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Previous studies yielded discouraging results for item-level locally differentially private linear regression with $s^*$-sparsity assumption, where the minimax rate for $nm$ samples is $\mathcal{O}(s^{*}d / nm\varepsilon^2)$. This can be challenging for high-dimensional data, where the dimension $d$ is extremely large. In this work, we investigate user-level locally differentially private sparse linear regression. We show that with $n$ users each contributing $m$ samples, the linear dependency of dimension $d$ can be eliminated, yielding an error upper bound of $\mathcal{O}(s^{*2} / nm\varepsilon^2)$. We propose a framework that first selects candidate variables and then conducts estimation in the narrowed low-dimensional space, which is extendable to general sparse estimation problems with tight error bounds. Experiments on both synthetic and real datasets demonstrate the superiority of the proposed methods. Both the theoretical and empirical results suggest that, with the same number of samples, locally private sparse estimation is better conducted when multiple samples per user are available.

We initiate the study of locally differentially private (LDP) learning with public features. We define semi-feature LDP, where some features are publicly available while the remaining ones, along with the label, require protection under local differential privacy. Under semi-feature LDP, we demonstrate that the mini-max convergence rate for non-parametric regression is significantly reduced compared to that of classical LDP. Then we propose HistOfTree, an estimator that fully leverages the information contained in both public and private features. Theoretically, HistOfTree reaches the mini-max optimal convergence rate. Empirically, HistOfTree achieves superior performance on both synthetic and real data. We also explore scenarios where users have the flexibility to select features for protection manually. In such cases, we propose an estimator and a data-driven parameter tuning strategy, leading to analogous theoretical and empirical results.

Magnetic Weyl semimetal (MWS) is a unique topological state with open surface Fermi arc states and other exotic transport phenomena. However, most reported MWSs show multiple pairs of Weyl points and complicated Fermi surfaces, which increases the difficulty of the investigation into the intrinsic chiral transport property. In this wor, we successfully synthesized a soft magnetic Weyl semimetal Eu$_3$In$_2$As$_4$ with a single pair of Weyl points under magnetic fields. The Shubnikov de Haas (SdH) oscillation with a single frequency, as well as a linear hall resistance with the same carrier density, is observed up to 50 Tesla, indicating a single pair of Weyl points around the Fermi level with a massless fermion ($m^* = 0.121 m_0$, $\pi$ Berry phase). Such a single pair of Weyl points is further confirmed by the density functional theory calculations. The magnetic ordering and band topology can be easily tuned by the external magnetic field. The field-induced MWS Eu$_3$In$_2$As$_4$ with a single pair of Weyl points is a good platform to detect chiral transport properties, including possible quantum anomalous Hall effect.

Quantum magneto-oscillations have long been a vital subject in condensed matter physics, with ubiquitous quantum phenomena and diverse underlying physical mechanisms. Here, we demonstrate the intrinsic and reproducible DC-current-driven quantum electro-oscillations with a periodicity in the inverse of the current (1/I), in quasi-one-dimensional charge-density-wave (CDW) insulators (TaSe$_4$)$_2$I and TaS$_3$ nanowires. Such oscillations manifest in the nearly infinite Fr\"ohlich conductivity region where the undamped CDW flow forms in a finite electric current, and finally disappear after the oscillation index n reaches 1. A systematic investigation on the effect of temperature and magnetic field establishes that the observed electro-oscillations are a coherent quantum phenomenon. We discuss the possibilities of the physical mechanisms, including the formation of sliding-driven inherent Floquet sidebands. Our results introduce a new member in the family of quantum oscillations, and shed light on plausible avenues to explore novel physics and potential applications of coherent density-wave condensates., Comment: 33 pages, 17 figures

The layered Mott insulator ${\alpha}$-RuCl${_3}$ has been extensively studied as a potential Kitaev quantum spin liquid candidate. Here, by constructing heterostructures with graphite, we employed electron tunneling measurements on few-layer ${\alpha}$-RuCl${_3}$ using a scanning tunneling microscopy/spectroscopy. Characteristic tunneling spectra were detected on ${\alpha}$-RuCl${_3}$ layers in proximity to graphite. In the single-layer ${\alpha}$-RuCl${_3}$ in direct contact with graphite, distinct states in the Mott-gap regime were observed. The in-gap states are demonstrated to be closely related to the electron orbitals in ${\alpha}$-RuCl${_3}$ and graphite, and to be sensitive to interfacial coupling, where a hybridization at the heterointerface is hypothesized. The in-gap states are also thought of as a charge reservoir for weakly doping the ${\alpha}$-RuCl${_3}$ upper-layers. It demonstrated that the weak doping effect causes a considerable decrease in the Mott-gap within the upper-layers, suggesting that an unconventional Mott-transition is occurring in these layers. The results show that the heterostructure comprised of ${\alpha}$-RuCl${_3}$ and graphite is a good platform for investigating the doping physics in ${\alpha}$-RuCl${_3}$. Therefore, tunneling into such a doped system is a useful probe for studying otherwise insulating spin-liquid candidates., Comment: Original data are available upon resonable request from corresponding author

Excitonic insulators are usually considered to form via the condensation of a soft charge mode of bound electron-hole pairs. This, however, presumes that the soft exciton is of spin-singlet character. Early theoretical considerations have also predicted a very distinct scenario, in which the condensation of magnetic excitons results in an antiferromagnetic excitonic insulator state. Here we report resonant inelastic x-ray scattering (RIXS) measurements of Sr3Ir2O7. By isolating the longitudinal component of the spectra, we identify a magnetic mode that is well-defined at the magnetic and structural Brillouin zone centers, but which merges with the electronic continuum in between these high-symmetry points and which decays upon heating concurrent with a decrease in the material's resistivity. We show that a bilayer Hubbard model, in which electron-hole pairs are bound by exchange interactions, consistently explains all the electronic and magnetic properties of Sr3Ir2O7 indicating that this material is a realization of the long-predicted antiferromagnetic excitonic insulators phase., Comment: accepted in Nature Communications; 6 pages main materials

We study the magnetism in NdFe$_2$Ga$_8$ by the neutron-diffraction and temperature-modulated linear {Gr\"uneisen} parameter measurements. Previous thermodynamical measurements have demonstrated that there are two magnetic transitions at 10 and 14.5 K, respectively. Neutron-diffraction measurements confirm that the lower one is an antiferromagnetic (AFM) transition with a commensurate magnetic structure. Both the commensurate and the incommensurate (IC) magnetic peaks are found below the higher transition but their intensities only gradually increase with decreasing temperature. Below 10 K, the commensurate peak intensity increases quickly with decreasing temperature, signaling the AFM transition, while the IC peak intensity disappears below 5 K. The linear {Gr\"uneisen} parameter along the $c$ axis, $\Gamma_c$, shows a hysteresis behavior that is different from the hysteresis behavior for the magnetization $M$. We give a discussion of the origin of the magnetism in NdFe$_2$Ga$_8$., Comment: 4 pages, 3 figures

Robust road segmentation is a key challenge in self-driving research. Though many image-based methods have been studied and high performances in dataset evaluations have been reported, developing robust and reliable road segmentation is still a major challenge. Data fusion across different sensors to improve the performance of road segmentation is widely considered an important and irreplaceable solution. In this paper, we propose a novel structure to fuse image and LiDAR point cloud in an end-to-end semantic segmentation network, in which the fusion is performed at decoder stage instead of at, more commonly, encoder stage. During fusion, we improve the multi-scale LiDAR map generation to increase the precision of the multi-scale LiDAR map by introducing pyramid projection method. Additionally, we adapted the multi-path refinement network with our fusion strategy and improve the road prediction compared with transpose convolution with skip layers. Our approach has been tested on KITTI ROAD dataset and has competitive performance., Comment: Accepted by Multimedia Tools and Applications