arXiv subjects

Lei Xu

Publications and source records attributed to Lei Xu.

At least 217 records · Page 12Linked to original sources

arXiv2020

TREVERSE: Trial-and-Error Lightweight Secure Reverse Authentication with Simulatable PUFs

A physical unclonable function (PUF) generates hardware intrinsic volatile secrets by exploiting uncontrollable manufacturing randomness. Although PUFs provide the potential for lightweight and secure authentication for increasing numbers of low-end Internet of Things devices, practical and secure mechanisms remain elusive. We aim to explore simulatable PUFs (SimPUFs) that are physically unclonable but efficiently modeled mathematically through privileged one-time PUF access to address the above problem. Given a challenge, a securely stored SimPUF in possession of a trusted server computes the corresponding response and its bit-specific reliability. Consequently, naturally noisy PUF responses generated by a resource limited prover can be immediately processed by a one-way function (OWF) and transmitted to the server, because the resourceful server can exploit the SimPUF to perform a trial-and-error search over likely error patterns to recover the noisy response to authenticate the prover. Security of trial-and-error reverse (TREVERSE) authentication under the random oracle model is guaranteed by the hardness of inverting the OWF. We formally evaluate the TREVERSE authentication capability with two SimPUFs experimentally derived from popular silicon PUFs.

cs.CR↗

arXiv2020

Enhanced Light-Matter Interactions in Dielectric Nanostructures via Machine Learning Approach

A key concept underlying the specific functionalities of metasurfaces, i.e. arrays of subwavelength nanoparticles, is the use of constituent components to shape the wavefront of the light, on-demand. Metasurfaces are versatile and novel platforms to manipulate the scattering, colour, phase or the intensity of the light. Currently, one of the typical approaches for designing a metasurface is to optimize one or two variables, among a vast number of fixed parameters, such as various materials' properties and coupling effects, as well as the geometrical parameters. Ideally, it would require a multi-dimensional space optimization through direct numerical simulations. Recently, an alternative approach became quite popular allowing to reduce the computational cost significantly based on a deep-learning-assisted method. In this paper, we utilize a deep-learning approach for obtaining high-quality factor (high-Q) resonances with desired characteristics, such as linewidth, amplitude and spectral position. We exploit such high-Q resonances for the enhanced light-matter interaction in nonlinear optical metasurfaces and optomechanical vibrations, simultaneously. We demonstrate that optimized metasurfaces lead up to 400+ folds enhancement of the third harmonic generation (THG); at the same time, they also contribute to 100+ folds enhancement in optomechanical vibrations. This approach can be further used to realize structures with unconventional scattering responses.

physics.optics↗

arXiv2020

Saliency-based Weighted Multi-label Linear Discriminant Analysis

In this paper, we propose a new variant of Linear Discriminant Analysis (LDA) to solve multi-label classification tasks. The proposed method is based on a probabilistic model for defining the weights of individual samples in a weighted multi-label LDA approach. Linear Discriminant Analysis is a classical statistical machine learning method, which aims to find a linear data transformation increasing class discrimination in an optimal discriminant subspace. Traditional LDA sets assumptions related to Gaussian class distributions and single-label data annotations. To employ the LDA technique in multi-label classification problems, we exploit intuitions coming from a probabilistic interpretation of class saliency to redefine the between-class and within-class scatter matrices. The saliency-based weights obtained based on various kinds of affinity encoding prior information are used to reveal the probability of each instance to be salient for each of its classes in the multi-label problem at hand. The proposed Saliency-based weighted Multi-label LDA approach is shown to lead to performance improvements in various multi-label classification problems.

cs.LG↗

arXiv2020

Broadband Control on Scattering Events with Interferometric Coherent Waves

We propose a universal strategy to realize a broadband control on arbitrary scatterers, through multiple coherent beams. By engineering the phases and amplitudes of incident beams, one can suppress the dominant scattering partial waves, making the obstacle lose its intrinsic responses in a broadband spectrum. The associated coherent beams generate a finite and static region, inside which the corresponding electric field intensity and Poynting vector vanish. As a solution to go beyond the sum-rule limit, our methodology is also irrespective of inherent system properties, as well as extrinsic operating wavelength, providing a non-invasive control on the wave-obstacles interaction for any kinds of shape.

physics.optics↗

arXiv2020

Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0.27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.

eess.IV↗

arXiv2020

Phase Transformation Characteristics of High-Temperature Shape Memory Alloy under Tension, Compression, and Bending Actuation Cycling

Shape Memory Alloys (SMAs) are a unique class of intermetallic alloys that can cyclically sustain large deformations and recover a designed geometry through a solid-to-solid phase transformation. SMAs provide favorable actuation energy density properties, making them suitable for engineering applications requiring a significant, repeated, work output. To facilitate the development and validation of an SMA constitutive model considering the evolving anisotropic material response for High-Temperature SMA (HTSMA), uniaxial and pure bending actuation cycling tests on HTSMA specimens are performed by a custom-built testing frames. The phase transformation characteristics for Ni$_{50.3}$TiHf$_{20}$ HTSMA under uniaxial tension/compression and four-point bending actuation cycles are investigated. The experimental results show that the polycrystalline HTSMAs has a strong tension-compression asymmetry under uniaxial actuation cycling loading conditions. Furthermore, the four-point beam bending test shows that there is an intrinsic phenomenon when HTSMAs are subjected to cyclic actuation bending conditions, i.e., the zero-strain neutral axis shifts as a result of the asymmetric tension-compression phase transformations and the asymmetric generation of TRIP strains on different sides of the beam. The conducted experiments provide invaluable information to develop and improve the SMA constitutive model considering tension-compression asymmetry and TRIP strain generation within a unified modeling effort. As future work, additional experiments on other HTSMA components, such as torque tubes and specimens with notches or cutouts, under actuation cycling would provide more comprehensive validation data and component performance for HTSMA-based actuators.

physics.app-ph↗

arXiv2020

A Three-dimensional Constitutive Model for Polycrystalline Shape Memory Alloys Under Large Strains Combined With Large Rotations

Shape Memory Alloys (SMAs) has been widely aware of working as actuators for active/smart morphing structures in the engineering industry. Because of the high actuation energy density of SMAs, compared to other active materials, structures integrated with SMA-based actuators has high advantage in terms of trade-offs between overall structure weight, integrity and functionality. The majority of available constitutive models for SMAs are developed within the infinitesimal strain regime. However, it was reported that particular SMAs can generate transformation strains nearly up to 8%-10%, for which the adopted infinitesimal strain assumption is no longer appropriate. Furthermore, industry applications may require SMA actuators undergo large rotation deformation at work. Combining the above two facts, a constitutive model for SMAs developed on a finite deformation framework is required. A three-dimensional constitutive model for SMAs considering large strains with large rotations is proposed in this work. This model utilizes the logarithmic strain as a finite strain measure for large deformation analysis so that its rate form hypo-elastic constitutive relation can be consistently integrated to deliver a free energy based hyper-elastic constitutive relation. The martensitic volume fraction and the second-order transformation strain tensor are chosen as the internal state variables to characterize the inelastic response exhibited by polycrystalline SMAs. The presented formulation and its numerical implementation scheme can be extended in future work for the incorporation of other inelastic phenomena such as transformation-induced plasticity, viscoplasticity and creep under large deformations.

cond-mat.mtrl-sci↗

arXiv2020

Finite strain constitutive modeling for shape memory alloys considering transformation-induced plasticity and two-way shape memory effect

This work presents a three-dimensional constitutive model for shape memory alloys considering the TRansformation-Induced Plasticity (TRIP) as well as the Two-Way Shape Memory Effect (TWSME) through a large deformation framework. The presented logarithmic strain based model is able to capture the large strains and rotations exhibited by SMAs under general thermomechanical cycling. By using the martensitic volume fraction, transformation strain, internal stress, and TRIP strain tensors as internal state variables, the model is capable to capture the stress-dependent TRIP generation when SMAs are subjected to a multiaxial stress state, as well as the TWSME for thermomechanically trained SMAs under load-free conditions. A detailed implementation procedure of the proposed model is presented through a user-defined material subroutine within a finite element framework allowing for solving different Boundary Value Problems (BVPs). Comprehensive instruction on calibrating the model parameters as well as the derivation of continuum tangent stiffness matrix are also provided. In the end, the simulated cyclic pseudoelastic and actuation responses by the presented model for a wide range of SMA material systems under both uniaxial and multiaxial stress states are compared against experimental results to validate the proposed modeling capabilities.

cond-mat.mtrl-sci↗

arXiv2020

Multipolar second-harmonic generation from high-$Q$ quasi-BIC states in nanoresonators

Multimode interference and multipolar interplay govern functionalities of optical nanoresonators and nonlinear nanoantennas. Recently, excitation of the high-quality supercavity modes (quasi-BIC states) in individual subwavelength dielectric particles has been predicted to boost the nonlinear frequency conversion at the nanoscale. Here, we put forward the multipolar model which captures the physics behind linear and nonlinear response driven by such high-$Q$ modes in nanoresonators. We show that formation of the quasi-BIC state in the AlGaAs nanodisk can be understood through multipolar transformations of coupled leaky modes. In particular, the hybridized axially symmetric TE-polarized modes can be viewed as superpositions of multipoles, with a basis of parent multipoles constituted mainly by magnetic dipoles and octupole. The quasi-BIC point in the parameter space appears where dipolar losses are totally suppressed. The efficient optical coupling to this state is implemented via azimuthally polarized beam illumination matching its multipolar origin. We establish a one-to-one correspondence between the standard non Hermitian coupled-mode theory and multipolar models that enables transparent interpretation of scattering characteristics. Using our approach, we derive the multipolar composition of the generated second-harmonic radiation from the AlGaAs nanodisk and validate it with full-wave numerical simulations. Back-action of the second-harmonic radiation onto the fundamental frequency is taken into account in the coupled nonlinear model with pump depletion. A hybrid metal-dielectric nanoantenna is proposed to augment the conversion efficiency up to tens of per cent and actualize the nonlinear parametric downconversion. Our findings delineate the in-depth conceptual framework and novel promising strategies in the design of functional elements for nonlinear nanophotonics applications.

physics.optics↗

arXiv2019

Modeling Tabular data using Conditional GAN

Modeling the probability distribution of rows in tabular data and generating realistic synthetic data is a non-trivial task. Tabular data usually contains a mix of discrete and continuous columns. Continuous columns may have multiple modes whereas discrete columns are sometimes imbalanced making the modeling difficult. Existing statistical and deep neural network models fail to properly model this type of data. We design TGAN, which uses a conditional generative adversarial network to address these challenges. To aid in a fair and thorough comparison, we design a benchmark with 7 simulated and 8 real datasets and several Bayesian network baselines. TGAN outperforms Bayesian methods on most of the real datasets whereas other deep learning methods could not.

cs.LG↗

arXiv2019

New Bounds on Augmenting Steps of Block-structured Integer Programs

We consider 4-block $n$-fold integer programs, whose constraint matrix consists of $n$ copies of small matrices $A$, $B$, and $D$, and one copy of $C$, in a specific block structure. All existing algorithms along this line of research follows an iterative augmentation framework, which relies on the so-called Graver basis of the constraint matrix that constitutes a set of fundamental augmenting steps. Bounding the $\ell_1$- or $\ell_\infty$-norm of elements of the Graver basis is the key to these algorithms. Hemmecke et al.~[Math. Prog. 2014] showed that 4-block $n$-fold IP has Graver elements of $\ell_\infty$-norm at most $O_{FPT}(n^{2^{s_{D}}})$, leading to an algorithm with a similar runtime; here, $s_{D}$ is the number of rows of matrix $D$ and $ O_{FPT}(1)$ hides a multiplicative factor that is only dependent on the small matrices $A,B,C,D$. We prove that the $\ell_{\infty}$-norm of the Graver elements of 4-block $n$-fold IP is upper bounded by $O_{FPT}(n^{s_{D}})$, improving significantly over the previous bound $O_{FPT} (n^{2^{s_{D}}})$. We also provide a matching lower bound of $Ω(n^{s_{D}})$ which even holds for arbitrary non-zero lattice elements, ruling out augmenting algorithm relying on even more restricted notions of augmentation than the Graver basis. We then consider a special case of 4-block $n$-fold in which $C$ is a zero matrix, called 3-block $n$-fold IP. We show that while even there the $\ell_{\infty}$-norm of its Graver elements is $Ω(n^{s_{D}})$, there exists a different decomposition into lattice elements whose $\ell_{\infty}$-norm is bounded by $ O_{FPT}(1)$, which allows us to provide improved upper bounds on the $\ell_{\infty}$-norm of Graver elements for 3-block $n$-fold IP.

cs.DS↗

arXiv2019

Spin-reversal energy barriers of 305 K for Fe$^{2+}$ $d^{6}$ ions with linear ligand coordination

A remarkably large magnetic anisotropy energy of 305 K is computed by quantum chemistry methods for divalent Fe$^{2+}$ $d^6$ substitutes at Li-ion sites with $D_{6h}$ point-group symmetry within the solid-state matrix of Li$_3$N. This is similar to values calculated by the same approach and confirmed experimentally for linearly coordinated monovalent Fe$^{1+}$ $d^7$ species, among the largest so far in the research area of single-molecule magnets. Our ab initio results therefore mark a new exciting exploration path in the search for superior single-molecule magnets, rooted in the $d_{xy}^{1.5} d_{x^2-y^2}^{1.5} d_{z^2}^1 d_{yz}^1 d_{zx}^1$ configuration of $d^6$ transition-metal ions with linear or quasilinear nearest-neighbor coordination. This $d^6$ axial anisotropy may be kept robust even for symmetries lower than $D_{6h}$, provided the ligand and farther-neighbor environment is engineered such that the $d_{xy}^{1.5} d_{x^2-y^2}^{1.5} d_{z^2}^1 d_{yz}^1 d_{zx}^1$ - $d_{xy}^{1} d_{x^2-y^2}^{1} d_{z^2}^2 d_{yz}^1 d_{zx}^1$ splitting remains large enough.

cond-mat.mtrl-sci↗

arXiv2019

Private and Atomic Exchange of Assets over Zero Knowledge Based Payment Ledger

Bitcoin brings a new type of digital currency that does not rely on a central system to maintain transactions. By benefiting from the concept of decentralized ledger, users who do not know or trust each other can still conduct transactions in a peer-to-peer manner. Inspired by Bitcoin, other cryptocurrencies were invented in recent years such as Ethereum, Dash, Zcash, Monero, Grin, etc. Some of these focus on enhancing privacy for instance crypto note or systems that apply the similar concept of encrypted notes used for transactions to enhance privacy (e.g., Zcash, Monero). However, there are few mechanisms to support the exchange of privacy-enhanced notes or assets on the chain, and at the same time preserving the privacy of the exchange operations. Existing approaches for fair exchanges of assets with privacy mostly rely on off-chain/side-chain, escrow or centralized services. Thus, we propose a solution that supports oblivious and privacy-protected fair exchange of crypto notes or privacy enhanced crypto assets. The technology is demonstrated by extending zero-knowledge based crypto notes. To address "privacy" and "multi-currency", we build a new zero-knowledge proving system and extend note format with new property to represent various types of tokenized assets or cryptocurrencies. By extending the payment protocol, exchange operations are realized through privacy enhanced transactions (e.g., shielded transactions). Based on the possible scenarios during the exchange operation, we add new constraints and conditions to the zero-knowledge proving system used for validating transactions publicly.

cs.CR↗

arXiv2019

Pushing the Limit of High-Q Mode of a Single Subwavelength Dielectric Nanocavity

High index dielectric nanostructure supports different types of resonant modes. However, it is very challenging to achieve high-Q factor in a single subwavelength dielectric nanoresonator due to non-hermtian property of the open system. Here, we present a universal approach of finding out a series of high-Q resonant modes in a single nonspherical dielectric nanocavity by exploring quasi-bound state in the continuum. Unlike conventional method relying on heavy computation (ie, frequency scanning by FDTD), our approach is built upon leaky mode engineering, through which many high-Q modes can be easily achieved by constructing avoid-crossing (or crossing) of the eigenvalue for pair leaky modes. The Q-factor can be up to 2.3*10^4 for square subwavelength nanowire (NW) (n=4), which is 64 times larger than the highest Q-factor (Q=360) reported so far in single subwavelength nanodisk. Such high-Q modes can be attributed to suppressed radiation in the corresponding eigenchannels and simultaneously quenched electric(magnetic) at momentum space. As a proof of concept, we experimentally demonstrate the emergence of the high-Q resonant modes (Q=380) in the scattering spectrum of a single silicon subwavelength nanowire.

physics.optics↗

arXiv2019

Robust Invisible Video Watermarking with Attention

The goal of video watermarking is to embed a message within a video file in a way such that it minimally impacts the viewing experience but can be recovered even if the video is redistributed and modified, allowing media producers to assert ownership over their content. This paper presents RivaGAN, a novel architecture for robust video watermarking which features a custom attention-based mechanism for embedding arbitrary data as well as two independent adversarial networks which critique the video quality and optimize for robustness. Using this technique, we are able to achieve state-of-the-art results in deep learning-based video watermarking and produce watermarked videos which have minimal visual distortion and are robust against common video processing operations.

cs.MM↗

arXiv2019

Multi-owner Secure Encrypted Search Using Searching Adversarial Networks

Searchable symmetric encryption (SSE) for multi-owner model draws much attention as it enables data users to perform searches over encrypted cloud data outsourced by data owners. However, implementing secure and precise query, efficient search and flexible dynamic system maintenance at the same time in SSE remains a challenge. To address this, this paper proposes secure and efficient multi-keyword ranked search over encrypted cloud data for multi-owner model based on searching adversarial networks. We exploit searching adversarial networks to achieve optimal pseudo-keyword padding, and obtain the optimal game equilibrium for query precision and privacy protection strength. Maximum likelihood search balanced tree is generated by probabilistic learning, which achieves efficient search and brings the computational complexity close to $\mathcal{O}(\log N)$. In addition, we enable flexible dynamic system maintenance with balanced index forest that makes full use of distributed computing. Compared with previous works, our solution maintains query precision above 95% while ensuring adequate privacy protection, and introduces low overhead on computation, communication and storage.

cs.CR↗

arXiv2019

On the three-dimensional stability of Poiseuille flow in a finite-length duct

The stability of a three-dimensional, incompressible, viscous flow through a finite-length duct is studied. A divergence-free basis technique is used to formulate the weak form of the problem. A SUPG (streamingline upwind Petrov-Galerkin) based scheme for eigenvalue problems is proposed to stabilize the solution. With proper boundary condtions, the least-stable eigenmodes and decay rates are computed. It is again found that the flows are asymptotically stable for all $Re$ up to $2500$. It is discovered that the least-stable eigenmodes have a boundary-layer-structure at high $Re$, although the Poiseuille base flow does not exhibits such structure. At these Reynolds numbers, the eigenmodes are dominant in the vicinity of the duct wall and are convected downstream. The boundary-layer-structure brings singularity to the modes at high $Re$ with unbounded perturbation gradient. It is shown that due to the singular structure of the least-stable eigenmodes, the linear Navier-Stoker operator tends to have pseudospectrua and the nonlinear mechanism kicks in when the perturbation energy is still small at high $Re$. The decreasing stable region as $Re$ increases is a result of both the decreasing decay rate and the singular structure of the least-stable modes. The result demonstrated that at very high $Re$, linearization of Navier-Stokes equation for duct flow may not be a good model problem with physical disturbances.

physics.flu-dyn↗

arXiv2019

MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data

Most automation in machine learning focuses on model selection and hyper parameter tuning, and many overlook the challenge of automatically defining predictive tasks. We still heavily rely on human experts to define prediction tasks, and generate labels by aggregating raw data. In this paper, we tackle the challenge of defining useful prediction problems on event-driven time-series data. We introduce MLFriend to address this challenge. MLFriend first generates all possible prediction tasks under a predefined space, then interacts with a data scientist to learn the context of the data and recommend good prediction tasks from all the tasks in the space. We evaluate our system on three different datasets and generate a total of 2885 prediction tasks and solve them. Out of these 722 were deemed useful by expert data scientists. We also show that an automatic prediction task discovery system is able to identify top 10 tasks that a user may like within a batch of 100 tasks.

cs.LG↗