Scalable Cell-Free Massive MIMO Systems with Finite Resolution ADCs/DACs over Spatially Correlated Rician Fading Channels

In this paper, an analytical framework for evaluating the performance of scalable cell-free massive MIMO (SCF-mMIMO) systems in which all user equipments (UEs) and access points (APs) employ finite resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) and operates under correlated Rician fading, is presented. By using maximal-ratio combining (MRC) detection, generic expressions for the uplink (UL) spectral efficiency (SE) for both distributed and centralized schemes are derived. In order to further reduce the computational complexity (CC) of the original local partial MMSE (LP-MMSE) and partial MMSE (P-MMSE) detectors, two novel scalable low complexity MMSE detectors are proposed for distributed and centralized schemes respectively, which achieves very similar SE performance. Furthermore, for the distributed scheme a novel partial large-scale fading decoding (P-LSFD) weighting vector is introduced and its analytical SE performance is very similar to the performance of an equivalent unscalable LSFD vector. Finally, a scalable algorithm jointly consisting of AP cluster formation, pilot assignment, and power control is proposed, which outperforms the conventional random pilot assignment and user-group based pilot assignment policies and, contrary to an equal power transmit strategy, it guarantees quality of service (QoS) fairness for all accessing UEs.

End-to-End Conversational Search for Online Shopping with Utterance Transfer

Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers. However, building such systems from scratch faces real word challenges from both imperfect product schema/knowledge and lack of training dialog data.In this work we first propose ConvSearch, an end-to-end conversational search system that deeply combines the dialog system with search. It leverages the text profile to retrieve products, which is more robust against imperfect product schema/knowledge compared with using product attributes alone. We then address the lack of data challenges by proposing an utterance transfer approach that generates dialogue utterances by using existing dialog from other domains, and leveraging the search behavior data from e-commerce retailer. With utterance transfer, we introduce a new conversational search dataset for online shopping. Experiments show that our utterance transfer method can significantly improve the availability of training dialogue data without crowd-sourcing, and the conversational search system significantly outperformed the best tested baseline.

Higher Auslanders defect and classifying substructures of n-exangulated categories

Herschend-Liu-Nakaoka introduced the notion of $n$-exangulated categories. It is not only a higher dimensional analogue of extriangulated categories defined by Nakaoka-Palu, but also gives a simultaneous generalization of $n$-exact categories and $(n+2)$-angulated categories. In this article, we give an $n$-exangulated version of Auslanders defect and Auslander-Reiten duality formula. Moreover, we also give a classification of substructures (=closed subbifunctors) of a given skeletally small $n$-exangulated category by using the category of defects.

Engineering of Ferroic Orders in Thin Films by Anionic Substitution

Multiferroics are a unique class of materials where magnetic and ferroelectric orders coexist. The research on multiferroics contributes significantly to the fundamental understanding of the strong correlations between different material degrees of freedom and provides an energy-efficient route toward the electrical control of magnetism. While multiple ABO3 oxide perovskites have been identified as being multiferroic, their magnetoelectric coupling strength is often weak, necessitating the material search in different compounds. Here, we report the observation of room-temperature multiferroic orders in multi-anion SrNbO3-xNx thin films. In these samples, the multi-anion state enables the room-temperature ferromagnetic ordering of the Nb d-electrons. Simultaneously, we find ferroelectric responses that originate from the structural symmetry breaking associated with both the off-center displacements of Nb and the geometric displacements of Sr, depending on the relative O-N arrangements within the Nb-centered octahedra. Our findings not only diversify the available multiferroic material pool but also demonstrate a new multiferroism design strategy via multi-anion engineering.

Neutron skin of $^{48}$Ca deduced from reaction and interaction cross sections

In our previous paper, we predicted $sigma_{rm R}$ for $^{40-60,62,64}$Ca+ $^{12}$C scattering at 280 MeV/u, using the Kyushu (chiral) $g$-matrix folding model with the densities calculated with D1S-GHFB with and without the AMP. Interaction cross sections $sigma_{rm I}$ are available for $^{42-51}$Ca + $^{12}$C scattering, whereas $sigma_{rm R}$ are available for p+$^{48}$Ca scattering. As for $^{48}$Ca, the high-resolution $E1$ polarizability experiment ($E1$pE) yields $r_{rm skin}^{48}(E1{rm pE}) =0.14 sim 0.20~{rm fm}$. We determine $r_{rm skin}^{48}({rm exp})$ from the data on $sigma_{rm R}$ for p+$^{48}$Ca scattering and from the data on $sigma_{rm I}$ for $^{48}$Ca+$^{12}$C scattering. We use the chiral (Kyushu) $g$-matrix folding model with the densities calculated with the Gogny-D1M Hartree-Fock-Bogoliubov with the AMP. The D1M-GHFB+AMP proton and neutron densities are scaled so as to reproduce the data under the condition that the radius $r_{rm p}$ of the scaled proton density equals the data $r_{rm p}({rm exp})$ of the electron scattering. The neutron radius $r_{rm n}$ thus obtained is an experimental value. Our results are $r_{rm skin}^{48}({rm exp})=-0.031sim 0.183$fm for p+$^{48}$Ca and $0.100 sim 0.218$fm for $^{48}$Ca + $^{12}$C scattering. Using the $r_{rm skin}^{48}$-$r_{rm skin}^{208}$ relation with a high correlation coefficient $R=0.99$, we have transformed $r_{rm skin}^{208}({rm PREXII})$ and $r_{rm skin}^{208}(E1{rm pE})$ to the corresponding values $r_{rm skin}^{48}({rm tPREXII})$ and $r_{rm skin}^{48}({rm t}E1{rm pE})$. The transformed data $r_{rm skin}^{48}({rm tPREXII})=0.190 sim 0.268$fm is consistent with $r_{rm skin}^{48}=0.102 sim 0.218$fm for $^{48}$Ca + $^{12}$C. Our final result is $r_{rm skin}^{48}=0.102 sim 0.218$fm determined from $^{48}$Ca + $^{12}$C scattering.

Inference on Individual Treatment Effects in Nonseparable Triangular Models

In nonseparable triangular models with a binary endogenous treatment and a binary instrumental variable, Vuong and Xu (2017) show that the individual treatment effects (ITEs) are identifiable. Feng, Vuong and Xu (2019) show that a kernel density estimator that uses nonparametrically estimated ITEs as observations is uniformly consistent for the density of the ITE. In this paper, we establish the asymptotic normality of the density estimator of Feng, Vuong and Xu (2019) and show that despite their faster rate of convergence, ITEs estimation errors have a non-negligible effect on the asymptotic distribution of the density estimator. We propose asymptotically valid standard errors for the density of the ITE that account for estimated ITEs as well as bias correction. Furthermore, we develop uniform confidence bands for the density of the ITE using nonparametric or jackknife multiplier bootstrap critical values. Our uniform confidence bands have correct coverage probabilities asymptotically with polynomial error rates and can be used for inference on the shape of the ITEs distribution.

The e-positivity of multivariate k-th order Eulerian polynomials

Inspired by the recent work of Chen and Fu on the e-positivity of trivariate second-order Eulerian polynomials, we show the e-positivity of a family of multivariate k-th order Eulerian polynomials. A relationship between the coefficients of this e-positive expansion and second-order Eulerian numbers is established. Moreover, we present a grammatical proof of the fact that the joint distribution of the ascent, descent and j-plateau statistics over k-Stirling permutations are symmetric distribution. By using symmetric transformation of grammars, a symmetric expansion of trivariate Schett polynomial is also established.

Scalable Computation of Monge Maps with General Costs

Monge map refers to the optimal transport map between two probability distributions and provides a principled approach to transform one distribution to another. In spite of the rapid developments of the numerical methods for optimal transport problems, computing the Monge maps remains challenging, especially for high dimensional problems. In this paper, we present a scalable algorithm for computing the Monge map between two probability distributions. Our algorithm is based on a weak form of the optimal transport problem, thus it only requires samples from the marginals instead of their analytic expressions, and can accommodate optimal transport between two distributions with different dimensions. Our algorithm is suitable for general cost functions, compared with other existing methods for estimating Monge maps using samples, which are usually for quadratic costs. The performance of our algorithms is demonstrated through a series of experiments with both synthetic and realistic data.

Noise Doesnt Lie: Towards Universal Detection of Deep Inpainting

Deep image inpainting aims to restore damaged or missing regions in an image with realistic contents. While having a wide range of applications such as object removal and image recovery, deep inpainting techniques also have the risk of being manipulated for image forgery. A promising countermeasure against such forgeries is deep inpainting detection, which aims to locate the inpainted regions in an image. In this paper, we make the first attempt towards universal detection of deep inpainting, where the detection network can generalize well when detecting different deep inpainting methods. To this end, we first propose a novel data generation approach to generate a universal training dataset, which imitates the noise discrepancies exist in real versus inpainted image contents to train universal detectors. We then design a Noise-Image Cross-fusion Network (NIX-Net) to effectively exploit the discriminative information contained in both the images and their noise patterns. We empirically show, on multiple benchmark datasets, that our approach outperforms existing detection methods by a large margin and generalize well to unseen deep inpainting techniques. Our universal training dataset can also significantly boost the generalizability of existing detection methods.

Reaction cross section of proton scattering consistent with PREX-II

Background: The neutron skin thickness $R_{rm skin}^{rm PV}$ of PREX-II is presented in Phys. Rev. Lett. {bf 126}, 172502 (2021). The reaction cross section $sigma_R$ is useful to determine the matter radius $R_m$ and $R_{rm skin}$. For proton scattering, the reaction cross section $sigma_R$ are available for $E_{rm in} > 400$ MeV. Method and results: We determine $R_n^{rm exp}=5.727 pm 0.071$ fm and $R_m^{rm exp}=5.617 pm 0.044$ fm from $R_p^{rm exp}$ = 5.444 fm and $R_{rm skin}^{rm PV}$. The $R_p^{rm GHFB}$ calculated with Gongny-D1S HFB (GHFB) with the angular momentum projection (AMP). agrees with $R_p^{rm exp}$. The neutron density calculated with GHFB+AMP is scaled so as to $R_n^{rm scaling}=5.727$ fm. The Love-Franey $t$-matrix model with the scaled densities reproduces the data on $sigma_R$. Aim: Our aim is to find the $sigma_R$ of proton scattering consistent with $R_{rm skin}^{rm PV}$. Conclusion: The $sigma_R$ of proton scattering consistent with $R_{rm skin}^{rm PV}$ are $sigma_R^{rm exp}$ at $E_{rm in} = 534.1, 549, 806$ MeV.