1,103 research outputs found
Mirage cosmology with an unstable probe D3-brane
We consider the mirage cosmology by an unstable probe brane whose action is
represented by BDI action with tachyon. We study how the presence of tachyon
affects the evolution of the brane inflation. At the early stage of the brane
inflation, the tachyon kinetic term can play an important role in curing the
superluminal expansion in mirage cosmology.Comment: 11 pages, improved presentation with some clarifications, typos
corrected, references adde
3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection
In this paper, we propose a new deep architecture for fusing camera and LiDAR
sensors for 3D object detection. Because the camera and LiDAR sensor signals
have different characteristics and distributions, fusing these two modalities
is expected to improve both the accuracy and robustness of 3D object detection.
One of the challenges presented by the fusion of cameras and LiDAR is that the
spatial feature maps obtained from each modality are represented by
significantly different views in the camera and world coordinates; hence, it is
not an easy task to combine two heterogeneous feature maps without loss of
information. To address this problem, we propose a method called 3D-CVF that
combines the camera and LiDAR features using the cross-view spatial feature
fusion strategy. First, the method employs auto-calibrated projection, to
transform the 2D camera features to a smooth spatial feature map with the
highest correspondence to the LiDAR features in the bird's eye view (BEV)
domain. Then, a gated feature fusion network is applied to use the spatial
attention maps to mix the camera and LiDAR features appropriately according to
the region. Next, camera-LiDAR feature fusion is also achieved in the
subsequent proposal refinement stage. The camera feature is used from the 2D
camera-view domain via 3D RoI grid pooling and fused with the BEV feature for
proposal refinement. Our evaluations, conducted on the KITTI and nuScenes 3D
object detection datasets demonstrate that the camera-LiDAR fusion offers
significant performance gain over single modality and that the proposed 3D-CVF
achieves state-of-the-art performance in the KITTI benchmark
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals
Brain-computer interface (BCI) is a communication system between humans and
computers reflecting human intention without using a physical control device.
Since deep learning is robust in extracting features from data, research on
decoding electroencephalograms by applying deep learning has progressed in the
BCI domain. However, the application of deep learning in the BCI domain has
issues with a lack of data and overconfidence. To solve these issues, we
proposed a novel data augmentation method, CropCat. CropCat consists of two
versions, CropCat-spatial and CropCat-temporal. We designed our method by
concatenating the cropped data after cropping the data, which have different
labels in spatial and temporal axes. In addition, we adjusted the label based
on the ratio of cropped length. As a result, the generated data from our
proposed method assisted in revising the ambiguous decision boundary into
apparent caused by a lack of data. Due to the effectiveness of the proposed
method, the performance of the four EEG signal decoding models is improved in
two motor imagery public datasets compared to when the proposed method is not
applied. Hence, we demonstrate that generated data by CropCat smooths the
feature distribution of EEG signals when training the model.Comment: 4 pages, 1 tabl
Decoding EEG-based Workload Levels Using Spatio-temporal Features Under Flight Environment
The detection of pilots' mental states is important due to the potential for
their abnormal mental states to result in catastrophic accidents. This study
introduces the feasibility of employing deep learning techniques to classify
different workload levels, specifically normal state, low workload, and high
workload. To the best of our knowledge, this study is the first attempt to
classify workload levels of pilots. Our approach involves the hybrid deep
neural network that consists of five convolutional blocks and one long
short-term memory block to extract the significant features from
electroencephalography signals. Ten pilots participated in the experiment,
which was conducted within the simulated flight environment. In contrast to
four conventional models, our proposed model achieved a superior grand--average
accuracy of 0.8613, surpassing other conventional models by at least 0.0597 in
classifying workload levels across all participants. Our model not only
successfully classified workload levels but also provided valuable feedback to
the participants. Hence, we anticipate that our study will make the significant
contributions to the advancement of autonomous flight and driving leveraging
artificial intelligence technology in the future.Comment: 5 pages, 3 figures, 1 table, 1 algorith
CCN1 Secretion Induced by Cigarette Smoking Extracts Augments IL-8 Release from Bronchial Epithelial Cells
Inflammation involves in many cigarette smoke (CS) related diseases including the chronic obstructive pulmonary disease (COPD). Lung epithelial cell released IL-8 plays a crucial role in CS induced lung inflammation. CS and cigarette smoke extracts (CSE) both induce IL-8 secretion and subsequently, IL-8 recruits inflammatory cells into the lung parenchyma. However, the molecular and cellular mechanisms by which CSE triggers IL-8 release remain not completely understood. In this study, we identified a novel extracellular matrix (ECM) molecule, CCN1, which mediated CSE induced IL-8 secretion by lung epithelial cells. We first found that CS and CSE up-regulated CCN1 expression and secretion in lung epithelial cells in vivo and in vitro. CSE up-regulated CCN1 via induction of reactive oxygen spices (ROS) and endoplasmic reticulum (ER) stress. p38 MAPK and JNK activation were also found to mediate the signal pathways in CSE induced CCN1. CCN1 was secreted into ECM via Golgi and membrane channel receptor aquaporin4. After CSE exposure, elevated ECM CCN1 functioned via an autocrine or paracrine manner. Importantly, CCN1 activated Wnt pathway receptor LRP6, subsequently stimulated Wnt pathway component Dvl2 and triggered beta-catenin translocation from cell membrane to cytosol and nucleus. Treatment of Wnt pathway inhibitor suppressed CCN1 induced IL-8 secretion from lung epithelial cells. Taken together, CSE increased CCN1 expression and secretion in lung epithelial cells via induction of ROS and ER stress. Increased ECM CCN1 resulted in augmented IL-8 release through the activation of Wnt pathway
Clinical outcomes of FOLFIRINOX in locally advanced pancreatic cancer: A single center experience
Systemic chemotherapy or chemoradiotherapy is the initial primary option for patients with locally advanced pancreatic cancer (LAPC). This study analyzed the effect of FOLFIRINOX and assessed the factors influencing conversion to surgical resectability for LAPC.Sixty-four patients with LAPC who received FOLFIRINOX as initial chemotherapy were enrolled retrospectively. Demographic characteristics, tumor status, interval/dosage/cumulative relative dose intensity (cRDI) of FOLFIRINOX, conversion to resection, and clinical outcomes were reviewed and factors associated with conversion to resectability after FOLFIRINOX were analyzed.After administration of FOLFIRINOX (median 9 cycles, 70% of cRDI), the median patient overall survival (OS) was 17.0 months. Fifteen of 64 patients underwent surgery and R0 resection was achieved in 11 patients. During a median follow-up time of 9.4 months after resection, cumulative recurrence rate was 28.5% at 18 months after resection. The estimated median OS was significantly longer for the resected group (>40 months vs 13 months). There were no statistical differences between the resected and non-resected groups in terms of baseline characteristics, tumor status and hematologic adverse effects. The patients who received standard dose of FOLFIRINOX had higher probability of subsequent resection compared with patients who received reduced dose, although cRDIs did not differ between groups.FOLFIRINOX is an active regimen in patients with LAPC, given acceptable resection rates and promising R0 resection rates. Additionally, our data demonstrate it is advantageous for obtaining resectability to administer FOLFIRINOX without dose reduction
PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels
The recent advent of play-to-earn (P2E) systems in massively multiplayer
online role-playing games (MMORPGs) has made in-game goods interchangeable with
real-world values more than ever before. The goods in the P2E MMORPGs can be
directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn
via blockchain networks. Unlike traditional in-game goods, once they had been
written to the blockchains, P2E goods cannot be restored by the game operation
teams even with chargeback fraud such as payment fraud, cancellation, or
refund. To tackle the problem, we propose a novel chargeback fraud prediction
method, PU GNN, which leverages graph attention networks with PU loss to
capture both the players' in-game behavior with P2E token transaction patterns.
With the adoption of modified GraphSMOTE, the proposed model handles the
imbalanced distribution of labels in chargeback fraud datasets. The conducted
experiments on three real-world P2E MMORPG datasets demonstrate that PU GNN
achieves superior performances over previously suggested methods.Comment: Under Review, Industry Trac
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