83 research outputs found
Cache-Enabled in Cooperative Cognitive Radio Networks for Transmission Performance
The proliferation of mobile devices that support the acceleration of data services (especially smartphones) has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary contents. Thus, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay from the backhaul link to the secondary base station. First, in terms of the content caching and the transmission strategies, we provide a cooperation scheme to maximize the secondary user’s effective data transmission rates under the constraint of the primary users target rate. Then, we investigate the impact of the caching allocation and prove that the formulated problem is a concave problem with regard to the caching capacity allocation for any given power allocation. Furthermore, we obtain the joint caching and power allocation by an effective bisection search algorithm. Finally, our results show that the content caching cooperation scheme can achieve significant performance gain for the primary and secondary systems over the traditional two-hop relay cooperation without caching
Practical and Secure Circular Range Search on Private Spatial Data
With the location-based services (LBS) booming, the volume of spatial data inevitably explodes. In order to reduce local storage and computational overhead, users tend to outsource data and initiate queries to the cloud. However, sensitive data or queries may be compromised if cloud server has access to raw data and plaintext token. To cope with this problem, searchable encryption for geometric range is applied. Geometric range search has wide applications in many scenarios, especially the circular range search.
In this paper, a practical and secure circular range search scheme (PSCS) is proposed to support searching for spatial data in a circular range. With our scheme, a semi-honest cloud server will return data for a given circular range correctly without uncovering index privacy or query privacy. We propose a polynomial split algorithm which can decompose the inner product calculation neatly. Then, we define the security of our PSCS formally and prove that it is secure under same-closeness-pattern chosen-plaintext attacks (CLS-CPA) in theory. In addition, we demonstrate the efficiency and accuracy through analysis and experiments compared with existing schemes
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception
Perception is crucial in the realm of autonomous driving systems, where
bird's eye view (BEV)-based architectures have recently reached
state-of-the-art performance. The desirability of self-supervised
representation learning stems from the expensive and laborious process of
annotating 2D and 3D data. Although previous research has investigated
pretraining methods for both LiDAR and camera-based 3D object detection, a
unified pretraining framework for multimodal BEV perception is missing. In this
study, we introduce CALICO, a novel framework that applies contrastive
objectives to both LiDAR and camera backbones. Specifically, CALICO
incorporates two stages: point-region contrast (PRC) and region-aware
distillation (RAD). PRC better balances the region- and scene-level
representation learning on the LiDAR modality and offers significant
performance improvement compared to existing methods. RAD effectively achieves
contrastive distillation on our self-trained teacher model. CALICO's efficacy
is substantiated by extensive evaluations on 3D object detection and BEV map
segmentation tasks, where it delivers significant performance improvements.
Notably, CALICO outperforms the baseline method by 10.5% and 8.6% on NDS and
mAP. Moreover, CALICO boosts the robustness of multimodal 3D object detection
against adversarial attacks and corruption. Additionally, our framework can be
tailored to different backbones and heads, positioning it as a promising
approach for multimodal BEV perception
Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving
3D object detection is an essential perception task in autonomous driving to
understand the environments. The Bird's-Eye-View (BEV) representations have
significantly improved the performance of 3D detectors with camera inputs on
popular benchmarks. However, there still lacks a systematic understanding of
the robustness of these vision-dependent BEV models, which is closely related
to the safety of autonomous driving systems. In this paper, we evaluate the
natural and adversarial robustness of various representative models under
extensive settings, to fully understand their behaviors influenced by explicit
BEV features compared with those without BEV. In addition to the classic
settings, we propose a 3D consistent patch attack by applying adversarial
patches in the 3D space to guarantee the spatiotemporal consistency, which is
more realistic for the scenario of autonomous driving. With substantial
experiments, we draw several findings: 1) BEV models tend to be more stable
than previous methods under different natural conditions and common corruptions
due to the expressive spatial representations; 2) BEV models are more
vulnerable to adversarial noises, mainly caused by the redundant BEV features;
3) Camera-LiDAR fusion models have superior performance under different
settings with multi-modal inputs, but BEV fusion model is still vulnerable to
adversarial noises of both point cloud and image. These findings alert the
safety issue in the applications of BEV detectors and could facilitate the
development of more robust models.Comment: 8 pages, CVPR202
HMGA2 promotes adipogenesis by activating C/EBPβ-mediated expression of PPARγ
AbstractAdipogenesis is orchestrated by a highly ordered network of transcription factors including peroxisome-proliferator activated receptor-gamma (PPARγ) and CCAAT-enhancer binding protein (C/EBP) family proteins. High mobility group protein AT-hook 2 (HMGA2), an architectural transcription factor, has been reported to play an essential role in preadipocyte proliferation, and its overexpression has been implicated in obesity in mice and humans. However, the direct role of HMGA2 in regulating the gene expression program during adipogenesis is not known. Here, we demonstrate that HMGA2 is required for C/EBPβ-mediated expression of PPARγ, and thus promotes adipogenic differentiation. We observed a transient but marked increase of Hmga2 transcript at an early phase of differentiation of mouse 3T3-L1 preadipocytes. Importantly, Hmga2 knockdown greatly impaired adipocyte formation, while its overexpression promoted the formation of mature adipocytes. We found that HMGA2 colocalized with C/EBPβ in the nucleus and was required for the recruitment of C/EBPβ to its binding element at the Pparγ2 promoter. Accordingly, HMGA2 and C/EBPβ cooperatively enhanced the Pparγ2 promoter activity. Our results indicate that HMGA2 is an essential constituent of the adipogenic transcription factor network, and thus its function may be affected during the course of obesity
Visual Representation Learning with Transformer: A Sequence-to-Sequence Perspective
Visual representation learning is the key of solving various vision problems.
Relying on the seminal grid structure priors, convolutional neural networks
(CNNs) have been the de facto standard architectures of most deep vision
models. For instance, classical semantic segmentation methods often adopt a
fully-convolutional network (FCN) with an encoder-decoder architecture. The
encoder progressively reduces the spatial resolution and learns more abstract
visual concepts with larger receptive fields. Since context modeling is
critical for segmentation, the latest efforts have been focused on increasing
the receptive field, through either dilated (i.e., atrous) convolutions or
inserting attention modules. However, the FCN-based architecture remains
unchanged. In this paper, we aim to provide an alternative perspective by
treating visual representation learning generally as a sequence-to-sequence
prediction task. Specifically, we deploy a pure Transformer to encode an image
as a sequence of patches, without local convolution and resolution reduction.
With the global context modeled in every layer of the Transformer, stronger
visual representation can be learned for better tackling vision tasks. In
particular, our segmentation model, termed as SEgmentation TRansformer (SETR),
excels on ADE20K (50.28% mIoU, the first position in the test leaderboard on
the day of submission), Pascal Context (55.83% mIoU) and reaches competitive
results on Cityscapes. Further, we formulate a family of Hierarchical
Local-Global (HLG) Transformers characterized by local attention within windows
and global-attention across windows in a hierarchical and pyramidal
architecture. Extensive experiments show that our method achieves appealing
performance on a variety of visual recognition tasks (e.g., image
classification, object detection and instance segmentation and semantic
segmentation).Comment: Extended version of CVPR 2021 paper arXiv:2012.1584
Deep learning-based edge caching for multi-cluster heterogeneous networks
© 2019, Springer-Verlag London Ltd., part of Springer Nature. In this work, we consider a time and space evolution cache refreshing in multi-cluster heterogeneous networks. We consider a two-step content placement probability optimization. At the initial complete cache refreshing optimization, the joint optimization of the activated base station density and the content placement probability is considered. And we transform this optimization problem into a GP problem. At the following partial cache refreshing optimization, we take the time–space evolution into consideration and derive a convex optimization problem subjected to the cache capacity constraint and the backhaul limit constraint. We exploit the redundant information in different content popularity using the deep neural network to avoid the repeated calculation because of the change in content popularity distribution at different time slots. Trained DNN can provide online response to content placement in a multi-cluster HetNet model instantaneously. Numerical results demonstrate the great approximation to the optimum and generalization ability
Lightweight conductive graphene/thermoplastic polyurethane foams with ultrahigh compressibility for piezoresistive sensing
Lightweight conductive porous graphene/thermoplastic polyurethane (TPU) foams with ultrahigh compressibility were successfully fabricated by using the thermal induced phase separation (TISP) technique. The density and porosity of the foams were calculated to be about 0.11 g cm−3 and 90% owing to the porous structure. Compared with pure TPU foams, the addition of graphene could effectively increase the thickness of the cell wall and hinder the formation of small holes, leading to a robust porous structure with excellent compression property. Meanwhile, the cell walls with small holes and a dendritic structure were observed due to the flexibility of graphene, endowing the foam with special positive piezoresistive behaviors and peculiar response patterns with a deflection point during the cyclic compression. This could effectively enhance the identifiability of external compression strain when used as piezoresistive sensors. In addition, larger compression sensitivity was achieved at a higher compression rate. Due to high porosity and good elasticity of TPU, the conductive foams demonstrated good compressibility and stable piezoresistive sensing signals at a strain of up to 90%. During the cyclic piezoresistive sensing test under different compression strains, the conductive foam exhibited good recoverability and reproducibility after the stabilization of cyclic loading. All these suggest that the fabricated conductive foam possesses great potential to be used as lightweight, flexible, highly sensitive, and stable piezoresistive sensors
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Most recent semantic segmentation methods adopt a fully-convolutional network
(FCN) with an encoder-decoder architecture. The encoder progressively reduces
the spatial resolution and learns more abstract/semantic visual concepts with
larger receptive fields. Since context modeling is critical for segmentation,
the latest efforts have been focused on increasing the receptive field, through
either dilated/atrous convolutions or inserting attention modules. However, the
encoder-decoder based FCN architecture remains unchanged. In this paper, we aim
to provide an alternative perspective by treating semantic segmentation as a
sequence-to-sequence prediction task. Specifically, we deploy a pure
transformer (ie, without convolution and resolution reduction) to encode an
image as a sequence of patches. With the global context modeled in every layer
of the transformer, this encoder can be combined with a simple decoder to
provide a powerful segmentation model, termed SEgmentation TRansformer (SETR).
Extensive experiments show that SETR achieves new state of the art on ADE20K
(50.28% mIoU), Pascal Context (55.83% mIoU) and competitive results on
Cityscapes. Particularly, we achieve the first position in the highly
competitive ADE20K test server leaderboard on the day of submission.Comment: CVPR 2021. Project page at https://fudan-zvg.github.io/SETR
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