312 research outputs found
Learning Diverse Tone Styles for Image Retouching
Image retouching, aiming to regenerate the visually pleasing renditions of
given images, is a subjective task where the users are with different aesthetic
sensations. Most existing methods deploy a deterministic model to learn the
retouching style from a specific expert, making it less flexible to meet
diverse subjective preferences. Besides, the intrinsic diversity of an expert
due to the targeted processing on different images is also deficiently
described. To circumvent such issues, we propose to learn diverse image
retouching with normalizing flow-based architectures. Unlike current flow-based
methods which directly generate the output image, we argue that learning in a
style domain could (i) disentangle the retouching styles from the image
content, (ii) lead to a stable style presentation form, and (iii) avoid the
spatial disharmony effects. For obtaining meaningful image tone style
representations, a joint-training pipeline is delicately designed, which is
composed of a style encoder, a conditional RetouchNet, and the image tone style
normalizing flow (TSFlow) module. In particular, the style encoder predicts the
target style representation of an input image, which serves as the conditional
information in the RetouchNet for retouching, while the TSFlow maps the style
representation vector into a Gaussian distribution in the forward pass. After
training, the TSFlow can generate diverse image tone style vectors by sampling
from the Gaussian distribution. Extensive experiments on MIT-Adobe FiveK and
PPR10K datasets show that our proposed method performs favorably against
state-of-the-art methods and is effective in generating diverse results to
satisfy different human aesthetic preferences. Source code and pre-trained
models are publicly available at https://github.com/SSRHeart/TSFlow
Network Pricing with Investment Waiting Cost based on Real Options under Uncertainties
Existing capacity-based network pricing uses discounted cash flows to calculate costs, unable to reflect the uncertainties and flexibilities in distribution networks. Such shortcoming could distort the cost-reflectivity of pricing signals, particularly those for renewables and flexible technologies, causing more constraints and curtailment issues in networks. This paper proposes a new pricing method, Incremental Cost Network Pricing based on Real Options (ICOC), which can reflect network user uncertainties on network investment by using real options. Under this concept, network operators can delay investment for a certain period by paying waiting cost based on options value until more information is available, thus avoiding non-reversible investment due to uncertainties. The options cost will be levied on network users as i) rewards if they can provide flexibilities to the system; or ii) waiting costs if they present uncertainties to the system. The reward or cost is determined by a binomial tree pricing under a risk-neutral condition, which is added onto asset present value as the total cost to be recovered. Such cost is allocated to network users based on their nodal incremental costs. The proposed method is demonstrated on a practical network with different users, i) uncertain, ii) flexible; iii) certain and nonflexible.</p
Melatonin protects against ovarian damage by inhibiting autophagy in granulosa cells in rats
Objectives: This study sought to further verify the protective mechanism of Melatonin (MT) against ovarian damage through animal model experiments and to lay a theoretical and experimental foundation for exploring new approaches for ovarian damage treatment.
Method: The wet weight and ovarian index of rat ovaries were weighted, and the morphology of ovarian tissues and the number of follicles in the pathological sections of collected ovarian tissues were recorded. And the serum sex hormone levels, the key proteins of the autophagy pathway (PI3K, AKT, mTOR, LC3II, LC3I, and Agt5) in rat ovarian tissues, as well as the viability and mortality of ovarian granulosa cells in each group were measured by ELISA, western blotting, CCK8 kit and LDH kit, respectively.
Results: The results showed that MT increased ovarian weight and improved the ovarian index in ovarian damage rats. Also, MT could improve autophagy-induced ovarian tissue injury, increase the number of primordial follicles, primary follicles, and sinus follicles, and decrease the number of atretic follicles. Furthermore, MT upregulated serum AMH, INH-B, and E2 levels downregulated serum FSH and LH levels in ovarian damage rats and activated the PI3K/AKT/mTOR signaling pathway. Besides, MT inhibited autophagic apoptosis of ovarian granulosa cells and repressed the expression of key proteins in the autophagic pathway and reduced the expression levels of Agt5 and LC3II/I.
Conclusions: MT inhibits granulosa cell autophagy by activating the PI3K/Akt/mTOR signaling pathway, thereby exerting a protective effect against ovarian damage
Controlling thermal emission with metasurfaces and its applications
Thermal emission caused by the thermal motion of the charged particles is
commonly broadband, un-polarized, and incoherent, like a melting pot of
electromagnetic waves, which makes it unsuitable for infrared applications in
many cases requiring specific thermal emission properties. Metasurfaces,
characterized by two-dimensional subwavelength artificial nanostructures, have
been extensively investigated for their flexibility in tuning optical
properties, which provide an ideal platform for shaping thermal emission.
Recently, remarkable progress was achieved not only in tuning thermal emission
in multiple degrees of freedom, such as wavelength, polarization, radiation
angle, coherence, and so on but also in applications of compact and integrated
optical devices. Here, we review the recent advances in the regulation of
thermal emission through metasurfaces and corresponding infrared applications,
such as infrared sensing, radiative cooling, and thermophotovoltaic devices.Comment: 28 pages, 10 figure
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Surface-Bound Cucurbit[8]uril Catenanes on Magnetic Nanoparticles Exhibiting Molecular Recognition.
We demonstrate the preparation of surface-bound cucurbit[8]uril (CB[8]) catenanes on silica nanoparticles (NPs), where CB[8] was employed as a tethered supramolecular "handcuff" to selectively capture target guest molecules. In this catenane, CB[8] was threaded onto a methyl viologen (MV(2+) ) axle and immobilized onto silica NPs. The formation of CB[8] catenanes on NPs were confirmed by UV/Vis titration experiments and lithographic characterization, demonstrating a high density of CB[8] on the silica NPs surface, 0.56 nm(-2) . This CB[8] catenane system exhibits specific molecular recognition towards certain aromatic molecules such as perylene bis(diimide), naphthol and aromatic amino acids, and thus it can act as a nanoscale molecular receptor for target guests. Furthermore, we also demonstrate its use as an efficient and recyclable nano-platform for peptide separation. By embedding magnetic NPs inside silica NPs, separation could be achieved by simply applying an external magnetic field. Moreover, the peptides captured by the catenanes could be released by reversible single-electron reduction of MV(2+) . The entire process demonstrated high recoverability.X. Ren thanks the CSC Cambridge Scholarship for financial support and
Dr. Ziyi Yu for template preparation. Y. Wu is financially supported
by the EP/L504920/1, J. Liu by the Marie Curie FP7 SASSYPOL ITN
(607602) programme, and G. Wu by the Leverhulme Trust.This is the author accepted manuscript. The final version is available from Wiley at http://dx.doi.org/10.1002/asia.201600875
Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution
Video super-resolution (VSR) aiming to reconstruct a high-resolution (HR)
video from its low-resolution (LR) counterpart has made tremendous progress in
recent years. However, it remains challenging to deploy existing VSR methods to
real-world data with complex degradations. On the one hand, there are few
well-aligned real-world VSR datasets, especially with large super-resolution
scale factors, which limits the development of real-world VSR tasks. On the
other hand, alignment algorithms in existing VSR methods perform poorly for
real-world videos, leading to unsatisfactory results. As an attempt to address
the aforementioned issues, we build a real-world 4 VSR dataset, namely
MVSR4, where low- and high-resolution videos are captured with
different focal length lenses of a smartphone, respectively. Moreover, we
propose an effective alignment method for real-world VSR, namely EAVSR. EAVSR
takes the proposed multi-layer adaptive spatial transform network (MultiAdaSTN)
to refine the offsets provided by the pre-trained optical flow estimation
network. Experimental results on RealVSR and MVSR4 datasets show the
effectiveness and practicality of our method, and we achieve state-of-the-art
performance in real-world VSR task. The dataset and code will be publicly
available
Synthesis and fungicidal activity of pyrazole derivatives containing 1,2,3,4-tetrahydroquinoline
Additional file 3. Structural information (CIF) for Compound 10g
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