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Researcher profile

Peng Liu

Peng Liu contributes to research discovery and scholarly infrastructure.

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gr-qchep-thMachine LearningCryptography and SecurityComputer VisionArtificial Intelligenceastro-ph.HEComputation and Languagephysics.opticsApplicationsComputationDistributed, Parallel, and Cluster Computingeess.ASeess.IVeess.SPInformation Theorymath.ITMethodologyquant-phSoundcond-mat.mes-hallcond-mat.mtrl-scicond-mat.str-elhep-phmath.PRmath.QAMultimedianucl-thphysics.app-phphysics.comp-phphysics.ins-detq-fin.MF

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Published work

55 published item(s)

preprint2026arXiv

X-OmniClaw Technical Report: A Unified Mobile Agent for Multimodal Understanding and Interaction

Inspired by the development of OpenClaw, there is a growing demand for mobile-based personal agents capable of handling complex and intuitive interactions. In this technical report, we introduce X-OmniClaw, a unified mobile agent designed for multimodal understanding and interaction in the Android ecosystem. This unified architecture of perception, memory, and action enables the agent to handle complex mobile tasks with high contextual awareness. Specifically, Omni Perception provides a unified multimodal ingress pipeline that integrates UI states, real-world visual contexts, and speech inputs, leveraging a temporal alignment module to decompose raw data into structured multimodal intent representations. Omni Memory leverages multimodal memory optimization to enhance personalized intelligence by integrating runtime working memory for task continuity with long-term personal memory distilled from local data, enabling highly context-aware and personalized interactions. Finally, Omni Action employs a hybrid grounding strategy that combines structural XML metadata with visual perception for robust interaction. Through Behavior Cloning and Trajectory Replay, the system captures user navigation as reusable skills, enabling precise direct-access execution. Demonstrations across diverse scenarios show that X-OmniClaw effectively enhances interaction efficiency and task reliability, providing a practical architectural blueprint for the next generation of mobile-native personal assistants.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Black holes with scalar hair: Extending from and beyond the Schwarzschild solution

We construct novel scalarized black hole (BH) solutions beyond the general relativity (GR) framework. These scalarized BH solutions are extended from the Schwarzschild one and the non-Schwarzschild one in the pure Einstein-Weyl gravity. By studying the BH entropy and free energy, we demonstrate that the scalarized BH extending from the Schwarzschild one exhibits thermodynamically preferred. We obtain these novel solutions by directly solving the full fourth-order equations of motion. This narrows the problematic solution space obtained by commonly adopted second-order reduction to physically valid spaces. Our findings also unveil the evasion of the no-hair theorem within the realm of higher-derivative gravity.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Sensing Aided Covert Communications: Turning Interference into Allies

In this paper, we investigate the realization of covert communication in a general radar-communication cooperation system, which includes integrated sensing and communications as a special example. We explore the possibility of utilizing the sensing ability of radar to track and jam the aerial adversary target attempting to detect the transmission. Based on the echoes from the target, the extended Kalman filtering technique is employed to predict its trajectory as well as the corresponding channels. Depending on the maneuvering altitude of adversary target, two channel state information (CSI) models are considered, with the aim of maximizing the covert transmission rate by jointly designing the radar waveform and communication transmit beamforming vector based on the constructed channels. For perfect CSI under the free-space propagation model, by decoupling the joint design, we propose an efficient algorithm to guarantee that the target cannot detect the transmission. For imperfect CSI due to the multi-path components, a robust joint transmission scheme is proposed based on the property of the Kullback-Leibler divergence. The convergence behaviour, tracking MSE, false alarm and missed detection probabilities, and covert transmission rate are evaluated. Simulation results show that the proposed algorithms achieve accurate tracking. For both channel models, the proposed sensing-assisted covert transmission design is able to guarantee the covertness, and significantly outperforms the conventional schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Broadband high-resolution integrated spectrometer architecture & data processing method

Up-to-date network telemetry is the key enabler for resource optimization by capacity scaling, fault recovery, and network reconfiguration among other means. Reliable optical performance monitoring in general and, specifically, the monitoring of the spectral profile of WDM signals in fixed- and flex- grid architectures across the entire C-band, remains challenging. This article describes a two-stage spectrometer architecture amenable to integration on a single chip that can measure quantitatively the spectrum across the entire C-band with a resolution of 1 GHz approximately. The first stage consists of a ring resonator with intra-ring phase shifter to provide a tuneable fine filter. The second stage makes use of an AWG subsystem and novel processing algorithm to synthesize a tuneable coarse filter with a flat passband which isolates individual resonances of a multiplicity of ring resonances. Due to its maturity and low loss, CMOS compatible Si$_3$N$_4$ is chosen for integration. A fabricated ring resonator functioning over the entire C-band with 1.3 GHz FWHM bandwidth resonances tunable over a complete free spectral range of 50 GHz is experimentally demonstrated. The complete system operation is demonstrated using an industry standard simulation tool and AWG constructor data. The operation of the circuit is invariant to the optical path length between individual components substantially improving robustness to fabrication process variations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A novel holographic quantum phase transition and butterfly velocity

In this paper, we make a systematical and in-depth exploration on the phase structure and the behaviors of butterfly velocity in an Einstein-Maxwell-dilaton-axions (EMDA) model. Depending on the model parameter, there are two kinds of mechanisms driving quantum phase transition (QPT) in this model. One is the infrared (IR) geometry to be renormalization group (RG) unstable, and the other is the strength of lattice deformation leading to some kind of bifurcating solution. We also find a novel QPT in the metal phases. The study on the behavior of the butterfly velocity crossing QPT indicates that the butterfly velocity or its first derivative exhibiting local extreme depends on the QPT mechanism. Further, the scaling behaviors of the butterfly velocity in the zero-temperature limit confirm that different phases are controlled by different IR geometries. Therefore, the butterfly velocity is a good probe to QPT and it also provides a possible way to study QPT beyond holography.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Survey on Active Deep Learning: From Model-driven to Data-driven

Which samples should be labelled in a large data set is one of the most important problems for trainingof deep learning. So far, a variety of active sample selection strategies related to deep learning havebeen proposed in many literatures. We defined them as Active Deep Learning (ADL) only if theirpredictor is deep model, where the basic learner is called as predictor and the labeling schemes iscalled selector. In this survey, three fundamental factors in selector designation were summarized. Wecategory ADL into model-driven ADL and data-driven ADL, by whether its selector is model-drivenor data-driven. The different characteristics of the two major type of ADL were addressed in indetail respectively. Furthermore, different sub-classes of data-driven and model-driven ADL are alsosummarized and discussed emphatically. The advantages and disadvantages between data-driven ADLand model-driven ADL are thoroughly analyzed. We pointed out that, with the development of deeplearning, the selector in ADL also is experiencing the stage from model-driven to data-driven. Finally,we make discussion on ADL about its uncertainty, explanatory, foundations of cognitive science etc.and survey on the trend of ADL from model-driven to data-driven.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Symmetry-orientated Divide-and-Conquer Method for Crystal Structure Prediction

Crystal structure prediction has been a subject of topical interest, but remains a substantial challenge, especially for complex structures as it deals with the global minimization of the extremely rugged high-dimensional potential energy surface. In this manuscript, a symmetry-orientated divide-and-conquer scheme was proposed to construct a symmetry tree graph, where the entire search space is decomposed into a finite number of symmetry-dependent subspaces. An artificial intelligence-based symmetry selection strategy was subsequently devised to select the low-lying subspaces with high symmetries for global exploration and in-depth exploitation. Our approach can significantly simplify the problem of crystal structure prediction by avoiding exploration of the most complex P1 subspace on the entire search space and have the advantage of preserving the crystal symmetry during structure evolution, making it well suitable for predicting the complex crystal structures. The effectiveness of the method has been validated by successful prediction of the candidate structures of binary Lennard-Jones mixtures and high-pressure phase of ice, containing more than one hundred atoms in the simulation cell. The work, therefore, opens up an opportunity towards achieving the long-sought goal for crystal structure prediction of complex systems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation

Open-domain conversational systems are assumed to generate equally good responses on multiple domains. Previous work achieved good performance on the single corpus, but training and evaluating on multiple corpora from different domains are less studied. This paper explores methods of generating relevant responses for each of multiple multi-domain corpora. We first examine interleaved learning which intermingles multiple corpora as the baseline. We then investigate two multi-domain learning methods, labeled learning and multi-task labeled learning, which encode each corpus through a unique corpus embedding. Furthermore, we propose Domain-specific Frequency (DF), a novel word-level importance weight that measures the relative importance of a word for a specific corpus compared to other corpora. Based on DF, we propose weighted learning, a method that integrates DF to the loss function. We also adopt DF as a new evaluation metric. Extensive experiments show that our methods gain significant improvements on both automatic and human evaluation. We share our code and data for reproducibility

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Bayesian Nonparametric Mixtures of Exponential Random Graph Models for Ensembles of Networks

Ensembles of networks arise in various fields where multiple independent networks are observed on the same set of nodes, for example, a collection of brain networks constructed on the same brain regions for different individuals. However, there are few models that describe both the variations and characteristics of networks in an ensemble at the same time. In this paper, we propose to model the ensemble of networks using a Dirichlet Process Mixture of Exponential Random Graph Models (DPM-ERGMs), which divides the ensemble into different clusters and models each cluster of networks using a separate Exponential Random Graph Model (ERGM). By employing a Dirichlet process mixture, the number of clusters can be determined automatically and changed adaptively with the data provided. Moreover, in order to perform full Bayesian inference for DPM-ERGMs, we employ the intermediate importance sampling technique inside the Metropolis-within-slice sampling scheme, which addressed the problem of sampling from the intractable ERGMs on an infinite sample space. We also demonstrate the performance of DPM-ERGMs with both simulated and real datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Correlated self-heterodyne method for ultra-low-noise laser linewidth measurements

Narrow-linewidth lasers are important to many applications spanning precision metrology to sensing systems. Characterization of these lasers requires precise measurements of their frequency noise spectra. Here we demonstrate a correlated self-heterodyne (COSH) method capable of measuring frequency noise as low as 0.01 Hz$^2$/Hz at 1 MHz offset frequency. The measurement setup is characterized by both commercial and lab-built lasers, and features low optical power requirements, fast acquisition time and high intensity noise rejection.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dynamic Properties of Two-Dimensional Latticed Holographic System

We study the anisotropic properties of dynamical quantities: direct current (DC) conductivity, butterfly velocity, and charge diffusion. The anisotropy plays a crucial role in determining the phase structure of the two-lattice system. Even a small deviation from isotropy can lead to distinct phase structures, as well as the IR fixed points of our holographic systems. In particular, for anisotropic cases, the most important property is that the IR fixed point can be non-AdS$_2 \times \mathbb R^2$ even for metallic phases. As that of a one-lattice system, the butterfly velocity and the charge diffusion can also diagnose the quantum phase transition (QPT) in this two-dimensional anisotropic latticed system.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dynamical spontaneous scalarization in Einstein-Maxwell-scalar theory

We study the linear instability and the nonlinear dynamical evolution of the Reissner-Nordström (RN) black hole in the Einstein-Maxwell-scalar theory in asymptotic flat spacetime. We focus on the coupling function $f(ϕ)=e^{-bϕ^2}$ which allows both the scalar-free RN solution and scalarized black hole solution. We first present the evolution of system parameters during dynamic scalarization. For parameter regions where spontaneous scalarization occurs, we find that the evolution of the scalar field at the horizon is dominated by the fundamental unstable mode from linear analysis at early times. At late times, the nonlinear evolution can be viewed as the perturbation of scalarized black holes.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Efficient Joint DOA and TOA Estimation for Indoor Positioning with 5G Picocell Base Stations

The ubiquity, large bandwidth, and spatial diversity of the fifth generation (5G) cellular signal render it a promising candidate for accurate positioning in indoor environments where the global navigation satellite system (GNSS) signal is absent. In this paper, a joint angle and delay estimation (JADE) scheme is designed for 5G picocell base stations (gNBs) which addresses two crucial issues to make it both effective and efficient in realistic indoor environments. Firstly, the direction-dependence of the array modeling error for picocell gNB as well as its impact on JADE is revealed. This error is mitigated by fitting the array response measurements to a vector-valued function and pre-calibrating the ideal steering-vector with the fitted function. Secondly, based on the deployment reality that 5G picocell gNBs only have a small-scale antenna array but have a large signal bandwidth, the proposed scheme decouples the estimation of time-of-arrival (TOA) and direction-of-arrival (DOA) to reduce the huge complexity induced by two-dimensional joint processing. It employs the iterative-adaptive-approach (IAA) to resolve multipath signals in the TOA domain, followed by a conventional beamformer (CBF) to retrieve the desired line-of-sight DOA. By further exploiting a dimension-reducing pre-processing module and accelerating spectrum computing by fast Fourier transforms, an efficient implementation is achieved for real-time JADE. Numerical simulations demonstrate the superiority of the proposed method in terms of DOA estimation accuracy. Field tests show that a triangulation positioning error of 0.44 m is achieved for 90% cases using only DOAs estimated at two separated receiving points.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Entanglement Wedge Minimum Cross-Section for Holographic Aether Gravity

We study the entanglement wedge cross-section (EWCS) in holographic Aether gravity theory, a gravity theory with Lorentz symmetry violation while keeping the general covariance intact. We find that only a limited parameter space is allowed to obtain a black brane with positive Hawking temperature. Subject to these allowed parameter regions, we find that the EWCS could exhibit non-monotonic behaviors with system parameters. Meanwhile, the holographic entanglement entropy (HEE), and the corresponding mutual information (MI), can only exhibit monotonic behaviors. These phenomena suggest that the EWCS could capture much more rich content of the entanglement than that of the HEE and the MI. The role of the Lorentz violation in determining the behaviors of quantum information-related quantities is also analyzed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Entanglement Wedge Minimum Cross-Section in Holographic Axion Gravity Theories

We study the mixed state entanglement properties in two holographic axion models by examining the behavior of the entanglement wedge minimum cross section (EWCS), and comparing it with the holographic entanglement entropy (HEE) and mutual information (MI). We find that the behavior of HEE, MI and EWCS with Hawking temperature is monotonic, while the behavior with the axion parameter $k$ is more rich, which depends on the size of the configuration and the values of the other two parameters. Interestingly, the EWCS monotonically increases with the coupling constant $κ$ between the axion field and the Maxwell field, while HEE and MI can be non-monotonic. It suggests that the EWCS, as a mixed state entanglement measure, captures distinct degrees of freedom from the HEE and MI indeed. We also provide analytical understandings for most of the numerical results.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Gradient-Based Meta-Learning Using Uncertainty to Weigh Loss for Few-Shot Learning

Model-Agnostic Meta-Learning (MAML) is one of the most successful meta-learning techniques for few-shot learning. It uses gradient descent to learn commonalities between various tasks, enabling the model to learn the meta-initialization of its own parameters to quickly adapt to new tasks using a small amount of labeled training data. A key challenge to few-shot learning is task uncertainty. Although a strong prior can be obtained from meta-learning with a large number of tasks, a precision model of the new task cannot be guaranteed because the volume of the training dataset is normally too small. In this study, first,in the process of choosing initialization parameters, the new method is proposed for task-specific learner adaptively learn to select initialization parameters that minimize the loss of new tasks. Then, we propose two improved methods for the meta-loss part: Method 1 generates weights by comparing meta-loss differences to improve the accuracy when there are few classes, and Method 2 introduces the homoscedastic uncertainty of each task to weigh multiple losses based on the original gradient descent,as a way to enhance the generalization ability to novel classes while ensuring accuracy improvement. Compared with previous gradient-based meta-learning methods, our model achieves better performance in regression tasks and few-shot classification and improves the robustness of the model to the learning rate and query sets in the meta-test set.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

KiPA22 Report: U-Net with Contour Regularization for Renal Structures Segmentation

Three-dimensional (3D) integrated renal structures (IRS) segmentation is important in clinical practice. With the advancement of deep learning techniques, many powerful frameworks focusing on medical image segmentation are proposed. In this challenge, we utilized the nnU-Net framework, which is the state-of-the-art method for medical image segmentation. To reduce the outlier prediction for the tumor label, we combine contour regularization (CR) loss of the tumor label with Dice loss and cross-entropy loss to improve this phenomenon.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Mixed State Entanglement For Holographic Systems With A Scalar Hair

We study the mixed state entanglement of an asymptotic AdS black hole system with scalar hair. Through numerical calculations, we find that the holographic entanglement entropy (HEE) presents a non-monotonic behavior with the system parameter, depending on the size of the subregion. In addition, the mutual information (MI) also shows non-monotonic behavior in certain ranges of system parameters. However, the entanglement wedge minimum cross-section (EWCS), which is a mixed state entanglement measure, increases monotonically with the AdS radius; meanwhile, shows non-monotonic with the temperature. We also give analytical understandings of the phenomena above.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters

The ever-growing model size and scale of compute have attracted increasing interests in training deep learning models over multiple nodes. However, when it comes to training on cloud clusters, especially across remote clusters, huge challenges are faced. In this work, we introduce a general framework, Nebula-I, for collaboratively training deep learning models over remote heterogeneous clusters, the connections between which are low-bandwidth wide area networks (WANs). We took natural language processing (NLP) as an example to show how Nebula-I works in different training phases that include: a) pre-training a multilingual language model using two remote clusters; and b) fine-tuning a machine translation model using knowledge distilled from pre-trained models, which run through the most popular paradigm of recent deep learning. To balance the accuracy and communication efficiency, in Nebula-I, parameter-efficient training strategies, hybrid parallel computing methods and adaptive communication acceleration techniques are jointly applied. Meanwhile, security strategies are employed to guarantee the safety, reliability and privacy in intra-cluster computation and inter-cluster communication. Nebula-I is implemented with the PaddlePaddle deep learning framework, which can support collaborative training over heterogeneous hardware, e.g. GPU and NPU. Experiments demonstrate that the proposed framework could substantially maximize the training efficiency while preserving satisfactory NLP performance. By using Nebula-I, users can run large-scale training tasks over cloud clusters with minimum developments, and the utility of existed large pre-trained models could be further promoted. We also introduced new state-of-the-art results on cross-lingual natural language inference tasks, which are generated based upon a novel learning framework and Nebula-I.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Optical Wavelength Meter with Machine Learning Enhanced Precision

Diverse applications in photonics and microwave engineering require a means of measurement of the instantaneous frequency of a signal. A photonic implementation typically applies an interferometer equipped with three or more output ports to measure the frequency dependent phase shift provided by an optical delay line. The components constituting the interferometer are prone to impairments which results in erroneous measurements. It is shown that the information to be retrieved is encoded by a three-component vector that lies on a circular cone within a three-dimensional Cartesian object space. The measured data belongs to the image of the object space under a linear map that describes the action of the interferometer. Assisted by a learning algorithm, an inverse map from the image space into the object space is constructed. The inverse map compensates for a variety of impairments while being robust to noise. Simulation results demonstrate that, to the extent the interferometer model captures all significant impairments, a precision limited only by the level of random noise is attainable. A wavelength meter architecture is fabricated on Si3N4 photonic integration platform to prove the method experimentally. Applied to the measured data, greater than an order of magnitude improvement in precision is achieved by the proposed method compared to the conventional method.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning

Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Directly applying off-policy algorithms to offline RL usually fails due to the extrapolation error caused by the out-of-distribution (OOD) actions. Previous methods tackle such problem by penalizing the Q-values of OOD actions or constraining the trained policy to be close to the behavior policy. Nevertheless, such methods typically prevent the generalization of value functions beyond the offline data and also lack precise characterization of OOD data. In this paper, we propose Pessimistic Bootstrapping for offline RL (PBRL), a purely uncertainty-driven offline algorithm without explicit policy constraints. Specifically, PBRL conducts uncertainty quantification via the disagreement of bootstrapped Q-functions, and performs pessimistic updates by penalizing the value function based on the estimated uncertainty. To tackle the extrapolating error, we further propose a novel OOD sampling method. We show that such OOD sampling and pessimistic bootstrapping yields provable uncertainty quantifier in linear MDPs, thus providing the theoretical underpinning for PBRL. Extensive experiments on D4RL benchmark show that PBRL has better performance compared to the state-of-the-art algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Reflected Entropy in Double Holography

Recently, the reflected entropy is proposed in holographic approach to describe the entanglement of a bipartite quantum system in a mixed state, which is identified as the area of the reflected minimal surface inside the entanglement wedge. In this paper, we study the reflected entropy in the doubly holographic setup, which contains the degrees of freedom of quantum matter in the bulk. In this context, we propose a notion of quantum entanglement wedge cross-section, which may describe the reflected entropy with higher-order quantum corrections. We numerically compute the reflected entropy in pure AdS background and black hole background in four dimensions, respectively. In general, the reflected entropy contains the contribution from the geometry on the brane and the contribution from the CFT. We compute their proportion for different Newton constants and find that their behaviors are in agreement with the results based on the semi-classical gravity and the correlation of CFT coupled to the bath CFT.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Robust IRS-aided Secrecy Transmission with Location Optimization

In this paper, we propose a robust secrecy transmission scheme for intelligent reflecting surface (IRS) aided communication systems. Different from all the existing works where IRS has already been deployed at a fixed location, we take the location of IRS as a variable to maximize the secrecy rate (SR) under the outage probability constraint by jointly optimizing the location of IRS, transmit beamformer and IRS phase shifts with imperfect channel state information (CSI) of Eve, where we consider two cases: a) the location of Eve is known; b) only a suspicious area of Eve is available. We show a critical observation that CSI models are different before and after IRS deployment, thus the optimization problem could be decomposed and solved via a two-stage framework. For case a), in the first stage, universal upper bounds of outage probabilities only related to the location of IRS are derived which can be optimized via successive convex approximation (SCA) method. In the second stage, we develop an alternative optimization (AO) algorithm to optimize beamformer and phase shifts iteratively. For case b), we propose a Max-Min SR scheme based on two-stage framework, where the location of IRS is optimized based on the worst location of Eve. Simulation results indicate the importance of the location of IRS optimization.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection

As an increasing number of deep-learning-based malware scanners have been proposed, the existing evasion techniques, including code obfuscation and polymorphic malware, are found to be less effective. In this work, we propose a reinforcement learning-based semantics-preserving (i.e.functionality-preserving) attack against black-box GNNs (GraphNeural Networks) for malware detection. The key factor of adversarial malware generation via semantic Nops insertion is to select the appropriate semanticNopsand their corresponding basic blocks. The proposed attack uses reinforcement learning to automatically make these "how to select" decisions. To evaluate the attack, we have trained two kinds of GNNs with five types(i.e., Backdoor, Trojan-Downloader, Trojan-Ransom, Adware, and Worm) of Windows malware samples and various benign Windows programs. The evaluation results have shown that the proposed attack can achieve a significantly higher evasion rate than three baseline attacks, namely the semantics-preserving random instruction insertion attack, the semantics-preserving accumulative instruction insertion attack, and the semantics-preserving gradient-based instruction insertion attack.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification

Deep convolutional neural network (DCNN) models have been widely explored for skin disease diagnosis and some of them have achieved the diagnostic outcomes comparable or even superior to those of dermatologists. However, broad implementation of DCNN in skin disease detection is hindered by small size and data imbalance of the publically accessible skin lesion datasets. This paper proposes a novel single-model based strategy for classification of skin lesions on small and imbalanced datasets. First, various DCNNs are trained on different small and imbalanced datasets to verify that the models with moderate complexity outperform the larger models. Second, regularization DropOut and DropBlock are added to reduce overfitting and a Modified RandAugment augmentation strategy is proposed to deal with the defects of sample underrepresentation in the small dataset. Finally, a novel Multi-Weighted New Loss (MWNL) function and an end-to-end cumulative learning strategy (CLS) are introduced to overcome the challenge of uneven sample size and classification difficulty and to reduce the impact of abnormal samples on training. By combining Modified RandAugment, MWNL and CLS, our single DCNN model method achieved the classification accuracy comparable or superior to those of multiple ensembling models on different dermoscopic image datasets. Our study shows that this method is able to achieve a high classification performance at a low cost of computational resources and inference time, potentially suitable to implement in mobile devices for automated screening of skin lesions and many other malignancies in low resource settings.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards Better Data Augmentation using Wasserstein Distance in Variational Auto-encoder

VAE, or variational auto-encoder, compresses data into latent attributes, and generates new data of different varieties. VAE based on KL divergence has been considered as an effective technique for data augmentation. In this paper, we propose the use of Wasserstein distance as a measure of distributional similarity for the latent attributes, and show its superior theoretical lower bound (ELBO) compared with that of KL divergence under mild conditions. Using multiple experiments, we demonstrate that the new loss function exhibits better convergence property and generates artificial images that could better aid the image classification tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards Understanding and Harnessing the Effect of Image Transformation in Adversarial Detection

Deep neural networks (DNNs) are threatened by adversarial examples. Adversarial detection, which distinguishes adversarial images from benign images, is fundamental for robust DNN-based services. Image transformation is one of the most effective approaches to detect adversarial examples. During the last few years, a variety of image transformations have been studied and discussed to design reliable adversarial detectors. In this paper, we systematically synthesize the recent progress on adversarial detection via image transformations with a novel classification method. Then, we conduct extensive experiments to test the detection performance of image transformations against state-of-the-art adversarial attacks. Furthermore, we reveal that each individual transformation is not capable of detecting adversarial examples in a robust way, and propose a DNN-based approach referred to as \emph{AdvJudge}, which combines scores of 9 image transformations. Without knowing which individual scores are misleading or not misleading, AdvJudge can make the right judgment, and achieve a significant improvement in detection rate. Finally, we utilize an explainable AI tool to show the contribution of each image transformation to adversarial detection. Experimental results show that the contribution of image transformations to adversarial detection is significantly different, the combination of them can significantly improve the generic detection ability against state-of-the-art adversarial attacks.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Analyzing the Overhead of Filesystem Protection Using Linux Security Modules

Over the years, the complexity of the Linux Security Module (LSM) is keeping increasing, and the count of the authorization hooks is nearly doubled. It is important to provide up-to-date measurement results of LSM for system practitioners so that they can make prudent trade-offs between security and performance. This work evaluates the overhead of LSM for file accesses on Linux v5.3.0. We build a performance evaluation framework for LSM. It has two parts, an extension of LMBench2.5 to evaluate the overhead of file operations for different security modules, and a security module with tunable latency for policy enforcement to study the impact of the latency of policy enforcement on the end-to-end latency of file operations. In our evaluation, we find opening a file would see about 87% (Linux v5.3) performance drop when the kernel is integrated with SELinux hooks (policy enforcement disabled) than without, while the figure was 27% (Linux v2.4.2). We found that performance of the above downgrade is affected by two parts, policy enforcement and hook placement. To further investigate the impact of policy enforcement and hook placement respectively, we build a Policy Testing Module, which reuses hook placements of LSM, while alternating latency of policy enforcement. With this module, we are able to quantitatively estimate the impact of the latency of policy enforcement on the end-to-end latency of file operations by using a multiple linear regression model and count policy authorization frequencies for each syscall. We then discuss and justify the evaluation results with static analysis on our enhanced syscalls' call graphs.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Bayesian spectral density approach for identification and uncertainty quantification of bridge section's flutter derivatives operated in turbulent flow

This study presents a Bayesian spectral density approach for identification and uncertainty quantification of flutter derivatives of bridge sections utilizing buffeting displacement responses, where the wind tunnel test is conducted in turbulent flow. Different from traditional time-domain approaches (e.g., least square method and stochastic subspace identification), the newly-proposed approach is operated in frequency domain. Based on the affine invariant ensemble sampler algorithm, Markov chain Monte-Carlo sampling is employed to accomplish the Bayesian inference. The probability density function of flutter derivatives is modeled based on complex Wishart distribution, where probability serves as the measure. By the Bayesian spectral density approach, the most probable values and corresponding posterior distributions (namely identification uncertainty here) of each flutter derivative can be obtained at the same time. Firstly, numerical simulations are conducted and the identified results are accurate. Secondly, thin plate model, flutter derivatives of which have theoretical solutions, is chosen to be tested in turbulent flow for the sake of verification. The identified results of thin plate model are consistent with the theoretical solutions. Thirdly, the center-slotted girder model, which is widely-utilized long-span bridge sections in current engineering practice, is employed to investigate the applicability of the proposed approach on a general bridge section. For the center-slotted girder model, the flutter derivatives are also extracted by least square method in uniform flow to cross validate the newly-proposed approach. The identified results by two different approaches are compatible.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Dynamical scalarization in Einstein-Maxwell-dilaton theory

We study the process of fully nonlinear dynamical scalarization starting from a charged black hole or a naked singularity in asymptotically flat spacetime in the Einstein-Maxwell-dilaton theory. Initially the dilaton field is negligible compared to the gravitational and the Maxwell field. Then the dilaton field experiences an immediate growth, later it oscillates with damping amplitude and finally settles down to a finite value. For a hairy black hole develops from an original Reissner-Nordström black hole, since the dilaton oscillation and decay are almost independent of the coupling parameter, unlike the Anti-de Sitter spacetime it is not easy to distinguish the resulting hairy black hole from the original asymptotically flat charged hole. For a hairy black hole evolves from an original naked singularity, the resulting hairy black hole has rich structures. In the scalarization process, the naked singularity is soon enveloped by one outer horizon, then another horizon is developed and in the end a stable hairy black hole forms and two horizons degenerate into one to protect the singularity. The hairy black hole mass saturates exponentially in the scalarization.

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preprint2021arXiv

Evolution of Anti-de Sitter black holes in Einstein-Maxwell-dilaton theory

We study the nonlinear evolution of the spherical symmetric black holes under a small neutral scalar field perturbation in Einstein-Maxwell-dilaton theory with coupling function $f(ϕ)=e^{-bϕ}$ in asymptotic anti-de Sitter spacetime. The non-minimal coupling between scalar and Maxwell fields allows the transmission of the energy from the Maxwell field to the scalar field, but also behaves as a repulsive force for the scalar. The scalar field oscillates with damping amplitude and converges to a final value by a power law. The irreducible mass of the black hole increases abruptly at initial times and then saturates to the final value exponentially. The saturating rate is twice the decaying rate of the dominant mode of the scalar. The effects of the black hole charge, the cosmological constant and the coupling parameter on the evolution are studied in detail. When the initial configuration is a naked singularity spacetime with a large charge to mass ratio, a horizon will form soon and hide the singularity.

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preprint2021arXiv

GPT Conjecture: Understanding the Trade-offs between Granularity, Performance and Timeliness in Control-Flow Integrity

Performance/security trade-off is widely noticed in CFI research, however, we observe that not every CFI scheme is subject to the trade-off. Motivated by the key observation, we ask three questions. Although the three questions probably cannot be directly answered, they are inspiring. We find that a deeper understanding of the nature of the trade-off will help answer the three questions. Accordingly, we proposed the GPT conjecture to pinpoint the trade-off in designing CFI schemes, which says that at most two out of three properties (fine granularity, acceptable performance, and preventive protection) could be achieved.

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preprint2021arXiv

Instability of regularized 4D charged Einstein-Gauss-Bonnet de-Sitter black holes

We studied the instability of the regularized 4D charged Einstein-Gauss-Bonnet de-Sitter black holes under charged scalar perturbations. The unstable modes satisfy the superradiant condition, but not all modes satisfying the superradiant condition are unstable. The instability occurs when the cosmological constant is small and the black hole charge is not too large. The Gauss-Bonnet coupling constant makes the unstable black hole more unstable when both the black hole charge and cosmological constant are small, and makes the stable black hole more stable when the black hole charge is large.

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preprint2021arXiv

Low-cost and high-performance data augmentation for deep-learning-based skin lesion classification

Although deep convolutional neural networks (DCNNs) have achieved significant accuracy in skin lesion classification comparable or even superior to those of dermatologists, practical implementation of these models for skin cancer screening in low resource settings is hindered by their limitations in computational cost and training dataset. To overcome these limitations, we propose a low-cost and high-performance data augmentation strategy that includes two consecutive stages of augmentation search and network search. At the augmentation search stage, the augmentation strategy is optimized in the search space of Low-Cost-Augment (LCA) under the criteria of balanced accuracy (BACC) with 5-fold cross validation. At the network search stage, the DCNNs are fine-tuned with the full training set in order to select the model with the highest BACC. The efficiency of the proposed data augmentation strategy is verified on the HAM10000 dataset using EfficientNets as a baseline. With the proposed strategy, we are able to reduce the search space to 60 and achieve a high BACC of 0.853 by using a single DCNN model without external database, suitable to be implemented in mobile devices for DCNN-based skin lesion detection in low resource settings.

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preprint2021arXiv

Security and Privacy for Artificial Intelligence: Opportunities and Challenges

The increased adoption of Artificial Intelligence (AI) presents an opportunity to solve many socio-economic and environmental challenges; however, this cannot happen without securing AI-enabled technologies. In recent years, most AI models are vulnerable to advanced and sophisticated hacking techniques. This challenge has motivated concerted research efforts into adversarial AI, with the aim of developing robust machine and deep learning models that are resilient to different types of adversarial scenarios. In this paper, we present a holistic cyber security review that demonstrates adversarial attacks against AI applications, including aspects such as adversarial knowledge and capabilities, as well as existing methods for generating adversarial examples and existing cyber defence models. We explain mathematical AI models, especially new variants of reinforcement and federated learning, to demonstrate how attack vectors would exploit vulnerabilities of AI models. We also propose a systematic framework for demonstrating attack techniques against AI applications and reviewed several cyber defences that would protect AI applications against those attacks. We also highlight the importance of understanding the adversarial goals and their capabilities, especially the recent attacks against industry applications, to develop adaptive defences that assess to secure AI applications. Finally, we describe the main challenges and future research directions in the domain of security and privacy of AI technologies.

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preprint2021arXiv

Sojourn times of Gaussian related random fields

This paper is concerned with the asymptotic analysis of sojourn times of random fields with continuous sample paths. Under a very general framework we show that there is an interesting relationship between tail asymptotics of sojourn times and that of supremum. Moreover, we establish the uniform double-sum method to derive the tail asymptotics of sojourn times. In the literature, based on the pioneering research of S. Berman the sojourn times have been utilised to derive the tail asymptotics of supremum of Gaussian processes. In this paper we show that the opposite direction is even more fruitful, namely knowing the asymptotics of supremum o f random processes and fields (in particular Gaussian) it is possible to establish the asymptotics of their sojourn times. We illustrate our findings considering i) two dimensional Gaussian random fields, ii) chi-process generated by stationary Gaussian processes and iii) stationary Gaussian queueing processes.

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preprint2021arXiv

Spotting Silent Buffer Overflows in Execution Trace through Graph Neural Network Assisted Data Flow Analysis

A software vulnerability could be exploited without any visible symptoms. When no source code is available, although such silent program executions could cause very serious damage, the general problem of analyzing silent yet harmful executions is still an open problem. In this work, we propose a graph neural network (GNN) assisted data flow analysis method for spotting silent buffer overflows in execution traces. The new method combines a novel graph structure (denoted DFG+) beyond data-flow graphs, a tool to extract {\tt DFG+} from execution traces, and a modified Relational Graph Convolutional Network as the GNN model to be trained. The evaluation results show that a well-trained model can be used to analyze vulnerabilities in execution traces (of previously-unseen programs) without support of any source code. Our model achieves 94.39\% accuracy on the test data and successfully locates 29 out of 30 real-world silent buffer overflow vulnerabilities. Leveraging deep learning, the proposed method is, to our best knowledge, the first general-purpose analysis method for silent buffer overflows. It is also the first method to spot silent buffer overflows in global variables, stack variables, or heap variables without crossing the boundary of allocated chunks.

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preprint2021arXiv

Using Deep Learning to Solve Computer Security Challenges: A Survey

Although using machine learning techniques to solve computer security challenges is not a new idea, the rapidly emerging Deep Learning technology has recently triggered a substantial amount of interests in the computer security community. This paper seeks to provide a dedicated review of the very recent research works on using Deep Learning techniques to solve computer security challenges. In particular, the review covers eight computer security problems being solved by applications of Deep Learning: security-oriented program analysis, defending return-oriented programming (ROP) attacks, achieving control-flow integrity (CFI), defending network attacks, malware classification, system-event-based anomaly detection, memory forensics, and fuzzing for software security.

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preprint2021arXiv

VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention

This paper proposes VARA-TTS, a non-autoregressive (non-AR) text-to-speech (TTS) model using a very deep Variational Autoencoder (VDVAE) with Residual Attention mechanism, which refines the textual-to-acoustic alignment layer-wisely. Hierarchical latent variables with different temporal resolutions from the VDVAE are used as queries for residual attention module. By leveraging the coarse global alignment from previous attention layer as an extra input, the following attention layer can produce a refined version of alignment. This amortizes the burden of learning the textual-to-acoustic alignment among multiple attention layers and outperforms the use of only a single attention layer in robustness. An utterance-level speaking speed factor is computed by a jointly-trained speaking speed predictor, which takes the mean-pooled latent variables of the coarsest layer as input, to determine number of acoustic frames at inference. Experimental results show that VARA-TTS achieves slightly inferior speech quality to an AR counterpart Tacotron 2 but an order-of-magnitude speed-up at inference; and outperforms an analogous non-AR model, BVAE-TTS, in terms of speech quality.

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preprint2020arXiv

Change of measure under the hard-to-borrow model

As the Securities and Exchange Commission(SEC) has implemented a new regulation on short-sellings, short-sellers are required to repurchase stocks once the clearing risk rises to a certain level. Avellaneda and Lipkin proposed a fully coupled SDE system to describe the mechanism which is referred as Hard-To-Borrow(HTB) models. Guiyuan Ma obtained the PDE system for both American and European options. There is a technical error in Guiyuan Ma where two correlated Brownian motion should be converted before change of measure. In this paper, I will provide supplement conditions.

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preprint2020arXiv

Echoes from phantom wormholes

We study the time evolution of the test scalar and electromagnetic fields perturbations in configurations of phantom wormholes surrounded by dark energy with state parameter $ω< -1$. We observe obvious signals of echoes reflecting wormholes properties and disclose the physical reasons behind such phenomena. In particular, we find that the dark energy equation of state has a clear imprint in echoes in wave perturbations. When $ω$ approaches the phantom divide $ω=-1$ from below, the delay time of echoes becomes longer. The echo of gravitational wave is likely to be detected in the near future, the signature of the dark energy equation of state in the echo spectrum can serve as a local measurement of the dark energy.

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preprint2020arXiv

Logic Bugs in IoT Platforms and Systems: A Review

In recent years, IoT platforms and systems have been rapidly emerging. Although IoT is a new technology, new does not mean simpler (than existing networked systems). Contrarily, the complexity (of IoT platforms and systems) is actually being increased in terms of the interactions between the physical world and cyberspace. The increased complexity indeed results in new vulnerabilities. This paper seeks to provide a review of the recently discovered logic bugs that are specific to IoT platforms and systems. In particular, 17 logic bugs and one weakness falling into seven categories of vulnerabilities are reviewed in this survey.

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preprint2020arXiv

Looking for the possible gluon condensation signature in sub-TeV gamma-ray spectra: from active galactic nuclei to gamma ray bursts

The gluon condensation in the proton as a dynamical model is used to treat a series of unsolved puzzles in sub-TeV gamma ray spectra, they include the broken power-law of blazar&#39;s radiation, the hardening confusion of 1ES 1426+428, Mkn 501, and the recently recorded sub-TeV gamma spectra of GRB 180720B and GRB 190114C. We find that the above anomalous phenomena in gamma ray energy spectra can be understood with the simple broken power law based on a QCD gluon condensation effect.

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preprint2020arXiv

Outcome-Guided Disease Subtyping for High-Dimensional Omics Data

High-throughput microarray and sequencing technology have been used to identify disease subtypes that could not be observed otherwise by using clinical variables alone. The classical unsupervised clustering strategy concerns primarily the identification of subpopulations that have similar patterns in gene features. However, as the features corresponding to irrelevant confounders (e.g. gender or age) may dominate the clustering process, the resulting clusters may or may not capture clinically meaningful disease subtypes. This gives rise to a fundamental problem: can we find a subtyping procedure guided by a pre-specified disease outcome? Existing methods, such as supervised clustering, apply a two-stage approach and depend on an arbitrary number of selected features associated with outcome. In this paper, we propose a unified latent generative model to perform outcome-guided disease subtyping constructed from omics data, which improves the resulting subtypes concerning the disease of interest. Feature selection is embedded in a regularization regression. A modified EM algorithm is applied for numerical computation and parameter estimation. The proposed method performs feature selection, latent subtype characterization and outcome prediction simultaneously. To account for possible outliers or violation of mixture Gaussian assumption, we incorporate robust estimation using adaptive Huber or median-truncated loss function. Extensive simulations and an application to complex lung diseases with transcriptomic and clinical data demonstrate the ability of the proposed method to identify clinically relevant disease subtypes and signature genes suitable to explore toward precision medicine.

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preprint2020arXiv

Practical Verification of MapReduce Computation Integrity via Partial Re-execution

Big data processing is often outsourced to powerful, but untrusted cloud service providers that provide agile and scalable computing resources to weaker clients. However, untrusted cloud services do not ensure the integrity of data and computations while clients have no control over the outsourced computation or no means to check the correctness of the execution. Despite a growing interest and recent progress in verifiable computation, the existing techniques are still not practical enough for big data processing due to high verification overhead. In this paper, we present a solution called V-MR (Verifiable MapReduce), which is a framework that verifies the integrity of MapReduce computation outsourced in the untrusted cloud via partial re-execution. V-MR is practically effective and efficient in that (1) it can detect the violation of MapReduce computation integrity and identify the malicious workers involved in the that produced the incorrect computation. (2) it can reduce the overhead of verification via partial re-execution with carefully selected input data and program code using program analysis. The experiment results of a prototype of V-MR show that V-MR can verify the integrity of MapReduce computation effectively with small overhead for partial re-execution.

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preprint2020arXiv

Regression and Learning with Pixel-wise Attention for Retinal Fundus Glaucoma Segmentation and Detection

Observing retinal fundus images by an ophthalmologist is a major diagnosis approach for glaucoma. However, it is still difficult to distinguish the features of the lesion solely through manual observations, especially, in glaucoma early phase. In this paper, we present two deep learning-based automated algorithms for glaucoma detection and optic disc and cup segmentation. We utilize the attention mechanism to learn pixel-wise features for accurate prediction. In particular, we present two convolutional neural networks that can focus on learning various pixel-wise level features. In addition, we develop several attention strategies to guide the networks to learn the important features that have a major impact on prediction accuracy. We evaluate our methods on the validation dataset and The proposed both tasks&#39; solutions can achieve impressive results and outperform current state-of-the-art methods. \textit{The code is available at \url{https://github.com/cswin/RLPA}}.

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preprint2020arXiv

Regularised Text Logistic Regression: Key Word Detection and Sentiment Classification for Online Reviews

Online customer reviews have become important for managers and executives in the hospitality and catering industry who wish to obtain a comprehensive understanding of their customers&#39; demands and expectations. We propose a Regularized Text Logistic (RTL) regression model to perform text analytics and sentiment classification on unstructured text data, which automatically identifies a set of statistically significant and operationally insightful word features, and achieves satisfactory predictive classification accuracy. We apply the RTL model to two online review datasets, Restaurant and Hotel, from TripAdvisor. Our results demonstrate satisfactory classification performance compared with alternative classifiers with a highest true positive rate of 94.9%. Moreover, RTL identifies a small set of word features, corresponding to 3% for Restaurant and 20% for Hotel, which boosts working efficiency by allowing managers to drill down into a much smaller set of important customer reviews. We also develop the consistency, sparsity and oracle property of the estimator.

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preprint2020arXiv

Secure and efficient synchronization scheme for quantum key distribution

To establish a time reference frame between two users in quantum key distribution, a synchronization calibration process is usually applied for the case of using gated mode single-photon detectors (SPDs). Traditionally, the synchronization calibration is independently implemented by the line length measurement for each SPD. However, this will leave a loophole which has been experimentally demonstrated by a special attack. Here, we propose an alternative synchronization scheme by fixing the relative delay of the signal time window among all SPDs and jointly performing the line length measurement with multiple SPDs under combining low-precision with high-precision synchronization. The new scheme is not only immune to the vulnerability but also improves the synchronization time from usually a few seconds to tens of milliseconds.

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preprint2020arXiv

Speaker Independent and Multilingual/Mixlingual Speech-Driven Talking Head Generation Using Phonetic Posteriorgrams

Generating 3D speech-driven talking head has received more and more attention in recent years. Recent approaches mainly have following limitations: 1) most speaker-independent methods need handcrafted features that are time-consuming to design or unreliable; 2) there is no convincing method to support multilingual or mixlingual speech as input. In this work, we propose a novel approach using phonetic posteriorgrams (PPG). In this way, our method doesn&#39;t need hand-crafted features and is more robust to noise compared to recent approaches. Furthermore, our method can support multilingual speech as input by building a universal phoneme space. As far as we know, our model is the first to support multilingual/mixlingual speech as input with convincing results. Objective and subjective experiments have shown that our model can generate high quality animations given speech from unseen languages or speakers and be robust to noise.

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preprint2020arXiv

Towards classification parity across cohorts

Recently, there has been a lot of interest in ensuring algorithmic fairness in machine learning where the central question is how to prevent sensitive information (e.g. knowledge about the ethnic group of an individual) from adding &#34;unfair&#34; bias to a learning algorithm (Feldman et al. (2015), Zemel et al. (2013)). This has led to several debiasing algorithms on word embeddings (Qian et al. (2019) , Bolukbasi et al. (2016)), coreference resolution (Zhao et al. (2018a)), semantic role labeling (Zhao et al. (2017)), etc. Most of these existing work deals with explicit sensitive features such as gender, occupations or race which doesn&#39;t work with data where such features are not captured due to privacy concerns. In this research work, we aim to achieve classification parity across explicit as well as implicit sensitive features. We define explicit cohorts as groups of people based on explicit sensitive attributes provided in the data (age, gender, race) whereas implicit cohorts are defined as groups of people with similar language usage. We obtain implicit cohorts by clustering embeddings of each individual trained on the language generated by them using a language model. We achieve two primary objectives in this work : [1.] We experimented and discovered classification performance differences across cohorts based on implicit and explicit features , [2] We improved classification parity by introducing modification to the loss function aimed to minimize the range of model performances across cohorts.

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preprint2019arXiv

Bias induced spin state transition mediated by electron excitations

Recent experiments reported that spin-state transitions were realized by applying bias voltages. But these bias-induced spin state transitions (BISSTs) are not fully understood, especially the mechanism. It is well known that the metal-to-ligand charge transfer excitation (MLCT) and the metal-centered excitation (MC) activated by light radiation can induce the transition from low spin (LS) to high spin (HS) and that from HS to LS. Moreover, electronic excitations are accessible by inelastic cotunneling in molecular junctions with bias voltages applied. Based on these two experimental facts, we propose the MLCT basically leads to the BISST from LS to HS, and the MC results in the BISST from HS to LS. The rationality of the mechanism is demonstrated by comparing first-principles results and experimental observations. The calculated voltage threshold for activating the MLCT (MC) is close to the experimental voltage for observing the BISST from LS to HS (from HS to LS). The activation of MLCT (MC) depends on the bias polarity, which can explain the bias-polarity dependence of BISST in the experiment. Our study is important for further design of molecular spintronic devices working on spin state transition.

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preprint2019arXiv

Holographic superconductor induced by charge density waves

Understanding the role of charge density wave (CDW) in high-temperature superconductivity is a longstanding challenge in condensed matter physics. We construct a holographic superconductor model in which the $U(1)$ symmetry is spontaneously broken only due to the presence of CDWs, rather than previously known free charges with constant density. Below the critical temperature of superconductivity, CDW phase and superconducting phase coexist, which is also justified by the numerical results of optical conductivity. The competitive and cooperative relations between CDW phase and superconducting phase are observed. This work supports the opinion that the appearance of pseudo-gap in CDW phase promotes the pre-pairing of electrons as well as holes such that the formation of superconductivity benefits from the presence of CDW.

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preprint2019arXiv

Mixed State Entanglement for Holographic Axion Model

We study the mixed state entanglement in a holographic axion model. We find that the holographic entanglement entropy (HEE), mutual information (MI) and entanglement of purification (EoP) exhibit very distinct behaviors with system parameters. The HEE exhibits universal monotonic behavior with system parameters, while the behaviors of MI and EoP relate to the specific system parameters and configurations. We find that MI and EoP can characterize mixed state entanglement better than HEE since they are less affected by thermal effects. Moreover, we argue that EoP is more suitable for describing mixed state entanglement than MI. Because the MI of large configurations are still dictated by the thermal entropy, while the EoP will never be controlled only by the thermal effects.

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