arXiv subjects

Yang Yu

Publications and source records attributed to Yang Yu.

At least 379 records · Page 21Linked to original sources

arXiv2020

An authenticated and secure accounting system for international emissions trading

Expanding multi-country emissions trading system is considered as crucial to fill the existing mitigation gap for the 2\degree C climate target. Trustworthy emissions accounting is the cornerstone of such a system encompassing different jurisdictions. However, traditional emissions measuring, reporting, and verification practices that support data authenticity might not be applicable as detailed data from large utilities and production facilities to be covered in the multi-country emissions trading system are usually highly sensitive and of severe national security concern. In this study, we propose a cryptographic framework for an authenticated and secure emissions accounting system that can resolve this data dilemma. We demonstrate that integrating a sequence of cryptographic protocols can preserve data authenticity and security for a stylized multi-country emissions trading system. We call for more research to promote applications of modern cryptography in future international climate governance to build trust and strengthen collaboration.

econ.GN↗

arXiv2020

OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs

Providing appropriate structures around human resources can streamline operations and thus facilitate the competitiveness of an organization. To achieve this goal, modern organizations need to acquire an accurate and timely understanding of human resource grouping while faced with an ever-changing environment. The use of process mining offers a promising way to help address the need through utilizing event log data stored in information systems. By extracting knowledge about the actual behavior of resources participating in business processes from event logs, organizational models can be constructed, which facilitate the analysis of the de facto grouping of human resources relevant to process execution. Nevertheless, open research gaps remain to be addressed when applying the state-of-the-art process mining to analyze resource grouping. For one, the discovery of organizational models has only limited connections with the context of process execution. For another, a rigorous solution that evaluates organizational models against event log data is yet to be proposed. In this paper, we aim to tackle these research challenges by developing a novel framework built upon a richer definition of organizational models coupling resource grouping with process execution knowledge. By introducing notions of conformance checking for organizational models, the framework allows effective evaluation of organizational models, and therefore provides a foundation for analyzing and improving resource grouping based on event logs. We demonstrate the feasibility of this framework by proposing an approach underpinned by the framework for organizational model discovery, and also conduct experiments on real-life event logs to discover and evaluate organizational models.

cs.DB↗

arXiv2020

Angular Embedding: A New Angular Robust Principal Component Analysis

As a widely used method in machine learning, principal component analysis (PCA) shows excellent properties for dimensionality reduction. It is a serious problem that PCA is sensitive to outliers, which has been improved by numerous Robust PCA (RPCA) versions. However, the existing state-of-the-art RPCA approaches cannot easily remove or tolerate outliers by a non-iterative manner. To tackle this issue, this paper proposes Angular Embedding (AE) to formulate a straightforward RPCA approach based on angular density, which is improved for large scale or high-dimensional data. Furthermore, a trimmed AE (TAE) is introduced to deal with data with large scale outliers. Extensive experiments on both synthetic and real-world datasets with vector-level or pixel-level outliers demonstrate that the proposed AE/TAE outperforms the state-of-the-art RPCA based methods.

cs.LG↗

arXiv2020

RetroXpert: Decompose Retrosynthesis Prediction like a Chemist

Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate or assist in the retrosynthesis analysis, various retrosynthesis prediction algorithms have been proposed. However, most of them are cumbersome and lack interpretability about their predictions. In this paper, we devise a novel template-free algorithm for automatic retrosynthetic expansion inspired by how chemists approach retrosynthesis prediction. Our method disassembles retrosynthesis into two steps: i) identify the potential reaction center of the target molecule through a novel graph neural network and generate intermediate synthons, and ii) generate the reactants associated with synthons via a robust reactant generation model. While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation.

q-bio.QM↗

arXiv2020

Mining Generalized Features for Detecting AI-Manipulated Fake Faces

Recently, AI-manipulated face techniques have developed rapidly and constantly, which has raised new security issues in society. Although existing detection methods consider different categories of fake faces, the performance on detecting the fake faces with "unseen" manipulation techniques is still poor due to the distribution bias among cross-manipulation techniques. To solve this problem, we propose a novel framework that focuses on mining intrinsic features and further eliminating the distribution bias to improve the generalization ability. Firstly, we focus on mining the intrinsic clues in the channel difference image (CDI) and spectrum image (SI) from the camera imaging process and the indispensable step in AI manipulation process. Then, we introduce the Octave Convolution (OctConv) and an attention-based fusion module to effectively and adaptively mine intrinsic features from CDI and SI. Finally, we design an alignment module to eliminate the bias of manipulation techniques to obtain a more generalized detection framework. We evaluate the proposed framework on four categories of fake faces datasets with the most popular and state-of-the-art manipulation techniques, and achieve very competitive performances. To further verify the generalization ability of the proposed framework, we conduct experiments on cross-manipulation techniques, and the results show the advantages of our method.

cs.CV↗

arXiv2020

Error Bounds of Imitating Policies and Environments

Imitation learning trains a policy by mimicking expert demonstrations. Various imitation methods were proposed and empirically evaluated, meanwhile, their theoretical understanding needs further studies. In this paper, we firstly analyze the value gap between the expert policy and imitated policies by two imitation methods, behavioral cloning and generative adversarial imitation. The results support that generative adversarial imitation can reduce the compounding errors compared to behavioral cloning, and thus has a better sample complexity. Noticed that by considering the environment transition model as a dual agent, imitation learning can also be used to learn the environment model. Therefore, based on the bounds of imitating policies, we further analyze the performance of imitating environments. The results show that environment models can be more effectively imitated by generative adversarial imitation than behavioral cloning, suggesting a novel application of adversarial imitation for model-based reinforcement learning. We hope these results could inspire future advances in imitation learning and model-based reinforcement learning.

cs.LG↗

arXiv2020

Finite Temperature Auxiliary Field Quantum Monte Carlo in the Canonical Ensemble

Finite temperature auxiliary field-based Quantum Monte Carlo methods, including Determinant Quantum Monte Carlo (DQMC) and Auxiliary Field Quantum Monte Carlo (AFQMC), have historically assumed pivotal roles in the investigation of the finite temperature phase diagrams of a wide variety of multidimensional lattice models and materials. Despite their utility, however, these techniques are typically formulated in the grand canonical ensemble, which makes them difficult to apply to condensates like superfluids and difficult to benchmark against alternative methods that are formulated in the canonical ensemble. Working in the grand canonical ensemble is furthermore accompanied by the increased overhead associated with having to determine the chemical potentials that produce desired fillings. Given this backdrop, in this work, we present a new recursive approach for performing AFQMC simulations in the canonical ensemble that does not require knowledge of chemical potentials. To derive this approach, we exploit the convenient fact that AFQMC solves the many-body problem by decoupling many-body propagators into integrals over one-body problems to which non-interacting theories can be applied. We benchmark the accuracy of our technique on illustrative Bose and Fermi Hubbard models and demonstrate that it can converge more quickly to the ground state than grand canonical AFQMC simulations. We believe that our novel use of HS-transformed operators to implement algorithms originally derived for non-interacting systems will motivate the development of a variety of other methods and anticipate that our technique will enable direct performance comparisons against other many-body approaches formulated in the canonical ensemble.

quant-ph↗

arXiv2020

Difference-in-Differences: Bridging Normalization and Disentanglement in PG-GAN

What mechanisms causes GAN's entanglement? Although developing disentangled GAN has attracted sufficient attention, it is unclear how entanglement is originated by GAN transformation. We in this research propose a difference-in-difference (DID) counterfactual framework to design experiments for analyzing the entanglement mechanism in on of the Progressive-growing GAN (PG-GAN). Our experiment clarify the mechanisms how pixel normalization causes PG-GAN entanglement during a input-unit-ablation transformation. We discover that pixel normalization causes object entanglement by in-painting the area occupied by ablated objects. We also discover the unit-object relation determines whether and how pixel normalization causes objects entanglement. Our DID framework theoretically guarantees that the mechanisms that we discover is solid, explainable and comprehensively.

cs.CV↗

arXiv2020

Switchable next-nearest-neighbor coupling for controlled two-qubit operations

In a superconducting quantum processor with nearest neighbor coupling, the dispersive interaction between adjacent qubits can result in an effective next-nearest-neighbor coupling whose strength depends on the state of the intermediary qubit. Here, we theoretically explore the possibility of engineering this next-nearest-neighbor coupling to implement controlled two-qubit operations where the intermediary qubit controls the operation on the next-nearest neighbor pair of qubits. In particular, in a system comprising two types of superconducting qubits with anharmonicities of opposite-sign arranged in an -A-B-A- pattern, where the unwanted static ZZ coupling between adjacent qubits could be heavily suppressed, a switchable coupling between the next-nearest-neighbor qubits can be achieved via the intermediary qubit, the qubit state of which functions as an on/off switch for this coupling. Therefore, depending on the adopted activating scheme, various controlled two-qubit operations such as controlled-iSWAP gate can be realized, potentially enabling circuit depth reductions as to a standard decomposition approach for implementing generic quantum algorithms.

quant-ph↗

arXiv2020

In Situ characterization of the proton enhanced conductivity of 20nm TiO2 thin-films obtained on the surface of optical fiber

We report in situ characterized TiO2 thin-films deposited on optical fiber, having thicknesses in the 20-100nm range, and having enhanced conductivity values of 700S/cm upon interacting with hydrogen. This conductivity was achieved in pure hydrogen at 800-900C, having a measured activation energy of 0.26eV of the hopping type. Given the variability in the observed results, it is postulated that the highest conductivity achievable may be much greater than what is currently demonstrated. The conductivity is retained after cooling to ambient temperatures as confirmed by Hall measurements, and subsequent grazing-incidence x-ray diffraction and TEM measurements show the films to be in the rutile phase. The exceptional conductivity in these films is hypothesized to result from direct proton incorporation into the lattice populating the conduction band with excess electrons, or from altering the Titania lattice to form conductive Magneli phases. The films did not display any evidence of transformations, however formation of Magneli phases was confirmed for powders. These interesting results, observed by examining 20nm films on the surface of optical fiber in combination with the first impedance spectroscopy performed on films on optical fiber in high temperature Fuel Cell type environments, confirm hypotheses arrived at in prior publications where thin-films of Titania had optical properties which could only be explained by the current claim. Titania thin-films on optical-fiber are being explored for high temperature hydrogen derived energy generation, thermo-photonic energy conversion, and associated sensors due to their unique interactions with hydrogen.

cond-mat.mtrl-sci↗

arXiv2020

Synthesis of Cu mono-component metallic glass by the deposition on amorphous SiO$_2$ substrate: a molecular dynamics study

In this work, we simulated the physical vapor deposition (PVD) process of Cu atoms on the amorphous SiO$_2$ substrate. The resulting Cu thin layer exhibit amorphous structure. The Cu liquid quenching from 2000 K to 50 K was also simulated with different cooling rate to form the Cu metallic glass for comparison. The Cu glasses from the two different processes (PVD and quenching) revealed the same radial distribution function but different local structure from the Voronoi tessellation analysis. The PVD glass exhibit higher densities and lower potential energy compared with the melt-quenched counterpart, which corresponded to the properties of ultrastable glasses.

cond-mat.mtrl-sci↗

arXiv2020

Large photoluminescence enhancement by an out-of-plane magnetic field in exfoliated WS$_2$ flakes

We report an out-of-plane magnetic field induced large photoluminescence enhancement in WS${}_2$ flakes at $4$ K, in contrast to the photoluminescence enhancement provided by in-plane field in general. Two mechanisms for the enhancement are proposed. One is a larger overlap of electron and hole caused by the magnetic field induced confinement. The other is that the energy difference between $Λ$ and K valleys is reduced by magnetic field, and thus enhancing the corresponding indirect-transition trions. Meanwhile, the Landé g factor of the trion is measured as $-0.8$, whose absolute value is much smaller than normal exciton, which is around $|-4|$. A model for the trion g factor is presented, confirming that the smaller absolute value of Landé g factor is a behavior of this $Λ$-K trion. By extending the valley space, we believe this work provides a further understanding of the valleytronics in monolayer transition metal dichalcogenides.

cond-mat.mes-hall↗

arXiv2020

Reinforced Epidemic Control: Saving Both Lives and Economy

Saving lives or economy is a dilemma for epidemic control in most cities while smart-tracing technology raises people's privacy concerns. In this paper, we propose a solution for the life-or-economy dilemma that does not require private data. We bypass the private-data requirement by suppressing epidemic transmission through a dynamic control on inter-regional mobility that only relies on Origin-Designation (OD) data. We develop DUal-objective Reinforcement-Learning Epidemic Control Agent (DURLECA) to search mobility-control policies that can simultaneously minimize infection spread and maximally retain mobility. DURLECA hires a novel graph neural network, namely Flow-GNN, to estimate the virus-transmission risk induced by urban mobility. The estimated risk is used to support a reinforcement learning agent to generate mobility-control actions. The training of DURLECA is guided with a well-constructed reward function, which captures the natural trade-off relation between epidemic control and mobility retaining. Besides, we design two exploration strategies to improve the agent's searching efficiency and help it get rid of local optimums. Extensive experimental results on a real-world OD dataset show that DURLECA is able to suppress infections at an extremely low level while retaining 76\% of the mobility in the city. Our implementation is available at https://github.com/anyleopeace/DURLECA/.

cs.AI↗

arXiv2020

Local Neighbor Propagation Embedding

Manifold Learning occupies a vital role in the field of nonlinear dimensionality reduction and its ideas also serve for other relevant methods. Graph-based methods such as Graph Convolutional Networks (GCN) show ideas in common with manifold learning, although they belong to different fields. Inspired by GCN, we introduce neighbor propagation into LLE and propose Local Neighbor Propagation Embedding (LNPE). With linear computational complexity increase compared with LLE, LNPE enhances the local connections and interactions between neighborhoods by extending $1$-hop neighbors into $n$-hop neighbors. The experimental results show that LNPE could obtain more faithful and robust embeddings with better topological and geometrical properties.

cs.LG↗

arXiv2020

Experimental Realization of Universal Time-optimal non-Abelian Geometric Gates

Based on the geometrical nature of quantum phases, non-adiabatic holonomic quantum control (NHQC) has become a standard technique for enhancing robustness in constructing quantum gates. However, the conventional approach of NHQC is sensitive to control instability, as it requires the driving pulses to cover a fixed pulse area. Furthermore, even for small-angle rotations, all operations need to be completed with the same duration of time. Here we experimentally demonstrate a time-optimal and unconventional approach of NHQC (called TOUNHQC), which can optimize the operation time of any holonomic gate. Compared with the conventional approach, TOUNHQC provides an extra layer of robustness to decoherence and control errors. The experiment involves a scalable architecture of superconducting circuit, where we achieved a fidelity of 99.51% for a single qubit gate using interleaved randomized benchmarking. Moreover, a two-qubit holonomic control-phase gate has been implemented where the gate error can be reduced by as much as 18% compared with NHQC.

quant-ph↗

arXiv2020

Simultaneously exciting two atoms with photon-mediated Raman interaction

We propose an approach to simultaneously excite two atoms by using cavity-assisted Raman process in combination with cavity photon-mediated interaction. The system consists of a two-level atom and a $Λ$-type or V-type three-level atom, which are coupled together with a cavity mode. Having derived the effective Hamiltonian, we find that under certain circumstances a single photon can simultaneously excite two atoms. In addition, multiple photons and even a classical field can also simultaneously excite two atoms. As an example, we show a scheme to realize our proposal in a circuit QED setup, which is artificial atoms coupled with a cavity. The dynamics and the quantum statistical properties of the process are investigated with experimentally feasible parameters.

quant-ph↗

arXiv2020

Design of Convergence-Optimized Non-binary LDPC Codes over Binary Erasure Channel

In this letter, we present a hybrid iterative decoder for non-binary low density parity check (LDPC) codes over binary erasure channel (BEC), based on which the recursion of the erasure probability is derived to design non-binary LDPC codes with convergence-optimized degree distributions. The resulting one-step decoding tree is cycle-free and achieves lower decoding complexity. Experimental studies show that the proposed convergence-optimization algorithm accelerates the convergence process by 33%.

eess.SP↗

arXiv2020

Design of Low Complexity Non-binary LDPC Codes with an Approximated Performance-Complexity Tradeoff

By presenting an approximated performance-complexity tradeoff (PCT) algorithm,a low-complexity non-binary low density parity check (LDPC) code over q-ary-input symmetric-output channel is designed in this manuscript which converges faster than the threshold-optimized non-binary LDPC codes in the low error rate regime. We examine our algorithm by both hard and soft decision decoders.Moreover, simulation shows that the approximated PCT algorithm has accelerated the convergence process by 30% regarding the number of the decoding iterations.

eess.SP↗