Search

This paper investigates data-driven cooperative output regulation for continuous-time multi-agent systems with unknown network topology. Unlike existing studies that typically assume a known network topology to directly compute controller parameters, a novel approach is proposed that allows for the computation of the parameter without prior knowledge of the topology. A lower bound on the minimum non-zero eigenvalue of the Laplacian matrix is estimated using only edge weight bounds, enabling the output regulation controller design to be independent of global network information. Additionally, the common need for state derivative measurements is eliminated, reducing the amount of data requirements. Furthermore, necessary and sufficient conditions are established to ensure that the data are informative for cooperative output regulation, leading to the design of a distributed output regulation controller. For the case with noisy data, the bound of the output error is provided, which is positively correlated with the noise bound, and a distributed controller is constructed for the approximate cooperative output regulation. Finally, the effectiveness of the proposed methods is verified through numerical simulations.

Thermodynamics of degenerate Fermi gases has been extensively studied through various aspects such as Pauli blocking effects, collective modes, BCS superfluidity, and more. Despite this, multi-component fermions with imbalanced spin configurations remain largely unexplored, particularly beyond the two-component scenario. In this work, we generalize the thermodynamic study of SU($N$) fermions to spin-imbalanced configurations based on density fluctuations. Theoretically, we provide closed-form expressions of density fluctuation across all temperature ranges for general spin population setups. Experimentally, after calibrating the measurements with deeply degenerate $^{173}$Yb Fermi gases under spin-balanced configurations ($N\leq$~6), we examine the density fluctuations in spin-imbalanced systems. Specifically, we investigate two-species and four-species configurations to validate our theoretical predictions. Our analysis indicates that interaction enhancement effects can be significant even in highly spin-imbalanced systems. Finally, as an application, we use this approach to examine the decoherence process. Our study provides a deeper understanding of the thermodynamic features of spin-imbalanced multi-component Fermi gases and opens new avenues for exploring complex quantum many-body systems., Comment: 6 pages, 4 figures, supplementary

The microscopic origin of the remarkable optoelectronic properties of one of the most studied contemporary materials remains unclear. Here, we identify the existence of magnetic interactions between intermolecular proton orbitals in CH$_3$NH$_3$PbI$_3$ and CH$_3$NH$_3$PbBr$_3$. In particular, a unique sharp drop and a pronounced step-up discontinuity in the magnetic susceptibility at the tetragonal-to-cubic phase transitions are identified in CH$_3$NH$_3$PbI$_3$ and CH$_3$NH$_3$PbBr$_3$, respectively. The magnetic interactions in the orthorhombic and tetragonal phases are dependent on thermal history and lattice orientation while nearly independent of the applied external magnetic field. In CH$_3$NH$_3$PbBr$_3$, the CH$_3$ and NH$_3$$^+$ components reorient in an uncorrelated fashion resulting the cubic phase to also exhibit magnetic anisotropy. Our findings provide a potential link connecting the highly light-absorbing CH$_3$NH$_3$$^+$ and the exceptional properties of the charge carriers of the inorganic framework in hybrid perovskite solar cells., Comment: Manuscript + Supplementary Material file (17 + 6 pages, 4 + 2 figures)

The $\beta$ and $\gamma$ phases of methylammonium chloride CH$_3$NH$_3$Cl and methylammonium bromide CH$_3$NH$_3$Br are identified to be ferroelectric $via$ pyroelectric current and dielectric constant measurements. The magnetic susceptibility also exhibits pronounced discontinuities at the Curie temperatures. We attribute the origin of spontaneous polarization to the emergence of two groups of proton orbital magnetic moments from the uncorrelated motion of the CH$_3$ and NH$_3$ groups in the $\beta$ and $\gamma$ phases. The two inequivalent frameworks of intermolecular orbital resonances interact with each other to distort the lattice in a non-centrosymmetric fashion. Our findings indicate that the structural instabilities in molecular frameworks are magnetic in origin as well as provide a new pathway toward uncovering new organic ferroelectrics., Comment: 5 pages, 5 figures

Mixed solid compounds are employed in a vast array of applications so an accurate determination of their chemical compositions is of crucial importance. All current characterization methods require specially-treated samples so the availability of a more practical method with similar accuracy should alleviate the quantification process. In this work, we show how the doping concentration $\delta$ (or isotope concentration) of a mixed solid compound in powdered form, where both parent compounds are diamagnetic, can be obtained from the measurement of the mass magnetization. We exploit the additive nature of the molar magnetic susceptibility $\chi_{Mol}$ and molar mass to construct two equations with the same two unknowns in the $\chi_{Mol}$ vs. $\delta$ space to simultaneously solve $\chi_{Mol}$ and $\delta$ of a mixed solid. Eight examples are provided to show the wide applicability of this method: NH$_{4(1-\delta)}$D$_{4\delta}$Br (where D = $^2$H), NH$_4$I$_{1-\delta}$Br$_\delta$, (NH$_4$H$_2$)$_{1-\delta}$(ND$_4$D$_2$)$_\delta$PO$_4$, C$_{48}$H$_{22+6\delta}$Br$_{6(1-\delta)}$O$_{32}$Zr$_6$, [creatine]$_{1-\delta}$[$_D$-glucose]$_\delta$, [$_L$-glutamic acid]$_{1-\delta}$[$_L$-leucine]$_\delta$, [terephthalic acid]$_{1-\delta}$[trimesic acid]$_\delta$ and [p-terphenyl]$_{1-\delta}$[triphenylphosphine]$_\delta$. Experimental errors of ~1.2% were obtained for $\delta$ from average sample masses of 16.6 mg in powdered form rendering the presented approach an attractive choice for characterizing the ratios of mixed solids., Comment: Main article: 19 pages, 12 Figures; Supplementary Information: 7 pages, 9 Tables and 4 Figures

Crafting effective topic models for brief texts, like tweets and news headlines, is essential for capturing the swift shifts in social dynamics. Traditional topic models, however, often fall short in accurately representing the semantic intricacies of short texts due to their brevity and lack of contextual data. In our study, we harness the advanced capabilities of Large Language Models (LLMs) to introduce a novel approach termed "Topic Refinement". This approach does not directly involve itself in the initial modeling of topics but focuses on improving topics after they have been mined. By employing prompt engineering, we direct LLMs to eliminate off-topic words within a given topic, ensuring that only contextually relevant words are preserved or substituted with ones that fit better semantically. This method emulates human-like scrutiny and improvement of topics, thereby elevating the semantic quality of the topics generated by various models. Our comprehensive evaluation across three unique datasets has shown that our topic refinement approach significantly enhances the semantic coherence of topics., Comment: 6 pages, 4 figures

The concept of non-Hermiticity has expanded the understanding of band topology leading to the emergence of counter-intuitive phenomena. One example is the non-Hermitian skin effect (NHSE), which involves the concentration of eigenstates at the boundary. However, despite the potential insights that can be gained from high-dimensional non-Hermitian quantum systems in areas like curved space, high-order topological phases, and black holes, the realization of this effect in high dimensions remains unexplored. Here, we create a two-dimensional (2D) non-Hermitian topological band for ultracold fermions in spin-orbit-coupled optical lattices with tunable dissipation, which exhibits the NHSE. We first experimentally demonstrate pronounced nonzero spectral winding numbers in the complex energy plane with non-zero dissipation, which establishes the existence of 2D skin effect. Further, we observe the real-space dynamical signature of NHSE in real space by monitoring the center of mass motion of atoms. Finally, we also demonstrate that a pair of exceptional points (EPs) are created in the momentum space, connected by an open-ended bulk Fermi arc, in contrast to closed loops found in Hermitian systems. The associated EPs emerge and shift with increasing dissipation, leading to the formation of the Fermi arc. Our work sets the stage for further investigation into simulating non-Hermitian physics in high dimensions and paves the way for understanding the interplay of quantum statistics with NHSE., Comment: 13 pages, 5 figures with supplementary information

In this paper, we consider the morphisms from projective spaces to flag varieties. We show that the morphisms can only be constant under some special conditions. As a consequence, we prove that the splitting types of unsplit uniform $r$-bundles on $\mathbb{P}^m$ can not be $(a_1,\dots,a_1,a_2,\dots,a_{r-k+1})$ for $1\le k\le m-2$., Comment: 12 pages

We report on a study exploring the design of nanoparticles that can enhance their laser refrigeration efficiency for applications in levitated optomechanics. In particular, we developed lanthanide-doped nanocrystals with an inert shell coating and compared their performance with bare nanocrystals. While optically levitated, we studied the refrigeration of both types of nanoparticles while varying the pressure. We found that the core-shell design shows an improvement in the minimum final temperature: a fourth of the core-shell nanoparticles showed a significant cooling compared to almost none of the bare nanoparticles. Furthermore, we measured a core-shell nanoparticle cooling down to a temperature of 147 K at 26 mbar in the underdamped regime. Our study is a first step towards engineering nanoparticles that are suitable for achieving absolute (centre-of-mass and internal temperature) cooling in levitation, opening new avenues for force sensing and the realization of macroscopic quantum superpositions., Comment: Any comments are welcome !

Effective oil spill segmentation in Synthetic Aperture Radar (SAR) images is critical for marine oil pollution cleanup, and proper image representation is helpful for accurate image segmentation. In this paper, we propose an effective oil spill image segmentation network named SRCNet by leveraging SAR image representation and the training for oil spill segmentation simultaneously. Specifically, our proposed segmentation network is constructed with a pair of deep neural nets with the collaboration of the seminal representation that describes SAR images, where one deep neural net is the generative net which strives to produce oil spill segmentation maps, and the other is the discriminative net which trys its best to distinguish between the produced and the true segmentations, and they thus built a two-player game. Particularly, the seminal representation exploited in our proposed SRCNet originates from SAR imagery, modelling with the internal characteristics of SAR images. Thus, in the training process, the collaborated seminal representation empowers the mapped generative net to produce accurate oil spill segmentation maps efficiently with small amount of training data, promoting the discriminative net reaching its optimal solution at a fast speed. Therefore, our proposed SRCNet operates effective oil spill segmentation in an economical and efficient manner. Additionally, to increase the segmentation capability of the proposed segmentation network in terms of accurately delineating oil spill details in SAR images, a regularisation term that penalises the segmentation loss is devised. This encourages our proposed SRCNet for accurately segmenting oil spill areas from SAR images. Empirical experimental evaluations from different metrics validate the effectiveness of our proposed SRCNet for oil spill image segmentation., Comment: arXiv admin note: substantial text overlap with arXiv:2301.01202

In light of the recent measurement of the nonzero Cosmic Microwave Background (CMB) polarization rotation angle from the Planck 2018 data, we explore the possibility that such a cosmic birefringence effect is induced by coupling a fermionic current with photons via a Chern-Simons-like term. We begin our discussion by rederiving the general formulae of the cosmic birefringence angle with correcting a mistake in the previous study. We then identify the fermions in the current as the left-handed electron neutrinos and asymmetric dark matter (ADM) particles, since the rotation angle is sourced by the number density difference between particles and antiparticles. For the electron neutrino case, with the value of the degeneracy parameter $\xi_{\nu_e}$ recently measured by the EMPRESS survey, we find a large parameter space which can explain the CMB photon polarization rotations. On the other hand, for the ADM solution, we consider two benchmark cases with $M_\chi = 5$~GeV and 5~keV. The former is the natural value of the ADM mass if the observed ADM and baryon asymmetry in the Universe are produced by the same mechanism, while the latter provides a warm DM candidate. In addition, we explore the experimental constraints from the CMB power spectra and the DM direct detections., Comment: 29 pages, 5 figures; published version

Successful implementation of oil spill segmentation in Synthetic Aperture Radar (SAR) images is vital for marine environmental protection. In this paper, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images. Specifically, our proposed segmentation network is constructed with two deep neural modules running in an interactive manner, where one is the inference module to achieve latent feature variable inference from SAR images, and the other is the generative module to produce oil spill segmentation maps by drawing the latent feature variables as inputs. Thus, to yield accurate segmentation, we take into account the intrinsic distribution of backscatter values in SAR images and embed it in our segmentation model. The intrinsic distribution originates from SAR imagery, describing the physical characteristics of oil spills. In the training process, the formulated intrinsic distribution guides efficient learning of optimal latent feature variable inference for oil spill segmentation. The efficient learning enables the training of our proposed DGNet with a small amount of image data. This is economically beneficial to oil spill segmentation where the availability of oil spill SAR image data is limited in practice. Additionally, benefiting from optimal latent feature variable inference, our proposed DGNet performs accurate oil spill segmentation. We evaluate the segmentation performance of our proposed DGNet with different metrics, and experimental evaluations demonstrate its effective segmentations.

In this paper, we characterize homogeneous arithmetically Cohen-Macaulay (ACM) bundles over exceptional Grassmannians in terms of their associated data. We show that there are only finitely many irreducible homogeneous ACM bundles by twisting line bundles over exceptional Grassmannians. As a consequence, we prove that some exceptional Grassmannians are of wild representation type., Comment: 20 pages. arXiv admin note: text overlap with arXiv:2206.09172, arXiv:2211.00950

With the application of the fifth-generation wireless communication technologies, more smart terminals are being used and generating huge amounts of data, which has prompted extensive research on how to handle and utilize these wireless data. Researchers currently focus on the research on the upper-layer application data or studying the intelligent transmission methods concerning a specific problem based on a large amount of data generated by the Monte Carlo simulations. This article aims to understand the endogenous relationship of wireless data by constructing a knowledge graph according to the wireless communication protocols, and domain expert knowledge and further investigating the wireless endogenous intelligence. We firstly construct a knowledge graph of the endogenous factors of wireless core network data collected via a 5G/B5G testing network. Then, a novel model based on graph convolutional neural networks is designed to learn the representation of the graph, which is used to classify graph nodes and simulate the relation prediction. The proposed model realizes the automatic nodes classification and network anomaly cause tracing. It is also applied to the public datasets in an unsupervised manner. Finally, the results show that the classification accuracy of the proposed model is better than the existing unsupervised graph neural network models, such as VGAE and ARVGE.

Sepsis is a leading cause of death in the ICU. It is a disease requiring complex interventions in a short period of time, but its optimal treatment strategy remains uncertain. Evidence suggests that the practices of currently used treatment strategies are problematic and may cause harm to patients. To address this decision problem, we propose a new medical decision model based on historical data to help clinicians recommend the best reference option for real-time treatment. Our model combines offline reinforcement learning and deep reinforcement learning to solve the problem of traditional reinforcement learning in the medical field due to the inability to interact with the environment, while enabling our model to make decisions in a continuous state-action space. We demonstrate that, on average, the treatments recommended by the model are more valuable and reliable than those recommended by clinicians. In a large validation dataset, we find out that the patients whose actual doses from clinicians matched the decisions made by AI has the lowest mortality rates. Our model provides personalized and clinically interpretable treatment decisions for sepsis to improve patient care.

In this paper, we characterize homogeneous arithmetically Cohen-Macaulay (ACM) bundles over isotropic Grassmannians of types $B$, $C$ and $D$ in term of step matrices. We show that there are only finitely many irreducible homogeneous ACM bundles by twisting line bundles over these isotropic Grassmannians. So we classify all homogeneous ACM bundles over isotropic Grassmannians combining the results on usual Grassmannians by Costa and Mir{\'o}-Roig. Moreover, if the irreducible initialized homogeneous ACM bundles correspond to some special highest weights, then they can be characterized by succinct forms., Comment: 26 pages