual model, including the trustor model (TrustorSVD) and a trustee model (TrusteeSVD) based on an existing rating-only recommendation model called SVD++, which are integrated by the weighted average and yield the final model, TT-SVD. The experimental results show that our model outperforms the state of the art including SVD and TrustSVD in both the ”all users” and ”cold start users” cases, and the accuracy improvement can reach a maximum of 29%. Complexity analysis shows that our model is equally suitable for the case of large sparse datasets. In a word, our model can effectively solve the sparse decision problem by introducing the two-way trust recommendation, and hence improve the efficiency of the intelligent recommendation systems
Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG
Walking-assistive devices require adaptive control methods to ensure smooth
transitions between various modes of locomotion. For this purpose, detecting
human locomotion modes (e.g., level walking or stair ascent) in advance is
crucial for improving the intelligence and transparency of such robotic
systems. This study proposes Deep-STF, a unified end-to-end deep learning model
designed for integrated feature extraction in spatial, temporal, and frequency
dimensions from surface electromyography (sEMG) signals. Our model enables
accurate and robust continuous prediction of nine locomotion modes and 15
transitions at varying prediction time intervals, ranging from 100 to 500 ms.
In addition, we introduced the concept of 'stable prediction time' as a
distinct metric to quantify prediction efficiency. This term refers to the
duration during which consistent and accurate predictions of mode transitions
are made, measured from the time of the fifth correct prediction to the
occurrence of the critical event leading to the task transition. This
distinction between stable prediction time and prediction time is vital as it
underscores our focus on the precision and reliability of mode transition
predictions. Experimental results showcased Deep-STP's cutting-edge prediction
performance across diverse locomotion modes and transitions, relying solely on
sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other
machine learning techniques, achieving an outstanding average prediction
accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy
only marginally decreased to 93.00%. The averaged stable prediction times for
detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the
100-500 ms time advances.Comment: 10 pages,7 figure
Metagenomic Sequencing Identifies Highly Diverse Assemblages of Dinoflagellate Cysts in Sediments From Ships\u27 Ballast Tanks
Ships\u27 ballast tanks have long been known as vectors for the introduction of organisms. We applied next-generation sequencing to detect dinoflagellates (mainly as cysts) in 32 ballast tank sediments collected during 2001-2003 from ships entering the Great Lakes or Chesapeake Bay and subsequently archived. Seventy-three dinoflagellates were fully identified to species level by this metagenomic approach and single-cell polymerase chain reaction (PCR)-based sequencing, including 19 toxic species, 36 harmful algal bloom (HAB) forming species, 22 previously unreported as producing cysts, and 55 reported from ballast tank sediments for the first time (including 13 freshwater species), plus 545 operational taxonomic units (OTUs) not fully identified due to a lack of reference sequences, indicating tank sediments are repositories of many previously undocumented taxa. Analyses indicated great heterogeneity of species composition among samples from different sources. Light and scanning electron microscopy and single-cell PCR sequencing supported and confirmed results of the metagenomic approach. This study increases the number of fully identified dinoflagellate species from ballast tank sediments to 142 (\u3e 50% increase). From the perspective of ballast water management, the high diversity and spatiotemporal heterogeneity of dinoflagellates in ballast tanks argues for continuing research and stringent adherence to procedures intended to prevent unintended introduction of non-indigenous toxic and HAB-forming species
Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG
Predicting lower limb motion intent is vital for controlling exoskeleton
robots and prosthetic limbs. Surface electromyography (sEMG) attracts
increasing attention in recent years as it enables ahead-of-time prediction of
motion intentions before actual movement. However, the estimation performance
of human joint trajectory remains a challenging problem due to the inter- and
intra-subject variations. The former is related to physiological differences
(such as height and weight) and preferred walking patterns of individuals,
while the latter is mainly caused by irregular and gait-irrelevant muscle
activity. This paper proposes a model integrating two gait cycle-inspired
learning strategies to mitigate the challenge for predicting human knee joint
trajectory. The first strategy is to decouple knee joint angles into motion
patterns and amplitudes former exhibit low variability while latter show high
variability among individuals. By learning through separate network entities,
the model manages to capture both the common and personalized gait features. In
the second, muscle principal activation masks are extracted from gait cycles in
a prolonged walk. These masks are used to filter out components unrelated to
walking from raw sEMG and provide auxiliary guidance to capture more
gait-related features. Experimental results indicate that our model could
predict knee angles with the average root mean square error (RMSE) of
3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the best
performance in relevant literatures that has been reported, with reduced RMSE
by at least 9.5%
Ou: Automating the Parallelization of Zero-Knowledge Protocols
A zero-knowledge proof (ZKP) is a powerful cryptographic primitive used in many decentralized or privacy-focused applications. However, the high overhead of ZKPs can restrict their practical applicability. We design a programming language, Ou, aimed at easing the programmer\u27s burden when writing efficient ZKPs, and a compiler framework, Lian, that automates the analysis and distribution of statements to a computing cluster. Lian uses programming language semantics, formal methods, and combinatorial optimization to automatically partition an Ou program into efficiently sized chunks for parallel ZK-proving and/or verification.
We contribute:
• A front-end language where users can write proof statements as imperative programs in a familiar syntax;
• A compiler architecture and implementation that automatically analyzes the program and compiles it into an optimized IR that can be lifted to a variety of ZKP constructions; and
• A cutting algorithm, based on Pseudo-Boolean optimization and Integer Linear Programming, that reorders instructions and then partitions the program into efficiently sized chunks for parallel evaluation and efficient state reconciliation
Testing Electron-phonon Coupling for the Superconductivity in Kagome Metal
In crystalline materials, electron-phonon coupling (EPC) is a ubiquitous
many-body interaction that drives conventional Bardeen-Cooper-Schrieffer
superconductivity. Recently, in a new kagome metal ,
superconductivity that possibly intertwines with time-reversal and spatial
symmetry-breaking orders is observed. Density functional theory calculations
predicted weak EPC strength,, supporting an unconventional pairing
mechanism in . However, experimental determination of
is still missing, hindering a microscopic understanding of the intertwined
ground state of . Here, using 7-eV laser-based angle-resolved
photoemission spectroscopy and Eliashberg function analysis, we determine an
intermediate =0.45~0.6 at T=6 K for both Sb 5p and V 3d electronic
bands, which can support a conventional superconducting transition temperature
on the same magnitude of experimental value in . Remarkably,
the EPC on the V 3d-band enhances to ~0.75 as the superconducting
transition temperature elevated to 4.4 K in .
Our results provide an important clue to understand the pairing mechanism in
the Kagome superconductor .Comment: To appear in Nature Communication
Extrachromosomal circular DNA (eccDNA) characteristics in the bile and plasma of advanced perihilar cholangiocarcinoma patients and the construction of an eccDNA-related gene prognosis model
Extrachromosomal DNAs (eccDNAs) frequently carry amplified oncogenes. This investigation aimed to examine the occurrence and role of eccDNAs in individuals diagnosed with advanced perihilar cholangiocarcinoma (pCCA) who exhibited distinct prognostic outcomes. Five patients with poor survival outcomes and five with better outcomes were selected among patients who received first-line hepatic arterial infusion chemotherapy from June 2021 to June 2022. The extracted eccDNAs were amplified for high-throughput sequencing. Genes associated with the differentially expressed eccDNAs were analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The differentially expressed bile eccDNA-related genes were used to construct a prognostic model. Across all 10 patients, a total of 19,024 and 3,048 eccDNAs were identified in bile and plasma, respectively. The concentration of eccDNA detected in the bile was 9-fold higher than that in plasma. The chromosome distribution of the eccDNAs were similar between bile and matched plasma. GO and KEGG pathway analyses showed enrichment in the mitogen-activated protein kinase (MAPK) and Wnt/β-catenin pathways in patients with poor survival outcomes. According to the prognostic model constructed by eccDNA-related genes, the high-risk group of cholangiocarcinoma patients displayed significantly shorter overall survival (p < 0.001). Moreover, the degree of infiltration of immunosuppressive cells was higher in patients in the high-risk group. In conclusion, EccDNA could be detected in bile and plasma of pCCA patients, with a higher concentration. A prognostic model based on eccDNA-related genes showed the potential to predict the survival and immune microenvironment of patients with cholangiocarcinoma
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