945 research outputs found
Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks
In this paper, we study the problem of processing continuous range queries in
a hierarchical wireless sensor network. Contrasted with the traditional
approach of building networks in a "flat" structure using sensor devices of the
same capability, the hierarchical approach deploys devices of higher capability
in a higher tier, i.e., a tier closer to the server. While query processing in
flat sensor networks has been widely studied, the study on query processing in
hierarchical sensor networks has been inadequate. In wireless sensor networks,
the main costs that should be considered are the energy for sending data and
the storage for storing queries. There is a trade-off between these two costs.
Based on this, we first propose a progressive processing method that
effectively processes a large number of continuous range queries in
hierarchical sensor networks. The proposed method uses the query merging
technique proposed by Xiang et al. as the basis and additionally considers the
trade-off between the two costs. More specifically, it works toward reducing
the storage cost at lower-tier nodes by merging more queries, and toward
reducing the energy cost at higher-tier nodes by merging fewer queries (thereby
reducing "false alarms"). We then present how to build a hierarchical sensor
network that is optimal with respect to the weighted sum of the two costs. It
allows for a cost-based systematic control of the trade-off based on the
relative importance between the storage and energy in a given network
environment and application. Experimental results show that the proposed method
achieves a near-optimal control between the storage and energy and reduces the
cost by 0.989~84.995 times compared with the cost achieved using the flat
(i.e., non-hierarchical) setup as in the work by Xiang et al.Comment: 41 pages, 20 figure
Continuous Facial Motion Deblurring
We introduce a novel framework for continuous facial motion deblurring that
restores the continuous sharp moment latent in a single motion-blurred face
image via a moment control factor. Although a motion-blurred image is the
accumulated signal of continuous sharp moments during the exposure time, most
existing single image deblurring approaches aim to restore a fixed number of
frames using multiple networks and training stages. To address this problem, we
propose a continuous facial motion deblurring network based on GAN (CFMD-GAN),
which is a novel framework for restoring the continuous moment latent in a
single motion-blurred face image with a single network and a single training
stage. To stabilize the network training, we train the generator to restore
continuous moments in the order determined by our facial motion-based
reordering process (FMR) utilizing domain-specific knowledge of the face.
Moreover, we propose an auxiliary regressor that helps our generator produce
more accurate images by estimating continuous sharp moments. Furthermore, we
introduce a control-adaptive (ContAda) block that performs spatially deformable
convolution and channel-wise attention as a function of the control factor.
Extensive experiments on the 300VW datasets demonstrate that the proposed
framework generates a various number of continuous output frames by varying the
moment control factor. Compared with the recent single-to-single image
deblurring networks trained with the same 300VW training set, the proposed
method show the superior performance in restoring the central sharp frame in
terms of perceptual metrics, including LPIPS, FID and Arcface identity
distance. The proposed method outperforms the existing single-to-video
deblurring method for both qualitative and quantitative comparisons
Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition
A dramatic increase in real-world video volume with extremely diverse and
emerging topics naturally forms a long-tailed video distribution in terms of
their categories, and it spotlights the need for Video Long-Tailed Recognition
(VLTR). In this work, we summarize the challenges in VLTR and explore how to
overcome them. The challenges are: (1) it is impractical to re-train the whole
model for high-quality features, (2) acquiring frame-wise labels requires
extensive cost, and (3) long-tailed data triggers biased training. Yet, most
existing works for VLTR unavoidably utilize image-level features extracted from
pretrained models which are task-irrelevant, and learn by video-level labels.
Therefore, to deal with such (1) task-irrelevant features and (2) video-level
labels, we introduce two complementary learnable feature aggregators. Learnable
layers in each aggregator are to produce task-relevant representations, and
each aggregator is to assemble the snippet-wise knowledge into a video
representative. Then, we propose Minority-Oriented Vicinity Expansion (MOVE)
that explicitly leverages the class frequency into approximating the vicinity
distributions to alleviate (3) biased training. By combining these solutions,
our approach achieves state-of-the-art results on large-scale VideoLT and
synthetically induced Imbalanced-MiniKinetics200. With VideoLT features from
ResNet-50, it attains 18% and 58% relative improvements on head and tail
classes over the previous state-of-the-art method, respectively.Comment: Accepted to AAAI 2023. Code is available at
https://github.com/wjun0830/MOV
Leveraging Hidden Positives for Unsupervised Semantic Segmentation
Dramatic demand for manpower to label pixel-level annotations triggered the
advent of unsupervised semantic segmentation. Although the recent work
employing the vision transformer (ViT) backbone shows exceptional performance,
there is still a lack of consideration for task-specific training guidance and
local semantic consistency. To tackle these issues, we leverage contrastive
learning by excavating hidden positives to learn rich semantic relationships
and ensure semantic consistency in local regions. Specifically, we first
discover two types of global hidden positives, task-agnostic and task-specific
ones for each anchor based on the feature similarities defined by a fixed
pre-trained backbone and a segmentation head-in-training, respectively. A
gradual increase in the contribution of the latter induces the model to capture
task-specific semantic features. In addition, we introduce a gradient
propagation strategy to learn semantic consistency between adjacent patches,
under the inherent premise that nearby patches are highly likely to possess the
same semantics. Specifically, we add the loss propagating to local hidden
positives, semantically similar nearby patches, in proportion to the predefined
similarity scores. With these training schemes, our proposed method achieves
new state-of-the-art (SOTA) results in COCO-stuff, Cityscapes, and Potsdam-3
datasets. Our code is available at: https://github.com/hynnsk/HP.Comment: Accepted to CVPR 202
Solid tumors of the pancreas can put on a mask through cystic change
<p>Abstract</p> <p>Background</p> <p>Solid pancreatic tumors such as pancreatic ductal adenocarcinoma (PDAC), solid pseudopapillary tumor (SPT), and pancreatic endocrine tumor (PET) may occasionally manifest as cystic lesions. In this study, we have put together our accumulated experience with cystic manifestations of various solid tumors of the pancreas.</p> <p>Methods</p> <p>From 2000 to 2006, 376 patients with pancreatic solid tumor resections were reviewed. Ten (2.66%) of these tumors appeared on radiological imaging studies as cystic lesions. We performed a retrospective review of medical records and pathologic findings of these 10 cases.</p> <p>Results</p> <p>Of the ten cases in which solid tumors of the pancreas manifested as cystic lesions, six were PDAC with cystic degeneration, two were SPT undergone complete cystic change, one was cystic PET, and one was a cystic schwannoma. The mean tumor size of the cystic portion in PDAC was 7.3 cm, and three patients were diagnosed as 'pseudocyst' with or without cancer. Two SPT were found incidentally in young women and were diagnosed as other cystic neoplasms. One cystic endocrine tumor was preoperatively suspected as intraductal papillary mucinous neoplasm or mucinous cystic neoplasm.</p> <p>Conclusions</p> <p>Cystic changes of pancreas solid tumors are extremely rare. However, the possibility of cystic manifestation of pancreas solid tumors should be kept in mind.</p
Microfluidic Cell Culture Device
Microfluidic devices for cell culturing and methods for using the same are disclosed. One device includes a substrate and membrane. The substrate includes a reservoir in fluid communication with a passage. A bio-compatible fluid may be added to the reservoir and passage. The reservoir is configured to receive and retain at least a portion of a cell mass. The membrane acts as a barrier to evaporation of the bio-compatible fluid from the passage. A cover fluid may be added to cover the bio-compatible fluid to prevent evaporation of the bio-compatible fluid
- …