424 research outputs found
Unsupervised Feature Learning by Deep Sparse Coding
In this paper, we propose a new unsupervised feature learning framework,
namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer
architecture for visual object recognition tasks. The main innovation of the
framework is that it connects the sparse-encoders from different layers by a
sparse-to-dense module. The sparse-to-dense module is a composition of a local
spatial pooling step and a low-dimensional embedding process, which takes
advantage of the spatial smoothness information in the image. As a result, the
new method is able to learn several levels of sparse representation of the
image which capture features at a variety of abstraction levels and
simultaneously preserve the spatial smoothness between the neighboring image
patches. Combining the feature representations from multiple layers, DeepSC
achieves the state-of-the-art performance on multiple object recognition tasks.Comment: 9 pages, submitted to ICL
A Novel Admission Control Model in Cloud Computing
With the rapid development of Cloud computing technologies and wide adopt of
Cloud services and applications, QoS provisioning in Clouds becomes an
important research topic. In this paper, we propose an admission control
mechanism for Cloud computing. In particular we consider the high volume of
simultaneous requests for Cloud services and develop admission control for
aggregated traffic flows to address this challenge. By employ network calculus,
we determine effective bandwidth for aggregate flow, which is used for making
admission control decision. In order to improve network resource allocation
while achieving Cloud service QoS, we investigate the relationship between
effective bandwidth and equivalent capacity. We have also conducted extensive
experiments to evaluate performance of the proposed admission control
mechanism
THE ROLE OF SELECTIVE AUTOPHAGY AND CELL SIGNALING IN FUNGAL DEVELOPMENT AND PATHOGENESIS
Ph.DDOCTOR OF PHILOSOPH
Integrated Sensing, Computation, and Communication: System Framework and Performance Optimization
Integrated sensing, computation, and communication (ISCC) has been recently
considered as a promising technique for beyond 5G systems. In ISCC systems, the
competition for communication and computation resources between sensing tasks
for ambient intelligence and computation tasks from mobile devices becomes an
increasingly challenging issue. To address it, we first propose an efficient
sensing framework with a novel action detection module. It can reduce the
overhead of computation resource by detecting whether the sensing target is
static. Subsequently, we analyze the sensing performance of the proposed
framework and theoretically prove its effectiveness with the help of the
sampling theorem. Then, we formulate a sensing accuracy maximization problem
while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve
it, we propose an optimal resource allocation strategy, in which the minimal
resource is allocated to computation tasks, and the rest is devoted to sensing
tasks. Besides, a threshold selection policy is derived. Compared with the
conventional schemes, the results further demonstrate the necessity of the
proposed sensing framework. Finally, a real-world test of action recognition
tasks based on USRP B210 is conducted to verify the sensing performance
analysis, and extensive experiments demonstrate the performance improvement of
our proposal by comparing it with some benchmark schemes
SYNC-CLIP: Synthetic Data Make CLIP Generalize Better in Data-Limited Scenarios
Prompt learning is a powerful technique for transferring Vision-Language
Models (VLMs) such as CLIP to downstream tasks. However, the prompt-based
methods that are fine-tuned solely with base classes may struggle to generalize
to novel classes in open-vocabulary scenarios, especially when data are
limited. To address this issue, we propose an innovative approach called
SYNC-CLIP that leverages SYNthetiC data for enhancing the generalization
capability of CLIP. Based on the observation of the distribution shift between
the real and synthetic samples, we treat real and synthetic samples as distinct
domains and propose to optimize separate domain prompts to capture
domain-specific information, along with the shared visual prompts to preserve
the semantic consistency between two domains. By aligning the cross-domain
features, the synthetic data from novel classes can provide implicit guidance
to rebalance the decision boundaries. Experimental results on three model
generalization tasks demonstrate that our method performs very competitively
across various benchmarks. Notably, SYNC-CLIP outperforms the state-of-the-art
competitor PromptSRC by an average improvement of 3.0% on novel classes across
11 datasets in open-vocabulary scenarios.Comment: Under Revie
Preparation and optoelectronic properties of silver nanowires
In this paper, polyol method was used to prepare different silver nanowires solutions by changing the concentration of
FeCl3·6H2O solution, and the solutions were spin-coated on conductive glass substrates to form silver nanowires fi lms. The eff ect of the
concentration of FeCl3·6H2O solution on the structure, surface morphology and optoelectronic properties of silver nanowires fi lms were
investigated. When 600 μM FeCl3·6H2O solution was added, the fi lm had a high haze value of 0.099 at 550 nm and a low sheet resistance of
5.92 Ω/sq. The light trapping ability and electrical conductivity of silver nanowires fi lms are improved
- …