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Register a new userNanophotonic chiral sensing: How does it actually work?
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Nanophotonic chiral sensing has recently attracted a lot of attention. The idea is to exploit the strong light-matter interaction in nanophotonic resonators to determine the concentration of chiral molecules at ultra-low thresholds, which is highly attractive for numerous applications in life science and chemistry. However, a thorough understanding of the underlying interactions is still missing. The theoretical description relies on either simple approximations or on purely numerical approaches. We close this gap and present a general theory of chiral light-matter interactions in arbitrary resonators. Our theory describes the chiral interaction as a perturbation of the resonator modes, also known as resonant states or quasi-normal modes. We observe two dominant contributions: A chirality-induced resonance shift and changes in the modes excitation and emission efficiencies. Our theory brings new and deep insights for tailoring and enhancing chiral light-matter interactions. Furthermore, it allows to predict spectra much more efficiently in comparison to conventional approaches. This is particularly true as chiral interactions are inherently weak and therefore perturbation theory fits extremely well for this problem.
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While feedback is important in theoretical models, we do not really know if it works in reality. Feedback from jets appears to be sufficient to keep the cooling flows in clusters from cooling too much and it may be sufficient to regulate black hole growth in dominant cluster galaxies. Only about 10% of all quasars, however, have powerful radio jets, so jet-related feedback cannot be generic. The outflows could potentially be a more common form of AGN feedback, but measuring mass and energy outflow rates is a challenging task, the main unknown being the location and geometry of the absorbing medium. Using a novel technique, we made first such measurement in NGC 4051 using XMM data and found the mass and energy outflow rates to be 4 to 5 orders of magnitude below those required for efficient feedback. To test whether the outflow velocity in NGC 4051 is unusually low, we compared the ratio of outflow velocity to escape velocity in a sample of AGNs and found it to be generally less than one. It is thus possible that in most Seyferts the feedback is not sufficient and may not be necessary.
Partial bosonisation of the two-dimensional Hubbard model focuses the functional renormalisation flow on channels in which interactions become strong and local order sets in. We compare the momentum structure of the four-fermion vertex, obtained on the basis of a patching approximation, to an effective bosonic description. For parameters in the antiferromagnetic phase near the onset of local antiferromagnetic order, the interaction of the electrons is indeed well described by the exchange of collective bosonic degrees of freedom. The residual four-fermion vertex after the subtraction of the bosonic exchange contribution is small. We propose that similar partial bosonisation techniques can improve the accuracy of renormalisation flow studies also for the case of competing order.
There is a longstanding discrepancy between the observed Galactic classical nova rate of $sim 10$ yr$^{-1}$ and the predicted rate from Galactic models of $sim 30$--50 yr$^{-1}$. One explanation for this discrepancy is that many novae are hidden by interstellar extinction, but the degree to which dust can obscure novae is poorly constrained. We use newly available all-sky three-dimensional dust maps to compare the brightness and spatial distribution of known novae to that predicted from relatively simple models in which novae trace Galactic stellar mass. We find that only half ($sim 48$%) of novae are expected to be easily detectable ($g lesssim 15$) with current all-sky optical surveys such as the All-Sky Automated Survey for Supernovae (ASAS-SN). This fraction is much lower than previously estimated, showing that dust does substantially affect nova detection in the optical. By comparing complementary survey results from ASAS-SN, OGLE-IV, and the Palomar Gattini IR-survey in the context of our modeling, we find a tentative Galactic nova rate of $sim 40$ yr$^{-1}$, though this could decrease to as low as $sim 30$ yr$^{-1}$ depending on the assumed distribution of novae within the Galaxy. These preliminary estimates will be improved in future work through more sophisticated modeling of nova detection in ASAS-SN and other surveys.
The development of neural networks and pretraining techniques has spawned many sentence-level tagging systems that achieved superior performance on typical benchmarks. However, a relatively less discussed topic is what if more context information is introduced into current top-scoring tagging systems. Although several existing works have attempted to shift tagging systems from sentence-level to document-level, there is still no consensus conclusion about when and why it works, which limits the applicability of the larger-context approach in tagging tasks. In this paper, instead of pursuing a state-of-the-art tagging system by architectural exploration, we focus on investigating when and why the larger-context training, as a general strategy, can work. To this end, we conduct a thorough comparative study on four proposed aggregators for context information collecting and present an attribute-aided evaluation method to interpret the improvement brought by larger-context training. Experimentally, we set up a testbed based on four tagging tasks and thirteen datasets. Hopefully, our preliminary observations can deepen the understanding of larger-context training and enlighten more follow-up works on the use of contextual information.
Generative adversarial networks (GANs) have been widely used and have achieved competitive results in semi-supervised learning. This paper theoretically analyzes how GAN-based semi-supervised learning (GAN-SSL) works. We first prove that, given a fixed generator, optimizing the discriminator of GAN-SSL is equivalent to optimizing that of supervised learning. Thus, the optimal discriminator in GAN-SSL is expected to be perfect on labeled data. Then, if the perfect discriminator can further cause the optimization objective to reach its theoretical maximum, the optimal generator will match the true data distribution. Since it is impossible to reach the theoretical maximum in practice, one cannot expect to obtain a perfect generator for generating data, which is apparently different from the objective of GANs. Furthermore, if the labeled data can traverse all connected subdomains of the data manifold, which is reasonable in semi-supervised classification, we additionally expect the optimal discriminator in GAN-SSL to also be perfect on unlabeled data. In conclusion, the minimax optimization in GAN-SSL will theoretically output a perfect discriminator on both labeled and unlabeled data by unexpectedly learning an imperfect generator, i.e., GAN-SSL can effectively improve the generalization ability of the discriminator by leveraging unlabeled information.
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