arXiv subjects

Yan Lyu

Publications and source records attributed to Yan Lyu.

35 records · Page 2Linked to original sources

arXiv2024

UAV Path Planning for Object Observation with Quality Constraints: A Dynamic Programming Approach

This paper addresses a UAV path planning task that seeks to observe a set of objects while satisfying the observation quality constraint. A dynamic programming algorithm is proposed that enables the UAV to observe the target objects with the shortest path while subjecting to the observation quality constraint. The objects have their own facing direction and restricted observation range. With an observing order, the algorithm achieves $(1+ε)$-approximation ratio in theory and runs in polynomial time. The extensive results demonstrate that the algorithm produces near-optimal solutions, the effectiveness of which is also tested and proved in the Airsim simulator, a realistic virtual environment.

cs.RO↗

arXiv2024

Scale setting and hadronic properties in light quark sector with $(2+1)$-flavor Wilson fermions at the physical point

We report scale setting and hadronic properties for our new lattice QCD gauge configuration set (HAL-conf-2023). We employ $(2+1)$-flavor nonperturbatively improved Wilson fermions with stout smearing and the Iwasaki gauge action on a $96^4$ lattice, and generate configurations of 8,000 trajectories at the physical point. We show the basic properties of the configurations such as the plaquette value, topological charge distribution and their auto-correlation times. The scale setting is performed by detailed analyses of the $Ω$ baryon mass. We calculate the physical results of quark masses, decay constants of pseudoscalar mesons and single hadron spectra in light quark sector. The masses of the stable hadrons are found to agree with the experimental values within a sub-percent level.

hep-lat↗

arXiv2024

Building Hadron Potentials from Lattice QCD with Deep Neural Networks

In this study, we develop a deep learning method to learn hadronic interactions unsupervisedly from the correlation functions calculated in lattice QCD simulations. We present our approach of using deep neural networks to model the inter-hadron potentials that are learned from Nambu-Bethe-Salpeter (NBS) wave functions. This enables the incorporation of most general forms of potentials into the Schrödinger-type equation for detailed analysis of hadronic interactions. Our results include validations with separable potentials, as well as the local and non-local potentials for the $Ω_{ccc}-Ω_{ccc}$ system. The neural networks accurately capture the essential features of these interactions, providing a reliable tool for predicting and analyzing hadron scattering properties, potentially bridging the experimental observables and lattice QCD data.

hep-lat↗

arXiv2024

i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance

Ride-hailing platforms have been facing the challenge of balancing demand and supply. Existing vehicle reposition techniques often treat drivers as homogeneous agents and relocate them deterministically, assuming compliance with the reposition. In this paper, we consider a more realistic and driver-centric scenario where drivers have unique cruising preferences and can decide whether to take the recommendation or not on their own. We propose i-Rebalance, a personalized vehicle reposition technique with deep reinforcement learning (DRL). i-Rebalance estimates drivers' decisions on accepting reposition recommendations through an on-field user study involving 99 real drivers. To optimize supply-demand balance and enhance preference satisfaction simultaneously, i-Rebalance has a sequential reposition strategy with dual DRL agents: Grid Agent to determine the reposition order of idle vehicles, and Vehicle Agent to provide personalized recommendations to each vehicle in the pre-defined order. This sequential learning strategy facilitates more effective policy training within a smaller action space compared to traditional joint-action methods. Evaluation of real-world trajectory data shows that i-Rebalance improves driver acceptance rate by 38.07% and total driver income by 9.97%.

cs.AI↗

arXiv2024

Tailoring Physical Properties of Crystals through Synthetic Temperature Control: A Case Study for new Polymorphic NbFeTe2 phases

Growth parameters play a significant role in the crystal quality and physical properties of layered materials. Here we present a case study on a van der Waals magnetic NbFeTe2 material. Two different types of polymorphic NbFeTe2 phases, synthesized at different temperatures, display significantly different behaviors in crystal symmetry, electronic structure, electrical transport, and magnetism. While the phase synthesized at low temperature showing behavior consistent with previous reports, the new phase synthesized at high temperature, has completely different physical properties, such as metallic resistivity, long-range ferromagnetic order, anomalous Hall effect, negative magnetoresistance, and distinct electronic structures. Neutron diffraction reveals out-of-plane ferromagnetism below 70K, consistent with the electrical transport and magnetic susceptibility studies. Our work suggests that simply tuning synthetic parameters in a controlled manner could be an effective route to alter the physical properties of existing materials potentially unlocking new states of matter, or even discovering new materials.

cond-mat.str-el↗

arXiv2024

Doubly charmed tetraquark $T_{cc}^+$ in (2+1)-flavor QCD near physical point

We study the doubly charmed tetraquark state $T_{cc}^+$ by the HAL QCD method applied to the $D^*D$ system in $(2+1)$ flavor lattice QCD at nearly physical pion mass, $m_π= 146$ MeV. We obtain the attractive potential at all distances in the $S$-wave of the isoscalar $D^* D$ system, whose long distance behavior is well described by the two-pion exchange (TPE), and it generates a virtual pole near $D^* D$ threshold with a pole position $E_{\rm pole} = -59 (^{+53}_{-99}) (^{+2}_{-67})$ keV and an inverse scattering length $1/a_0=0.05(5)(^{+2}_{-2})$ fm$^{-1}$. The virtual pole turns into a loosely bound state pole if the pion mass in the TPE potential is extrapolated to the physical value, $m_π=135$ MeV. The potential at the physical pion mass is shown to give a semi-quantitative description of the $D^0 D^0π^+$ mass spectrum at the LHCb.

hep-lat↗

arXiv2023

Indication of a p-$ϕ$ bound state from a correlation function analysis

The existence of a nucleon-$ϕ$ (N-$ϕ$) bound state has been subject of theoretical and experimental investigations for decades. In this letter, indication of a \pphi bound state is found, using for the first time two-particle correlation functions as alternative to invariant mass spectra. Newly available lattice calculations for the spin 3/2 \Nphi interaction by the HAL QCD collaboration are used to constrain the spin 1/2 counterpart from the fit of the experimental \pphi correlation function measured by ALICE. The corresponding scattering length and effective range are $f_0^{(1/2)}=\left(-1.54^{+0.53}_{-0.53}(\mathrm{stat.})^{+0.16}_{-0.09}(\mathrm{syst.})+i\cdot0.00^{+0.35}_{-0.00}(\mathrm{stat.})^{+0.16}_{-0.00}(\mathrm{syst.})\right)$~fm and $d_0^{(1/2)}=\left(0.39^{+0.09}_{-0.09}(\mathrm{stat.})^{+0.02}_{-0.03}(\mathrm{syst.})+i\cdot0.00^{+0.00}_{-0.04}(\mathrm{stat.})^{+0.00}_{-0.02}(\mathrm{syst.})\right)$~fm, respectively. The results imply the appearance of a \pphi bound state with an estimated binding energy in the range of $12.8-56.1$ MeV.

nucl-ex↗

arXiv2023

Doubly Charmed Tetraquark $T^+_{cc}$ from Lattice QCD near Physical Point

The doubly charmed tetraquark $T^+_{cc}$ recently discovered by the LHCb Collaboration is studied on the basis of $(2+1)$-flavor lattice QCD simulations of the $D^*D$ system with nearly physical pion mass $m_π=146$ MeV. The interaction of $D^*D$ in the isoscalar and $S$-wave channel, derived from the hadronic spacetime correlation by the HAL QCD method, is attractive for all distances and leads to a near-threshold virtual state with a pole position $E_\text{pole}=-59\left(^{+53}_{-99}\right)\left(^{+2}_{-67}\right)$ keV and a large scattering length $1/a_0=0.05(5)\left(^{+2}_{-2}\right)~\text{fm}^{-1}$. The virtual state is shown to evolve into a loosely bound state as $m_π$ decreases to its physical value by using a potential modified to $m_π=135$ MeV based on the pion-exchange interaction. Such a potential is found to give a semiquantitative description of the LHCb data on the $D^0D^0π^+$ mass spectrum. Future study is necessary to perform physical-point simulations with the isospin-breaking and open three-body-channel effects taken into account.

hep-lat↗

arXiv2023

TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations

Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning. For debiasing tasks, the doubly robust (DR) method and its variants show superior performance due to the double robustness property, that is, DR is unbiased when either imputed errors or learned propensities are accurate. However, our theoretical analysis reveals that DR usually has a large variance. Meanwhile, DR would suffer unexpectedly large bias and poor generalization caused by inaccurate imputed errors and learned propensities, which usually occur in practice. In this paper, we propose a principled approach that can effectively reduce bias and variance simultaneously for existing DR approaches when the error imputation model is misspecified. In addition, we further propose a novel semi-parametric collaborative learning approach that decomposes imputed errors into parametric and nonparametric parts and updates them collaboratively, resulting in more accurate predictions. Both theoretical analysis and experiments demonstrate the superiority of the proposed methods compared with existing debiasing methods.

cs.IR↗

arXiv2022

Multiple Robust Learning for Recommendation

In recommender systems, a common problem is the presence of various biases in the collected data, which deteriorates the generalization ability of the recommendation models and leads to inaccurate predictions. Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate. In this paper, we propose a multiple robust (MR) estimator that can take the advantage of multiple candidate imputation and propensity models to achieve unbiasedness. Specifically, the MR estimator is unbiased when any of the imputation or propensity models, or a linear combination of these models is accurate. Theoretical analysis shows that the proposed MR is an enhanced version of DR when only having a single imputation and propensity model, and has a smaller bias. Inspired by the generalization error bound of MR, we further propose a novel multiple robust learning approach with stabilization. We conduct extensive experiments on real-world and semi-synthetic datasets, which demonstrates the superiority of the proposed approach over state-of-the-art methods.

cs.IR↗

arXiv2022

Attractive $N$-$ϕ$ Interaction and Two-Pion Tail from Lattice QCD near Physical Point

First results on the interaction between the $ϕ$-meson and the nucleon ($N$) are presented based on the ($2+1$)-flavor lattice QCD simulations with nearly physical quark masses. Using the HAL QCD method, the spacetime correlation of the $N$-$ϕ$ system in the spin 3/2 channel is converted into the $N$-$ϕ$ scattering phase shift through the interaction potential. The $N$-$ϕ$ potential appears to be a combination of a short-range attractive core and a long-range attractive tail. The latter is found to be consistent with the two-pion exchange (TPE) obtained from the interaction between a color-dipole and the nucleon. The resultant scattering length and effective range for $m_π=$ 146.4 MeV are $ a^{(3/2)}_0=-1.43(23)_{\rm stat.}\left(^{+36}_{-06}\right)_{\rm syst.} {\rm fm}$ and $ r^{(3/2)}_{\rm eff}=2.36(10)_{\rm stat.}\left(^{+02}_{-48}\right)_{\rm syst.} {\rm fm}$, respectively. The magnitude of the scattering length is shown to have nontrivial dependence of $m_π$ and is sensitive to the existence of the long-range tail from TPE.

hep-lat↗

arXiv2022

Optimized Two-Baryon Operators in Lattice QCD

A set of optimized interpolating operators which are dominantly coupled to each eigenstate of two baryons on the lattice is constructed by the HAL QCD method. To test its validity, we consider heavy dibaryons $Ω_{3Q}Ω_{3Q}$ ($Q=s,c$) calculated by (2+1)-flavor lattice QCD simulations with nearly physical pion mass. The optimized two-baryon operators are shown to provide effective energies of the ground and excited states separately stable as a function of the Euclidean time. Also they agree to the eigenenergies in a finite lattice box obtained from the leading-order HAL QCD potential $V({\boldsymbol{r}})$ within statistical errors. The overlapping factors between the optimized sink operators and the state created by the wall-type source operator indicate that $V( \boldsymbol{r})$ can be reliably extracted, no matter whether the spacetime correlation of two baryons is dominated by the ground state or the excited state. It is suggested that the optimized set of operators is useful for variational studies of hadron-hadron interactions.

hep-lat↗

arXiv2022

A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems

Accurate recommendation and reliable explanation are two key issues for modern recommender systems. However, most recommendation benchmarks only concern the prediction of user-item ratings while omitting the underlying causes behind the ratings. For example, the widely-used Yahoo!R3 dataset contains little information on the causes of the user-movie ratings. A solution could be to conduct surveys and require the users to provide such information. In practice, the user surveys can hardly avoid compliance issues and sparse user responses, which greatly hinders the exploration of causality-based recommendation. To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios. To illustrate the use of our framework, we construct a semi-synthetic dataset with Causal Tags And Ratings (CTAR), based on the movies as well as their descriptive tags and rating information collected from a famous movie rating website. Using the collected data and the causal graph, the user-item-ratings and their corresponding user-item-tags are automatically generated, which provides the reasons (selected tags) why the user rates the items. Descriptive statistics and baseline results regarding the CTAR dataset are also reported. The proposed data generation framework is not limited to recommendation, and the released APIs can be used to generate customized datasets for other research tasks.

cs.IR↗

arXiv2022

IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality

With the increasing pervasiveness of Artificial Intelligence (AI), many visual analytics tools have been proposed to examine fairness, but they mostly focus on data scientist users. Instead, tackling fairness must be inclusive and involve domain experts with specialized tools and workflows. Thus, domain-specific visualizations are needed for algorithmic fairness. Furthermore, while much work on AI fairness has focused on predictive decisions, less has been done for fair allocation and planning, which require human expertise and iterative design to integrate myriad constraints. We propose the Intelligible Fair Allocation (IF-Alloc) Framework that leverages explanations of causal attribution (Why), contrastive (Why Not) and counterfactual reasoning (What If, How To) to aid domain experts to assess and alleviate unfairness in allocation problems. We apply the framework to fair urban planning for designing cities that provide equal access to amenities and benefits for diverse resident types. Specifically, we propose an interactive visual tool, Intelligible Fair City Planner (IF-City), to help urban planners to perceive inequality across groups, identify and attribute sources of inequality, and mitigate inequality with automatic allocation simulations and constraint-satisfying recommendations. We demonstrate and evaluate the usage and usefulness of IF-City on a real neighborhood in New York City, US, with practicing urban planners from multiple countries, and discuss generalizing our findings, application, and framework to other use cases and applications of fair allocation.

cs.HC↗

arXiv2021

Finite volume analysis on systematics of the derivative expansion in HAL QCD method

We study the convergence of the derivative expansion in HAL QCD method from the finite volume analysis. Employing the (2+1)-flavor lattice QCD data obtained at nearly physical light quark masses $(m_π, m_K) \simeq (146, 525)$ MeV and the physical charm quark mass, we study two representative systems, $ΩΩ$ and $Ω_{ccc}Ω_{ccc}$ in the $^1S_0$ channel, where both systems were found to have a shallow bound state in our previous studies. The HAL QCD potentials are determined at the leading-order in the derivative expansion, from which finite-volume eigenmodes are obtained. Utilizing the eigenmode projection, we find that the correlation functions are dominated by the ground state (first excited state) in the case of $ΩΩ$ ($Ω_{ccc}Ω_{ccc}$). In both $ΩΩ$ and $Ω_{ccc}Ω_{ccc}$, the spectra obtained from eigenmode-projected temporal correlators are found to be consistent with those from the HAL QCD potential for both the ground and first excited state. These results show that the derivative expansion is well converged in these systems, and also provide a first explicit evidence that the HAL QCD method enables us to reliably extract the binding energy of the ground state even from the correlator dominated by excited scattering states.

hep-lat↗

arXiv2021

Most charming dibaryon near unitarity

We present a first study on a pair of triply charmed baryons, $Ω_{ccc}Ω_{ccc}$ in the $^1S_0$ channel, on the basis of the HAL QCD method. The measurements are perfomed on the $(2+1)$-flavor lattice QCD configurations with nearly physical light-quark masses and physical charm-quark mass. We show that the system with the Coulomb repulsion taking into account the charge form factor of $Ω_{ccc}$ leads to the scattering length $a^\mathrm{C}_0\simeq-19$ fm and the effective range $r^\mathrm{C}_\mathrm{eff}\simeq0.45$ fm, which indicates $Ω_{ccc}Ω_{ccc}$ is located in the unitary regime.

hep-lat↗

arXiv2021

Dibaryon with highest charm number near unitarity from lattice QCD

A pair of triply charmed baryons, $Ω_{ccc}Ω_{ccc}$, is studied as an ideal dibaryon system by (2+1)-flavor lattice QCD with nearly physical light-quark masses and the relativistic heavy quark action with the physical charm quark mass. The spatial baryon-baryon correlation is related to their scattering parameters on the basis of the HAL QCD method. The $Ω_{ccc}Ω_{ccc}$ in the ${^1S_0}$ channel taking into account the Coulomb repulsion with the charge form factor of $Ω_{ccc}$ leads to the scattering length $a^{\rm C}_0\simeq -19~\text{fm}$ and the effective range $r^{\rm C}_{\mathrm{eff}}\simeq 0.45~\text{fm}$. The ratio $r^{\rm C}_{\mathrm{eff}}/a^{\rm C}_0 \simeq -0.024$, whose magnitude is considerably smaller than that of the dineutron ($-0.149$), indicates that $Ω_{ccc}Ω_{ccc}$ is located in the unitary regime.

hep-lat↗