323 research outputs found
Throughput Maximization Leveraging Just-Enough SNR Margin and Channel Spacing Optimization
Flexible optical network is a promising technology to accommodate
high-capacity demands in next-generation networks. To ensure uninterrupted
communication, existing lightpath provisioning schemes are mainly done with the
assumption of worst-case resource under-provisioning and fixed channel spacing,
which preserves an excessive signal-to-noise ratio (SNR) margin. However, under
a resource over-provisioning scenario, the excessive SNR margin restricts the
transmission bit-rate or transmission reach, leading to physical layer resource
waste and stranded transmission capacity. To tackle this challenging problem,
we leverage an iterative feedback tuning algorithm to provide a just-enough SNR
margin, so as to maximize the network throughput. Specifically, the proposed
algorithm is implemented in three steps. First, starting from the high SNR
margin setup, we establish an integer linear programming model as well as a
heuristic algorithm to maximize the network throughput by solving the problem
of routing, modulation format, forward error correction, baud-rate selection,
and spectrum assignment. Second, we optimize the channel spacing of the
lightpaths obtained from the previous step, thereby increasing the available
physical layer resources. Finally, we iteratively reduce the SNR margin of each
lightpath until the network throughput cannot be increased. Through numerical
simulations, we confirm the throughput improvement in different networks and
with different baud-rates. In particular, we find that our algorithm enables
over 20\% relative gain when network resource is over-provisioned, compared to
the traditional method preserving an excessive SNR margin.Comment: submitted to IEEE JLT, Jul. 17th, 2021. 14 pages, 8 figure
Throughput Maximization in Multi-Band Optical Networks with Column Generation
Multi-band transmission is a promising technical direction for spectrum and
capacity expansion of existing optical networks. Due to the increase in the
number of usable wavelengths in multi-band optical networks, the complexity of
resource allocation problems becomes a major concern. Moreover, the
transmission performance, spectrum width, and cost constraint across optical
bands may be heterogeneous. Assuming a worst-case transmission margin in U, L,
and C-bands, this paper investigates the problem of throughput maximization in
multi-band optical networks, including the optimization of route, wavelength,
and band assignment. We propose a low-complexity decomposition approach based
on Column Generation (CG) to address the scalability issue faced by traditional
methodologies. We numerically compare the results obtained by our CG-based
approach to an integer linear programming model, confirming the near-optimal
network throughput. Our results also demonstrate the scalability of the
CG-based approach when the number of wavelengths increases, with the
computation time in the magnitude order of 10 s for cases varying from 75 to
1200 wavelength channels per link in a 14-node network.Comment: 6 pages, 4 figures, submitted to IEEE International Conference on
Communications 2024 (ICC2024). (Note on arXiv: for beginners in the area of
column generation, please refer to the example computation in the file
. I have uploaded it to this arXiv
project along with other source files.
TCMGIS-II based prediction of medicinal plant distribution for conservation planning: a case study of Rheum tanguticum
<p>Abstract</p> <p>Background</p> <p>Many medicinal plants are increasingly endangered due to overexploitation and habitat destruction. To provide reliable references for conservation planning and regional management, this study focuses on large-scale distribution prediction of <it>Rheum tanguticum </it>Maxim. ex Balf (<it>Dahuang</it>).</p> <p>Methods</p> <p>Native habitats were determined by specimen examination. An improved version of GIS-based program for the distribution prediction of traditional Chinese medicine (TCMGIS-II) was employed to integrate national geographic, climate and soil type databases of China. Grid-based distance analysis of climate factors was based on the Mikowski distance and the analysis of soil types was based on grade division. The database of resource survey was employed to assess the reliability of prediction result.</p> <p>Results</p> <p>A total of 660 counties of 17 provinces in China, covering a land area of 3.63 × 10<sup>6 </sup>km<sup>2</sup>, shared similar ecological factors with those of native habitats appropriate for <it>R. tanguticum </it>growth.</p> <p>Conclusion</p> <p>TCMGIS-II modeling found the potential habitats of target medicinal plants for their conservation planning. This technology is useful in conservation planning and regional management of medicinal plant resources.</p
Few shot font generation via transferring similarity guided global style and quantization local style
Automatic few-shot font generation (AFFG), aiming at generating new fonts
with only a few glyph references, reduces the labor cost of manually designing
fonts. However, the traditional AFFG paradigm of style-content disentanglement
cannot capture the diverse local details of different fonts. So, many
component-based approaches are proposed to tackle this problem. The issue with
component-based approaches is that they usually require special pre-defined
glyph components, e.g., strokes and radicals, which is infeasible for AFFG of
different languages. In this paper, we present a novel font generation approach
by aggregating styles from character similarity-guided global features and
stylized component-level representations. We calculate the similarity scores of
the target character and the referenced samples by measuring the distance along
the corresponding channels from the content features, and assigning them as the
weights for aggregating the global style features. To better capture the local
styles, a cross-attention-based style transfer module is adopted to transfer
the styles of reference glyphs to the components, where the components are
self-learned discrete latent codes through vector quantization without manual
definition. With these designs, our AFFG method could obtain a complete set of
component-level style representations, and also control the global glyph
characteristics. The experimental results reflect the effectiveness and
generalization of the proposed method on different linguistic scripts, and also
show its superiority when compared with other state-of-the-art methods. The
source code can be found at https://github.com/awei669/VQ-Font.Comment: Accepted by ICCV 202
Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments
Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP
community. Recent works of SRL mainly fall into two lines: 1) BIO-based; 2)
span-based. Despite ubiquity, they share some intrinsic drawbacks of not
considering internal argument structures, potentially hindering the model's
expressiveness. The key challenge is arguments are flat structures, and there
are no determined subtree realizations for words inside arguments. To remedy
this, in this paper, we propose to regard flat argument spans as latent
subtrees, accordingly reducing SRL to a tree parsing task. In particular, we
equip our formulation with a novel span-constrained TreeCRF to make tree
structures span-aware and further extend it to the second-order case. We
conduct extensive experiments on CoNLL05 and CoNLL12 benchmarks. Results reveal
that our methods perform favorably better than all previous syntax-agnostic
works, achieving new state-of-the-art under both end-to-end and w/ gold
predicates settings.Comment: COLING 202
Mining Word Boundaries in Speech as Naturally Annotated Word Segmentation Data
Inspired by early research on exploring naturally annotated data for Chinese
word segmentation (CWS), and also by recent research on integration of speech
and text processing, this work for the first time proposes to mine word
boundaries from parallel speech/text data. First we collect parallel
speech/text data from two Internet sources that are related with CWS data used
in our experiments. Then, we obtain character-level alignments and design
simple heuristic rules for determining word boundaries according to pause
duration between adjacent characters. Finally, we present an effective
complete-then-train strategy that can better utilize extra naturally annotated
data for model training. Experiments demonstrate our approach can significantly
boost CWS performance in both cross-domain and low-resource scenarios.Comment: latest versio
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