arXiv subjects

Meng Han

Publications and source records attributed to Meng Han.

At least 109 records · Page 6Linked to original sources

arXiv2023

Decay properties of $D_{s0}^*(2317)^+$ as a conventional $c\bar s$ meson

Taking $D_{s0}^{*}(2317)^{+ }$ as a conventional $c\bar s$ meson, we calculate its dominant strong and electromagnetic decays in the framework of the Bethe-Salpeter method. Our results are $Γ(D_{s0}^{*+}\to D_s^+π^0) = 7.83^{+1.97}_{-1.55}$ keV and $Γ(D_{s0}^{*+}\to D_s^{*+}γ) = 2.55^{+0.37}_{-0.45}$ keV. The contributions of the different partial waves from the initial and final state wave functions to the decay width are also calculated, and we find that the relativistic corrections in both decay processes are very large.

hep-ph↗

arXiv2023

Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs

Logical reasoning over incomplete knowledge graphs to answer complex logical queries is a challenging task. With the emergence of new entities and relations in constantly evolving KGs, inductive logical reasoning over KGs has become a crucial problem. However, previous PLMs-based methods struggle to model the logical structures of complex queries, which limits their ability to generalize within the same structure. In this paper, we propose a structure-modeled textual encoding framework for inductive logical reasoning over KGs. It encodes linearized query structures and entities using pre-trained language models to find answers. For structure modeling of complex queries, we design stepwise instructions that implicitly prompt PLMs on the execution order of geometric operations in each query. We further separately model different geometric operations (i.e., projection, intersection, and union) on the representation space using a pre-trained encoder with additional attention and maxout layers to enhance structured modeling. We conduct experiments on two inductive logical reasoning datasets and three transductive datasets. The results demonstrate the effectiveness of our method on logical reasoning over KGs in both inductive and transductive settings.

cs.CL↗

arXiv2023

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

Advancements in wearable medical devices in IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), we are witnessing how efficient healthcare services are provided to patients and how healthcare professionals are effectively used AI-based models to analyze the data collected from IoHT devices for the treatment of various diseases. To avoid privacy breaches, these data must be processed and analyzed in compliance with the legal rules and regulations such as HIPAA and GDPR. Federated learning is a machine leaning based approach that allows multiple entities to collaboratively train a ML model without sharing their data. This is particularly useful in the healthcare domain where data privacy and security are big concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy Enhancing Technologies (PETs) are a set of tools and techniques that are designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users' personal information and sensitive data from unauthorized access and tracking. This paper reviews PETs in detail and comprehensively in relation to FL in the IoHT setting and identifies several key challenges for future research.

cs.NI↗

arXiv2023

Observation of Nuclear-wavepacket Interference in Ultrafast Inter-atomic Energy Transfer

We report the experimental observation of quantum interference in the nuclear wave-packet dynamics driving ultrafast excitation-energy transfer in argon dimers below the threshold of interatomic Coulombic decay (ICD). Using time-resolved photoion-photoion coincidence spectroscopy and quantum dynamics simulations, we reveal that the electronic relaxation dynamics of the inner-valence $3s$ hole on one atom leading to a $4s$ or $4p$ excitation on the other one is influenced by nuclear quantum dynamics in the initial state, giving rise to a deep, periodic modulation on the kinetic-energy-release (KER) spectra of the coincident Ar$^+$-Ar$^+$ ion pairs. Moreover, the time-resolved KER spectra show characteristic fingerprints of quantum interference effects during the energy-transfer process. Our findings pave the way to elucidating quantum-interference effects in ultrafast charge- and energy-transfer dynamics in more complex systems.

physics.atm-clus↗

arXiv2023

ATA-Cache: Contention Mitigation for GPU Shared L1 Cache with Aggregated Tag Array

GPU shared L1 cache is a promising architecture while still suffering from high resource contentions. We present a GPU shared L1 cache architecture with an aggregated tag array that minimizes the L1 cache contentions and takes full advantage of inter-core locality. The key idea is to decouple and aggregate the tag arrays of multiple L1 caches so that the cache requests can be compared with all tag arrays in parallel to probe the replicated data in other caches. The GPU caches are only accessed by other GPU cores when replicated data exists, filtering out unnecessary cache accesses that cause high resource contentions. The experimental results show that GPU IPC can be improved by 12% on average for applications with a high inter-core locality.

cs.AR↗

arXiv2023

Separation of Wigner and Continuum-continuum Delays by Mirror-symmetry-broken Attosecond Interferometry

Photoionization of matter is one of the fastest electronic processes in nature. Experimental measurements of photoionization dynamics have become possible through attosecond metrology. However, all experiments reported to date contain a so-far unavoidable measurement-induced contribution, known as continuum-continuum (CC) or Coulomb-laser-coupling delay. Exploiting the recently characterized circularly polarized attosecond pulse trains, we introduce the concept of mirror-symmetry-broken attosecond interferometry, which enables the direct and separate measurement of both the native one-photon ionization delays as well as the continuum-continuum delays. Our technique solves the longstanding challenge of experimentally isolating both the native one-photon-ionization (or Wigner) delays and the measurement-induced (CC) delays. This advance opens the door to a new generation of precision measurements that is likely to drive major progress in experimental and theoretical attosecond science with implications for benchmarking the accuracy of electronic-structure and electron-dynamics methods.

quant-ph↗

arXiv2023

Laser-assisted Fano resonance: attosecond quantum control and dynamical imaging

A Fano resonance arises from the pathway interference between discrete and continuum states, playing a fundamental role in many branches of physics, chemistry and material science. Here, we introduce the concept of a laser-assisted Fano resonance, created from two interferometric pathways that are coupled together by an additional laser field, which introduces a controllable phase delay between them and results in a generalized Fano lineshape that can be actively controlled on the {\it attosecond} time scale. Based on our experimental results of unprecedented resolution, we dynamically image a resonant electron wave packet during its evolution directly in the time domain, extracting both the amplitude and the phase, which allows for the measurement of the {\it resonant} photoionization time delay. Ab-initio calculations and simulations employing a physically transparent two-level model agree with our experimental results, laying the groundwork for extending our concepts into attosecond quantum control of complex systems.

quant-ph↗

arXiv2023

Data-Driven Distributionally Robust Scheduling of Community Integrated Energy Systems with Uncertain Renewable Generations Considering Integrated Demand Response

A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and infinity-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional stochastic programming and robust optimization, it is verified that the proposed DRO model properly balances the relationship between economical operation and robustness while exhibiting stronger adaptability. Furthermore, our approach outperforms other commonly used DRO methods with better operational economy, lower renewable power curtailment rate, and higher computational efficiency.

eess.SY↗

arXiv2022

Attosecond metrology in circular polarization

Attosecond metrology with linearly polarized light pulses is the basis of a highly successful research area. An even broader impact can be expected from a generalized metrology that covers two-dimensional polarization states, enabling notably the study of chiroptical phenomena on the electronic time scale. Here, we introduce and demonstrate a comprehensive approach to the generation and complete characterization of elliptically to circularly polarized attosecond pulses. The generation relies on a simple plug-in device of unprecedented simplicity. For the characterization, we introduce SPARROW (Stokes-Parameter and Attosecond Resolved Reconstruction of Optical Waveforms), which encodes the attosecond-metrology information into the photoemission angle in the polarization plane and accesses all four Stokes parameters of the attosecond pulses. Our study demonstrates a physically transparent scheme for attosecond metrology with elliptical to fully circular polarizations, applicable to both table-top and accelerator-based light sources, which will unlock studies of chiral molecules, magnetic materials and novel chiroptical phenomena on the most fundamental time scales.

physics.optics↗

arXiv2022

Two-center Interference in the Photoionization Delays of Kr2

We present the experimental observation of two-center interference in the ionization time delays of Kr2. Using attosecond electron-ion-coincidence spectroscopy, we simultaneously measure the photoionization delays of krypton monomer and dimer. The relative time delay is found to oscillate as a function of the electron kinetic energy, an effect that is traced back to constructive and destructive interference of the photoelectron wave packets that are emitted or scattered from the two atomic centers. Our interpretation of the experimental results is supported by solving the time-independent Schrodinger equation of a 1D double-well potential, as well as coupled-channel multiconfigurational quantum-scattering calculations of Kr2. This work opens the door to the study of a broad class of quantum-interference effects in photoionization delays and demonstrates the potential of attosecond coincidence spectroscopy for studying weakly bound systems.

physics.atom-ph↗

arXiv2022

"Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution

Understanding the decision process of neural networks is hard. One vital method for explanation is to attribute its decision to pivotal features. Although many algorithms are proposed, most of them solely improve the faithfulness to the model. However, the real environment contains many random noises, which may leads to great fluctuations in the explanations. More seriously, recent works show that explanation algorithms are vulnerable to adversarial attacks. All of these make the explanation hard to trust in real scenarios. To bridge this gap, we propose a model-agnostic method \emph{Median Test for Feature Attribution} (MeTFA) to quantify the uncertainty and increase the stability of explanation algorithms with theoretical guarantees. MeTFA has the following two functions: (1) examine whether one feature is significantly important or unimportant and generate a MeTFA-significant map to visualize the results; (2) compute the confidence interval of a feature attribution score and generate a MeTFA-smoothed map to increase the stability of the explanation. Experiments show that MeTFA improves the visual quality of explanations and significantly reduces the instability while maintaining the faithfulness. To quantitatively evaluate the faithfulness of an explanation under different noise settings, we further propose several robust faithfulness metrics. Experiment results show that the MeTFA-smoothed explanation can significantly increase the robust faithfulness. In addition, we use two scenarios to show MeTFA's potential in the applications. First, when applied to the SOTA explanation method to locate context bias for semantic segmentation models, MeTFA-significant explanations use far smaller regions to maintain 99\%+ faithfulness. Second, when tested with different explanation-oriented attacks, MeTFA can help defend vanilla, as well as adaptive, adversarial attacks against explanations.

cs.AI↗

arXiv2022

The partial waves of $B^{*}_{2}(5747)$ and their contributions in strong decays

By adopting the relativistic Bethe-Salpeter method, the OZI allowed strong decays of the $2^+$ state $B^{*}_{2}(5747)^{0}$ are studied, emphasis is paid to the relativistic corrections. We first study the partial waves in the wave functions used, find that there are $P$, $D$ and $F$ partial waves in $B^{*}_{2}(5747)^{0}$ meson, and the ratios $P:D:F=1:0.421:0.051$. We also find $S:P:D=1:0.354:0.046$ for $B^*$, and $S:P=1:0.343$ for $B$ meson. The large components of the $D$ wave in $B^{*}_{2}(5747)^{0}$ and $P$ wave in $B^{(*)}$ means that large relativistic effects existing in these states. Second, we calculate the strong decays, the obtained total decay width $Γ(B^{*}_{2}(5747)^{0})=25.9$ MeV and the branching fraction $Γ(B^{*}_{2}(5747)^{0} \to B^{*}π)$ / $Γ(B^{*}_{2}(5747)^{0} \to Bπ)=0.96$ are consistent well with experimental data. Third, we study the contributions of different partial waves in the initial and final wave functions, find that the relativistic effects are about $15\%$ and $11\%$ for $B^{*}_{2}(5747)^{0} \to Bπ$ and $B^{*}_{2}(5747)^{0} \to B^{*}π$, respectively, which are much smaller than our expected, showing that the relativistic corrections are cancelled to each other in these decays.

hep-ph↗

arXiv2022

Abnormally High Thermal Conductivity in Fivefold Twinned Diamond Nanowires

Fivefold twins (5FTs), discovered nearly 200 years ago, are a common multiply twinned structure that usually dramatically deteriorate the thermal transport properties of nanomaterials. Here, we report the anomalous thermal conductivity ($κ$) in a novel fivefold twinned diamond nanowires (5FT-DNWs). The $κ$ of 5FT-DNWs is effectively enhanced by the defects of 5FT boundaries, and non-monotonically changes with the cross-sectional area ($\textit{S}$). Above the critical $\textit{S}$ = 7.1 nm$^{2}$, 5FT-DNWs show a constant value of $κ$, whereas below it, there appears a sharp increase in $κ$ with decreasing $\textit{S}$. More importantly, 5FT-DNWs with minimal $\textit{S}$ show a superior $κ$ over the bulk diamond. By confirming the Normal-process-dominated scattering event, it is demonstrated that the phonon hydrodynamic behavior plays a determinative role in abnormally high $κ$ of 5FT-DNWs with small $\textit{S}$. The super-transported phonon hydrodynamic phenomenon unveiled in the twinned diamond nanowires may provide a new route for pursuing highly thermally conductive nanomaterials.

physics.comp-ph↗

arXiv2021

The generalized Hamilton principle and non-Hermitian quantum theory

The Hamilton principle is a variation principle describing the isolated and conservative systems, its Lagrange function is the difference between kinetic energy and potential energy. By Feynman path integration, we can obtain the Hermitian quantum theory, i.e., the standard Schrodinger equation. In this paper, we have given the generalized Hamilton principle, which can describe the open system (mass or energy exchange systems) and nonconservative force systems or dissipative systems. On this basis, we have given the generalized Lagrange function, it has to do with the kinetic energy, potential energy and the work of nonconservative forces to do. With the Feynman path integration, we have given the non-Hermitian quantum theory of the nonconservative force systems. Otherwise, we have given the generalized Hamiltonian function for the particle exchanging heat with the outside world, which is the sum of kinetic energy, potential energy and thermal energy, and further given the equation of quantum thermodynamics.

quant-ph↗

arXiv2021

Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems with an Electric Vehicle Charging Station: A Bi-level Approach

A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand response and multiple renewable uncertainties. To this end, a novel bi-level optimal dispatching model for the CIES with an EVCS in multi-stakeholder scenarios is established in this paper. In this model, an integrated demand response program is designed to promote a balance between energy supply and demand while maintaining a user comprehensive satisfaction within an acceptable range. To further tap the potential of demand response through flexibly guiding users' energy consumption and electric vehicles' behaviors (charging, discharging and providing spinning reserves), a dynamic pricing mechanism combining time-of-use and real-time pricing is put forward. In the solution phase, by using sequence operation theory (SOT), the original chance-constrained programming (CCP) model is converted into a readily solvable mixed-integer linear programming (MILP) formulation and finally solved by CPLEX solver. The simulation results on a practical CIES located in North China demonstrate that the presented method manages to balance the interests between CIES and EVCS via the coordination of flexible demand response and uncertain renewables.

eess.SY↗

arXiv2020

The quantum tunnel effect of photon in one-dimensional photonic crystals

In the paper, we have given the quantum transmissivity, probability density and probability current density of photon in one-dimensional photonic crystals $(AB)^N$ with the quantum theory approach. We find the quantum transmissivity is identical to the classical transmissivity. When the incident angle $θ$ and periodic number $N$ change the probability density and probability current density are approximate periodic change, and their amplitude are increased with the incident angles $θ$ and periodic number $N$ increasing. Otherwise, we find when the frequency of incident photon is corresponding to transmissivity $T=1$, the amplitude of the probability density is the largest. When the frequency of incident photon is corresponding to transmissivity $T=0$, the amplitude of the probability density attenuate rapidly to zero, it indicates that there is the quantum tunnel effect of photon in photonic crystals.

quant-ph↗

arXiv2020

Machine Learning Research Towards Combating COVID-19: Virus Detection, Spread Prevention, and Medical Assistance

COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, and predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspectives. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.

cs.CY↗

arXiv2020

Blockchain Architecture forAuditing Automation and TrustBuilding in Public Markets

Business transactions by public firms are required to be reported, verified, and audited periodically, which is traditionally a labor-intensive and time-consuming process. To streamline this procedure, we design FutureAB (Future Auditing Blockchain) which aims to automate the reporting and auditing process, thereby allowing auditors to focus on discretionary accounts to better detect and prevent fraud. We demonstrate how distributed-ledger technologies build investor trust and disrupt the auditing industry. Our multi-functional design indicates that auditing firms can automate transaction verification without the need for a trusted third party by collaborating and sharing their information while preserving data privacy (commitment scheme) and security (immutability). We also explore how smart contracts and wallets facilitate the computerization and implementation of our system on Ethereum. Finally, performance evaluation reveals the efficacy and scalability of FutureAB in terms of both encryption (0.012 seconds per transaction) and verification (0.001 seconds per transaction).

cs.CR↗