214 research outputs found
Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognition
Skeleton-based human action recognition is a longstanding challenge due to
its complex dynamics. Some fine-grain details of the dynamics play a vital role
in classification. The existing work largely focuses on designing incremental
neural networks with more complicated adjacent matrices to capture the details
of joints relationships. However, they still have difficulties distinguishing
actions that have broadly similar motion patterns but belong to different
categories. Interestingly, we found that the subtle differences in motion
patterns can be significantly amplified and become easy for audience to
distinct through specified view directions, where this property haven't been
fully explored before. Drastically different from previous work, we boost the
performance by proposing a conceptually simple yet effective Multi-view
strategy that recognizes actions from a collection of dynamic view features.
Specifically, we design a novel Skeleton-Anchor Proposal (SAP) module which
contains a Multi-head structure to learn a set of views. For feature learning
of different views, we introduce a novel Angle Representation to transform the
actions under different views and feed the transformations into the baseline
model. Our module can work seamlessly with the existing action classification
model. Incorporated with baseline models, our SAP module exhibits clear
performance gains on many challenging benchmarks. Moreover, comprehensive
experiments show that our model consistently beats down the state-of-the-art
and remains effective and robust especially when dealing with corrupted data.
Related code will be available on https://github.com/ideal-idea/SAP
EGFR inhibitor C225 increases the radiosensitivity of human lung squamous cancer cells
Background: The purpose of the present study is to investigate the direct biological effects of the epidermal growth factor receptor (EGFR) inhibitor C225 on the radiosensitivity of human lung squamous cancer cell-H520. H520 cells were treated with different dosage of (60)Co gamma ray irradiation (1.953 Gy/min) in the presence or absence of C225. The cellular proliferation, colony forming capacity, apoptosis, the cell cycle distribution as well as caspase-3 were analyzed in vitro. Results: We found that C225 treatment significantly increased radiosensitivity of H-520 cells to irradiation, and led to cell cycle arrest in G(1) phase, whereas (60)Co gamma ray irradiation mainly caused G(2) phase arrest. H-520 cells thus displayed both the G(1) and G(2) phase arrest upon treatment with C225 in combination with (60)Co gamma ray irradiation. Moreover, C225 treatment significantly increased the apoptosis percentage of H-520 cells (13.91% +/- 1.88%) compared with the control group (5.75% +/- 0.64%, P < 0.05). Conclusion: In this regard, C225 treatment may make H-520 cells more sensitive to irradiation through the enhancement of caspase-3 mediated tumor cell apoptosis and cell cycle arrest.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000284001800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701OncologySCI(E)PubMed2ARTICLE391
Hybrid phase-change Lattice Boltzmann simulation of vapor condensation on vertical subcooled walls
Saturated vapor condensation on homogenous and heterogeneous subcooled walls is presented in this study by adopting a hybrid phase-change multiple-relaxation-time Lattice Boltzmann model. The effects of wall wettability on the condensation process, including droplets’ growth, coalescence and falling, and the influence of vapor flow to condensation are investigated. The results demonstrate that the heat fluxes around the triple-phase contact lines are higher than that in other cold areas in homogeneous subcooled walls, which actually indicates the fact that filmwise condensation is preventing the continuous condensation process. Furthermore, the dropwise condensation can be formed more easily on the heterogeneous surface with a mixed surface wettability. At last, the dynamic process of condensation of continuous vapor flow is also investigated by considering the homogenous and heterogeneous subcooled surfaces. The results show that the heterogeneous surface with mixed wettability doesn’t contribute to the formation, growth of droplets, when compared to the homogeneous surface. It is expected that this study can bring more attentions to simulate condensation using multiphase LBM for complex geometries in heat transfer community
MNHMT2009-18008 Monte Carlo simulations of phonon backscattering in low-sized nanowires
ABSTRACT Monte Carlo model is used to study the roughness effects on lattice thermal conductivity of silicon nanowires. Based on the Matthiessen's rule, a roughness influence coefficient is adopted to amend the relaxation time
Mass distribution for single-lined hot subdwarf stars in LAMOST
Masses for 664 single-lined hot subdwarf stars identified in LAMOST were
calculated by comparing synthetic fluxes from spectral energy distribution
(SED) with observed fluxes from virtual observatory service. Three groups of
hot subdwarf stars were selected from the whole sample according to their
parallax precision to study the mass distributions. We found, that He-poor
sdB/sdOB stars present a wide mass distribution from 0.1 to 1.0
with a sharp mass peak around at 0.46 ,
which is consistent with canonical binary model prediction. He-rich
sdB/sdOB/sdO stars present a much flatter mass distribution than He-poor
sdB/sdOB stars and with a mass peak around 0.42 . By
comparing the observed mass distributions to the predictions of different
formation scenarios, we concluded that the binary merger channel, including two
helium white dwarfs (He-WDs) and He-WD + main sequence (MS) merger, cannot be
the only main formation channel for He-rich hot subdwarfs, and other formation
channels such as the surviving companions from type Ia supernovae (SNe Ia)
could also make impacts on producing this special population, especially for
He-rich hot subdwarfs with masses less than 0.44 . He-poor
sdO stars also present a flatter mass distribution with an inconspicuous peak
mass at 0.18 . The similar mass -
distribution between He-poor sdB/sdOB and sdO stars supports the scenario that
He-poor sdO stars could be the subsequent evolution stage of He-poor sdB/sdOB
stars.Comment: 38 pages, 13 figures, 3 tables, accepted for publication in Ap
2D2D HILIC-ELSD/UPLC-Q-TOF-MS Method for Acquiring Phospholipid Profiles and the Application in Caenorhabditis elegans
Phospholipids are the main constituent of cellular membranes and have recently been identified to have diagnostic value as biomarkers for many diseases. Accordingly, much emphasis is now laid on developing optimal analytical techniques for the phospholipid profiles of various biological samples. In the present study, different classes of phospholipids are first separated by optimized hydrophilic interaction chromatography with evaporative light scattering detector (HILIC-ELSD). The phospholipids in each class are then identified by ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Validation results confirm that this approach meets the requirements of quantitative analysis. Finally, the approach is adopted to analyze the phospholipid profiles in Caenorhabditis elegans. A total of 111 phospholipid species is identified according to the mass fragments. Major fatty acyl chains in phospholipids are found to be formed by oleic acid (C18:1), arachidonic acid (C20:4), and eicosapentaenoic acid (C20:5). Overall, this study improves current knowledge on analytical techniques of the phospholipid composition in C. elegans and provides a basis for future lipidomics research. Practical applications: Phospholipids reportedly play a crucial role in the development of many diseases. Until now, only a small portion of phospholipids in Caenorhabditis elegans has been reported by using one-dimensional analysis strategy. The offline 2D2D liquid chromatography method developed in this study identifies 111 phospholipid species in Caenorhabditis elegans. The obtained phospholipid profiles complement the lipid database of Caenorhabditis elegans. The study also provides the basis for the future development of a 2D online approach
Advanced Geological Prediction
Due to the particularity of the tunnel project, it is difficult to find out the exact geological conditions of the tunnel body during the survey stage. Once it encounters unfavorable geological bodies such as faults, fracture zones, and karst, it will bring great challenges to the construction and will easily cause major problems, economic losses, and casualties. Therefore, it is necessary to carry out geological forecast work in the tunnel construction process, which is of great significance for tunnel safety construction and avoiding major disaster accident losses. This lecture mainly introduces the commonly used methods of geological forecast in tunnel construction, the design principles, and contents of geological forecast and combines typical cases to show the implementation process of comprehensive geological forecast. Finally, the development direction of geological forecast theory, method, and technology is carried out. Prospects provide a useful reference for promoting the development of geological forecast of tunnels
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
Despite recent progress in reinforcement learning (RL) from raw pixel data,
sample inefficiency continues to present a substantial obstacle. Prior works
have attempted to address this challenge by creating self-supervised auxiliary
tasks, aiming to enrich the agent's learned representations with
control-relevant information for future state prediction. However, these
objectives are often insufficient to learn representations that can represent
the optimal policy or value function, and they often consider tasks with small,
abstract discrete action spaces and thus overlook the importance of action
representation learning in continuous control. In this paper, we introduce
TACO: Temporal Action-driven Contrastive Learning, a simple yet powerful
temporal contrastive learning approach that facilitates the concurrent
acquisition of latent state and action representations for agents. TACO
simultaneously learns a state and an action representation by optimizing the
mutual information between representations of current states paired with action
sequences and representations of the corresponding future states.
Theoretically, TACO can be shown to learn state and action representations that
encompass sufficient information for control, thereby improving sample
efficiency. For online RL, TACO achieves 40% performance boost after one
million environment interaction steps on average across nine challenging visual
continuous control tasks from Deepmind Control Suite. In addition, we show that
TACO can also serve as a plug-and-play module adding to existing offline visual
RL methods to establish the new state-of-the-art performance for offline visual
RL across offline datasets with varying quality
AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments
We present AutoMerge, a LiDAR data processing framework for assembling a
large number of map segments into a complete map. Traditional large-scale map
merging methods are fragile to incorrect data associations, and are primarily
limited to working only offline. AutoMerge utilizes multi-perspective fusion
and adaptive loop closure detection for accurate data associations, and it uses
incremental merging to assemble large maps from individual trajectory segments
given in random order and with no initial estimations. Furthermore, after
assembling the segments, AutoMerge performs fine matching and pose-graph
optimization to globally smooth the merged map. We demonstrate AutoMerge on
both city-scale merging (120km) and campus-scale repeated merging (4.5km x 8).
The experiments show that AutoMerge (i) surpasses the second- and third- best
methods by 14% and 24% recall in segment retrieval, (ii) achieves comparable 3D
mapping accuracy for 120 km large-scale map assembly, (iii) and it is robust to
temporally-spaced revisits. To the best of our knowledge, AutoMerge is the
first mapping approach that can merge hundreds of kilometers of individual
segments without the aid of GPS.Comment: 18 pages, 18 figur
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