arXiv subjects

Xi He

Publications and source records attributed to Xi He.

At least 55 records · Page 3Linked to original sources

arXiv2023

Preventing Inferences through Data Dependencies on Sensitive Data

Simply restricting the computation to non-sensitive part of the data may lead to inferences on sensitive data through data dependencies. Inference control from data dependencies has been studied in the prior work. However, existing solutions either detect and deny queries which may lead to leakage -- resulting in poor utility, or only protects against exact reconstruction of the sensitive data -- resulting in poor security. In this paper, we present a novel security model called full deniability. Under this stronger security model, any information inferred about sensitive data from non-sensitive data is considered as a leakage. We describe algorithms for efficiently implementing full deniability on a given database instance with a set of data dependencies and sensitive cells. Using experiments on two different datasets, we demonstrate that our approach protects against realistic adversaries while hiding only minimal number of additional non-sensitive cells and scales well with database size and sensitive data.

cs.DB↗

arXiv2023

Calculate electronic excited states using neural networks with effective core potential

The essence of atomic structure theory, quantum chemistry, and computational materials science is solving the multi-electron stationary Schrödinger equation. The Quantum Monte Carlo-based neural network wave function method has surpassed traditional post-Hartree-Fock methods in precision across various systems. However, its energy uncertainty is limited to 0.01%, posing challenges in accurately determining excited states and ionization energies, especially for elements beyond the fourth period. Using effective core potentials to account for inner electrons enhances the precision of vertical excitation and ionization energies. This approach has proved effective in computing ground state energies for elements like Lithium to Gallium and in calculating energy levels and wave functions for atoms and molecules with second and fourth period elements. Additionally, by integrating effective core potentials with Ferminet, we've achieved multiple excited state calculations with a precision comparable to experimental results, marking a significant advancement in practical applications and setting a new standard for theoretical excited state calculations.

physics.atom-ph↗

arXiv2023

CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation

The hybrid architecture of convolutional neural networks (CNNs) and Transformer are very popular for medical image segmentation. However, it suffers from two challenges. First, although a CNNs branch can capture the local image features using vanilla convolution, it cannot achieve adaptive feature learning. Second, although a Transformer branch can capture the global features, it ignores the channel and cross-dimensional self-attention, resulting in a low segmentation accuracy on complex-content images. To address these challenges, we propose a novel hybrid architecture of convolutional neural networks hand in hand with vision Transformers (CiT-Net) for medical image segmentation. Our network has two advantages. First, we design a dynamic deformable convolution and apply it to the CNNs branch, which overcomes the weak feature extraction ability due to fixed-size convolution kernels and the stiff design of sharing kernel parameters among different inputs. Second, we design a shifted-window adaptive complementary attention module and a compact convolutional projection. We apply them to the Transformer branch to learn the cross-dimensional long-term dependency for medical images. Experimental results show that our CiT-Net provides better medical image segmentation results than popular SOTA methods. Besides, our CiT-Net requires lower parameters and less computational costs and does not rely on pre-training. The code is publicly available at https://github.com/SR0920/CiT-Net.

eess.IV↗

arXiv2023

DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance

Recent years have witnessed the adoption of differential privacy (DP) in practical database systems like PINQ, FLEX, and PrivateSQL. Such systems allow data analysts to query sensitive data while providing a rigorous and provable privacy guarantee. However, the existing design of these systems does not distinguish data analysts of different privilege levels or trust levels. This design can have an unfair apportion of the privacy budget among the data analyst if treating them as a single entity, or waste the privacy budget if considering them as non-colluding parties and answering their queries independently. In this paper, we propose DProvDB, a fine-grained privacy provenance framework for the multi-analyst scenario that tracks the privacy loss to each single data analyst. Under this framework, when given a fixed privacy budget, we build algorithms that maximize the number of queries that could be answered accurately and apportion the privacy budget according to the privilege levels of the data analysts.

cs.DB↗

arXiv2023

A general method for solving light-like geodesics

A universal method to solve the differential equations of light-like geodesics is developed. The validity of this method depends on a new theorem, which is introduced for light-like geodesics in analogy to Beltrami's "geometrical" method for time-like geodesics. we apply the method to the Schwarzschild and Kerr spacetime as two examples. The general solutions of the light-like geodesic equations in the two spacetimes are derived straightforwadly. After setting $θ=π/2$, the general light-like geodesics in Schwarzschild spacetime reduce to the same expression as that in literatures. The method developed and results obtained in this paper may be useful in modeling dynamical phenomena in strong gravitaional fields like black holes since the solutions are expressed in terms of elliptic integrals, which can be calculated effectively.

gr-qc↗

arXiv2022

Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration

Differential privacy (DP) allows data analysts to query databases that contain users' sensitive information while providing a quantifiable privacy guarantee to users. Recent interactive DP systems such as APEx provide accuracy guarantees over the query responses, but fail to support a large number of queries with a limited total privacy budget, as they process incoming queries independently from past queries. We present an interactive, accuracy-aware DP query engine, CacheDP, which utilizes a differentially private cache of past responses, to answer the current workload at a lower privacy budget, while meeting strict accuracy guarantees. We integrate complex DP mechanisms with our structured cache, through novel cache-aware DP cost optimization. Our thorough evaluation illustrates that CacheDP can accurately answer various workload sequences, while lowering the privacy loss as compared to related work.

cs.CR↗

arXiv2022

Evolution of the cyclotron mass with doping in La$_{2-x}$Sr$_x$CuO$_4$

The recent observation of cyclotron resonance in optimally-doped La$_{2-x}$Sr$_x$CuO$_4$ using time-domain THz spectroscopy in high magnetic field has given new possibilities for the study of cuprate superconductors. One can measure the cyclotron mass in more disordered cuprates possesing short scattering times therefore expanding the study to materials and dopings in which quantum oscillations have not been observed. Here we present the measurement of the carrier mass of the hole doped cuprate La$_{2-x}$Sr$_x$CuO$_4$ across a a range of dopings spanning the slightly underdoped ($x=0.13$) to highly overdoped ($x=0.26$), near to the termination of the superconducting dome. These results reveal a systematic increase of $m_c$ with doping, up to values greater than thirteen times the bare electron mass. This is in contrast with those masses extracted from the heat capacity, which show a peak near the pseudogap critical point $p^*$ and/or Lifshitz transition. The cyclotron frequency is linear in field up to 31 T for all dopings giving no evidence for field induced Fermi surface reconstructions. The cyclotron mass is found to be positive for all dopings, but with a magnitude systematically below the heat capacity mass for under and optimally doped samples, while exceeding it for overdoped samples. Among other aspects, these results are surprising as photoemission reveals a Lifshitz transition in the middle of our doping range and the sign of the cyclotron mass determined from a finite frequency resonance is -- in conventional theories -- a {\it topological quantity} only sensitive to whether or not the Fermi surface is closed around holes or electrons. We see no sign of a divergence of the mass near $p^*$ nor near the Lifshitz transition, showing that any singularity -- if it exists -- is not strong enough to affect the cyclotron mass.

cond-mat.supr-con↗

arXiv2022

Effect of radiation-induced defects on the superfluid density and optical conductivity of overdoped La$_{2-x}$Sr$_x$CuO$_4$

Using a combination of time-domain THz spectroscopy (TDTS) and mutual inductance measurements, we have investigated the low-energy electrodynamic response of overdoped La$_{2-x}$Sr$_x$CuO$_4$ films that have been exposed to ion irradiation. Films went through three rounds of irradiation (2, 4, and 6 $\times 10^{13}$ ions/cm$^2$) and mutual inductance and TDTS experiments were performed between each step. Together with the as-grown film, this gives four different levels of disorder. The transport scattering rate that is measured directly in the THz experiments is an approximately linear function of the radiation dose at all temperatures. This is consistent with a proportionate increase in elastic scattering. In the superconducting state we find that the relation between $T_c$, the superfluid density, and the scattering rates are quantitatively at odds with the predictions based on the extant theory of Abrikosov-Gorkov-like pair breaking in a dirty $d$-wave superconductor. Increasing disorder causes only a small change in the superconducting transition temperature for the overdoped films, but the changes to the $ω\sim 0$ superfluid density are much larger.

cond-mat.supr-con↗

arXiv2022

Colossal transverse magnetoresistance due to nematic superconducting phase fluctuations in a copper oxide

Electronic anisotropy (or `nematicity') has been detected in all main families of cuprate superconductors by a range of experimental techniques -- electronic Raman scattering, THz dichroism, thermal conductivity, torque magnetometry, second-harmonic generation -- and was directly visualized by scanning tunneling microscope (STM) spectroscopy. Using angle-resolved transverse resistance (ARTR) measurements, a very sensitive and background-free technique that can detect 0.5$\%$ anisotropy in transport, we have observed it also in La$_{2-x}$Sr$_{x}$CuO$_{4}$ (LSCO) for $0.02 \leq x \leq 0.25$. Arguably the key enigma in LSCO is the rotation of the nematic director with temperature; this has not been seen before in any material. Here, we address this puzzle by measuring the angle-resolved transverse magnetoresistance (ARTMR) in LSCO. We report a discovery of colossal transverse magnetoresistance (CTMR) -- an order-of-magnitude drop in the transverse resistivity in the magnetic field of $6\,$T, while none is seen in the longitudinal resistivity. We show that the apparent rotation of the nematic director is caused by superconducting phase fluctuations, which are much more anisotropic than the normal-electron fluid, and their respective directors are not parallel. This qualitative conclusion is robust and follows straight from the raw experimental data. We quantify this by modelling the measured (magneto-)conductivity by a sum of two conducting channels that correspond to distinct anisotropic Drude and Cooper-pair effective mass tensors. Strikingly, the anisotropy of Cooper-pair stiffness is significantly larger than that of the normal electrons, and it grows dramatically on the underdoped side, where the fluctuations become effectively quasi-one dimensional.

cond-mat.supr-con↗

arXiv2022

Role of Oxygen States in the Low Valence Nickelate La$_4$Ni$_3$O$_8$

The discovery of superconductivity in square-planar low valence nickelates has ignited a vigorous debate regarding their essential electronic properties: Do these materials have appreciable oxygen charge-transfer character akin to the cuprates, or are they in a distinct Mott-Hubbard regime where oxygen plays a minimal role? Here, we resolve this question using O $K$-edge resonant inelastic x-ray scattering (RIXS) measurements of the low valence nickelate La$_{4}$Ni$_{3}$O$_{8}$ and a prototypical cuprate La$_{2-x}$Sr$_{x}$CuO$_{4}$ ($x=0.35$). As expected, the cuprate lies deep in the charge-transfer regime of the Zaanen-Sawatzky-Allen scheme. The nickelate, however, is not well described by either limit of the ZSA scheme and is found to be of mixed charge-transfer/Mott-Hubbard character with the Coulomb repulsion $U$ of similar size to the charge-transfer energy $Δ$. Nevertheless, the transition-metal-oxygen hopping is larger in La$_{4}$Ni$_{3}$O$_{8}$ than in La$_{2-x}$Sr$_{x}$CuO$_{4}$, leading to a significant superexchange interaction and an appreciable hole occupation of the ligand O orbitals in La$_{4}$Ni$_{3}$O$_{8}$ despite its larger $Δ$. Our results clarify the essential characteristics of low valence nickelates and put strong constraints on theoretical interpretations of superconductivity in these materials.

cond-mat.supr-con↗

arXiv2022

Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases

Organizations often collect private data and release aggregate statistics for the public's benefit. If no steps toward preserving privacy are taken, adversaries may use released statistics to deduce unauthorized information about the individuals described in the private dataset. Differentially private algorithms address this challenge by slightly perturbing underlying statistics with noise, thereby mathematically limiting the amount of information that may be deduced from each data release. Properly calibrating these algorithms -- and in turn the disclosure risk for people described in the dataset -- requires a data curator to choose a value for a privacy budget parameter, $ε$. However, there is little formal guidance for choosing $ε$, a task that requires reasoning about the probabilistic privacy-utility trade-off. Furthermore, choosing $ε$ in the context of statistical inference requires reasoning about accuracy trade-offs in the presence of both measurement error and differential privacy (DP) noise. We present Visualizing Privacy (ViP), an interactive interface that visualizes relationships between $ε$, accuracy, and disclosure risk to support setting and splitting $ε$ among queries. As a user adjusts $ε$, ViP dynamically updates visualizations depicting expected accuracy and risk. ViP also has an inference setting, allowing a user to reason about the impact of DP noise on statistical inferences. Finally, we present results of a study where 16 research practitioners with little to no DP background completed a set of tasks related to setting $ε$ using both ViP and a control. We find that ViP helps participants more correctly answer questions related to judging the probability of where a DP-noised release is likely to fall and comparing between DP-noised and non-private confidence intervals.

cs.CR↗

arXiv2021

The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection

Hyperparameter optimization is a ubiquitous challenge in machine learning, and the performance of a trained model depends crucially upon their effective selection. While a rich set of tools exist for this purpose, there are currently no practical hyperparameter selection methods under the constraint of differential privacy (DP). We study honest hyperparameter selection for differentially private machine learning, in which the process of hyperparameter tuning is accounted for in the overall privacy budget. To this end, we i) show that standard composition tools outperform more advanced techniques in many settings, ii) empirically and theoretically demonstrate an intrinsic connection between the learning rate and clipping norm hyperparameters, iii) show that adaptive optimizers like DPAdam enjoy a significant advantage in the process of honest hyperparameter tuning, and iv) draw upon novel limiting behaviour of Adam in the DP setting to design a new and more efficient optimizer.

stat.ML↗

arXiv2021

Quantum classifiers for domain adaptation

Transfer learning (TL), a crucial subfield of machine learning, aims to accomplish a task in the target domain with the acquired knowledge of the source domain. Specifically, effective domain adaptation (DA) facilitates the delivery of the TL task where all the data samples of the two domains are distributed in the same feature space. In this paper, two quantum implementations of the DA classifier are presented with quantum speedup compared with the classical DA classifier. One implementation, the quantum basic linear algebra subroutines (QBLAS)-based classifier, can predict the labels of the target domain data with logarithmic resources in the number and dimension of the given data. The other implementation efficiently accomplishes the DA task through a variational hybrid quantum-classical procedure.

quant-ph↗

arXiv2021

Exponential enhancement of quantum metrology using continuous variables

Coherence time is an important resource to generate enhancement in quantum metrology. In this work, based on continuous-variable models, we propose a new design of the signal-probe Hamiltonian which generates an exponential enhancement of measurement sensitivity. The key idea is to include into the system an ancilla that does not couple directly to the signal. An immediate benefit of such design is one can expand quantum Fisher information(QFI) into a power series in time, making it possible to achieve a higher-order time scaling in QFI. Specifically, one can design the interaction for a qubit-oscillator Ramsey interferometer to achieve a quartic time scaling, based on which, one can further design a chain of coupled harmonic oscillators to achieve an exponential time scaling in QFI. Our results show that linear scaling in both time and the number of coupling terms is sufficient to obtain exponential enhancement. Such exponential advantage is closely related to the characteristic commutation relations of quadratures.

quant-ph↗

arXiv2021

Quantum correlation alignment for unsupervised domain adaptation

Correlation alignment (CORAL), a representative domain adaptation (DA) algorithm, decorrelates and aligns a labelled source domain dataset to an unlabelled target domain dataset to minimize the domain shift such that a classifier can be applied to predict the target domain labels. In this paper, we implement the CORAL on quantum devices by two different methods. One method utilizes quantum basic linear algebra subroutines (QBLAS) to implement the CORAL with exponential speedup in the number and dimension of the given data samples. The other method is achieved through a variational hybrid quantum-classical procedure. In addition, the numerical experiments of the CORAL with three different types of data sets, namely the synthetic data, the synthetic-Iris data, the handwritten digit data, are presented to evaluate the performance of our work. The simulation results prove that the variational quantum correlation alignment algorithm (VQCORAL) can achieve competitive performance compared with the classical CORAL.

quant-ph↗

arXiv2021

Kamino: Constraint-Aware Differentially Private Data Synthesis

Organizations are increasingly relying on data to support decisions. When data contains private and sensitive information, the data owner often desires to publish a synthetic database instance that is similarly useful as the true data, while ensuring the privacy of individual data records. Existing differentially private data synthesis methods aim to generate useful data based on applications, but they fail in keeping one of the most fundamental data properties of the structured data -- the underlying correlations and dependencies among tuples and attributes (i.e., the structure of the data). This structure is often expressed as integrity and schema constraints, or with a probabilistic generative process. As a result, the synthesized data is not useful for any downstream tasks that require this structure to be preserved. This work presents Kamino, a data synthesis system to ensure differential privacy and to preserve the structure and correlations present in the original dataset. Kamino takes as input of a database instance, along with its schema (including integrity constraints), and produces a synthetic database instance with differential privacy and structure preservation guarantees. We empirically show that while preserving the structure of the data, Kamino achieves comparable and even better usefulness in applications of training classification models and answering marginal queries than the state-of-the-art methods of differentially private data synthesis.

cs.DB↗

arXiv2021

Scanning SQUID characterization of extremely overdoped $La_{2-x}Sr_{x}CuO_{4}$

Recently, advances in film synthesis methods have enabled a study of extremely overdoped $La_{2-x}Sr_{x}CuO_{4}$. This has revealed a surprising behavior of the superfluid density as a function of doping and temperature, the explanation of which is vividly debated. One popular class of models posits electronic phase separation, where the superconducting phase fraction decreases with doping, while some competing phase (e.g. ferromagnetic) progressively takes over. A problem with this scenario is that all the way up to the dome edge the superconducting transition remains sharp, according to mutual inductance measurements. However, the physically relevant scale is the Pearl penetration depth, $Λ_{P}$, and this technique probes the sample on a length scale $L$ that is much larger than $Λ_{P}$. In the present paper, we use local scanning SQUID measurements that probe the susceptibility of the sample on the scale $L << Λ_{P}$. Our SQUID maps show uniform landscapes of susceptibility and excellent overall agreement of the local penetration depth data with the bulk measurements. These results contribute an important piece to the puzzle of how high-temperature superconductivity vanishes on the overdoped side of the cuprates phase diagram.

cond-mat.supr-con↗

arXiv2020

Electron pairing in the pseudogap state revealed by shot noise in copper-oxide junctions

In the quest to understand high-temperature superconductivity in copper oxides, a vigorous debate has been focused on the pseudogap - a partial gap that opens over portions of the Fermi surface in the 'normal' state above the bulk critical temperature ($T_{c}$). The pseudogap has been attributed to precursor superconductivity, to the existence of preformed pairs, or to competing orders such as charge-density waves. A direct determination of the charge of carriers as a function of temperature and bias could help resolve among these alternatives. Here, we report measurements of the shot noise of tunneling current in high-quality La$_{2-x}$Sr$_{x}$CuO$_{4}$/La$_{2}$CuO$_{4}$/La$_{2-x}$Sr$_{x}$CuO$_{4}$ (LSCO/LCO/LSCO) heterostructures fabricated using atomic-layer-by-layer molecular beam epitaxy, for several doping levels. The data delineate three distinct regions in the bias voltage-temperature ($V-T$) space. Well outside the superconducting gap region, the shot noise agrees quantitatively with independent tunneling of charge-e carriers. Deep within the gap, shot noise is greatly enhanced, reminiscent of multiple Andreev reflections. Starting above $T_{c}$ and extending to biases much larger than the gap, there is a broad region in which the noise substantially exceeds the expectations of single-charge tunneling, indicating pairing of carriers. Pairs are detectable deep into the pseudogap region of temperature and bias.

cond-mat.supr-con↗