415 research outputs found
Modeling driver's evasive behavior during safety-critical lane changes:Two-dimensional time-to-collision and deep reinforcement learning
Lane changes are complex driving behaviors and frequently involve
safety-critical situations. This study aims to develop a lane-change-related
evasive behavior model, which can facilitate the development of safety-aware
traffic simulations and predictive collision avoidance systems. Large-scale
connected vehicle data from the Safety Pilot Model Deployment (SPMD) program
were used for this study. A new surrogate safety measure, two-dimensional
time-to-collision (2D-TTC), was proposed to identify the safety-critical
situations during lane changes. The validity of 2D-TTC was confirmed by showing
a high correlation between the detected conflict risks and the archived
crashes. A deep deterministic policy gradient (DDPG) algorithm, which could
learn the sequential decision-making process over continuous action spaces, was
used to model the evasive behaviors in the identified safety-critical
situations. The results showed the superiority of the proposed model in
replicating both the longitudinal and lateral evasive behaviors
LogGD:Detecting Anomalies from System Logs by Graph Neural Networks
Log analysis is one of the main techniques engineers use to troubleshoot
faults of large-scale software systems. During the past decades, many log
analysis approaches have been proposed to detect system anomalies reflected by
logs. They usually take log event counts or sequential log events as inputs and
utilize machine learning algorithms including deep learning models to detect
system anomalies. These anomalies are often identified as violations of
quantitative relational patterns or sequential patterns of log events in log
sequences. However, existing methods fail to leverage the spatial structural
relationships among log events, resulting in potential false alarms and
unstable performance. In this study, we propose a novel graph-based log anomaly
detection method, LogGD, to effectively address the issue by transforming log
sequences into graphs. We exploit the powerful capability of Graph Transformer
Neural Network, which combines graph structure and node semantics for log-based
anomaly detection. We evaluate the proposed method on four widely-used public
log datasets. Experimental results show that LogGD can outperform
state-of-the-art quantitative-based and sequence-based methods and achieve
stable performance under different window size settings. The results confirm
that LogGD is effective in log-based anomaly detection.Comment: 12 pages, 12 figure
Integrated network analysis and metabolomics reveal the molecular mechanism of Yinchen Sini decoction in CCl4-induced acute liver injury
Objective: Yinchen Sini decoction (YCSND), a traditional Chinese medicine (TCM) formula, plays a crucial role in the treatment of liver disease. However, the bioactive constituents and pharmacological mechanisms of action remain unclear. The present study aimed to reveal the molecular mechanism of YCSND in the treatment of acute liver injury (ALI) using integrated network analysis and metabolomics.Methods: Ultra-high-performance liquid chromatography coupled with Q-Exactive focus mass spectrum (UHPLC-QE-MS) was utilized to identify metabolites in YCSND, and high-performance liquid chromatography (HPLC) was applied to evaluate the quality of four botanical drugs in YCSND. Cell damage and ALI models in mice were established using CCl4. 1H-NMR metabolomics approach, along with histopathological observation using hematoxylin and eosin (H&E), biochemical measurements, and reverse transcription quantitative real-time PCR (RT-qPCR), was applied to evaluate the effect of YCSND on CCl4- induced ALI. Network analysis was conducted to predict the potential targets of YCSND in ALI.Result: Our results showed that 89 metabolites in YCSND were identified using UHPLC-QE-MS. YCSND protected against ALI by reducing the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), and malondialdehyde (MDA) contents and increasing those of superoxide dismutase (SOD), and glutathione (GSH) both in vivo and in vitro. The 1H-NMRmetabolic pattern revealed that YCSND reversed CCl4-induced metabolic abnormalities in the liver. Additionally, the Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis identified five pathways related to liver injury, including the PI3K-AKT, MAPK, HIF-1, apoptosis, and TNF signaling pathways. Moreover, RT-qPCR showed YCSND regulated the inflammatory response (Tlr4, Il6, Tnfα, Nfκb1, Ptgs2, and Mmp9) and apoptosis (Bcl2, Caspase3, Bax, and Mapk3), and inhibited PI3K-AKT signaling pathway (Pi3k and Akt1). Combined network analysis and metabolomics showed a link between the key targets (Tlr4, Ptgs2, and Mmp9) and vital metabolites (choline, xanthine, lactate, and 3-hydroxybutyric acid) of YCSND in ALI.Conclusion: Overall, the results contribute to the understanding of the therapeutic effects of YCSND on ALI, and indicate that the integrated network analysis and metabolomics could be a powerful strategy to reveal the pharmacological effects of TCM
Boundary-semantic collaborative guidance network with dual-stream feedback mechanism for salient object detection in optical remote sensing imagery
With the increasing application of deep learning in various domains, salient
object detection in optical remote sensing images (ORSI-SOD) has attracted
significant attention. However, most existing ORSI-SOD methods predominantly
rely on local information from low-level features to infer salient boundary
cues and supervise them using boundary ground truth, but fail to sufficiently
optimize and protect the local information, and almost all approaches ignore
the potential advantages offered by the last layer of the decoder to maintain
the integrity of saliency maps. To address these issues, we propose a novel
method named boundary-semantic collaborative guidance network (BSCGNet) with
dual-stream feedback mechanism. First, we propose a boundary protection
calibration (BPC) module, which effectively reduces the loss of edge position
information during forward propagation and suppresses noise in low-level
features without relying on boundary ground truth. Second, based on the BPC
module, a dual feature feedback complementary (DFFC) module is proposed, which
aggregates boundary-semantic dual features and provides effective feedback to
coordinate features across different layers, thereby enhancing cross-scale
knowledge communication. Finally, to obtain more complete saliency maps, we
consider the uniqueness of the last layer of the decoder for the first time and
propose the adaptive feedback refinement (AFR) module, which further refines
feature representation and eliminates differences between features through a
unique feedback mechanism. Extensive experiments on three benchmark datasets
demonstrate that BSCGNet exhibits distinct advantages in challenging scenarios
and outperforms the 17 state-of-the-art (SOTA) approaches proposed in recent
years. Codes and results have been released on GitHub:
https://github.com/YUHsss/BSCGNet.Comment: Accepted by TGR
Topological interfacial states in ferroelectric domain walls of two-dimensional bismuth
Using machine learning method, we investigate various domain walls for the
recently discovered single-element ferroelectrics bismuth monolayer [Nature
617, 67 (2023)]. Surprisingly, we find that the charged domain wall
configuration has a lower energy than the uncharged domain wall structure due
to its low electrostatic repulsion potential. Two stable charged domain wall
configurations exhibit topological interfacial states near their domain walls,
which is caused by the change of the Z_2 number between ferroelectric and
paraelectric states. Interestingly, different from the edge states of
topological insulators, the topological interfacial states related Dirac bands
are contributed from different edges which is caused by the build-in electric
field of FE. Our works thus indicate that domain walls in two-dimensional
bismuth can be a good platform for ferroelectric domain wall devices.Comment: 15 pages, 4 fig
Renal Tubular Cell-Derived Extracellular Vesicles Accelerate the Recovery of Established Renal Ischemia Reperfusion Injury
Ischemic renal injury is a complex syndrome; multiple cellular abnormalities cause accelerating cycles of inflammation, cellular damage, and sustained local ischemia. There is no single therapy that effectively resolves the renal damage after ischemia. However, infusions of normal adult rat renal cells have been a successful therapy in several rat renal failure models. The sustained broad renal benefit achieved by relatively few donor cells led to the hypothesis that extracellular vesicles (EV, largely exosomes) derived from these cells are the therapeutic effector in situ We now show that EV from adult rat renal tubular cells significantly improved renal function when administered intravenously 24 and 48 hours after renal ischemia in rats. Additionally, EV treatment significantly improved renal tubular damage, 4-hydroxynanoneal adduct formation, neutrophil infiltration, fibrosis, and microvascular pruning. EV therapy also markedly reduced the large renal transcriptome drift observed after ischemia. These data show the potential utility of EV to limit severe renal ischemic injury after the occurrence
A New Anthracene Derivative from Marine Streptomyces sp. W007 Exhibiting Highly and Selectively Cytotoxic Activities
A new anthracene derivative, 3-hydroxy-1-keto-3-methyl-8-methoxy-1,2,3, 4-tetrahydro-benz[α]anthracene, was isolated from the marine strain Streptomyces sp. W007, and its structure was established by spectroscopic analysis including mass spectra, 1D- and 2D-NMR (1H–1H COSY, HMBC, HSQC and NOESY) experiments. 3-hydroxy-1-keto-3-methyl-8-methoxy-1,2,3,4-tetrahydro-benz[α]anthracene showed cytotoxicity against human lung adenocarcinoma cell line A549
FBNet: Feedback Network for Point Cloud Completion
The rapid development of point cloud learning has driven point cloud
completion into a new era. However, the information flows of most existing
completion methods are solely feedforward, and high-level information is rarely
reused to improve low-level feature learning. To this end, we propose a novel
Feedback Network (FBNet) for point cloud completion, in which present features
are efficiently refined by rerouting subsequent fine-grained ones. Firstly,
partial inputs are fed to a Hierarchical Graph-based Network (HGNet) to
generate coarse shapes. Then, we cascade several Feedback-Aware Completion
(FBAC) Blocks and unfold them across time recurrently. Feedback connections
between two adjacent time steps exploit fine-grained features to improve
present shape generations. The main challenge of building feedback connections
is the dimension mismatching between present and subsequent features. To
address this, the elaborately designed point Cross Transformer exploits
efficient information from feedback features via cross attention strategy and
then refines present features with the enhanced feedback features. Quantitative
and qualitative experiments on several datasets demonstrate the superiority of
proposed FBNet compared to state-of-the-art methods on point completion task.Comment: The first two authors contributed equally to this work. The source
code and model are available at
https://github.com/hikvision-research/3DVision/. Accepted to ECCV 2022 as
oral presentatio
- …