arXiv subjects

Peng Cheng

Publications and source records attributed to Peng Cheng.

At least 199 records · Page 11Linked to original sources

arXiv2023

Realization of a two-dimensional checkerboard lattice in monolayer Cu$_2$N

Two-dimensional checkerboard lattice, the simplest line-graph lattice, has been intensively studied as a toy model, while material design and synthesis remain elusive. Here, we report theoretical prediction and experimental realization of the checkerboard lattice in monolayer Cu$_2$N. Experimentally, monolayer Cu$_2$N can be realized in the well-known N/Cu(100) and N/Cu(111) systems that were previously mistakenly believed to be insulators. Combined angle-resolved photoemission spectroscopy measurements, first-principles calculations, and tight-binding analysis show that both systems host checkerboard-derived hole pockets near the Fermi level. In addition, monolayer Cu$_2$N has outstanding stability in air and organic solvents, which is crucial for further device applications.

cond-mat.mtrl-sci↗

arXiv2023

Bound excitons and bandgap engineering in violet phosphorus

Violet phosphorus (VP), the most stable phosphorus allotrope, is a van der Waals semiconductor that can be used to construct $\textit{p}$-type nanodevices. Recently, high-quality VP crystals have been synthesized while a deep insight into their excitonic properties and bandgap tailoring approaches, which are crucial for their optoelectronic device applications, is still lacking. Here, we study the optical properties of ultrathin VP by second harmonic generation, photoluminescence, and optical absorption spectroscopy. We observed strong bound exciton emission that is 0.48 eV away from the free exciton emission, which is among the largest in 2D materials. In addition, the bandgaps of VP are highly sensitive to the number of layers and external strain, which provides convenient approaches for bandgap engineering. The strong bound exciton emission and tunable bandgaps make VP a promising material in optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2023

Tutel: Adaptive Mixture-of-Experts at Scale

Sparsely-gated mixture-of-experts (MoE) has been widely adopted to scale deep learning models to trillion-plus parameters with fixed computational cost. The algorithmic performance of MoE relies on its token routing mechanism that forwards each input token to the right sub-models or experts. While token routing dynamically determines the amount of expert workload at runtime, existing systems suffer inefficient computation due to their static execution, namely static parallelism and pipelining, which does not adapt to the dynamic workload. We present Flex, a highly scalable stack design and implementation for MoE with dynamically adaptive parallelism and pipelining. Flex designs an identical layout for distributing MoE model parameters and input data, which can be leveraged by all possible parallelism or pipelining methods without any mathematical inequivalence or tensor migration overhead. This enables adaptive parallelism/pipelining optimization at zero cost during runtime. Based on this key design, Flex also implements various MoE acceleration techniques. Aggregating all techniques, Flex finally delivers huge speedup at any scale -- 4.96x and 5.75x speedup of a single MoE layer over 16 and 2,048 A100 GPUs, respectively, over the previous state-of-the-art. Our evaluation shows that Flex efficiently and effectively runs a real-world MoE-based model named SwinV2-MoE, built upon Swin Transformer V2, a state-of-the-art computer vision architecture. On efficiency, Flex accelerates SwinV2-MoE, achieving up to 1.55x and 2.11x speedup in training and inference over Fairseq, respectively. On effectiveness, the SwinV2-MoE model achieves superior accuracy in both pre-training and down-stream computer vision tasks such as COCO object detection than the counterpart dense model, indicating the readiness of Flex for end-to-end real-world model training and inference.

cs.DC↗

arXiv2023

Fast Blind Recovery of Linear Block Codes over Noisy Channels

This paper addresses the blind recovery of the parity check matrix of an (n,k) linear block code over noisy channels by proposing a fast recovery scheme consisting of 3 parts. Firstly, this scheme performs initial error position detection among the received codewords and selects the desirable codewords. Then, this scheme conducts Gaussian elimination (GE) on a k-by-k full-rank matrix and uses a threshold and the reliability associated to verify the recovered dual words, aiming to improve the reliability of recovery. Finally, it performs decoding on the received codewords with partially recovered dual words. These three parts can be combined into different schemes for different noise level scenarios. The GEV that combines Gaussian elimination and verification has a significantly lower recovery failure probability and a much lower computational complexity than an existing Canteaut-Chabaud-based algorithm, which relies on GE on n-by-n full-rank matrices. The decoding-aided recovery (DAR) and error-detection-&-codeword-selection-&-decoding-aided recovery (EDCSDAR) schemes can improve the code recovery performance over GEV for high noise level scenarios, and their computational complexities remain much lower than the Canteaut-Chabaud-based algorithm.

cs.IT↗

arXiv2023

Polarised parton distribution functions and proton spin

Supposing there exists an effective charge which defines an evolution scheme for both unpolarised and polarised parton distribution functions (DFs) that is all-orders exact and using Ansätze for hadron-scale proton polarised valence quark DFs, constrained by flavour-separated axial charges and insights from perturbative quantum chromodynamics, predictions are delivered for all proton polarised DFs at the scale $ζ_{\rm C}^2 = 3\,$GeV$^2$. The pointwise behaviour of the predicted DFs and, consequently, their moments, compare favourably with results inferred from data. Notably, flavour-separated singlet polarised DFs are small. On the other hand, the polarised gluon DF, $ΔG(x;ζ_{\rm C})$, is large and positive. Using our result, we predict $\int_{0.05}^1\,dx\,ΔG(x;ζ_{\rm C}) = 0.214(4)$ and that experimental measurements of the proton flavour-singlet axial charge should return $a_0^{\rm E}(ζ_{\rm C}) = 0.35(2)$.

hep-ph↗

arXiv2023

Replica wormhole as a vacuum-to-vacuum transition

The recent developments related to the black hole information paradox have brought us a confusing object: the replica wormhole. We are trying to better understand the object from the viewpoint of the thermo-mixed double and spontaneous symmetry breaking. It was shown that the replica wormhole can be regarded as a transition between different degenerate vacua, and the corresponding gravitational partition function should be controlled by the manifold of the degenerate vacua. We also check the wormhole partition function in 2-dimensional Jackiw-Teitelboim gravity and show that the wormhole saddle is indeed controlled by the dimension of the degenerate vacua. Moreover, it is suggested that the replica wormhole geometries connecting different vacua can be related to the measurement process of soft hair that compares different vacuum configurations.

hep-th↗

arXiv2023

Multiple magnetic transitions and complex magnetic structures in Fe$_2$SiSe$_4$ with the sawtooth lattice

The sawtooth lattice shares some structural similarities with the kagome lattice and may attract renewed research interest. Here, we report a comprehensive study on the physical properties of Fe$_2$SiSe$_4$, an unexplored member in the olivine chalcogenides with the sawtooth lattice of Fe. Our results show that Fe$_2$SiSe$_4$ is a magnetic semiconductor with band gap of 0.66~eV. It first undergoes an antiferromagnetic transition at T$_{m1}$=110~K, then an ferrimagnetic-like one at T$_{m2}$=50~K and finally a magnetic transition at T$_{m3}$=25~K which is likely driven by the thermal populations of spin-orbit manifold on the Fe site. Neutron diffraction analysis reveals a non-collinear antiferromagnetic structure with propagation vector $\mathbf{q_1}$=(0,0,0) at T$_{m2}$<T<T$_{m1}$. Interestingly, below T$_{m2}$, an additional antiferromagnetic structure with $\mathbf{q_2}$=(0,0.5,0) appears and Fe$_2$SiSe$_4$ exhibits a complex double-$\mathbf{q}$ magnetic structure which has never been observed in sawtooth olivines. Density functional theory calculations suggest this complex noncollinear magnetic structure may originate from the competing antiferromagnetic interactions for both intra- and inter-chain in the sawtooth lattice. Furthermore, band structural calculations show that Fe$_2$SiSe$_4$ has quasi-flat band features near the valence and conduction bands. Based on the above results, we propose Fe$_2$SiSe$_4$ as a new material platform to condensed matter researches.

cond-mat.mtrl-sci↗

arXiv2023

Flat equivariant gerbes: holonomies and dualities

We examine the role of global topological data associated to choices of holonomy for flat gauge fields in string compactification. Our study begins with perturbative string compactification on compact flat manifolds preserving 8 supercharges in 5 dimensions. By including non-trivial holonomy for Wilson lines in the heterotic string and for the B-field gerbe in the type II string we find worldsheet dualities that relate these backgrounds to other string compactifications. While our simple examples allow for explicit analysis, the concepts and some of the methods extend to a broader class of compactifications and have implications for string dualities, perturbative and otherwise.

hep-th↗

arXiv2023

Dynamic Private Task Assignment under Differential Privacy

Data collection is indispensable for spatial crowdsourcing services, such as resource allocation, policymaking, and scientific explorations. However, privacy issues make it challenging for users to share their information unless receiving sufficient compensation. Differential Privacy (DP) is a promising mechanism to release helpful information while protecting individuals' privacy. However, most DP mechanisms only consider a fixed compensation for each user's privacy loss. In this paper, we design a task assignment scheme that allows workers to dynamically improve their utility with dynamic distance privacy leakage. Specifically, we propose two solutions to improve the total utility of task assignment results, namely Private Utility Conflict-Elimination (PUCE) approach and Private Game Theory (PGT) approach, respectively. We prove that PUCE achieves higher utility than the state-of-the-art works. We demonstrate the efficiency and effectiveness of our PUCE and PGT approaches on both real and synthetic data sets compared with the recent distance-based approach, Private Distance Conflict-Elimination (PDCE). PUCE is always better than PDCE slightly. PGT is 50% to 63% faster than PDCE and can improve 16% utility on average when worker range is large enough.

cs.CR↗

arXiv2023

Hydrogels enable negative pressure in water for efficient heat utilization and transfer

Metastable water in negative pressure can provide giant passive driving pressure up to several megapascals for efficient evaporation-driven flow, however, the practical applications with negative pressure are rare due to the challenges of generating and maintaining large negative pressure. In this work, we report a novel structure with thin hydrogel films as evaporation surfaces and robust porous substrates as the supports, and obtain a high negative pressure of -1.61 MPa through water evaporation. Molecular dynamics simulations elucidate the essential role of strong interaction between water molecules and polymer chains in generating the negative pressure. With such a large negative pressure, we demonstrate a streaming potential generator that spontaneously converts environmental energy into electricity and outputs a voltage of 1.06 V. Moreover, we propose a "negative pressure heat pipe" for the first time, which achieves a high heat transfer density of 11.2 kW cm-2 with a flow length of 1 m, showing the potential of negative pressure in efficient heat utilization and transfer.

physics.chem-ph↗

arXiv2023

Topological Hall Effect Driven by Short-Range Magnetic Orders in EuZn$_2$As$_2$

Short-range (SR) magnetic orders such as magnetic glass orders or fluctuations in a quantum system usually host exotic states or critical behaviors. As the long-range (LR) magnetic orders, SR magnetic orders can also break time-reversal symmetry and drive the non-zero Berry curvature leading to novel transport properties. In this work, we report that in EuZn$_2$As$_2$ compound, besides the LR A-type antiferromagnetic (AF) order, the SR magnetic order is observed in a wide temperature region. The magnetization measurements and electron spin resonance (ESR) measurements reveal the ferromagnetic (FM) correlations for this SR magnetic order which results in an obvious anomalous Hall effect above the AF transition. Moreover the ESR results reveal that this FM SR order coexists with LR AF order exhibiting anisotropic magnetic correlations below the AF transition. The interactions of LR and SR magnetism evolving with temperature and field can host non-zero spin charility and berry curvature leading the additional topological Hall contribution even in a centrosymmetric simple AF system. Our results indicate that EuZn$_2$As$_2$ is a fertile platform to investigate exotic magnetic and electronic states.

cond-mat.str-el↗

arXiv2023

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.

cs.LG↗

arXiv2022

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.

cs.CR↗

arXiv2022

An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context

One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluctuated bandwidth in congestion control. Existing works on adaptation to unknown environment contexts either assume the contexts are the same for the whole episode or assume the context variables are Markovian. However, in many real-world applications, the environment context usually stays stable for a stochastic period and then changes in an abrupt and unpredictable manner within an episode, resulting in a segment structure, which existing works fail to address. To leverage the segment structure of piecewise stable context in real-world applications, in this paper, we propose a \textit{\textbf{Se}gmented \textbf{C}ontext \textbf{B}elief \textbf{A}ugmented \textbf{D}eep~(SeCBAD)} RL method. Our method can jointly infer the belief distribution over latent context with the posterior over segment length and perform more accurate belief context inference with observed data within the current context segment. The inferred belief context can be leveraged to augment the state, leading to a policy that can adapt to abrupt variations in context. We demonstrate empirically that SeCBAD can infer context segment length accurately and outperform existing methods on a toy grid world environment and Mujuco tasks with piecewise-stable context.

cs.LG↗

arXiv2022

Evaporating black holes and late-stage loss of soft hair

We present a paradox for evaporating black holes, which is common in most schemes trying to avoid the firewall by decoupling early and late radiation. At the late stage of the black hole evaporation, the decoupling between early and late radiation can not be realized because the black hole has a very small coarse-grained entropy, then we are faced with the firewall again. We call the problem hair-loss paradox as a pun on losing black hole soft hair during the black hole evaporation and the situation that the information paradox has put so much pressure on researchers.

hep-th↗

arXiv2022

FedSiam-DA: Dual-aggregated Federated Learning via Siamese Network under Non-IID Data

Federated learning is a distributed learning that allows each client to keep the original data locally and only upload the parameters of the local model to the server. Despite federated learning can address data island, it remains challenging to train with data heterogeneous in a real application. In this paper, we propose FedSiam-DA, a novel dual-aggregated contrastive federated learning approach, to personalize both local and global models, under various settings of data heterogeneity. Firstly, based on the idea of contrastive learning in the siamese network, FedSiam-DA regards the local and global model as different branches of the siamese network during the local training and controls the update direction of the model by constantly changing model similarity to personalize the local model. Secondly, FedSiam-DA introduces dynamic weights based on model similarity for each local model and exercises the dual-aggregated mechanism to further improve the generalization of the global model. Moreover, we provide extensive experiments on benchmark datasets, the results demonstrate that FedSiam-DA achieves outperforming several previous FL approaches on heterogeneous datasets.

cs.LG↗

arXiv2022

TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural Backdoors

Neural backdoors represent one primary threat to the security of deep learning systems. The intensive research has produced a plethora of backdoor attacks/defenses, resulting in a constant arms race. However, due to the lack of evaluation benchmarks, many critical questions remain under-explored: (i) what are the strengths and limitations of different attacks/defenses? (ii) what are the best practices to operate them? and (iii) how can the existing attacks/defenses be further improved? To bridge this gap, we design and implement TROJANZOO, the first open-source platform for evaluating neural backdoor attacks/defenses in a unified, holistic, and practical manner. Thus far, focusing on the computer vision domain, it has incorporated 8 representative attacks, 14 state-of-the-art defenses, 6 attack performance metrics, 10 defense utility metrics, as well as rich tools for in-depth analysis of the attack-defense interactions. Leveraging TROJANZOO, we conduct a systematic study on the existing attacks/defenses, unveiling their complex design spectrum: both manifest intricate trade-offs among multiple desiderata (e.g., the effectiveness, evasiveness, and transferability of attacks). We further explore improving the existing attacks/defenses, leading to a number of interesting findings: (i) one-pixel triggers often suffice; (ii) training from scratch often outperforms perturbing benign models to craft trojan models; (iii) optimizing triggers and trojan models jointly greatly improves both attack effectiveness and evasiveness; (iv) individual defenses can often be evaded by adaptive attacks; and (v) exploiting model interpretability significantly improves defense robustness. We envision that TROJANZOO will serve as a valuable platform to facilitate future research on neural backdoors.

cs.LG↗

arXiv2022

Observation of Gapped Dirac Cones in a Two-Dimensional Su-Schrieffer-Heeger Lattice

The Su-Schrieffer-Heeger (SSH) model in a two-dimensional rectangular lattice features gapless or gapped Dirac cones with topological edge states along specific peripheries. While such a simple model has been recently realized in photonic/acoustic lattices and electric circuits, its material realization in condensed matter systems is still lacking. Here, we study the atomic and electronic structure of a rectangular Si lattice on Ag(001) by angle-resolved photoemission spectroscopy and theoretical calculations. We demonstrate that the Si lattice hosts gapped Dirac cones at the Brillouin zone corners. Our tight-binding analysis reveals that the Dirac bands can be described by a 2D SSH model with anisotropic polarizations. The gap of the Dirac cone is driven by alternative hopping amplitudes in one direction and staggered potential energies in the other one and hosts topological edge states. Our results establish an ideal platform to explore the rich physical properties of the 2D SSH model.

cond-mat.mtrl-sci↗