arXiv subjects

Ting Wang

Publications and source records attributed to Ting Wang.

At least 199 records · Page 11Linked to original sources

arXiv2022

Construction of Strong-Uniform Fuzzy Partitions of Arbitrary Dimensions

Strong-uniform fuzzy partition is necessary for the accuracy of fuzzy partition-based histograms. Most previous research focused on constructing one-dimensional strong-uniform fuzzy partitions. While to the best of our knowledge, few have been reported for high-dimensional cases. In order to fill this theoretical vacancy, this paper proves the existence of high-dimensional strong-uniform fuzzy partitions via proposing an analytic formula to construct strong-uniform fuzzy partitions of arbitrary dimensions.

math.GM↗

arXiv2022

Ultrafast coherent control of a hole spin qubit in a germanium quantum dot

Operation speed and coherence time are two core measures for the viability of a qubit. Strong spin-orbit interaction (SOI) and relatively weak hyperfine interaction make holes in germanium (Ge) intriguing candidates for spin qubits with rapid, all-electrical coherent control. Here we report ultrafast single-spin manipulation in a hole-based double quantum dot in a germanium hut wire (GHW). Mediated by the strong SOI, a Rabi frequency exceeding 540 MHz is observed at a magnetic field of 100 mT, setting a record for ultrafast spin qubit control in semiconductor systems. We demonstrate that the strong SOI of heavy holes (HHs) in our GHW, characterized by a very short spin-orbit length of 1.5 nm, enables the rapid gate operations we accomplish. Our results demonstrate the potential of ultrafast coherent control of hole spin qubits to meet the requirement of DiVincenzo's criteria for a scalable quantum information processor.

cond-mat.mes-hall↗

arXiv2022

Computation and application of generalized linear mixed model derivatives using lme4

Maximum likelihood estimation of generalized linear mixed models(GLMMs) is difficult due to marginalization of the random effects. Computing derivatives of a fitted GLMM's likelihood (with respect to model parameters) is also difficult, especially because the derivatives are not by-products of popular estimation algorithms. In this paper, we describe GLMM derivatives along with a quadrature method to efficiently compute them, focusing on lme4 models with a single clustering variable. We describe how psychometric results related to IRT are helpful for obtaining these derivatives, as well as for verifying the derivatives' accuracies. After describing the derivative computation methods, we illustrate the many possible uses of these derivatives, including robust standard errors, score tests of fixed effect parameters, and likelihood ratio tests of non-nested models. The derivative computation methods and applications described in the paper are all available in easily-obtained R packages.

stat.ME↗

arXiv2021

APTSHIELD: A Stable, Efficient and Real-time APT Detection System for Linux Hosts

Advanced Persistent Threat (APT) attack usually refers to the form of long-term, covert and sustained attack on specific targets, with an adversary using advanced attack techniques to destroy the key facilities of an organization. APT attacks have caused serious security threats and massive financial loss worldwide. Academics and industry thereby have proposed a series of solutions to detect APT attacks, such as dynamic/static code analysis, traffic detection, sandbox technology, endpoint detection and response (EDR), etc. However, existing defenses are failed to accurately and effectively defend against the current APT attacks that exhibit strong persistent, stealthy, diverse and dynamic characteristics due to the weak data source integrity, large data processing overhead and poor real-time performance in the process of real-world scenarios. To overcome these difficulties, in this paper we propose APTSHIELD, a stable, efficient and real-time APT detection system for Linux hosts. In the aspect of data collection, audit is selected to stably collect kernel data of the operating system so as to carry out a complete portrait of the attack based on comprehensive analysis and comparison of existing logging tools; In the aspect of data processing, redundant semantics skipping and non-viable node pruning are adopted to reduce the amount of data, so as to reduce the overhead of the detection system; In the aspect of attack detection, an APT attack detection framework based on ATT\&CK model is designed to carry out real-time attack response and alarm through the transfer and aggregation of labels. Experimental results on both laboratory and Darpa Engagement show that our system can effectively detect web vulnerability attacks, file-less attacks and remote access trojan attacks, and has a low false positive rate, which adds far more value than the existing frontier work.

cs.CR↗

arXiv2021

MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare

Deep neural networks (DNNs) have been broadly adopted in health risk prediction to provide healthcare diagnoses and treatments. To evaluate their robustness, existing research conducts adversarial attacks in the white/gray-box setting where model parameters are accessible. However, a more realistic black-box adversarial attack is ignored even though most real-world models are trained with private data and released as black-box services on the cloud. To fill this gap, we propose the first black-box adversarial attack method against health risk prediction models named MedAttacker to investigate their vulnerability. MedAttacker addresses the challenges brought by EHR data via two steps: hierarchical position selection which selects the attacked positions in a reinforcement learning (RL) framework and substitute selection which identifies substitute with a score-based principle. Particularly, by considering the temporal context inside EHRs, it initializes its RL position selection policy by using the contribution score of each visit and the saliency score of each code, which can be well integrated with the deterministic substitute selection process decided by the score changes. In experiments, MedAttacker consistently achieves the highest average success rate and even outperforms a recent white-box EHR adversarial attack technique in certain cases when attacking three advanced health risk prediction models in the black-box setting across multiple real-world datasets. In addition, based on the experiment results we include a discussion on defending EHR adversarial attacks.

cs.LG↗

arXiv2021

Backdoor Attack through Frequency Domain

Backdoor attacks have been shown to be a serious threat against deep learning systems such as biometric authentication and autonomous driving. An effective backdoor attack could enforce the model misbehave under certain predefined conditions, i.e., triggers, but behave normally otherwise. However, the triggers of existing attacks are directly injected in the pixel space, which tend to be detectable by existing defenses and visually identifiable at both training and inference stages. In this paper, we propose a new backdoor attack FTROJAN through trojaning the frequency domain. The key intuition is that triggering perturbations in the frequency domain correspond to small pixel-wise perturbations dispersed across the entire image, breaking the underlying assumptions of existing defenses and making the poisoning images visually indistinguishable from clean ones. We evaluate FTROJAN in several datasets and tasks showing that it achieves a high attack success rate without significantly degrading the prediction accuracy on benign inputs. Moreover, the poisoning images are nearly invisible and retain high perceptual quality. We also evaluate FTROJAN against state-of-the-art defenses as well as several adaptive defenses that are designed on the frequency domain. The results show that FTROJAN can robustly elude or significantly degenerate the performance of these defenses.

cs.CR↗

arXiv2021

Martingale product estimators for sensitivity analysis in computational statistical physics

We introduce a new class of estimators for the linear response of steady states of stochastic dynamics. We generalize the likelihood ratio approach and formulate the linear response as a product of two martingales, hence the name "martingale product estimators". We present a systematic derivation of the martingale product estimator, and show how to construct such estimator so its bias is consistent with the weak order of the numerical scheme that approximates the underlying stochastic differential equation. Motivated by the estimation of transport properties in molecular systems, we present a rigorous numerical analysis of the bias and variance for these new estimators in the case of Langevin dynamics. We prove that the variance is uniformly bounded in time and derive a specific form of the estimator for second-order splitting schemes for Langevin dynamics. For comparison, we also study the bias and variance of a Green-Kubo estimator, motivated, in part, by its variance growing linearly in time. Presented analysis shows that the new martingale product estimators, having uniformly bounded variance in time, offer a competitive alternative to the traditional Green-Kubo estimator. We compare on illustrative numerical tests the new estimators with results obtained by the Green-Kubo method.

math.NA↗

arXiv2021

Auto robust relative radiometric normalization via latent change noise modelling

Relative radiometric normalization(RRN) of different satellite images of the same terrain is necessary for change detection, object classification/segmentation, and map-making tasks. However, traditional RRN models are not robust, disturbing by object change, and RRN models precisely considering object change can not robustly obtain the no-change set. This paper proposes auto robust relative radiometric normalization methods via latent change noise modeling. They utilize the prior knowledge that no change points possess small-scale noise under relative radiometric normalization and that change points possess large-scale radiometric noise after radiometric normalization, combining the stochastic expectation maximization method to quickly and robustly extract the no-change set to learn the relative radiometric normalization mapping functions. This makes our model theoretically grounded regarding the probabilistic theory and mathematics deduction. Specifically, when we select histogram matching as the relative radiometric normalization learning scheme integrating with the mixture of Gaussian noise(HM-RRN-MoG), the HM-RRN-MoG model achieves the best performance. Our model possesses the ability to robustly against clouds/fogs/changes. Our method naturally generates a robust evaluation indicator for RRN that is the no-change set root mean square error. We apply the HM-RRN-MoG model to the latter vegetation/water change detection task, which reduces the radiometric contrast and NDVI/NDWI differences on the no-change set, generates consistent and comparable results. We utilize the no-change set into the building change detection task, efficiently reducing the pseudo-change and boosting the precision.

cs.CV↗

arXiv2021

Generation of photonic hooks from patchy microcylinders

The photonic hook (PH) is new type of curved light beam which has promising applications in various fields such as nanoparticle manipulation, super-resolution imaging, etc. Herein, we proposed a new approach of utilizing patchy microcylinders for the generation of PHs. Numerical simulation based on the finite-difference time-domain method was used to investigate the field distribution characteristics of the PHs. By rotating the patchy microcylinder, PHs with different curvatures can be effectively generated, and the PH with a bending angle of 28.4$^\circ$ and a full-width-half-maximum of 0.36 $λ$ can be obtained from 1 $μ$m-diameter patchy microcylinders.

physics.optics↗

arXiv2021

Towards Robust Reasoning over Knowledge Graphs

Answering complex logical queries over large-scale knowledge graphs (KGs) represents an important artificial intelligence task, entailing a range of applications. Recently, knowledge representation learning (KRL) has emerged as the state-of-the-art approach, wherein KG entities and the query are embedded into a latent space such that entities that answer the query are embedded close to the query. Yet, despite its surging popularity, the potential security risks of KRL are largely unexplored, which is concerning, given the increasing use of such capabilities in security-critical domains (e.g., cyber-security and healthcare). This work represents a solid initial step towards bridging this gap. We systematize the potential security threats to KRL according to the underlying attack vectors (e.g., knowledge poisoning and query perturbation) and the adversary's background knowledge. More importantly, we present ROAR(Reasoning Over Adversarial Representations), a new class of attacks that instantiate a variety of such threats. We demonstrate the practicality of ROAR in two representative use cases (i.e., cyber-threat hunting and drug repurposing). For instance, ROAR attains over 99% attack success rate in misleading the threat intelligence engine to give pre-defined answers for target queries, yet without any impact on non-target ones. Further, we discuss potential countermeasures against ROAR, including filtering of poisoning facts and robust training with adversarial queries, which leads to several promising research directions.

cs.CR↗

arXiv2021

Backdoor Pre-trained Models Can Transfer to All

Pre-trained general-purpose language models have been a dominating component in enabling real-world natural language processing (NLP) applications. However, a pre-trained model with backdoor can be a severe threat to the applications. Most existing backdoor attacks in NLP are conducted in the fine-tuning phase by introducing malicious triggers in the targeted class, thus relying greatly on the prior knowledge of the fine-tuning task. In this paper, we propose a new approach to map the inputs containing triggers directly to a predefined output representation of the pre-trained NLP models, e.g., a predefined output representation for the classification token in BERT, instead of a target label. It can thus introduce backdoor to a wide range of downstream tasks without any prior knowledge. Additionally, in light of the unique properties of triggers in NLP, we propose two new metrics to measure the performance of backdoor attacks in terms of both effectiveness and stealthiness. Our experiments with various types of triggers show that our method is widely applicable to different fine-tuning tasks (classification and named entity recognition) and to different models (such as BERT, XLNet, BART), which poses a severe threat. Furthermore, by collaborating with the popular online model repository Hugging Face, the threat brought by our method has been confirmed. Finally, we analyze the factors that may affect the attack performance and share insights on the causes of the success of our backdoor attack.

cs.CL↗

arXiv2021

On the Security Risks of AutoML

Neural Architecture Search (NAS) represents an emerging machine learning (ML) paradigm that automatically searches for models tailored to given tasks, which greatly simplifies the development of ML systems and propels the trend of ML democratization. Yet, little is known about the potential security risks incurred by NAS, which is concerning given the increasing use of NAS-generated models in critical domains. This work represents a solid initial step towards bridging the gap. Through an extensive empirical study of 10 popular NAS methods, we show that compared with their manually designed counterparts, NAS-generated models tend to suffer greater vulnerability to various malicious attacks (e.g., adversarial evasion, model poisoning, and functionality stealing). Further, with both empirical and analytical evidence, we provide possible explanations for such phenomena: given the prohibitive search space and training cost, most NAS methods favor models that converge fast at early training stages; this preference results in architectural properties associated with attack vulnerability (e.g., high loss smoothness and low gradient variance). Our findings not only reveal the relationships between model characteristics and attack vulnerability but also suggest the inherent connections underlying different attacks. Finally, we discuss potential remedies to mitigate such drawbacks, including increasing cell depth and suppressing skip connects, which lead to several promising research directions.

cs.LG↗

arXiv2021

Silicon photonic-electronic neural network for fibre nonlinearity compensation

In optical communication systems, fibre nonlinearity is the major obstacle in increasing the transmission capacity. Typically, digital signal processing techniques and hardware are used to deal with optical communication signals, but increasing speed and computational complexity create challenges for such approaches. Highly parallel, ultrafast neural networks using photonic devices have the potential to ease the requirements placed on the digital signal processing circuits by processing the optical signals in the analogue domain. Here we report a silicon photonice-lectronic neural network for solving fibre nonlinearity compensation of submarine optical fibre transmission systems. Our approach uses a photonic neural network based on wavelength-division multiplexing built on a CMOS-compatible silicon photonic platform. We show that the platform can be used to compensate optical fibre nonlinearities and improve the signal quality (Q)-factor in a 10,080 km submarine fibre communication system. The Q-factor improvement is comparable to that of a software-based neural network implemented on a 32-bit graphic processing unit-assisted workstation. Our reconfigurable photonic-electronic integrated neural network promises to address pressing challenges in high-speed intelligent signal processing.

physics.app-ph↗

arXiv2021

Graph Backdoor

One intriguing property of deep neural networks (DNNs) is their inherent vulnerability to backdoor attacks -- a trojan model responds to trigger-embedded inputs in a highly predictable manner while functioning normally otherwise. Despite the plethora of prior work on DNNs for continuous data (e.g., images), the vulnerability of graph neural networks (GNNs) for discrete-structured data (e.g., graphs) is largely unexplored, which is highly concerning given their increasing use in security-sensitive domains. To bridge this gap, we present GTA, the first backdoor attack on GNNs. Compared with prior work, GTA departs in significant ways: graph-oriented -- it defines triggers as specific subgraphs, including both topological structures and descriptive features, entailing a large design spectrum for the adversary; input-tailored -- it dynamically adapts triggers to individual graphs, thereby optimizing both attack effectiveness and evasiveness; downstream model-agnostic -- it can be readily launched without knowledge regarding downstream models or fine-tuning strategies; and attack-extensible -- it can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks, constituting severe threats for a range of security-critical applications. Through extensive evaluation using benchmark datasets and state-of-the-art models, we demonstrate the effectiveness of GTA. We further provide analytical justification for its effectiveness and discuss potential countermeasures, pointing to several promising research directions.

cs.LG↗

arXiv2021

Chiral photonic circuits for deterministic spin transfer

Chiral quantum optics has attracted considerable interest in the field of quantum information science. Exploiting the spin-polarization properties of quantum emitters and engineering rational photonic nanostructures has made it possible to transform information from spin to path encoding. Here, compact chiral photonic circuits with deterministic circularly polarized chiral routing and beamsplitting are demonstrated using two laterally adjacent waveguides coupled with quantum dots. Chiral routing arises from the electromagnetic field chirality in waveguide, and beamsplitting is obtained via the evanescent field coupling. The spin- and position-dependent directional spontaneous emission are achieved by spatially selective micro-photoluminescence measurements, with a chiral contrast of up to 0.84 in the chiral photonic circuits. This makes a significant advancement for broadening the application scenarios of chiral quantum optics and developing scalable quantum photonic networks.

physics.optics↗

arXiv2021

Byzantine-Resilient Federated Machine Learning via Over-the-Air Computation

Federated learning (FL) is recognized as a key enabling technology to provide intelligent services for future wireless networks and industrial systems with delay and privacy guarantees. However, the performance of wireless FL can be significantly degraded by Byzantine attack, such as data poisoning attack, model poisoning attack and free-riding attack. To design the Byzantine-resilient FL paradigm in wireless networks with limited radio resources, we propose a novel communication-efficient robust model aggregation scheme via over-the-air computation (AirComp). This is achieved by applying the Weiszfeld algorithm to obtain the smoothed geometric median aggregation against Byzantine attack. The additive structure of the Weiszfeld algorithm is further leveraged to match the signal superposition property of multiple-access channels via AirComp, thereby expediting the communication-efficient secure aggregation process of FL. Numerical results demonstrate the robustness against Byzantine devices and good learning performance of the proposed approach.

cs.IT↗

arXiv2021

Anisotropic g-Factor and Spin-Orbit Field in a Ge Hut Wire Double Quantum Dot

Holes in nanowires have drawn significant attention in recent years because of the strong spin-orbit interaction, which plays an important role in constructing Majorana zero modes and manipulating spin-orbit qubits. Here, from the strongly anisotropic leakage current in the spin blockade regime for a double dot, we extract the full g-tensor and find that the spin-orbit field is in plane with an azimuthal angle of 59° to the axis of the nanowire. The direction of the spin-orbit field indicates a strong spin-orbit interaction along the nanowire, which may have originated from the interface inversion asymmetry in Ge hut wires. We also demonstrate two different spin relaxation mechanisms for the holes in the Ge hut wire double dot: spin-flip cotunneling to the leads, and spin-orbit interaction within the double dot. These results help establish feasibility of a Ge-based quantum processor.

cond-mat.mes-hall↗

arXiv2021

Molecular Dynamics Simulation of the Interaction between Cracks in Single-Crystal Aluminum

The interaction between cracks, as well as their propagation, in single-crystal aluminum is investigated at the atomic scale using the molecular dynamics method and the modified embedded atom method. The results demonstrated that the crack propagation in aluminum is a quite complex process, which is accompanied by micro-crack growth, merging, stress shielding, dislocation emission, and phase transformation of the crystal structure. The main deformation mechanisms at the front of the fatigue crack are holes, slip bands, and cross-slip bands. During crack propagation, there are interactions between cracks. Such interactions inhibit the phase transition at the crack tip, and also affect the direction and speed of crack propagation. The intensity of the interaction between two cracks decreases with the increase of the distance between them and increases with increasing crack size. Moreover, while this interaction has no effect on the elastic modulus of the material, it affects its strength limit.

cond-mat.mtrl-sci↗