354 research outputs found
Capacity Limitation and Optimization Strategy for Flexible Point-to-Multi-Point Optical Networks
Point-to-multi-point (PtMP) optical networks become the main solutions for
network-edge applications such as passive optical networks and radio access
networks. Entropy-loading digital subcarrier multiplexing (DSCM) is the core
technology to achieve low latency and approach high capacity for flexible PtMP
optical networks. However, the high peak-to-average power ratio of the
entropy-loading DSCM signal limits the power budget and restricts the capacity,
which can be reduced effectively by clipping operation. In this paper, we
derive the theoretical capacity limitation of the flexible PtMP optical
networks based on the entropy-loading DSCM signal. Meanwhile, an optimal
clipping ratio for the clipping operation is acquired to approach the highest
capacity limitation. Based on an accurate clipping-noise model under the
optimal clipping ratio, we establish a three-dimensional look-up table for
bit-error ratio, spectral efficiency, and link loss. Based on the
three-dimensional look-up table, an optimization strategy is proposed to
acquire optimal spectral efficiencies for achieving a higher capacity of the
flexible PtMP optical networks.Comment: The paper has been submitted to the IEEE Transactions on
Communication
Accurate and Efficient Estimation of Small P-values with the Cross-Entropy Method: Applications in Genomic Data Analysis
Small -values are often required to be accurately estimated in large scale
genomic studies for the adjustment of multiple hypothesis tests and the ranking
of genomic features based on their statistical significance. For those
complicated test statistics whose cumulative distribution functions are
analytically intractable, existing methods usually do not work well with small
-values due to lack of accuracy or computational restrictions. We propose a
general approach for accurately and efficiently calculating small -values
for a broad range of complicated test statistics based on the principle of the
cross-entropy method and Markov chain Monte Carlo sampling techniques. We
evaluate the performance of the proposed algorithm through simulations and
demonstrate its application to three real examples in genomic studies. The
results show that our approach can accurately evaluate small to extremely small
-values (e.g. to ). The proposed algorithm is helpful
to the improvement of existing test procedures and the development of new test
procedures in genomic studies.Comment: 34 pages, 1 figure, 4 table
Cerebral Microvascular Endothelial Cell Apoptosis after Ischemia: Role of Enolase-Phosphatase 1 Activation and Aci-Reductone Dioxygenase 1 Translocation
Comparison of the Effects of Saline and Alkaline Stress on Growth, Photosynthesis and Water-Soluble Carbohydrate of Oat Seedling (Avena sativa L)
UATVR: Uncertainty-Adaptive Text-Video Retrieval
With the explosive growth of web videos and emerging large-scale
vision-language pre-training models, e.g., CLIP, retrieving videos of interest
with text instructions has attracted increasing attention. A common practice is
to transfer text-video pairs to the same embedding space and craft cross-modal
interactions with certain entities in specific granularities for semantic
correspondence. Unfortunately, the intrinsic uncertainties of optimal entity
combinations in appropriate granularities for cross-modal queries are
understudied, which is especially critical for modalities with hierarchical
semantics, e.g., video, text, etc. In this paper, we propose an
Uncertainty-Adaptive Text-Video Retrieval approach, termed UATVR, which models
each look-up as a distribution matching procedure. Concretely, we add
additional learnable tokens in the encoders to adaptively aggregate
multi-grained semantics for flexible high-level reasoning. In the refined
embedding space, we represent text-video pairs as probabilistic distributions
where prototypes are sampled for matching evaluation. Comprehensive experiments
on four benchmarks justify the superiority of our UATVR, which achieves new
state-of-the-art results on MSR-VTT (50.8%), VATEX (64.5%), MSVD (49.7%), and
DiDeMo (45.8%). The code is available at https://github.com/bofang98/UATVR.Comment: To appear at ICCV202
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