arXiv subjects

Xinyi Huang

Publications and source records attributed to Xinyi Huang.

53 records · Page 3Linked to original sources

arXiv2023

Dual-comb Spectroscopy of Ammonia Formation in Non-thermal Plasmas

Plasma-activated chemical transformations promise the efficient synthesis of salient chemical products. However, the reaction pathways that lead to desirable products are often unknown, and key quantum-state-resolved information regarding the involved molecular species is lacking. Here we use quantum cascade laser dual-comb spectroscopy (QCL-DCS) to probe plasma-activated NH$_3$ generation with rotational and vibrational state resolution, quantifying state-specific number densities via broadband spectral analysis. The measurements reveal unique translational, rotational and vibrational temperatures for NH$_3$ products, indicative of a highly reactive, non-thermal environment. Ultimately, we postulate on the energy transfer mechanisms that explain trends in temperatures and number densities observed for NH$_3$ generated in low-pressure nitrogen-hydrogen (N$_2$-H$_2$) plasmas.

physics.chem-ph↗

arXiv2022

Efficiency Boosting of Secure Cross-platform Recommender Systems over Sparse Data

Fueled by its successful commercialization, the recommender system (RS) has gained widespread attention. However, as the training data fed into the RS models are often highly sensitive, it ultimately leads to severe privacy concerns, especially when data are shared among different platforms. In this paper, we follow the tune of existing works to investigate the problem of secure sparse matrix multiplication for cross-platform RSs. Two fundamental while critical issues are addressed: preserving the training data privacy and breaking the data silo problem. Specifically, we propose two concrete constructions with significantly boosted efficiency. They are designed for the sparse location insensitive case and location sensitive case, respectively. State-of-the-art cryptography building blocks including homomorphic encryption (HE) and private information retrieval (PIR) are fused into our protocols with non-trivial optimizations. As a result, our schemes can enjoy the HE acceleration technique without privacy trade-offs. We give formal security proofs for the proposed schemes and conduct extensive experiments on both real and large-scale simulated datasets. Compared with state-of-the-art works, our two schemes compress the running time roughly by 10* and 2.8*. They also attain up to 15* and 2.3* communication reduction without accuracy loss.

cs.CR↗

arXiv2022

SIMC 2.0: Improved Secure ML Inference Against Malicious Clients

In this paper, we study the problem of secure ML inference against a malicious client and a semi-trusted server such that the client only learns the inference output while the server learns nothing. This problem is first formulated by Lehmkuhl \textit{et al.} with a solution (MUSE, Usenix Security'21), whose performance is then substantially improved by Chandran et al.'s work (SIMC, USENIX Security'22). However, there still exists a nontrivial gap in these efforts towards practicality, giving the challenges of overhead reduction and secure inference acceleration in an all-round way. We propose SIMC 2.0, which complies with the underlying structure of SIMC, but significantly optimizes both the linear and non-linear layers of the model. Specifically, (1) we design a new coding method for homomorphic parallel computation between matrices and vectors. It is custom-built through the insight into the complementarity between cryptographic primitives in SIMC. As a result, it can minimize the number of rotation operations incurred in the calculation process, which is very computationally expensive compared to other homomorphic operations e.g., addition, multiplication). (2) We reduce the size of the garbled circuit (GC) (used to calculate nonlinear activation functions, e.g., ReLU) in SIMC by about two thirds. Then, we design an alternative lightweight protocol to perform tasks that are originally allocated to the expensive GCs. Compared with SIMC, our experiments show that SIMC 2.0 achieves a significant speedup by up to $17.4\times $ for linear layer computation, and at least $1.3\times$ reduction of both the computation and communication overheads in the implementation of non-linear layers under different data dimensions. Meanwhile, SIMC 2.0 demonstrates an encouraging runtime boost by $2.3\sim 4.3\times$ over SIMC on different state-of-the-art ML models.

cs.CR↗

arXiv2022

New Secure Sparse Inner Product with Applications to Machine Learning

Sparse inner product (SIP) has the attractive property of overhead being dominated by the intersection of inputs between parties, independent of the actual input size. It has intriguing prospects, especially for boosting machine learning on large-scale data, which is tangled with sparse data. In this paper, we investigate privacy-preserving SIP problems that have rarely been explored before. Specifically, we propose two concrete constructs, one requiring offline linear communication which can be amortized across queries, while the other has sublinear overhead but relies on the more computationally expensive tool. Our approach exploits state-of-the-art cryptography tools including garbled Bloom filters (GBF) and Private Information Retrieval (PIR) as the cornerstone, but carefully fuses them to obtain non-trivial overhead reductions. We provide formal security analysis of the proposed constructs and implement them into representative machine learning algorithms including k-nearest neighbors, naive Bayes classification and logistic regression. Compared to the existing efforts, our method achieves $2$-$50\times$ speedup in runtime and up to $10\times$ reduction in communication.

cs.CR↗

arXiv2022

Simulation of classical Ising-like magnetism with a Mott insulator of paired atoms

Quantum simulation of the XXZ model with a two-component Bose or Fermi Hubbard model based on a Mott insulator background has been widely used in the investigations of quantum magnetism with ultracold neutral atoms. In most cases, the diagonal spin-spin interaction is always accompanied by a large spin-exchange interaction which hinders the formation of long-range magnetic order at low temperature. Here we show that the spin-exchange interaction can be strongly reduced in a Mott insulator of paired atoms, while the diagonal spin-spin interaction remains unaffected. Thus, the effective magnetic model is quite close to an exact classical Ising model in the textbook. And we analysed an experimentally achievable three-component Fermi-Hubbard model of $\mathrm{{}^{6}Li}$ with two hyperfine levels of atoms paired in the lattice. We find the long-range antiferromagnetic order of such a three-component Fermi-Hubbard model can be much stronger than that of a typical two-component Fermi-Hubbard model at low temperature. And we discussed the possiblity of simulating an exact two-dimensional ferromagnetic Ising model in a Mott insulator of paired bosonic atoms. Our results may be useful for experimental investigation of the long-range Ising-like magnetism with ultracold neutral atoms under thermal equilibrium.

cond-mat.quant-gas↗

arXiv2022

Hercules: Boosting the Performance of Privacy-preserving Federated Learning

In this paper, we address the problem of privacy-preserving federated neural network training with $N$ users. We present Hercules, an efficient and high-precision training framework that can tolerate collusion of up to $N-1$ users. Hercules follows the POSEIDON framework proposed by Sav et al. (NDSS'21), but makes a qualitative leap in performance with the following contributions: (i) we design a novel parallel homomorphic computation method for matrix operations, which enables fast Single Instruction and Multiple Data (SIMD) operations over ciphertexts. For the multiplication of two $h\times h$ dimensional matrices, our method reduces the computation complexity from $O(h^3)$ to $O(h)$. This greatly improves the training efficiency of the neural network since the ciphertext computation is dominated by the convolution operations; (ii) we present an efficient approximation on the sign function based on the composite polynomial approximation. It is used to approximate non-polynomial functions (i.e., ReLU and max), with the optimal asymptotic complexity. Extensive experiments on various benchmark datasets (BCW, ESR, CREDIT, MNIST, SVHN, CIFAR-10 and CIFAR-100) show that compared with POSEIDON, Hercules obtains up to 4% increase in model accuracy, and up to 60$\times$ reduction in the computation and communication cost.

cs.CR↗

arXiv2021

Geo-Context Aware Study of Vision-Based Autonomous Driving Models and Spatial Video Data

Vision-based deep learning (DL) methods have made great progress in learning autonomous driving models from large-scale crowd-sourced video datasets. They are trained to predict instantaneous driving behaviors from video data captured by on-vehicle cameras. In this paper, we develop a geo-context aware visualization system for the study of Autonomous Driving Model (ADM) predictions together with large-scale ADM video data. The visual study is seamlessly integrated with the geographical environment by combining DL model performance with geospatial visualization techniques. Model performance measures can be studied together with a set of geospatial attributes over map views. Users can also discover and compare prediction behaviors of multiple DL models in both city-wide and street-level analysis, together with road images and video contents. Therefore, the system provides a new visual exploration platform for DL model designers in autonomous driving. Use cases and domain expert evaluation show the utility and effectiveness of the visualization system.

cs.HC↗

arXiv2021

Second Moment Closure Modeling and DNS of Stratified Shear Layers

Buoyant shear layers are encountered in many engineering and environmental applications and have been studied by researchers in the context of experiments and modeling for decades. Often, these flows have high Reynolds and Richardson numbers, and this leads to significant/intractable space-time resolution requirements for DNS or LES modeling. On the other hand, many of the important physical mechanisms in these systems, such as stress anisotropy, wake stabilization, and regime transition, inherently render eddy viscosity-based RANS modeling inappropriate. Accordingly, we pursue second-moment closure (SMC), i.e., full Reynolds stress/flux/variance modeling, for moderate Reynolds number non-stratified and stratified shear layers for which DNS is possible. A range of sub-model complexity is pursued for the diffusion of stresses, density fluxes and variance, pressure strain and scrambling, and dissipation. These sub-models are evaluated in terms of how well they are represented by DNS in comparison to the exact Reynolds averaged terms, and how well they impact the accuracy of the full RANS closure. For the non-stratified case, the SMC model predicts the shear layer growth rate and Reynolds shear stress profiles accurately. Stress anisotropy and budgets are captured only qualitatively. Comparing DNS of exact and modeled terms, inconsistencies in model performance and assumptions are observed, including inaccurate prediction of individual statistics, non-negligible pressure diffusion, and dissipation anisotropy. For the stratified case, shear layer and gradient Richardson number growth rates, and stress, flux, and variance decay rates, are captured with less accuracy than corresponding flow parameters in the non-stratified case. These studies lead to several recommendations for model improvement.

physics.flu-dyn↗

arXiv2021

3D flow motions in the viscous sublayer

We employ novel digital Fresnel reflection holography to capture the 3D flows within the viscous sublayer of a smooth-wall turbulent channel flow at Reτ=400. The measurements reveal unsteady and diverse flow patterns in the sublayer including nearly uniform high and low speed flows and strong small-scale (on the order of viscous wall units) spanwise meandering motions. The probability density functions (PDFs) of wall shear stresses show a clear discrepancy in high stress range with those from direct numerical simulation (DNS), which is attributed to the unresolved streamwise and spanwise motions by DNS. Moreover, the PDF of Lagrangian particle accelerations yields a stretched exponential shape like that in homogenous isotropic turbulence, indicating strong intermittency in the sublayer. We find a significant fraction of high accelerations is associated with the small-scale meandering motions. Our study helps explain the effect of sublayer-scale roughness on reducing drag and flow separation reported in the literature.

physics.flu-dyn↗

arXiv2021

Determining a priori a RANS model's applicable range via global epistemic uncertainty quantification

Calibrating a Reynolds-averaged Navier-Stokes (RANS) model against data leads to an improvement. Determining {\it a priori} if such an improvement generalizes to flows outside the calibration data is an outstanding challenge. This work attempts to address this challenge via global epistemic Uncertainty Quantification (UQ). Unlike the available epistemic UQ methods that are local and tell us a model's uncertainty at one specific flow condition, the global epistemic UQ method presented in this work tells us also whether a perturbation of the original model would generalize. Specifically, the global epistemic UQ method evaluates a potential improvement in terms of its "effectiveness" and "inconsistency". Any improvement can be put in one of the following four quadrants: first, high effectiveness, low inconsistency; second, high effectiveness, high inconsistency; third, low effectiveness, low inconsistency; and fourth, low effectiveness, high inconsistency. An improvement would generalize if and only if it is in the high effectiveness and low inconsistency quadrant. To demonstrate the concept, we apply the global epistemic UQ to full Reynolds stress modeling of a stratified shear layer. The global epistemic UQ results point to a model coefficient in the pressure-strain correlation closure (among others) as effective and consistent for predicting the quantity of interest of shear layer's growth. We calibrate the model coefficient such that our RANS matches direct numerical simulation data at one flow condition. We show that the calibrated model generalizes to several other test flow conditions. On the other hand, when calibrating a high inconsistency term, we get a model that works at only the calibrated condition.

physics.flu-dyn↗

arXiv2020

Towards Annotation-Free Evaluation of Cross-Lingual Image Captioning

Cross-lingual image captioning, with its ability to caption an unlabeled image in a target language other than English, is an emerging topic in the multimedia field. In order to save the precious human resource from re-writing reference sentences per target language, in this paper we make a brave attempt towards annotation-free evaluation of cross-lingual image captioning. Depending on whether we assume the availability of English references, two scenarios are investigated. For the first scenario with the references available, we propose two metrics, i.e., WMDRel and CLinRel. WMDRel measures the semantic relevance between a model-generated caption and machine translation of an English reference using their Word Mover's Distance. By projecting both captions into a deep visual feature space, CLinRel is a visual-oriented cross-lingual relevance measure. As for the second scenario, which has zero reference and is thus more challenging, we propose CMedRel to compute a cross-media relevance between the generated caption and the image content, in the same visual feature space as used by CLinRel. The promising results show high potential of the new metrics for evaluation with no need of references in the target language.

cs.CV↗

arXiv2020

Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images

Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However, visual analytics tools are lacking for the specific application of x-ray image classification with multiple structural attributes. In this paper, we present an interactive system for domain scientists to visually study the multiple attributes learning models applied to x-ray scattering images. It allows domain scientists to interactively explore this important type of scientific images in embedded spaces that are defined on the model prediction output, the actual labels, and the discovered feature space of neural networks. Users are allowed to flexibly select instance images, their clusters, and compare them regarding the specified visual representation of attributes. The exploration is guided by the manifestation of model performance related to mutual relationships among attributes, which often affect the learning accuracy and effectiveness. The system thus supports domain scientists to improve the training dataset and model, find questionable attributes labels, and identify outlier images or spurious data clusters. Case studies and scientists feedback demonstrate its functionalities and usefulness.

cs.CV↗

arXiv2019

Visual Understanding of Multiple Attributes Learning Model of X-Ray Scattering Images

This extended abstract presents a visualization system, which is designed for domain scientists to visually understand their deep learning model of extracting multiple attributes in x-ray scattering images. The system focuses on studying the model behaviors related to multiple structural attributes. It allows users to explore the images in the feature space, the classification output of different attributes, with respect to the actual attributes labelled by domain scientists. Abundant interactions allow users to flexibly select instance images, their clusters, and compare them visually in details. Two preliminary case studies demonstrate its functionalities and usefulness.

cs.LG↗

arXiv2017

Relationship between critical current and flux-flow resistivity in the mixed state of Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$

We studied the temperature and magnetic field dependence of vortex dissipation and critical current in the mixed-state of unconventional superconducting alloys Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$ ($0.044 \leq x \leq 0.100$) through current-voltage measurements. Our results reveal that all the electric field $E$ vs current density $j$ curves in the Ohmic regime merge to one point ($j_0,E_0$) and that there is a simple relationship between the critical current density $j_c$ and flux-flow resistivity $ρ_{\rm ff}$: $ρ_{\rm ff}/ρ_{\rm n} = (1- j_{c}/j_{0})^{-1}$, where $ρ_{\rm n}=E_0/j_0$ is the normal-state resistivity just above the superconducting transition. In addition, $E_0$ is positive for all five dopings, reflecting the abnormal behavior of the flux-flow resistivity $ρ_{\rm ff}$: it increases with decreasing magnetic field. In contrast, $E_0$ is negative for the conventional superconductor Nb since, as expected, $ρ_{\rm ff}$ decreases with decreasing magnetic field. Furthermore, in the under-doped and over-doped single crystals of Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$, the parameter $E_0$ remains temperature independent, while it decreases with increasing temperature for the single crystals around optimal doping ($ 0.060\leq x\leq 0.072 $). This result points to the co-existence of superconductivity with some other phase around optimal doping.

cond-mat.supr-con↗

arXiv2016

Universality and unconventional enhancement of flux-flow resistivity in Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$

Measurements of the current-voltage characteristics were performed on Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$ single crystals with doping level $0.044 \leq x \leq 0.1$. An unconventional increase in the flux-flow resistivity $ρ_{\rm ff}$ with decreasing magnetic field was observed across this doping range. Such an abnormal field dependence of flux-flow resistivity is in contrast with the linear field dependence of $ρ_{\rm ff}$ in conventional type-II superconductors, but is similar to the behavior recently observed in the heavy-fermion superconductor CeCoIn$_5$. A significantly enhanced $ρ_{\rm ff}$ was found for the x=0.06 single crystals, implying a strong single-particle energy dissipation around the vortex cores. At different temperatures and fields and for a given doping concentration, the normalized $ρ_{\rm ff}$ scales with normalized field and temperature. The doping level dependence of these parameters strongly suggests that the abnormal upturn flux-flow resisitivity is likely related to the enhancement of spin fluctuations around the vortex cores of the optimally doped samples.

cond-mat.supr-con↗

arXiv2014

Pressure studies of the quantum critical alloy Ce(0.93)Yb(0.07)CoIn5

Here we present our experimental and theoretical study of the effects of pressure on the transport properties of the heavy-fermion alloy Ce(1-x)Yb(x)CoIn5 with x~0.07. We specifically choose this value of ytterbium concentration because the magnetic-field-induced quantum critical point, which separates the antiferromagnetic and paramagnetic states at zero temperature, approaches zero, as has been established in previous studies. Our measurements show that pressure further suppresses quantum fluctuations in this alloy, just as it does in the parent compound CeCoIn5. In contrast, the square-root temperature dependent part of resistivity remains insensitive to pressure, indicating that the heavy-quasiparticles are not involved in the scattering processes leading to such a temperature dependent resistivity. We demonstrate that the growth of the coherence temperature with pressure, as well as the decrease of the residual resistivity, can be accurately described by employing the coherent potential approximation for a disordered Kondo lattice.

cond-mat.str-el↗

arXiv2011

Correlated disorder in Kondo lattice

Motivated by recent experiments on disordered heavy-fermion materials, we study the effect of correlated disorder in Kondo lattice. Correlations between the impurities are considered at the two-particle level. We use mean-field theory approximation for the Anderson lattice model to calculate how the emergence of coherence in the Kondo lattice is impacted by correlations between impurities. We show that the rate at which disorder suppresses coherence temperature depends on the length of impurity correlations. As impurity concentration increases, we generally find that the suppression of coherence temperature is significantly reduced. The results are discussed in the context of available experimental data.

cond-mat.str-el↗