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Researcher profile

Peng Cheng

Peng Cheng contributes to research discovery and scholarly infrastructure.

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cond-mat.mtrl-sciMachine Learningcond-mat.supr-conCryptography and SecurityDatabasescond-mat.mes-hallcond-mat.str-eleess.SPArtificial IntelligenceDistributed, Parallel, and Cluster ComputingInformation Theorymath.IThep-thphysics.comp-phComputer Visioneess.SYgr-qcGraphicshep-exhep-lathep-phInformation RetrievalMultiagent SystemsNetworking and Internet Architecturenucl-exnucl-thSoftware EngineeringSoundSystems and Control

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Published work

39 published item(s)

preprint2026arXiv

Attack-Resistant Watermarking for AIGC Image Forensics via Diffusion-based Semantic Deflection

Protecting the copyright of user-generated AI images is an emerging challenge as AIGC becomes pervasive in creative workflows. Existing watermarking methods (1) remain vulnerable to real-world adversarial threats, often forced to trade off between defenses against spoofing and removal attacks; and (2) cannot support semantic-level tamper localization. We introduce PAI, a training-free inherent watermarking framework for AIGC copyright protection, plug-and-play with diffusion-based AIGC services. PAI simultaneously provides three key functionalities: robust ownership verification, attack detection, and semantic-level tampering localization. Unlike existing inherent watermark methods that only embed watermarks at noise initialization of diffusion models, we design a novel key-conditioned deflection mechanism that subtly steers the denoising trajectory according to the user key. Such trajectory-level coupling further strengthens the semantic entanglement of identity and content, thereby further enhancing robustness against real-world threats. Moreover, we also provide a theoretical analysis proving that only the valid key can pass verification. Experiments across 12 attack methods show that PAI achieves 98.43\% verification accuracy, improving over SOTA methods by 37.25\% on average, and retains strong tampering localization performance even against advanced AIGC edits. Our code is available at https://github.com/QingyuLiu/PAI.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Beyond Content: A Comprehensive Speech Toxicity Dataset and Detection Framework Incorporating Paralinguistic Cues

Toxic speech detection has become a crucial challenge in maintaining safe online communication environments. However, existing approaches to toxic speech detection often neglect the contribution of paralinguistic cues, such as emotion, intonation, and speech rate, which are key to detecting speech toxicity. Moreover, current toxic speech datasets are predominantly text-based, limiting the development of models that can capture paralinguistic cues.To address these challenges, we present ToxiAlert-Bench, a large-scale audio dataset comprising over 30,000 audio clips annotated with seven major toxic categories and twenty fine-grained toxic labels. Uniquely, our dataset annotates toxicity sources -- distinguishing between textual content and paralinguistic origins -- for comprehensive toxic speech analysis.Furthermore, we propose a dual-head neural network with a multi-stage training strategy tailored for toxic speech detection. This architecture features two task-specific classification headers: one for identifying the source of sensitivity (textual or paralinguistic), and the other for categorizing the specific toxic type. The training process involves independent head training followed by joint fine-tuning to reduce task interference. To mitigate data class imbalance, we incorporate class-balanced sampling and weighted loss functions.Our experimental results show that leveraging paralinguistic features significantly improves detection performance. Our method consistently outperforms existing baselines across multiple evaluation metrics, with a 21.1% relative improvement in Macro-F1 score and a 13.0% relative gain in accuracy over the strongest baseline, highlighting its enhanced effectiveness and practical applicability.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Federated Customization of Large Models: Approaches, Experiments, and Insights

In this article, we explore federated customization of large models and highlight the key challenges it poses within the federated learning framework. We review several popular large model customization techniques, including full fine-tuning, efficient fine-tuning, prompt engineering, prefix-tuning, knowledge distillation, and retrieval-augmented generation. Then, we discuss how these techniques can be implemented within the federated learning framework. Moreover, we conduct experiments on federated prefix-tuning, which, to the best of our knowledge, is the first trial to apply prefix-tuning in the federated learning setting. The conducted experiments validate its feasibility with performance close to centralized approaches. Further comparison with three other federated customization methods demonstrated its competitive performance, satisfactory efficiency, and consistent robustness.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Helicity Dependent Distribution Functions of the Proton and $Λ$ and $Σ^0$ Baryons

Using continuum Schwinger function methods, a coherent set of predictions for proton, $Λ$ and $Σ^0$ distribution functions (DFs) has been made available -- both helicity dependent and unpolarised. The results and comparisons between them reveal impacts of diquark correlations and SU$(3)$-flavour symmetry breaking, some of which are highlighted in this contribution. For instance: in-proton ratios of helicity-dependent/unpolarised valence-quark DFs are presented; it is highlighted that, were it not for the presence of axialvector diquarks in the $Σ^0$, the valence strange quark would carry none of the $Σ^0$ spin; and the sign and size of polarised gluon DFs is discussed -- at a scale typical of modern measurements, gluon partons carry roughly 40% of each octet baryon's spin.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Intertwined atomic-nanoscale-microscale structures via intralayer anisotropic Fe-chains in the layered ferromagnet FePd2Te2

Controlling mesoscale and nanoscale material structures and properties through self-organized atomic behavior is essential for atomic-scale manufacturing. However, direct and visual studies on the cross-scale effects of such atomic self-organization on mesoscopic structures remain scarce. Here, we report the intertwined atomic-nanoscale-mesoscale structures via the intralayer Fe-chains in the sandwich-like layered FePd2Te2 crystal by scanning tunneling microscopy (STM) and atomic force microscopy (AFM). The hierarchical orthogonal corrugated morphologies are directly revealed and attributed to its chain-orientation-determined twinning-domain effect. Both Fe-chains of middle-sublayer and two kinds of Te atoms of top-sublayer are further atomically resolved at the sub-Å level, indicating the critical effects of Pd-atoms/voids on the intra-layer anisotropic Fe-chains and the interlayer structural alignment. The thermal-induced and strain-related structural transitions of surface layer are further investigated and discussed based on the proposed filling model of Pd-voids by the intralayer Pd-atoms. Our work not only provides deep understanding of this exotic layered magnetic material, and will inspire more perspectives for tailoring its anisotropic atomic-to-mesoscale structures and properties.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

On the gravitational partition function under volume constraints

The Euclidean action serves as a bridge between gravitational thermodynamics and the partition function. In this work, we further examine the gravitational partition function under a fixed volume constraint, extending the fixed volume on-shell geometry in the massless case. Moving beyond this massless configuration, we construct solutions with nonzero mass functions, leading to a new class of volume-constrained Euclidean geometries (VCEGs). The VCEG contains both a boundary and a horizon, and its Euclidean action is determined solely by the contribution from the horizon. However, further investigation suggests that this boundary appears to be artificially constructed and can be extended, giving rise to the extended VCEGs. These geometries feature two horizons, each with a conical singularity, and their action is given by one-quarter of the sum of the areas of the two horizons. In general, the conical singularities on both horizons cannot be simultaneously removed, except at a critical mass $m = m^*$, which defines the critical extended VCEG. Configurations with conical singularities are interpreted as constrained gravitational instantons. An analysis of their contributions to the partition function and topology reveals a close analogy between the extended VCEGs and the Euclidean Schwarzschild-de Sitter static patch, suggesting that the volume constraint effectively plays a role akin to that of a cosmological constant in semiclassical quantum gravity.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Personalized w-Event Privacy for Infinite Stream Estimation

In applications such as event monitoring, log analysis, and video querying, $w$-event privacy protects individual data within a sliding time window while supporting accurate stream statistics. Existing studies on infinite data streams mainly assume homogeneous privacy requirements for all users, which cannot capture user-specific privacy preferences. This paper studies personalized $w$-event privacy for private data stream estimation. We first design the Personalized Window Size Mechanism (PWSM), which supports personalized privacy requirements at each time slot. Based on PWSM, we propose Personalized Budget Distribution (PBD) and Personalized Budget Absorption (PBA) to estimate streaming statistics under $\boldsymbol{w}$-Event $\boldsymbol{\mathcal{E}}$ Personalized Differential Privacy (($\boldsymbol{w}$, $\boldsymbol{\mathcal{E}}$)-EPDP). PBD guarantees that the budget reserved for the next time step is no smaller than the budget consumed in the previous release, while PBA improves the current budget by absorbing unused budgets from the previous $k$ time slots and borrowing from the next $k$ time slots. We further develop Dynamic Personalized Budget Distribution (DPBD) and Dynamic Personalized Budget Absorption (DPBA), which allow users to dynamically adjust privacy requirements while satisfying $(τ, \boldsymbol{w}_B, \boldsymbol{w}_F)$-Event $(\boldsymbol{\mathcal{E}}_B, \boldsymbol{\mathcal{E}}_F)$-Personalized Differential Privacy. We prove that all proposed methods achieve the corresponding personalized differential privacy guarantees and derive their error upper bounds. Experiments show that our methods reduce estimation error by at least $53.6\%$ compared with state-of-the-art algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Revisiting Graph Analytics Benchmark

The rise of graph analytics platforms has led to the development of various benchmarks for evaluating and comparing platform performance. However, existing benchmarks often fall short of fully assessing performance due to limitations in core algorithm selection, data generation processes (and the corresponding synthetic datasets), as well as the neglect of API usability evaluation. To address these shortcomings, we propose a novel graph analytics benchmark. First, we select eight core algorithms by extensively reviewing both academic and industrial settings. Second, we design an efficient and flexible data generator and produce eight new synthetic datasets as the default datasets for our benchmark. Lastly, we introduce a multi-level large language model (LLM)-based framework for API usability evaluation-the first of its kind in graph analytics benchmarks. We conduct comprehensive experimental evaluations on existing platforms (GraphX, PowerGraph, Flash, Grape, Pregel+, Ligra and G-thinker). The experimental results demonstrate the superiority of our proposed benchmark.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Discriminator-Guided Model-Based Offline Imitation Learning

Offline imitation learning (IL) is a powerful method to solve decision-making problems from expert demonstrations without reward labels. Existing offline IL methods suffer from severe performance degeneration under limited expert data. Including a learned dynamics model can potentially improve the state-action space coverage of expert data, however, it also faces challenging issues like model approximation/generalization errors and suboptimality of rollout data. In this paper, we propose the Discriminator-guided Model-based offline Imitation Learning (DMIL) framework, which introduces a discriminator to simultaneously distinguish the dynamics correctness and suboptimality of model rollout data against real expert demonstrations. DMIL adopts a novel cooperative-yet-adversarial learning strategy, which uses the discriminator to guide and couple the learning process of the policy and dynamics model, resulting in improved model performance and robustness. Our framework can also be extended to the case when demonstrations contain a large proportion of suboptimal data. Experimental results show that DMIL and its extension achieve superior performance and robustness compared to state-of-the-art offline IL methods under small datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Antiferromagnetic structure and magnetic properties of Dy2O2Te: An isostructural analog of the rare-earth superconductors R2O2Bi

The rare-earth compounds R2O2Bi (R=Tb, Dy, Er, Lu, Y) are newly discovered superconductors in the vicinity of a rare-earth magnetic long-range order. In this work, we determine the magnetic order of the parent compound Dy2O2Te by neutron scattering as the A-type antiferromagnetic structure below the Néel temperature TN=9.7K. The large staggered magnetic moment 9.4(1) μB per Dy at T=3.5K lies in the basal ab plane. In a magnetic field, anomalous magnetic properties including the bifurcation between zero-field- and field-cooling magnetization, a butterfly-shaped magnetic hysteresis, and slow magnetic relaxation emerge, which are related to the field-induced metamagnetic transitions in Dy2O2Te. Our experimental findings could stimulate further research on the relation between antiferromagnetism and superconductivity in these rare-earth compounds.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Efficient k-clique Listing with Set Intersection Speedup [Technical Report]

Listing all k-cliques is a fundamental problem in graph mining, with applications in finance, biology, and social network analysis. However, owing to the exponential growth of the search space as k increases, listing all k-cliques is algorithmically challenging. DDegree and DDegCol are the state-of-the-art algorithms that exploit ordering heuristics based on degree ordering and color ordering, respectively. Both DDegree and DDegCol induce high time and space overhead for set intersections cause they construct and maintain all induced subgraphs. Meanwhile, it is non-trivial to implement the data level parallelism to further accelerate on DDegree and DDegCol. In this paper, we propose two efficient algorithms SDegree and BitCol for k-clique listing. We mainly focus on accelerating the set intersections for k-clique listing. Both SDegree and BitCol exploit the data level parallelism for further acceleration with single instruction multiple data (SIMD) or vector instruction sets. Furthermore, we propose two preprocessing techniques Pre-Core and Pre-List, which run in linear time. The preprocessing techniques significantly reduce the size of the original graph and prevent exploring a large number of invalid nodes. In the theoretical analysis, our algorithms have a comparable time complexity and a slightly lower space complexity than the state-of-the-art algorithms. The comprehensive experiments reveal that our algorithms outperform the state-of-the-art algorithms by 3.75x for degree ordering and 5.67x for color ordering on average.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Exchange field enhanced upper critical field of the superconductivity in compressed antiferromagnetic EuTe2

We report high pressure studies on the C-type antiferromagnetic semiconductor EuTe2 up to 36.0 GPa. A structural transition from the I4/mcm to C2/m space group is identified at ~16 GPa. Superconductivity is discovered above ~5 GPa in both the I4/mcm and C2/m space groups. In the low-pressure phase (< 16 GPa), the antiferromagnetic transition temperature is enhanced with increasing pressure due to the enhanced magnetic exchange interactions. Magnetoresistance measurements indicate an interplay between the local moments of Eu2+ and the conduction electrons of Te 5p orbits. The upper critical field of the superconductivity is well above the Pauli limit. Across the structural transition to the high-pressure phase (> 16 GPa), EuTe2 becomes nonmagnetic and the superconducting transition temperature evolves smoothly with the upper critical field below the Pauli limit. Therefore, the high upper critical field of EuTe2 in the low-pressure phase is due to the exchange field compensation effect of the Eu magnetic order and the superconductivity in both structures may arise in the framework of the BCS theory.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Exfoliation of 2D van der Waals crystals in ultrahigh vacuum for interface engineering

Two-dimensional (2D) materials and their heterostructures have been intensively studied in recent years due to their potential applications in electronic, optoelectronic, and spintronic devices. Nonetheless, the realization of 2D heterostructures with atomically flat and clean interfaces remains challenging, especially for air-sensitive materials, which hinders the in-depth investigation of interface-induced phenomena and the fabrication of high-quality devices. Here, we circumvented this challenge by exfoliating 2D materials in an ultrahigh vacuum. Remarkably, ultraflat and clean substrate surfaces can assist the exfoliation of 2D materials, regardless of the substrate and 2D material, thus providing a universal method for the preparation of heterostructures with ideal interfaces. In addition, we studied the properties of two prototypical systems that cannot be achieved previously, including the electronic structure of monolayer phospherene and optical responses of transition metal dichalcogenides on different metal substrates. Our work paves the way to engineer rich interface-induced phenomena, such as proximity effects and moiré superlattices.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]

With the development of traffic prediction technology, spatiotemporal prediction models have attracted more and more attention from academia communities and industry. However, most existing researches focus on reducing model&#39;s prediction error but ignore the error caused by the uneven distribution of spatial events within a region. In this paper, we study a region partitioning problem, namely optimal grid size selection problem (OGSS), which aims to minimize the real error of spatiotemporal prediction models by selecting the optimal grid size. In order to solve OGSS, we analyze the upper bound of real error of spatiotemporal prediction models and minimize the real error by minimizing its upper bound. Through in-depth analysis, we find that the upper bound of real error will decrease then increase when the number of model grids increase from 1 to the maximum allowed value. Then, we propose two algorithms, namely Ternary Search and Iterative Method, to automatically find the optimal grid size. Finally, the experiments verify that the error of prediction has the same trend as its upper bound, and the change trend of the upper bound of real error with respect to the increase of the number of model grids will decrease then increase. Meanwhile, in a case study, by selecting the optimal grid size, the order dispatching results of a state-of-the-art prediction-based algorithm can be improved up to 13.6%, which shows the effectiveness of our methods on tuning the region partition for spatiotemporal prediction models.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Observation of one-dimensional Dirac fermions in silicon nanoribbons

Dirac materials, which feature Dirac cones in the reciprocal space, have been one of the hottest topics in condensed matter physics in the past decade. To date, 2D and 3D Dirac Fermions have been extensively studied, while their 1D counterparts are rare. Recently, Si nanoribbons (SiNRs), which are composed of alternating pentagonal Si rings, have attracted intensive attention. However, the electronic structure and topological properties of SiNRs are still elusive. Here, by angle-resolved photoemission spectroscopy, scanning tunneling microscopy/spectroscopy measurements, first-principles calculations, and tight-binding model analysis, we demonstrate the existence of 1D Dirac Fermions in SiNRs. Our theoretical analysis shows that the Dirac cones derive from the armchairlike Si chain in the center of the nanoribbon and can be described by the Su-Schrieffer-Heeger model. These results establish SiNRs as a platform for studying the novel physical properties in 1D Dirac materials.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Observation of topological flat bands in the kagome semiconductor Nb$_3$Cl$_8$

The destructive interference of wavefunctions in a kagome lattice can give rise to topological flat bands (TFBs) with a highly degenerate state of electrons. Recently, TFBs have been observed in several kagome metals, including Fe$_3$Sn$_2$, FeSn, CoSn, and YMn$_6$Sn$_6$. Nonetheless, kagome materials that are both exfoliable and semiconducting are lacking, which seriously hinders their device applications. Herein, we show that Nb$_3$Cl$_8$, which hosts a breathing kagome lattice, is gapped out because of the absence of inversion symmetry, while the TFBs survive because of the protection of the mirror reflection symmetry. By angle-resolved photoemission spectroscopy measurements and first-principles calculations, we directly observe the TFB and a moderate band gap in Nb$_3$Cl$_8$. By mechanical exfoliation, we successfully obtain monolayers of Nb$_3$Cl$_8$ and confirm that they are stable under ambient conditions. In addition, our calculations show that monolayers of Nb$_3$Cl$_8$ have a magnetic ground state, thus providing opportunities to study the interplay between geometry, topology, and magnetism.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Spatio-Temporal-Frequency Graph Attention Convolutional Network for Aircraft Recognition Based on Heterogeneous Radar Network

This paper proposes a knowledge-and-data-driven graph neural network-based collaboration learning model for reliable aircraft recognition in a heterogeneous radar network. The aircraft recognizability analysis shows that: (1) the semantic feature of an aircraft is motion patterns driven by the kinetic characteristics, and (2) the grammatical features contained in the radar cross-section (RCS) signals present spatial-temporal-frequency (STF) diversity decided by both the electromagnetic radiation shape and motion pattern of the aircraft. Then a STF graph attention convolutional network (STFGACN) is developed to distill semantic features from the RCS signals received by the heterogeneous radar network. Extensive experiment results verify that the STFGACN outperforms the baseline methods in terms of detection accuracy, and ablation experiments are carried out to further show that the expansion of the information dimension can gain considerable benefits to perform robustly in the low signal-to-noise ratio region.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards Comprehensively Understanding the Run-time Security of Programmable Logic Controllers: A 3-year Empirical Study

Programmable Logic Controllers (PLCs) are the core control devices in Industrial Control Systems (ICSs), which control and monitor the underlying physical plants such as power grids. PLCs were initially designed to work in a trusted industrial network, which however can be brittle once deployed in an Internet-facing (or penetrated) network. Yet, there is a lack of systematic empirical analysis of the run-time security of modern real-world PLCs. To close this gap, we present the first large-scale measurement on 23 off-the-shelf PLCs across 13 leading vendors. We find many common security issues and unexplored implications that should be more carefully addressed in the design and implementation. To sum up, the unsupervised logic applications can cause system resource/privilege abuse, which gives adversaries new means to hijack the control flow of a runtime system remotely (without exploiting memory vulnerabilities); 2) the improper access control mechanisms bring many unauthorized access implications; 3) the proprietary or semi-proprietary protocols are fragile regarding confidentiality and integrity protection of run-time data. We empirically evaluated the corresponding attack vectors on multiple PLCs, which demonstrates that the security implications are severe and broad. Our findings were reported to the related parties responsibly, and 20 bugs have been confirmed with 7 assigned CVEs.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

VeriFi: Towards Verifiable Federated Unlearning

Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving participant has the right to request to delete its private data from the global model. However, unlearning itself may not be enough to implement RTBF unless the unlearning effect can be independently verified, an important aspect that has been overlooked in the current literature. In this paper, we prompt the concept of verifiable federated unlearning, and propose VeriFi, a unified framework integrating federated unlearning and verification that allows systematic analysis of the unlearning and quantification of its effect, with different combinations of multiple unlearning and verification methods. In VeriFi, the leaving participant is granted the right to verify (RTV), that is, the participant notifies the server before leaving, then actively verifies the unlearning effect in the next few communication rounds. The unlearning is done at the server side immediately after receiving the leaving notification, while the verification is done locally by the leaving participant via two steps: marking (injecting carefully-designed markers to fingerprint the leaver) and checking (examining the change of the global model&#39;s performance on the markers). Based on VeriFi, we conduct the first systematic and large-scale study for verifiable federated unlearning, considering 7 unlearning methods and 5 verification methods. Particularly, we propose a more efficient and FL-friendly unlearning method, and two more effective and robust non-invasive-verification methods. We extensively evaluate VeriFi on 7 datasets and 4 types of deep learning models. Our analysis establishes important empirical understandings for more trustworthy federated unlearning.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Anomalies as Obstructions: from Dimensional Lifts to Swampland

We revisit the relation between the anomalies in four and six dimensions and the Chern-Simons couplings one dimension below. While the dimensional reduction of chiral theories is well-understood, the question which three and five-dimensional theories can come from a general circle reduction, and are hence liftable, is more subtle. We argue that existence of an anomaly cancellation mechanism is a necessary condition for liftability. In addition, the anomaly cancellation and the CS couplings in six and five dimensions respectively determine the central charges of string-like BPS objects that cannot be consistently decoupled from gravity, a.k.a. supergravity strings. Following the completeness conjecture and requiring that their worldsheet theory is unitary imposes bounds on the admissible theories. We argue that for the anomaly-free six-dimensional theories it is more advantageous to study the unitarity constraints obtained after reduction to five dimensions. In general these are slightly more stringent and can be cast in a more geometric form, highly reminiscent of the Kodaira positivity condition (KPC). Indeed, for the F-theoretic models which have an underlying Calabi-Yau threefold these can be directly compared. The unitarity constraints (UC) are in general weaker than KPC, and maybe useful in understanding the consistent models without F-theoretic realisation. We catalogue the cases when UC is more restrictive than KPC, hinting at more refined hidden structure in elliptic Calabi-Yau threefolds with certain singularity structure.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Extreme Suppression of Antiferromagnetic Order and Critical Scaling in a Two-Dimensional Random Quantum Magnet

Sr$_2$CuTeO$_6$ is a square-lattice Néel antiferromagnet with superexchange between first-neighbor $S=1/2$ Cu spins mediated by plaquette centered Te ions. Substituting Te by W, the affected impurity plaquettes have predominantly second-neighbor interactions, thus causing local magnetic frustration. Here we report a study of Sr$_2$CuTe$_{1-x}$W$_x$O$_6$ using neutron diffraction and $μ$SR techniques, showing that the Néel order vanishes already at $x = 0.025 \pm 0.005$. We explain this extreme order suppression using a two-dimensional Heisenberg spin model, demonstrating that a W-type impurity induces a deformation of the order parameter that decays with distance as $1/r^2$ at temperature $T=0$. The associated logarithmic singularity leads to loss of order for any $x>0$. Order for small $x>0$ and $T>0$ is induced by weak interplane couplings. In the nonmagnetic phase of Sr$_2$CuTe$_{1-x}$W$_x$O$_6$, the $μ$SR relaxation rate exhibits quantum critical scaling with a large dynamic exponent, $z \approx 3$, consistent with a random-singlet state.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Photochemical Synthesis of P-S-H Ternary Hydride at High Pressures

The recent discovery of room temperature superconductivity (283 K) in carbonaceous sulfur hydride (C-S-H) has attracted lots of interests in ternary hydrogen rich materials. In this report, ternary hydride P-S-H has been synthesized through photochemical reaction from elemental sulfur (S), phosphorus (P) and molecular hydrogen (H2) at high pressures and room temperature. The Raman spectroscopy under pressure shows that H2S and PH3 compounds are synthesized after laser heating at 0.9 GPa and a ternary van der Waals compound P-S-H is synthesized with a further compression to 4.6 GPa. The P-S-H compound is probably a mixed alloy of PH3 and (H2S)2H2 with a guest-host structure similar to the C-S-H system. The ternary hydride can persist up to 35.6 GPa at least and shows two phase transitions at approximately 23.6 GPa and 32.8 GPa, respectively. The P-S-H ternary hydride in this report is a competitive candidate for new hydride superconductors with near room-temperature transitions.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

RobOT: Robustness-Oriented Testing for Deep Learning Systems

Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, where adversarial examples (a.k.a.~bugs) of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the commonly used neuron coverage metrics by existing DL testing approaches are not correlated to model robustness. It is also not an effective measurement on the confidence of the model robustness after testing. In this work, we address this gap by proposing a novel testing framework called Robustness-Oriented Testing (RobOT). A key part of RobOT is a quantitative measurement on 1) the value of each test case in improving model robustness (often via retraining), and 2) the convergence quality of the model robustness improvement. RobOT utilizes the proposed metric to automatically generate test cases valuable for improving model robustness. The proposed metric is also a strong indicator on how well robustness improvement has converged through testing. Experiments on multiple benchmark datasets confirm the effectiveness and efficiency of RobOT in improving DL model robustness, with 67.02% increase on the adversarial robustness that is 50.65% higher than the state-of-the-art work DeepGini.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

CoinMagic: A Differential Privacy Framework for Ring Signature Schemes

By allowing users to obscure their transactions via including &#34;mixins&#34; (chaff coins), ring signature schemes have been widely used to protect a sender&#39;s identity of a transaction in privacy-preserving blockchain systems, like Monero and Bytecoin. However, recent works point out that the existing ring signature scheme is vulnerable to the &#34;chain-reaction&#34; analysis (i.e., the spent coin in a given ring signature can be deduced through elimination). Especially, when the diversity of mixins is low, the spent coin will have a high risk to be detected. To overcome the weakness, the ring signature should be consisted of a set of mixins with high diversity and produce observations having &#34;similar&#34; distributions for any two coins. In this paper, we propose a notion, namely $ε$-coin-indistinguishability ($ε$-CI), to formally define the &#34;similar&#34; distribution guaranteed through a differential privacy scheme. Then, we formally define the CI-aware mixins selection problem with disjoint-superset constraint (CIA-MS-DS), which aims to find a mixin set that has maximal diversity and satisfies the constraints of $ε$-CI and the budget. In CIA-MS-DS, each ring signature is either disjoint with or the superset of its preceding ring signatures. We prove that CIA-MS-DS is NP-hard and thus intractable. To solve the CIA-MS-DS problem, we propose two approximation algorithms, namely the Progressive Algorithm and the Game Theoretic Algorithm, with theoretic guarantees. Through extensive experiments on both real data sets and synthetic data sets, we demonstrate the efficiency and the effectiveness of our approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles

Envisioned as a promising component of the future wireless Internet-of-Things (IoT) networks, the non-orthogonal multiple access (NOMA) technique can support massive connectivity with a significantly increased spectral efficiency. Cooperative NOMA is able to further improve the communication reliability of users under poor channel conditions. However, the conventional system design suffers from several inherent limitations and is not optimized from the bit error rate (BER) perspective. In this paper, we develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL). We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner. On this basis, we construct multiple loss functions to quantify the BER performance and propose a novel multi-task oriented two-stage training method to solve the end-to-end training problem in a self-supervised manner. The learning mechanism of each DNN module is then analyzed based on information theory, offering insights into the proposed DNN architecture and its corresponding training method. We also adapt the proposed scheme to handle the power allocation (PA) mismatch between training and inference and incorporate it with channel coding to combat signal deterioration. Simulation results verify its advantages over orthogonal multiple access (OMA) and the conventional cooperative NOMA scheme in various scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Evidence the ferromagnetic order on CoSb layer of LaCoSb$_2$

The emergence of unconventional superconductivity is generally considered to be related to spin fluctuations. Unveiling the intriguing behaviors of spin fluctuations in parent compounds with layered transition-metal ions may shed light on the search for exotic unconventional superconductors. Here, based on the framework of the first-principles calculations, we theoretically propose that LaCoSb$_2$ is a weak antiferromagnetic layered metal with an in-plane ferromagnetic moment of 0.88 $μ_B$ at the Co sites, as a candidate parent compound of the cobalt-based superconductors. Importantly, this theoretical finding is experimentally supported by our magnetization measurements on polycrystalline samples of LaCo$_{0.78}$Sb$_2$. Following the symmetry analysis, we suggest a possible $p$-wave superconductivity hosted in doped LaCoSb$_2$ emerging at the verge of ferromagnetic spin fluctuations, which implies potential applications in topological quantum computing in future.

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preprint2020arXiv

Evolution of superconductivity and antiferromagnetic order in Ba(Fe$_{0.92-x}$Co$_{0.08}$V$_x$)$_2$As$_2$

The vanadium doping effects on superconductivity and magnetism of iron pnictides are investigated in Ba(Fe$_{0.92-x}$Co$_{0.08}$V$_x$)$_2$As$_2$ by transport, susceptibility and neutron scattering measurements. The doping of magnetic impurity V causes a fast suppression of superconductivity with T$_c$ reduced at a rate of 7.4~K/1\%V. On the other hand, the long-range commensurate $C$-type antiferromagnetic order is recovered upon the V doping. The value of ordered magnetic moments of Ba(Fe$_{0.92-x}$Co$_{0.08}$V$_x$)$_2$As$_2$ follows a dome-like evolution versus doping concentration x. A possible Griffiths-type antiferromagnetic region of multiple coexisting phases in the phase diagram of Ba(Fe$_{0.92-x}$Co$_{0.08}$V$_x$)$_2$As$_2$ is identified, in accordance with previous theoretical predictions based on a cooperative behavior of the magnetic impurities and the conduction electrons mediating the Ruderman-Kittel-Kasuya-Yosida interactions between them.

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preprint2020arXiv

Experimental evidence of monolayer AlB$_2$ with symmetry-protected Dirac cones

Monolayer AlB$_2$ is composed of two atomic layers: honeycomb borophene and triangular aluminum. In contrast with the bulk phase, monolayer AlB$_2$ is predicted to be a superconductor with a high critical temperature. Here, we demonstrate that monolayer AlB$_2$ can be synthesized on Al(111) via molecular beam epitaxy. Our theoretical calculations revealed that the monolayer AlB$_2$ hosts several Dirac cones along the $Γ$--M and $Γ$--K directions; these Dirac cones are protected by crystal symmetries and are thus resistant to external perturbations. The extraordinary electronic structure of the monolayer AlB$_2$ was confirmed via angle-resolved photoemission spectroscopy measurements. These results are likely to stimulate further research interest to explore the exotic properties arising from the interplay of Dirac fermions and superconductivity in two-dimensional materials.

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preprint2020arXiv

FastReID: A Pytorch Toolbox for General Instance Re-identification

General Instance Re-identification is a very important task in the computer vision, which can be widely used in many practical applications, such as person/vehicle re-identification, face recognition, wildlife protection, commodity tracing, and snapshop, etc.. To meet the increasing application demand for general instance re-identification, we present FastReID as a widely used software system in JD AI Research. In FastReID, highly modular and extensible design makes it easy for the researcher to achieve new research ideas. Friendly manageable system configuration and engineering deployment functions allow practitioners to quickly deploy models into productions. We have implemented some state-of-the-art projects, including person re-id, partial re-id, cross-domain re-id and vehicle re-id, and plan to release these pre-trained models on multiple benchmark datasets. FastReID is by far the most general and high-performance toolbox that supports single and multiple GPU servers, you can reproduce our project results very easily and are very welcome to use it, the code and models are available at https://github.com/JDAI-CV/fast-reid.

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preprint2020arXiv

Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference

Executing deep neural networks for inference on the server-class or cloud backend based on data generated at the edge of Internet of Things is desirable due primarily to the limited compute power of edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inference scheme incurs concerns regarding the privacy of the inference data transmitted by the edge devices to the curious backend. This paper presents a lightweight and unobtrusive approach to obfuscate the inference data at the edge devices. It is lightweight in that the edge device only needs to execute a small-scale neural network; it is unobtrusive in that the edge device does not need to indicate whether obfuscation is applied. Extensive evaluation by three case studies of free spoken digit recognition, handwritten digit recognition, and American sign language recognition shows that our approach effectively protects the confidentiality of the raw forms of the inference data while effectively preserving the backend&#39;s inference accuracy.

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preprint2020arXiv

Observation of quantum spin Hall states in Ta$_2$Pd$_3$Te$_5$

Two-dimensional topological insulators (2DTIs), which host the quantum spin Hall (QSH) effect, are one of the key materials in next-generation spintronic devices. To date, experimental evidence of the QSH effect has only been observed in a few materials, and thus, the search for new 2DTIs is at the forefront of physical and materials science. Here, we report experimental evidence of a 2DTI in the van der Waals material Ta$_2$Pd$_3$Te$_5$. First-principles calculations show that each monolayer of Ta$_2$Pd$_3$Te$_5$ is a 2DTI with weak interlayer interactions. Combined transport, angle-resolved photoemission spectroscopy, and scanning tunneling microscopy measurements confirm the existence of a band gap at the Fermi level and topological edge states inside the gap. These results demonstrate that Ta$_2$Pd$_3$Te$_5$ is a promising material for fabricating spintronic devices based on the QSH effect.

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preprint2020arXiv

Simulating Performance of ML Systems with Offline Profiling

We advocate that simulation based on offline profiling is a promising approach to better understand and improve the complex ML systems. Our approach uses operation-level profiling and dataflow based simulation to ensure it offers a unified and automated solution for all frameworks and ML models, and is also accurate by considering the various parallelization strategies in a real system.

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preprint2020arXiv

Spectrum Intelligent Radio: Technology, Development, and Future Trends

The advent of Industry 4.0 with massive connectivity places significant strains on the current spectrum resources, and challenges the industry and regulators to respond promptly with new disruptive spectrum management strategies. The current radio development, with certain elements of intelligence, is nowhere near showing an agile response to the complex radio environments. Following the line of intelligence, we propose to classify spectrum intelligent radio into three streams: classical signal processing, machine learning (ML), and contextual adaptation. We focus on the ML approach, and propose a new intelligent radio architecture with three hierarchical forms: perception, understanding, and reasoning. The proposed perception method achieves fully blind multi-level spectrum sensing. The understanding method accurately predicts the primary users&#39; coverage across a large area, and the reasoning method performs a near-optimal idle channel selection. Opportunities, challenges, and future visions are also discussed for the realization of a fully intelligent radio.

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preprint2020arXiv

Task Offloading for Large-Scale Asynchronous Mobile Edge Computing: An Index Policy Approach

Mobile-edge computing (MEC) offloads computational tasks from wireless devices to network edge, and enables real-time information transmission and computing. Most existing work concerns a small-scale synchronous MEC system. In this paper, we focus on a large-scale asynchronous MEC system with random task arrivals, distinct workloads, and diverse deadlines. We formulate the offloading policy design as a restless multi-armed bandit (RMAB) to maximize the total discounted reward over the time horizon. However, the formulated RMAB is related to a PSPACE-hard sequential decision-making problem, which is intractable. To address this issue, by exploiting the Whittle index (WI) theory, we rigorously establish the WI indexability and derive a scalable closed-form solution. Consequently, in our WI policy, each user only needs to calculate its WI and report it to the BS, and the users with the highest indices are selected for task offloading. Furthermore, when the task completion ratio becomes the focus, the shorter slack time less remaining workload (STLW) priority rule is introduced into the WI policy for performance improvement. When the knowledge of user offloading energy consumption is not available prior to the offloading, we develop Bayesian learning-enabled WI policies, including maximum likelihood estimation, Bayesian learning with conjugate prior, and prior-swapping techniques. Simulation results show that the proposed policies significantly outperform the other existing policies.

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preprint2020arXiv

Tunable magnetic properties in van der Waals crystals (Fe$_{1-x}$Co$_x$)$_5$GeTe$_2$

We report the doping effects of cobalt on van der Waals (vdW) magnet Fe$_5$GeTe$_2$. A series of (Fe$_{1-x}$Co$_x$)$_5$GeTe$_2$ (0$\leq$x$\leq$0.44) single crystals have been successfully grown, their structural, magnetic and transport properties are investigated. For x=0.20, The Curie temperature $T_C$ increases from 276~K to 337~K. Moreover, the magnetic easy-axis is reoriented to the $ab$-plane from the $c$-axis in undoped Fe$_5$GeTe$_2$ with largely enhanced magnetic anisotropy. These magnetic properties would make (Fe$_{0.8}$Co$_{0.2}$)$_5$GeTe$_2$ more effective in stabilizing magnetic order in the two-dimensional limit. A complex magnetic phase diagram is identified on the higher doping side. The x=0.44 crystal first orders ferromagnetically at $T_C$=363~K then undergoes an antiferromagnetic transition at $T_N$=335~K. Furthermore magnetic-field-induced spin-flop transitions are observed for the AFM ground state. Our work reveals (Fe$_{1-x}$Co$_x$)$_5$GeTe$_2$ as promising candidates for developing new spin-related applications and proposes a method to engineer the magnetic properties of vdW magnet.

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preprint2020arXiv

Universal mechanical exfoliation of large-area 2D crystals

Two-dimensional (2D) materials provide extraordinary opportunities for exploring phenomena arising in atomically thin crystals. Beginning with the first isolation of graphene, mechanical exfoliation has been a key to provide high-quality 2D materials but despite improvements it is still limited in yield, lateral size and contamination. Here we introduce a contamination-free, one-step and universal Au-assisted mechanical exfoliation method and demonstrate its effectiveness by isolating 40 types of single-crystalline monolayers, including elemental 2D crystals, metal-dichalcogenides, magnets and superconductors. Most of them are of millimeter-size and high-quality, as shown by transfer-free measurements of electron microscopy, photo spectroscopies and electrical transport. Large suspended 2D crystals and heterojunctions were also prepared with high-yield. Enhanced adhesion between the crystals and the substrates enables such efficient exfoliation, for which we identify a common rule that underpins a universal route for producing large-area monolayers and thus supports studies of fundamental properties and potential application of 2D materials.

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preprint2019arXiv

Domain wall pinning and hard magnetic phase in Co-doped bulk single crystalline Fe3GeTe2

We report the effects of cobalt doping on the magnetic properties of two-dimensional van der Waals ferromagnet Fe3GeTe2. Single crystals of (Fe{1-x}Cox)3GeTe2 with x=0-0.78 were successfully synthesized and characterized with x-ray diffraction, energy dispersive x-ray spectroscopy and magnetization measurements. Both the Curie-Weiss temperature and ferromagnetic (FM) ordered moment of Fe3GeTe2 are gradually suppressed upon Co doping. A kink in zero-field-cooling low field M(T) curve which is previously explained as an antiferromagnetic transition is observed for samples with x=0-0.58. Our detailed magnetization measurements and theoretical calculations strongly suggest that this kink is originated from the pinning of magnetic domain walls. The domain pinning effects are suddenly enhanced when the doping concentration of cobalt is around 50%, both the coercive field Hc and the magnetic remanence to saturated magnetization ratio MR/MS are largely improved and a hard magnetic phase emerges in bulk single crystal samples. The strong doping dependent magnetic properties suggest more spintronic applications of Fe3GeTe2.

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preprint2019arXiv

Privacy-preserving Distributed Machine Learning via Local Randomization and ADMM Perturbation

With the proliferation of training data, distributed machine learning (DML) is becoming more competent for large-scale learning tasks. However, privacy concerns have to be given priority in DML, since training data may contain sensitive information of users. In this paper, we propose a privacy-preserving ADMM-based DML framework with two novel features: First, we remove the assumption commonly made in the literature that the users trust the server collecting their data. Second, the framework provides heterogeneous privacy for users depending on data&#39;s sensitive levels and servers&#39; trust degrees. The challenging issue is to keep the accumulation of privacy losses over ADMM iterations minimal. In the proposed framework, a local randomization approach, which is differentially private, is adopted to provide users with self-controlled privacy guarantee for the most sensitive information. Further, the ADMM algorithm is perturbed through a combined noise-adding method, which simultaneously preserves privacy for users&#39; less sensitive information and strengthens the privacy protection of the most sensitive information. We provide detailed analyses on the performance of the trained model according to its generalization error. Finally, we conduct extensive experiments using real-world datasets to validate the theoretical results and evaluate the classification performance of the proposed framework.

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preprint2019arXiv

Quantum oscillations and electronic structures in large Chern number semimetal RhSn

We report the magnetoresistance, Hall effect, de Haas-van Alphen (dHvA) oscillations and the electronic structures of single crystal RhSn, which is a typical material of CoSi family holding a large Chern number. The large unsaturated magnetoresistance is observed with B//[001]. The Hall resistivity curve indicates that RhSn is a multi-band system with high mobility. Evident quantum oscillations have been observed, from which the light effective masses are extracted. Ten fundamental frequencies are extracted after the fast Fourier transform analysis of the dHvA oscillations with B//[001] configuration. The two low frequencies F$_1$ and F$_2$ do not change obviously and the two high frequencies F$_9$ and F$_{10}$ evolve into four when B rotates from B//[001] to B//[110], which is consistent with the band structure in the first-principles calculations with spin-orbit coupling (SOC). The extracted Berry phases of the relative pockets show a good agreement with the Chern number $\pm4$ (with SOC) in the first-principles calculations. Above all, our studies indicate that RhSn is an ideal platform to study the unconventional chiral fermions and the surface states.

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