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Lan Zhang

Lan Zhang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Cryptography and SecurityMachine Learningastro-ph.GAcond-mat.str-elArtificial Intelligencecond-mat.supr-conComputer Visionphysics.ins-detastro-ph.IMhep-exastro-ph.COeess.AShep-phHuman-Computer InteractionLogic in Computer ScienceNetworking and Internet Architecturenucl-exQuantitative MethodsRoboticsSocial and Information NetworksSound

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Published work

39 published item(s)

preprint2026arXiv

Beyond the Wrapper: Identifying Artifact Reliance in Static Malware Classifiers using TRUSTEE

Modern cybersecurity relies heavily on static machine-learning-based malware classifiers. However, transformations such as packing and other non-semantic modifications applied to executable files limit their reliability. Malware classifiers often learn these unnecessary artifacts rather than the true binary behavior because of the high association between maliciousness and packing. Moreover, these malware classifiers are black boxes, making it difficult to understand what they learn. To address this issue, we proposed a two-part framework using the post-hoc interpretability XAI tool TRUSTEE, followed by a manual analysis of the top features. We conducted several controlled experiments by varying the dataset composition ratios to understand their impact on the results. The top-ranked features across all experiments, identified by TRUSTEE, were predominantly packing artifacts, portable executable(PE) metadata, and n-grams at the string level, rather than malicious semantics. These results suggest that these malware classifiers are highly sensitive to dataset composition and can misinterpret packing as malicious behavior. Our proposed framework allows for the reproducible diagnosis of such biases and forms a guideline for building more robust and semantically meaningful malware detection models

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

CSGuard: Toward Forgery-Resistant Watermarking in Diffusion Models via Compressed Sensing Constraint

Latent-based diffusion model watermarking embeds watermarks into generated images' latent space to enable content attribution, offering a training-free solution for intellectual property protection and digital forensics. However, these methods exhibit a critical vulnerability to the forgery attack, attackers can extract the watermark by inverting the watermarked image and re-generating it with an arbitrary prompt, thereby enabling false attribution on malicious content. In this paper, we propose the CSGuard, the first forgery-resistant watermarking schema that leverages compressed sensing to bind the watermarked image generation and verification to a secret matrix. This ensures that only users possessing the secret matrix can correctly embed or verify the image watermark, prevents the illegal users from forgery without compromising generation quality and watermark integrity. Experimental results demonstrate that CSGuard achieves strong forgery resistance, reduces the attack success rate from 100.0\% to 28.12\%, and achieve 100\% detection rate on benign watermarked images without compromising watermarking effectiveness.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Metis: Learning to Jailbreak LLMs via Self-Evolving Metacognitive Policy Optimization

Red teaming is critical for uncovering vulnerabilities in Large Language Models (LLMs). While automated methods have improved scalability, existing approaches often rely on static heuristics or stochastic search, rendering them brittle against advanced safety alignment. To address this, we introduce Metis, a framework that reformulates jailbreaking as inference-time policy optimization within an adversarial Partially Observable Markov Decision Process (POMDP). Metis employs a self-evolving metacognitive loop to perform causal diagnosis of a target's defense logic and leverages structured feedback as a semantic gradient to refine its policy, offering enhanced interpretability through transparent reasoning traces. Extensive evaluations across 10 diverse models demonstrate that Metis achieves the strongest average Attack Success Rate (ASR) among compared methods at 89.2%, maintaining high efficacy on resilient frontier models (e.g., 76.0% on O1 and 78.0% on GPT-5-chat) where traditional baselines exhibit substantial performance degradation. By replacing redundant exploration with directed optimization, Metis reduces token costs by an average of 8.2x and up to 11.4x. Our analysis reveals that current defenses remain vulnerable to internally-steered, closed-loop reasoning trajectories under the tested settings, highlighting a critical need for next-generation defenses capable of reasoning about safety dynamically during inference.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Nice Fold or Hero Call: Learning Budget-Efficient Thinking for Adaptive Reasoning

Large reasoning models (LRMs) improve problem solving through extended reasoning, but often misallocate test-time compute. Existing efficiency methods reduce cost by compressing reasoning traces or conditioning budget on perceived difficulty, yet largely overlook solvability. As a result, they may spend large budgets on queries beyond the model's capability while compressing hard-but-solvable queries that require deeper reasoning. In this work, we formulate adaptive reasoning as a computational investment under uncertainty, where budget should follow the expected return of reasoning rather than perceived difficulty alone. To instantiate this principle, we propose Budget-Efficient Thinking (BET), a two-stage framework that combines behavioral cold-start with GRPO under an investment-cost-aware reward. By aligning solve-or-fold decisions with rollout-derived solvability, BET learns three behaviors: (1) short solve, answering easy queries concisely; (2) nice fold, abstaining early when continued reasoning has near-zero expected return; and (3) hero call, preserving sufficient compute for hard-but-solvable queries. Across seven benchmarks and three base models, BET reduces reasoning tokens by ~55% on average while achieving overall performance improvements, and transfers zero-shot from mathematical reasoning to scientific QA and logical reasoning with comparable efficiency gains.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles

Deep neural networks (DNNs) have become the essential components for various commercialized machine learning services, such as Machine Learning as a Service (MLaaS). Recent studies show that machine learning services face severe privacy threats - well-trained DNNs owned by MLaaS providers can be stolen through public APIs, namely model stealing attacks. However, most existing works undervalued the impact of such attacks, where a successful attack has to acquire confidential training data or auxiliary data regarding the victim DNN. In this paper, we propose ES Attack, a novel model stealing attack without any data hurdles. By using heuristically generated synthetic data, ES Attack iteratively trains a substitute model and eventually achieves a functionally equivalent copy of the victim DNN. The experimental results reveal the severity of ES Attack: i) ES Attack successfully steals the victim model without data hurdles, and ES Attack even outperforms most existing model stealing attacks using auxiliary data in terms of model accuracy; ii) most countermeasures are ineffective in defending ES Attack; iii) ES Attack facilitates further attacks relying on the stolen model.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Federated Semi-Supervised Domain Adaptation via Knowledge Transfer

Given the rapidly changing machine learning environments and expensive data labeling, semi-supervised domain adaptation (SSDA) is imperative when the labeled data from the source domain is statistically different from the partially labeled data from the target domain. Most prior SSDA research is centrally performed, requiring access to both source and target data. However, data in many fields nowadays is generated by distributed end devices. Due to privacy concerns, the data might be locally stored and cannot be shared, resulting in the ineffectiveness of existing SSDA research. This paper proposes an innovative approach to achieve SSDA over multiple distributed and confidential datasets, named by Federated Semi-Supervised Domain Adaptation (FSSDA). FSSDA integrates SSDA with federated learning based on strategically designed knowledge distillation techniques, whose efficiency is improved by performing source and target training in parallel. Moreover, FSSDA controls the amount of knowledge transferred across domains by properly selecting a key parameter, i.e., the imitation parameter. Further, the proposed FSSDA can be effectively generalized to multi-source domain adaptation scenarios. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of FSSDA design.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models

Federated learning enables multiple distributed devices to collaboratively learn a shared prediction model without centralizing their on-device data. Most of the current algorithms require comparable individual efforts for local training with the same structure and size of on-device models, which, however, impedes participation from resource-constrained devices. Given the widespread yet heterogeneous devices nowadays, in this paper, we propose an innovative federated learning framework with heterogeneous on-device models through Zero-shot Knowledge Transfer, named by FedZKT. Specifically, FedZKT allows devices to independently determine the on-device models upon their local resources. To achieve knowledge transfer across these heterogeneous on-device models, a zero-shot distillation approach is designed without any prerequisites for private on-device data, which is contrary to certain prior research based on a public dataset or a pre-trained data generator. Moreover, this compute-intensive distillation task is assigned to the server to allow the participation of resource-constrained devices, where a generator is adversarially learned with the ensemble of collected on-device models. The distilled central knowledge is then sent back in the form of the corresponding on-device model parameters, which can be easily absorbed on the device side. Extensive experimental studies demonstrate the effectiveness and robustness of FedZKT towards on-device knowledge agnostic, on-device model heterogeneity, and other challenging federated learning scenarios, such as heterogeneous on-device data and straggler effects.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Membership Inference Attacks and Defenses in Neural Network Pruning

Neural network pruning has been an essential technique to reduce the computation and memory requirements for using deep neural networks for resource-constrained devices. Most existing research focuses primarily on balancing the sparsity and accuracy of a pruned neural network by strategically removing insignificant parameters and retraining the pruned model. Such efforts on reusing training samples pose serious privacy risks due to increased memorization, which, however, has not been investigated yet. In this paper, we conduct the first analysis of privacy risks in neural network pruning. Specifically, we investigate the impacts of neural network pruning on training data privacy, i.e., membership inference attacks. We first explore the impact of neural network pruning on prediction divergence, where the pruning process disproportionately affects the pruned model's behavior for members and non-members. Meanwhile, the influence of divergence even varies among different classes in a fine-grained manner. Enlighten by such divergence, we proposed a self-attention membership inference attack against the pruned neural networks. Extensive experiments are conducted to rigorously evaluate the privacy impacts of different pruning approaches, sparsity levels, and adversary knowledge. The proposed attack shows the higher attack performance on the pruned models when compared with eight existing membership inference attacks. In addition, we propose a new defense mechanism to protect the pruning process by mitigating the prediction divergence based on KL-divergence distance, whose effectiveness has been experimentally demonstrated to effectively mitigate the privacy risks while maintaining the sparsity and accuracy of the pruned models.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Pay "Attention" to Adverse Weather: Weather-aware Attention-based Object Detection

Despite the recent advances of deep neural networks, object detection for adverse weather remains challenging due to the poor perception of some sensors in adverse weather. Instead of relying on one single sensor, multimodal fusion has been one promising approach to provide redundant detection information based on multiple sensors. However, most existing multimodal fusion approaches are ineffective in adjusting the focus of different sensors under varying detection environments in dynamic adverse weather conditions. Moreover, it is critical to simultaneously observe local and global information under complex weather conditions, which has been neglected in most early or late-stage multimodal fusion works. In view of these, this paper proposes a Global-Local Attention (GLA) framework to adaptively fuse the multi-modality sensing streams, i.e., camera, gated camera, and lidar data, at two fusion stages. Specifically, GLA integrates an early-stage fusion via a local attention network and a late-stage fusion via a global attention network to deal with both local and global information, which automatically allocates higher weights to the modality with better detection features at the late-stage fusion to cope with the specific weather condition adaptively. Experimental results demonstrate the superior performance of the proposed GLA compared with state-of-the-art fusion approaches under various adverse weather conditions, such as light fog, dense fog, and snow.

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

Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning

Despite the success of AI-enabled onboard perception, on-ramp merging has been one of the main challenges for autonomous driving. Due to limited sensing range of onboard sensors, a merging vehicle can hardly observe main road conditions and merge properly. By leveraging the wireless communications between connected and automated vehicles (CAVs), a merging CAV has potential to proactively obtain the intentions of nearby vehicles. However, CAVs can be prone to inaccurate observations, such as the noisy basic safety messages (BSM) and poor quality surveillance images. In this paper, we present a novel approach for Robust on-ramp merge of CAVs via Augmented and Multi-modal Reinforcement Learning, named by RAMRL. Specifically, we formulate the on-ramp merging problem as a Markov decision process (MDP) by taking driving safety, comfort driving behavior, and traffic efficiency into account. To provide reliable merging maneuvers, we simultaneously leverage BSM and surveillance images for multi-modal observation, which is used to learn a policy model through proximal policy optimization (PPO). Moreover, to improve data efficiency and provide better generalization performance, we train the policy model with augmented data (e.g., noisy BSM and noisy surveillance images). Extensive experiments are conducted with Simulation of Urban MObility (SUMO) platform under two typical merging scenarios. Experimental results demonstrate the effectiveness and efficiency of our robust on-ramp merging design.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

The mass of the Milky Way out to 100 kpc using halo stars

We use a distribution function analysis to estimate the mass of the Milky Way out to 100 kpc using a large sample of halo stars. These stars are compiled from the literature, and the vast majority (~98%) have 6D phase-space information. We pay particular attention to systematic effects, such as the dynamical influence of the Large Magellanic Cloud (LMC), and the effect of unrelaxed substructure. The LMC biases the (pre-LMC infall) halo mass estimates towards higher values, while realistic stellar halos from cosmological simulations tend to underestimate the true halo mass. After applying our method to the Milky Way data we find a mass within 100 kpc of M(< 100 kpc) = 6.07 +/- 0.29 (stat.) +/- 1.21 (sys.) x 10^11 M_Sun. For this estimate, we have approximately corrected for the reflex motion induced by the LMC using the Erkal et al. model, which assumes a rigid potential for the LMC and MW. Furthermore, stars that likely belong to the Sagittarius stream are removed, and we include a 5% systematic bias, and a 20% systematic uncertainty based on our tests with cosmological simulations. Assuming the mass-concentration relation for Navarro-Frenk-White haloes, our mass estimate favours a total (pre-LMC infall) Milky Way mass of M_200c = 1.01 +/- 0.24 x 10^12 M_Sun, or (post-LMC infall) mass of M_200c = 1.16 +/- 0.24 x 10^12 M_Sun when a 1.5 x 10^11 M_Sun mass of a rigid LMC is included.

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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.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Novel Decision Tree for Depression Recognition in Speech

Depression is a common mental disorder worldwide which causes a range of serious outcomes. The diagnosis of depression relies on patient-reported scales and psychiatrist interview which may lead to subjective bias. In recent years, more and more researchers are devoted to depression recognition in speech , which may be an effective and objective indicator. This study proposes a new speech segment fusion method based on decision tree to improve the depression recognition accuracy and conducts a validation on a sample of 52 subjects (23 depressed patients and 29 healthy controls). The recognition accuracy are 75.8% and 68.5% for male and female respectively on gender-dependent models. It can be concluded from the data that the proposed decision tree model can improve the depression classification performance.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling

Labeling data (e.g., labeling the people, objects, actions and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ability of deep learning models or accelerate them. Unfortunately, a single machine-learning model is not powerful enough to extract various semantic information from data. Given certain applications, such as image retrieval platforms and photo album management apps, it is often required to execute a collection of models to obtain sufficient labels. With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e.g., the number of high-confidence labels). Achieving this lofty goal is nontrivial since a model's output on any data item is content-dependent and unknown until we execute it. To tackle this, we propose an Adaptive Model Scheduling framework, consisting of 1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and 2) two heuristic algorithms to adaptively schedule the model execution order under a deadline or deadline-memory constraints respectively. The proposed framework doesn't require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on five diverse image datasets and 30 popular image labeling models to demonstrate the effectiveness of our design: our design could save around 53\% execution time without loss of any valuable labels.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Lifshitz Transition in Triangular Lattice Kondo-Heisenberg Model

Motivated by recent experimental progress on triangular lattice heavy-fermion compounds, we investigate possible Lifshitz transitions and the scanning tunnel microscope (STM) spectra of the Kondo-Heisenberg model on the triangular lattice. In the heavy Fermi liquid state, the introduced Heisenberg antiferromagnetic interaction ($J_H$) results in the twice Lifshitz transition at the case of the nearest-neighbour electron hopping but with next-nearest-neighbour hole hopping and the case of the nearest-neighbour hole hopping but with next-nearest-neighbour electron hopping, respectively. Driven by $J_H$, the Lifshitz transitions on triangular lattice are all continuous in contrast to the case in square lattice. Furthermore, the STM spectra shows rich line-shape which is influenced by the Kondo coupling $J_{K}$, $J_{H}$ and the ratio of the tunneling amplitude $t_{f}$ versus $t_{c}$. Our work provides a possible scenario to understand the Fermi surface topology and the quantum critical point in heavy-fermion compounds.

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Measuring the local dark matter density with LAMOST DR5 and Gaia DR2

We apply the vertical Jeans equation to the kinematics of Milky Way stars in the solar neighbourhood to measure the local dark matter density. More than 90,000 G- and K-type dwarf stars are selected from the cross-matched sample of LAMOST DR5 and Gaia DR2 for our analyses. The mass models applied consist of a single exponential stellar disc, a razor thin gas disc and a constant dark matter density. We first consider the simplified vertical Jeans equation which ignores the tilt term and assumes a flat rotation curve. Under a Gaussian prior on the total stellar surface density, the local dark matter density inferred from Markov Chain Monte Carlo simulations is $0.0133_{-0.0022}^{+0.0024}\ {\rm M}_{\odot}\,{\rm pc}^{-3}$. The local dark matter densities for subsamples in an azimuthal angle range of $-10^{\circ} < ϕ< 5^{\circ}$ are consistent within their 1$σ$ errors. However, the northern and southern subsamples show a large discrepancy due to plateaux in the northern and southern vertical velocity dispersion profiles. These plateaux may be the cause of the different estimates of the dark matter density between the north and south. Taking the tilt term into account has little effect on the parameter estimations and does not explain the north and south asymmetry. Taking half of the difference of $σ_{z}$ profiles as unknown systematic errors, we then obtain consistent measurements for the northern and southern subsamples. We discuss the influence of the vertical data range, the scale height of the tracer population, the vertical distribution of stars and the sample size on the uncertainty of the determination of the local dark matter density.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Tracing Kinematic and Chemical Properties of Sagittarius Stream by K-Giants, M-Giants, and BHB stars

We characterize the kinematic and chemical properties of $\sim$3,000 Sagittarius (Sgr) stream stars, including K-giants, M-giants, and BHBs, select from SEGUE-2, LAMOST, and SDSS separately in Integrals-of-Motion space. The orbit of Sgr stream is quite clear from the velocity vector in $X$-$Z$ plane. Stars traced by K-giants and M-giants present the apogalacticon of trailing steam is $\sim$ 100 kpc. The metallicity distributions of Sgr K-, M-giants, and BHBs present that the M-giants are on average the most metal-rich population, followed by K-giants and BHBs. All of the K-, M-giants, and BHBs indicate that the trailing arm is on average more metal-rich than leading arm, and the K-giants show that the Sgr debris is the most metal-poor part. The $α$-abundance of Sgr stars exhibits a similar trend with the Galactic halo stars at lower metallicity ([Fe/H] $<\sim$ $-$1.0 dex), and then evolve down to lower [$α$/Fe] than disk stars at higher metallicity, which is close to the evolution pattern of $α$-element of Milky Way dwarf galaxies. We find $V_Y$ and metallicity of K-giants have gradients along the direction of line-of-sight from the Galactic center in $X$-$Z$ plane, and the K-giants show that $V_Y$ increases with metallicity at [Fe/H] $>\sim-$1.5 dex. After dividing the Sgr stream into bright and faint stream according to their locations in equatorial coordinate, the K-giants and BHBs show that the bright and faint stream present different $V_Y$ and metallicities, the bright stream is on average higher in $V_Y$ and metallicity than the faint stream.

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preprint2015arXiv

A Comparison Study of Coupled and Decoupled Uplink Heterogeneous Cellular Networks

The evolution of mobile cellular networks has brought great changes of network architecture. For example, heterogeneous cellular network (HetNet) and Ultra dense network (UDN) have been proposed as promising techniques for 5G systems. Dense deployment of base stations (BSs) allows a mobile user to be able to access multiple BSs. Meanwhile the unbalance between UL and DL in HetNets, such as different received SINR threshold and traffic load, etc., becomes increasingly obvious. All these factors naturally inspire us to consider decoupling of uplink and downlink in radio access network. An interesting question is that whether the decoupled uplink (UL) /downlink (DL) access (DUDA) mode outperforms traditional coupled uplink (UL)/downlink (DL) access (CUDA) mode or not, and how big is the performance difference in terms of system rate, spectrum efficiency (SE) and energy efficiency (EE), etc. in HetNets. In this paper, we aim at thoroughly comparing the performance of the two modes based on stochastic geometry theory. In our analytical model, we take into account dynamic transmit power control in UL communication. Specifically, we employ fractional power control (FPC) to model a location-dependent channel state. Numerical results reveals that DUDA mode significantly outperforms CUDA mode in system rate, SE and EE in HetNets. In addition, DUDA mode improves load balance and potential fairness for both different type BSs and associated UEs.

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preprint2015arXiv

Coexistence of antiferromagnetism and superconductivity of t-t$^\prime$-J model on honeycomb lattice

Motivated by recent experimental study of antiferromagnetic property of honeycomb compound In$_{3}$Cu$_{2}$VO$_{9}$ [Yan \textit{et al.}, PRB \textbf{85}, 085102 (2012)], we explore possible superconductivity and its coexistence with antiferromagnetism. We use the t-t$^\prime$-J model on the honeycomb lattice as our starting point and employ the slave-boson mean-field theory. In the antiferromagnetic normal state, the characteristic doping evolution of Fermi surface shows that only one effective singe band is active, which suggests that the potential pairing symmetry is the time-reversal symmetry breaking $d+id$, rather than the extended $s$-wave. It is found that this superconducting state coexists with the antiferromagnetism in a broad doping regime, which is consistent with the numerical calculations. The local density of states and its thermodynamic property of the superconducting state has been studied in detail with an effective single-band picture for understanding other physical observable such as superfluid density. The present work may be useful in experimentally exploring possible superconductivity of this kind of materials on the honeycomb lattice and contributes to the understanding of the unconventional superconductivity on general two-dimensional correlated electron systems.

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preprint2015arXiv

Determining the local dark matter density with LAMOST data

Measurement of the local dark matter density plays an important role in both Galactic dynamics and dark matter direct detection experiments. However, the estimated values from previous works are far from agreeing with each other. In this work, we provide a well-defined observed sample with 1427 G \& K type main-sequence stars from the LAMOST spectroscopic survey, taking into account selection effects, volume completeness, and the stellar populations. We apply a vertical Jeans equation method containing a single exponential stellar disk, a razor thin gas disk, and a constant dark matter density distribution to the sample, and obtain a total surface mass density of $\rm {78.7 ^{+3.9}_{-4.7}\ M_{\odot}\ pc^{-2}}$ up to 1 kpc and a local dark matter density of $0.0159^{+0.0047}_{-0.0057}\,\rm M_{\odot}\,\rm pc^{-3}$. We find that the sampling density (i.e. number of stars per unit volume) of the spectroscopic data contributes to about two-thirds of the uncertainty in the estimated values. We discuss the effect of the tilt term in the Jeans equation and find it has little impact on our measurement. Other issues, such as a non-equilibrium component due to perturbations and contamination by the thick disk population, are also discussed.

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preprint2015arXiv

Extended $s$-wave pairing symmetry on the triangular lattice heavy fermion system

We investigate the pairing symmetry of the Kondo-Heisenberg model on triangular lattice, which is believed to capture the core competition of Kondo screening and local magnetic exchange interaction in heavy electron compounds. On the dominant background of the heavy fermion state, the introduction of the Heisenberg antiferromagnetic interaction ($J_H$) leads to superconducting pairing instability. Depending on the strength of the interactions, it is found that the pairing symmetry prefers an extended $s$-wave for small $J_H$ and high conduction electron density but a chiral $d_{x^2-y^2}+id_{xy}$-wave for large $J_H$ and low conduction electron density, which provides a phase diagram of pairing symmetry from the calculations of the ground-state energy. The transition between these two pairing symmetries is found to be first-order. Furthermore, we also analyze the phase diagram from the pairing strengths and find that the phase diagram obtained is qualitatively consistent with that based on the ground-state energy. In addition, we propose an effective single-band BCS Hamiltonian, which is able to describe the low-energy thermodynamic behaviors of the heavy fermion superconducting states. These results further deepen the understanding of the antiferromagnetic interaction which results in a geometric frustration for the model studied. Our work may provide a possible scenario to understand the pairing symmetry of the heavy fermion superconductivity, which is the one of active issues in very recent years.

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preprint2015arXiv

Fermionology in the Kondo-Heisenberg model: the case of CeCoIn$_{5}$

Fermi surface of heavy electron systems plays a fundamental role in understanding their variety of puzzling phenomena, for example, quantum criticality, strange metal behavior, unconventional superconductivity and even enigmatic phases with yet unknown order parameters. The spectroscopy measurement of typical heavy fermion superconductor CeCoIn$_{5}$ has demonstrated multi-Fermi surface structure, which has not been in detail studied theoretically in a model system like the Kondo-Heisenberg model. In this work, we make a step toward such an issue with revisiting the Kondo-Heisenberg model. It is surprising to find that the usual self-consistent calculation cannot reproduced the fermionology of the experimental observation of the system due to the unfounded sign binding between the hopping of the conduction electrons and the mean-field valence-bond order. To overcome such inconsistency, we assume that the sign binding should be relaxed and the mean-field valence-bond order can be considered as a free/fit parameter so as to meet with real-life experiments. Given the fermionology, the calculated effective mass enhancement, entropy, superfluid density and Knight shift are all in qualitative agreement with the experimental results of CeCoIn$_{5}$, which confirms our assumption. Our result supports a $d_{x^{2}-y^{2}}$-wave pairing structure in heavy fermion material CeCoIn$_{5}$. In addition, we have also provided the scanning tunneling microscopy (STM) spectra of the system, which is able to be tested by the present STM experiments.

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preprint2015arXiv

Superfluid response in heavy fermion superconductors

Motivated by recent London penetration depth measurement [H. Kim et al. Phys. Rev. Lett. \textbf{114}, 027003 (2015)] and novel composite pairing scenario [O. Erten, R. Flint and P. Coleman, Phys. Rev. Lett. \textbf{114}, 027002 (2015)] on Yb-doped heavy fermion superconductor CeCoIn$_{5}$, we revisit the issue of superfluid response in microscopic heavy fermion lattice model. However, it is found that in literature explicit expression of superfluid response function in heavy fermion superconductor is rare. In this paper, we make a contribution to this issue by investigating superfluid density response function in celebrated Kondo-Heisenberg model. To be specific, we derive corresponding formalism from an effective fermionic large-N mean-field pairing Hamiltonian, whose pairing interaction is assumed to originate from effective local antiferromagnetic exchange interaction. Interestingly, it is found that physically correct superfluid density formula can only be obtained if external electromagnetic field is directly coupled to heavy fermion quasi-particle. Such unique feature emphasizes the key role of Kondo-screening-renormalized quasi-particle for low-temperature/energy thermodynamics and transport behaviors. As an important application, the theoretical result is compared to experimental measurement in heavy fermion superconductor CeCoIn$_{5}$ and Yb-doped Ce$_{1-x}$Yb$_{x}$CoIn$_{5}$, where the agreement is fairly good and the transition of pairing symmetry in the latter one is explained as a simple doping effect. In addition, the requisite formalism for the commonly encountered nonmagnetic impurity and non-local electrodynamic effect are developed.

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preprint2014arXiv

A General Framework for Privacy-Preserving Distributed Greedy Algorithm

Increasingly more attention is paid to the privacy in online applications due to the widespread data collection for various analysis purposes. Sensitive information might be mined from the raw data during the analysis, and this led to a great privacy concern among people (data providers) these days. To deal with this privacy concerns, multitudes of privacy-preserving computation schemes are proposed to address various computation problems, and we have found many of them fall into a class of problems which can be solved by greedy algorithms. In this paper, we propose a framework for distributed greedy algorithms in which instances in the feasible set come from different parties. By our framework, most generic distributed greedy algorithms can be converted to a privacy preserving one which achieves the same result as the original greedy algorithm while the private information associated with the instances is still protected.

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preprint2014arXiv

A Resolution Prover for Coalition Logic

We present a prototype tool for automated reasoning for Coalition Logic, a non-normal modal logic that can be used for reasoning about cooperative agency. The theorem prover CLProver is based on recent work on a resolution-based calculus for Coalition Logic that operates on coalition problems, a normal form for Coalition Logic. We provide an overview of coalition problems and of the resolution-based calculus for Coalition Logic. We then give details of the implementation of CLProver and present the results for a comparison with an existing tableau-based solver.

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preprint2014arXiv

Cloud-based Privacy Preserving Image Storage, Sharing and Search

High-resolution cameras produce huge volume of high quality images everyday. It is extremely challenging to store, share and especially search those huge images, for which increasing number of cloud services are presented to support such functionalities. However, images tend to contain rich sensitive information (\eg, people, location and event), and people's privacy concerns hinder their readily participation into the services provided by untrusted third parties. In this work, we introduce PIC: a Privacy-preserving large-scale Image search system on Cloud. Our system enables efficient yet secure content-based image search with fine-grained access control, and it also provides privacy-preserving image storage and sharing among users. Users can specify who can/cannot search on their images when using the system, and they can search on others' images if they satisfy the condition specified by the image owners. Majority of the computationally intensive jobs are outsourced to the cloud side, and users only need to submit the query and receive the result throughout the entire image search. Specially, to deal with massive images, we design our system suitable for distributed and parallel computation and introduce several optimizations to further expedite the search process. We implement a prototype of PIC including both cloud side and client side. The cloud side is a cluster of computers with distributed file system (Hadoop HDFS) and MapReduce architecture (Hadoop MapReduce). The client side is built for both Windows OS laptops and Android phones. We evaluate the prototype system with large sets of real-life photos. Our security analysis and evaluation results show that PIC successfully protect the image privacy at a low cost of computation and communication.

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preprint2014arXiv

Enable Portrait Privacy Protection in Photo Capturing and Sharing

The wide adoption of wearable smart devices with onboard cameras greatly increases people's concern on privacy infringement. Here we explore the possibility of easing persons from photos captured by smart devices according to their privacy protection requirements. To make this work, we need to address two challenges: 1) how to let users explicitly express their privacy protection intention, and 2) how to associate the privacy requirements with persons in captured photos accurately and efficiently. Furthermore, the association process itself should not cause portrait information leakage and should be accomplished in a privacy-preserving way. In this work, we design, develop, and evaluate a protocol, that enables a user to flexibly express her privacy requirement and empowers the photo service provider (or image taker) to exert the privacy protection policy.Leveraging the visual distinguishability of people in the field-of-view and the dimension-order-independent property of vector similarity measurement, we achieves high accuracy and low overhead. We implement a prototype system, and our evaluation results on both the trace-driven and real-life experiments confirm the feasibility and efficiency of our system.

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preprint2014arXiv

One-dimensional Hubbard model with pair-hopping term: Pair-density wave, pair-superconductor and orthogonal metal

As the simplest non-Fermi liquids, orthogonal metals have gapped single-particle excitations but show normal metallic behaviors in thermodynamic and transport quantities. Such an exotic metallic state could be realized in a Hubbard model with pair-hopping term. However, it is difficult to solve this model in higher dimension, here we focus on its one-dimension version, in which a powerful U(1) abelian bosonization technique is available to capture \textit{analytically} its universal low-energy physics. It is found that the one-dimensional system is unable to provide an appropriate precursor for the desirable orthogonal metals in higher dimension and the occurrence of the featureless orthogonal metallic phase needs \textit{at least} a quasi-one-dimensional multi-leg ladder model. Nevertheless, two interesting quantum liquid phases are identified for the present one-dimensional model, namely, pair-density wave (PDW) state and pair-superconducting (PSC) state. The former describes the charge-density-wave order of cooper pairs while the latter shows the superfluidity of two cooper pairs. Needing not any explicit calculation, these two phases could have further implications in the two-dimensional case, namely, the PDW state may give rise to quantum electron liquid crystal states, which is believed to be responsible for the complex pseudogap phase in cuprate, and the PSC state with charge 4e can be related to certain nematic superconducting state. These predications call for further investigation on this model in higher dimension by available analytical methods and/or numerical calculations.

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preprint2014arXiv

Outsource Photo Sharing and Searching for Mobile Devices With Privacy Protection

With the proliferation of mobile devices, cloud-based photo sharing and searching services are becoming common due to the mobile devices' resource constrains. Meanwhile, there is also increasing concern about privacy in photos. In this work, we present a framework \ourprotocolNSP, which enables cloud servers to provide privacy-preserving photo sharing and search as a service to mobile device users. Privacy-seeking users can share their photos via our framework to allow only their authorized friends to browse and search their photos using resource-bounded mobile devices. This is achieved by our carefully designed architecture and novel outsourced privacy-preserving computation protocols, through which no information about the outsourced photos or even the search contents (including the results) would be revealed to the cloud servers. Our framework is compatible with most of the existing image search technologies, and it requires few changes to the existing cloud systems. The evaluation of our prototype system with 31,772 real-life images shows the communication and computation efficiency of our system.

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preprint2014arXiv

Study of the material photon and electron background and the liquid argon detector veto efficiency of the CDEX-10 experiment

The China Dark Matter Experiment (CDEX) is located at the China Jinping underground laboratory (CJPL) and aims to directly detect the WIMP flux with high sensitivity in the low mass region. Here we present a study of the predicted photon and electron backgrounds including the background contribution of the structure materials of the germanium detector, the passive shielding materials, and the intrinsic radioactivity of the liquid argon that serves as an anti-Compton active shielding detector. A detailed geometry is modeled and the background contribution has been simulated based on the measured radioactivities of all possible components within the GEANT4 program. Then the photon and electron background level in the energy region of interest (<10^-2 events kg-1 day-1 keV-1 (cpkkd)) is predicted based on Monte Carlo simulations. The simulated result is consistent with the design goal of CDEX-10 experiment, 0.1 cpkkd, which shows that the active and passive shield design of CDEX-10 is effective and feasible.

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preprint2013arXiv

Data Aggregation without Secure Channel: How to Evaluate a Multivariate Polynomial Securely

Much research has been conducted to securely outsource multiple parties' data aggregation to an untrusted aggregator without disclosing each individual's data, or to enable multiple parties to jointly aggregate their data while preserving privacy. However, those works either assume to have a secure channel or suffer from high complexity. Here we consider how an external aggregator or multiple parties learn some algebraic statistics (e.g., summation, product) over participants' data while any individual's input data is kept secret to others (the aggregator and other participants). We assume channels in our construction are insecure. That is, all channels are subject to eavesdropping attacks, and all the communications throughout the aggregation are open to others. We successfully guarantee data confidentiality under this weak assumption while limiting both the communication and computation complexity to at most linear.

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preprint2013arXiv

Introduction of the CDEX experiment

Weakly Interacting Massive Particles (WIMPs) are the candidates of dark matter in our universe. Up to now any direct interaction of WIMP with nuclei has not been observed yet. The exclusion limits of the spin-independent cross section of WIMP-nucleon which have been experimentally obtained is about 10^{-7}pb at high mass region and only 10^{-5}pb} at low mass region. China Jin-Ping underground laboratory CJPL is the deepest underground lab in the world and provides a very promising environment for direct observation of dark matter. The China Dark Matter Experiment (CDEX) experiment is going to directly detect the WIMP flux with high sensitivity in the low mass region. Both CJPL and CDEX have achieved a remarkable progress in recent two years. The CDEX employs a point-contact germanium semi-conductor detector PCGe whose detection threshold is less than 300 eV. We report the measurement results of Muon flux, monitoring of radioactivity and Radon concentration carried out in CJPL, as well describe the structure and performance of the 1 kg PCGe detector CDEX-1 and 10kg detector array CDEX-10 including the detectors, electronics, shielding and cooling systems. Finally we discuss the physics goals of the CDEX-1, CDEX-10 and the future CDEX-1T detectors.

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preprint2013arXiv

SilentSense: Silent User Identification via Dynamics of Touch and Movement Behavioral Biometrics

With the increased popularity of smartphones, various security threats and privacy leakages targeting them are discovered and investigated. In this work, we present \ourprotocoltight, a framework to authenticate users silently and transparently by exploiting dynamics mined from the user touch behavior biometrics and the micro-movement of the device caused by user's screen-touch actions. We build a "touch-based biometrics" model of the owner by extracting some principle features, and then verify whether the current user is the owner or guest/attacker. When using the smartphone, the unique operating dynamics of the user is detected and learnt by collecting the sensor data and touch events silently. When users are mobile, the micro-movement of mobile devices caused by touch is suppressed by that due to the large scale user-movement which will render the touch-based biometrics ineffective. To address this, we integrate a movement-based biometrics for each user with previous touch-based biometrics. We conduct extensive evaluations of our approaches on the Android smartphone, we show that the user identification accuracy is over 99%.

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preprint2013arXiv

The CDEX-1 1 kg Point-Contact Germanium Detector for Low Mass Dark Matter Searches

The CDEX Collaboration has been established for direct detection of light dark matter particles, using ultra-low energy threshold p-type point-contact germanium detectors, in China JinPing underground Laboratory (CJPL). The first 1 kg point-contact germanium detector with a sub-keV energy threshold has been tested in a passive shielding system located in CJPL. The outputs from both the point-contact p+ electrode and the outside n+ electrode make it possible to scan the lower energy range of less than 1 keV and at the same time to detect the higher energy range up to 3 MeV. The outputs from both p+ and n+ electrode may also provide a more powerful method for signal discrimination for dark matter experiment. Some key parameters, including energy resolution, dead time, decay times of internal X-rays, and system stability, have been tested and measured. The results show that the 1 kg point-contact germanium detector, together with its shielding system and electronics, can run smoothly with good performances. This detector system will be deployed for dark matter search experiments.

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preprint2013arXiv

The Gravitational Potential Near the Sun From SEGUE K-dwarf Kinematics

To constrain the Galactic gravitational potential near the Sun ($\sim$1.5 kpc), we derive and model the spatial and velocity distribution for a sample of 9000 K-dwarfs that have spectra from SDSS/SEGUE, which yield radial velocities and abundances ([Fe/H] & [$α$/Fe]). We first derive the spatial density distribution for stars of three abundance-selected sub-populations by accounting for the survey's selection function. The vertical profile of these sub-populations are simple exponentials and their vertical dispersion profile is nearly isothermal. To model these data, we apply the `vertical' Jeans Equation, which relates the observable tracer number density and vertical velocity dispersion to the gravitational potential or vertical force. We explore a number of functional forms for the vertical force law, and fit the dispersion and density profiles of all abundance selected sub-populations simultaneously in the same potential, and explore all parameter co-variances using MCMC. Our fits constrain a disk {\it mass} scale height $\lesssim$ 300 pc and the total surface mass density to be $67 \pm 6 M_{\odot} {\rm pc^{-2}}$ at $|z| = 1.0$ kpc of which the contribution from all stars is $42 \pm 5 M_{\odot} {\rm pc^{-2}}$ (presuming a contribution from cold gas of $13 M_{\odot} {\rm pc^{-2}}$). We find significant constraints on the local dark matter density of $0.0065\pm0.0023 M_{\odot} {\rm pc^{-3}}$ ($0.25\pm0.09 {\rm GeV cm^{-3}} $). Together with recent experiments this firms up the best estimate of $0.0075\pm0.0021 M_{\odot} {\rm pc^{-3}}$ ($0.28\pm0.08 {\rm GeV cm^{-3}} $), consistent with global fits of approximately round dark matter halos to kinematic data in the outskirts of the Galaxy.

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preprint2012arXiv

Message in a Sealed Bottle: Privacy Preserving Friending in Social Networks

Many proximity-based mobile social networks are developed to facilitate connections between any two people, or to help a user to find people with matched profile within a certain distance. A challenging task in these applications is to protect the privacy the participants' profiles and personal interests. In this paper, we design novel mechanisms, when given a preference-profile submitted by a user, that search a person with matching-profile in decentralized multi-hop mobile social networks. Our mechanisms are privacy-preserving: no participants' profile and the submitted preference-profile are exposed. Our mechanisms establish a secure communication channel between the initiator and matching users at the time when the matching user is found. Our rigorous analysis shows that our mechanism is secure, privacy-preserving, verifiable, and efficient both in communication and computation. Extensive evaluations using real social network data, and actual system implementation on smart phones show that our mechanisms are significantly more efficient then existing solutions.

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preprint2012arXiv

The vertical motions of mono-abundance sub-populations in the Milky Way disk

We present the vertical kinematics of stars in the Milky Way's stellar disk inferred from SDSS/SEGUE G-dwarf data, deriving the vertical velocity dispersion, σ_z, as a function of vertical height |z| and Galactocentric radius R for a set of 'mono-abundance' sub-populations of stars with very similar elemental abundances [α/Fe] and [Fe/H]. We find that all components exhibit nearly isothermal kinematics in |z|, and a slow outward decrease of the vertical velocity dispersion: σ_z (z,R|[α/Fe],[Fe/H]) ~ σ_z ([α/Fe],[Fe/H]) x \exp (-(R-R_0)/7 kpc}). The characteristic velocity dispersions of these components vary from ~ 15 km/s for chemically young, metal-rich stars, to >~ 50 km/s for metal poor stars. The mean σ_z gradient away from the mid plane is only 0.3 +/- 0.2 km/s/kpc. We find a continuum of vertical kinetic temperatures (~σ^2_z) as function of ([α/Fe],[Fe/H]), which contribute to the stellar surface mass density as Σ_{R_0}(σ^2_z) ~ \exp(-σ^2_z). The existence of isothermal mono-abundance populations with intermediate dispersions reject the notion of a thin-thick disk dichotomy. This continuum of disks argues against models where the thicker disk portions arise from massive satellite infall or heating; scenarios where either the oldest disk portion was born hot, or where internal evolution plays a major role, seem the most viable. The wide range of σ_z ([α/Fe],[Fe/H]) combined with a constant σ_z(z) for each abundance bin provides an independent check on the precision of the SEGUE abundances: δ_[α/Fe] ~ 0.07 dex and δ_[Fe/H] ~ 0.15 dex. The radial decline of the vertical dispersion presumably reflects the decrease in disk surface-mass density. This measurement constitutes a first step toward a purely dynamical estimate of the mass profile the disk in our Galaxy. [abridged]

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