arXiv subjects

Meng Qi

Publications and source records attributed to Meng Qi.

15 recordsLinked to original sources

arXiv2026

How Infrastructure and Streetscape Shape E-Scooter Route Choice: Evidence from Washington, DC

E-scooters have emerged as an important micromobility mode for short urban trips, yet evidence on route choice behavior remains limited. This study examines e-scooter route choice in Washington, DC using GPS trajectory data and a Path Size Logit model. In addition to roadway and infrastructure characteristics, the model incorporates visual streetscape features extracted from Google Street View imagery using computer vision techniques. The results show that the effectiveness of cycling infrastructure depends strongly on roadway context. On major roads, only protected bicycle facilities significantly increase route attractiveness, whereas on minor roads both protected and designated lanes provide utility gains. Sidewalks constitute the most frequently used riding environment, yet only asphalt-paved sidewalks are associated with positive utility, suggesting that sidewalk riding may reflect the absence of attractive on-street alternatives rather than a preference for pedestrian infrastructure. Tree coverage, particularly during summer, as well as building and wall coverage, are positively associated with route choice. Likelihood ratio tests and value-of-distance analysis indicate that roadway infrastructure exerts a stronger influence on route choice than visual streetscape features, although the latter provide additional explanatory power. These findings support targeted infrastructure investment and the integration of streetscape improvements as a complementary strategy for enhancing micromobility route attractiveness.

stat.AP↗

arXiv2026

Observation of robust macroscale structural superlubricity

Structural superlubricity (SSL) promises nearly frictionless and wearless sliding, but has until now been considered a special and extreme interfacial phenomenon limited to micro- and nanoscale contacts. Here, we demonstrate robust macroscale SSL within a single sub-millimeter graphite contact. Previously reported near-zero friction coefficients, where friction is nearly independent of normal load, have only been observed at microscale contacts under low loads. Our system expands both contact size and load into the macroscopic regime, exhibiting friction coefficients that fluctuate around zero and reach values as low as $10^{-6}$ across a broad load range from 1 mN to 0.5 N. Negative friction coefficients are also observed. Similar behavior is observed at graphite/MoS$_2$ interfaces, indicating that macroscale SSL is a generalizable phenomenon across flat layered materials. These findings overturn long-standing scaling limitations and establish macroscale SSL as a paradigm-shifting platform for next-generation mechanical and electromechanical systems.

cond-mat.mtrl-sci↗

arXiv2025

On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction

The predictions of click through rate (CTR) and conversion rate (CVR) play a crucial role in the success of ad-recommendation systems. A Deep Hierarchical Ensemble Network (DHEN) has been proposed to integrate multiple feature crossing modules and has achieved great success in CTR prediction. However, its performance for CVR prediction is unclear in the conversion ads setting, where an ad bids for the probability of a user's off-site actions on a third party website or app, including purchase, add to cart, sign up, etc. A few challenges in DHEN: 1) What feature-crossing modules (MLP, DCN, Transformer, to name a few) should be included in DHEN? 2) How deep and wide should DHEN be to achieve the best trade-off between efficiency and efficacy? 3) What hyper-parameters to choose in each feature-crossing module? Orthogonal to the model architecture, the input personalization features also significantly impact model performance with a high degree of freedom. In this paper, we attack this problem and present our contributions biased to the applied data science side, including: First, we propose a multitask learning framework with DHEN as the single backbone model architecture to predict all CVR tasks, with a detailed study on how to make DHEN work effectively in practice; Second, we build both on-site real-time user behavior sequences and off-site conversion event sequences for CVR prediction purposes, and conduct ablation study on its importance; Last but not least, we propose a self-supervised auxiliary loss to predict future actions in the input sequence, to help resolve the label sparseness issue in CVR prediction. Our method achieves state-of-the-art performance compared to previous single feature crossing modules with pre-trained user personalization features.

cs.LG↗

arXiv2024

Online MDP with Transition Prototypes: A Robust Adaptive Approach

In this work, we consider an online robust Markov Decision Process (MDP) where we have the information of finitely many prototypes of the underlying transition kernel. We consider an adaptively updated ambiguity set of the prototypes and propose an algorithm that efficiently identifies the true underlying transition kernel while guaranteeing the performance of the corresponding robust policy. To be more specific, we provide a sublinear regret of the subsequent optimal robust policy. We also provide an early stopping mechanism and a worst-case performance bound of the value function. In numerical experiments, we demonstrate that our method outperforms existing approaches, particularly in the early stage with limited data. This work contributes to robust MDPs by considering possible prior information about the underlying transition probability and online learning, offering both theoretical insights and practical algorithms for improved decision-making under uncertainty.

cs.LG↗

arXiv2024

Mechanism for Collaborative Federated Learning: Pitfalls of Shapley Values

This paper investigates the impact of mechanism design on collaborative learning systems enabled by federated learning (FL). We propose a multi-action collaborative federated learning (MCFL) framework, capturing the interplay between agent strategies, platform mechanisms, and FL algorithms--a "three-body problem" in collaborative learning. This work demonstrates how the convergence rate and computational efficiency of FL are endogenously determined by the agent participation equilibrium that is induced by the mechanism. By doing so, we establish a direct link between incentive design in collaborative learning systems and the performance of the underlying optimization algorithms, a connection that has been largely overlooked in the existing literature. Specifically, we characterize the equilibrium of agent participation under two prominent mechanisms: the Shapley Value (SV) and Marginal Contribution (MC) mechanisms. Although SV is fair in surplus allocation and budget balanced, it has a vital pitfall: agents are incentivized to split their data across newly created fake identities. This is critical especially in the MCFL setting as it leads to slow convergence of FL optimization, which increases the number of required synchronization/communication rounds even when the per-round cost is fixed. In contrast, while MC is not budget-balanced, it is robust to such strategic manipulation and is able to induce an equilibrium that maximizes the MCFL system efficiency. Overall, our study lays a foundation for jointly designing incentives and algorithms in MCFL systems. We provide insights on pitfalls of SV: it induces a system equilibrium that leads to tremendous training cost and slower convergence, ultimately undermining the effectiveness of collaborative learning.

cs.GT↗

arXiv2024

Stimulated Raman phase shift spectroscopy: a pathway to hyperfine fingerprint spectra

The principle and experimental realization of a novel Raman spectroscopic technique entitled stimulated Raman phase shift (SRPS) spectroscopy was demonstrated. This technique depends on the measurement of the stimulated Raman scattering (SRS) induced phase shift of Stokes light field ($\Delta$ $\phi$) that is related to the real part of the third order nonlinear susceptibility of SRS. In principle, the spectral lineshape of 1/|$\Delta$ $\phi$| is a delta function waveform, which is insensitive to the fluctuation of Stokes light intensity, the decoherence of phonon in materials, as well as the inhomogeneous fluorescence background. In order to measure 1/|$\Delta$ $\phi$|, a SRPS including a Mach-Zender interferometer and a signal processing device was developed. Using the developed spectrometer, the SRPS and stimulated Raman gain (SRG) spectra of neat dimethyl sulfoxide were detected simultaneously. Seven Raman peaks corresponding to specific molecule vibrational and rotational modes were observed in the SRPS spectra, while only two peaks could be identified in the SRG spectra without a priori knowledge. The linewidth of the Raman peak centered at 2913.283 cm$^{-1}$ indicating the v$_s$(CH$_3$)stretching mode of the methyl groups was less than 0.00036 cm$^{-1}$ in the measured SRPS spectra, which was almost four orders of magnitude narrower than that in the measured SRG spectra. Meanwhile, the detection signal-to-noise ratio of the Raman peak centered at 2913.283 cm$^{-1}$ was 25.3 dB, representing an increase of 14.3 dB compared to the SRG spectra. The reliability of SRPS technique was verified by 10 independent measurements, and the standard deviation of the Raman peak frequency was less than $\pm$0.338 cm$^{-1}$ . The SRPS technique paves the way for characterizing the hyperfine fingerprint of materials.

physics.optics↗

arXiv2021

Integrated Conditional Estimation-Optimization

Many real-world optimization problems involve uncertain parameters with probability distributions that can be estimated using contextual feature information. In contrast to the standard approach of first estimating the distribution of uncertain parameters and then optimizing the objective based on the estimation, we propose an integrated conditional estimation-optimization (ICEO) framework that estimates the underlying conditional distribution of the random parameter while considering the structure of the optimization problem. We directly model the relationship between the conditional distribution of the random parameter and the contextual features, and then estimate the probabilistic model with an objective that aligns with the downstream optimization problem. We show that our ICEO approach is asymptotically consistent under moderate regularity conditions and further provide finite performance guarantees in the form of generalization bounds. Computationally, performing estimation with the ICEO approach is a non-convex and often non-differentiable optimization problem. We propose a general methodology for approximating the potentially non-differentiable mapping from estimated conditional distribution to the optimal decision by a differentiable function, which greatly improves the performance of gradient-based algorithms applied to the non-convex problem. We also provide a polynomial optimization solution approach in the semi-algebraic case. Numerical experiments are also conducted to show the empirical success of our approach in different situations including with limited data samples and model mismatches.

stat.ML↗

arXiv2021

Smart Feasibility Pump: Reinforcement Learning for (Mixed) Integer Programming

In this work, we propose a deep reinforcement learning (DRL) model for finding a feasible solution for (mixed) integer programming (MIP) problems. Finding a feasible solution for MIP problems is critical because many successful heuristics rely on a known initial feasible solution. However, it is in general NP-hard. Inspired by the feasibility pump (FP), a well-known heuristic for searching feasible MIP solutions, we develop a smart feasibility pump (SFP) method using DRL. In addition to multi-layer perception (MLP), we propose a novel convolution neural network (CNN) structure for the policy network to capture the hidden information of the constraint matrix of the MIP problem. Numerical experiments on various problem instances show that SFP significantly outperforms the classic FP in terms of the number of steps required to reach the first feasible solution. Moreover, the CNN structure works without the projection of the current solution as the input, which saves the computational effort at each step of the FP algorithms to find projections. This highlights the representational power of the CNN structure.

cs.LG↗

arXiv2017

Electron Mobility in Polarization-doped Al$\mathrm{_{0-0.2}}$GaN with a Low Concentration Near 10$\mathrm{^{17}}$ cm$\mathrm{^{-3}}$

In this letter, carrier transport in graded Al$\mathrm{_x}$Ga$\mathrm{_{1-x}}$N with a polarization-induced n-type doping as low as ~ 10$\mathrm{^{17}}$ cm$\mathrm{^{-3}}$ is reported. The graded Al$\mathrm{_x}$Ga$\mathrm{_{1-x}}$N is grown by metal organic chemical vapor deposition on a sapphire substrate and a uniform n-type doping without any intentional doping is realized by linearly varying the Al composition from 0% to 20% over a thickness of 600 nm. A compensating center concentration of ~10$\mathrm{^{17}}$ cm$\mathrm{^{-3}}$ was also estimated. A peak mobility of 900 cm$\mathrm{^2}$/V$\mathrm \cdot$s at room temperature is extracted at an Al composition of ~ 7%, which represents the highest mobility achieved in n-Al$\mathrm{_{0.07}}$GaN with a carrier concentration ~10$\mathrm{^{17}}$ cm$\mathrm{^{-3}}$. Comparison between experimental data and theoretical models shows that, at this low doping concentration, both dislocation scattering and alloy scattering are significant in limiting electron mobility; and that a dislocation density of <10$\mathrm{^7}$ cm$\mathrm{^{-2}}$ is necessary to optimize mobility near 10$\mathrm{^{16}}$ cm$\mathrm{^{-3}}$. The findings in this study provide insight in key elements for achieving high mobility at low doping levels in GaN, a critical parameter in design of novel power electronics taking advantage of polarization doping.

cond-mat.mes-hall↗

arXiv2016

Strained GaN Quantum-Well FETs on Single Crystal Bulk AlN Substrates

We report the first realization of molecular beam epitaxy grown strained GaN quantum well field-effect transistors on single-crystal bulk AlN substrates. The fabricated double heterostructure FETs exhibit a two- dimensional electron gas (2DEG) density in excess of 2x10^13/cm2. Ohmic contacts to the 2DEG channel were formed by n+ GaN MBE regrowth process, with a contact resistance of 0.13 Ohm-mm. Raman spectroscopy using the quantum well as an optical marker reveals the strain in the quantum well, and strain relaxation in the regrown GaN contacts. A 65-nm-long rectangular-gate device showed a record high DC drain current drive of 2.0 A/mm and peak extrinsic transconductance of 250 mS/mm. Small-signal RF performance of the device achieved current gain cutoff frequency fT~120 GHz. The DC and RF performance demonstrate that bulk AlN substrates offer an attractive alternative platform for strained quantum well nitride transistors for future high-voltage and high-power microwave applications.

cond-mat.mtrl-sci↗

arXiv2015

Unique opportunity to harness polarization in GaN to override the conventional power electronics figure-of-merits

Owing to the large breakdown electric field, wide bandgap semiconductors such as SiC, GaN, Ga2O3 and diamond based power devices are the focus for next generation power switching applications. The unipolar trade-off relationship between the area specific-on resistance and breakdown voltage is often employed to compare the performance limitation among various materials. The GaN material system has a unique advantage due to its prominent spontaneous and piezoelectric polarization effects in GaN, AlN, InN, AlxInyGaN alloys and flexibility in inserting appropriate heterojunctions thus dramatically broaden the device design space.

cond-mat.mtrl-sci↗

arXiv2015

Dual Optical Marker Raman Characterization of Strained GaN-channels on AlN Using AlN/GaN/AlN Quantum Wells and 15N Isotopes

This work shows that the combination of ultrathin highly strained GaN quantum wells embedded in an AlN matrix, with controlled isotopic concentrations of Nitrogen enables a dual marker method for Raman spectroscopy. By combining these techniques, we demonstrate the effectiveness in studying strain in the vertical direction. This technique will enable the precise probing of properties of buried active layers in heterostructures, and can be extended in the future to vertical devices such as those used for optical emitters, and for power electronics.

cond-mat.mtrl-sci↗

arXiv2015

Band Anticrossing in Dilute Germanium Carbides Using Hybrid Functionals

Dilute germanium carbides (Ge1-xCx) offer a direct bandgap for compact silicon photonics, but widely varying results have been reported. This work uses ab initio simulations with HSE06 hybrid functionals and spin-orbit coupling to study the Ge1-xCx band structure behavior in the absence of defects. Contrary to Vegard's law, the conduction band minimum at k=0 is consistently found to decrease with increasing C content, while L and X valleys change much more slowly. A vanishing bandgap was observed for all alloys with x>0.017. Conduction bands deviate from a constant-potential band anticrossing model except near the center of the Brillouin zone.

physics.comp-ph↗

arXiv2014

Self-Assembled Ge QDs Formed by High Temperature Annealing on GaAs and AlxGa1-xAs (001)

This work studies the spontaneous self-assembly of Ge QDs on AlAs, GaAs, and AlGaAs by high temperature in-situ annealing in molecular beam epitaxy (MBE). The morphology of Ge dots formed on AlAs are observed by atom probe tomography, which revealed nearly spherical QDs with diameters approaching 10 nm and confirmed the complete absence of a wetting layer. Reflection high-energy electron diffraction (RHEED) and atomic force microscopy (AFM) of Ge annealed under similar conditions on GaAs and Al0.3Ga0.7As surfaces reveal the gradual suppression of QD formation with decreasing Al-content of the buffer. To investigate the prospects of using encapsulated Ge dots for upconverting photovoltaics, in which photocurrent can still be generated from photons with energy less than the host bandgap, Ge QDs are embedded into the active region of III-V PIN diodes by MBE. It is observed that orders of magnitude higher short-circuit current is obtained at photon energies below the GaAs bandgap compared with a reference PIN diode without Ge QDs. These results demonstrate the promise of Ge QDs for upconverting solar cells and the realization of device-quality integration of group IV and III-V semiconductors.

cond-mat.mtrl-sci↗

arXiv2013

Ge Quantum Dots Encapsulated by AlAs Grown by Molecular Beam Epitaxy on GaAs Without Extended Defects

We demonstrate nearly-spherical, strain-free, self-assembled Ge quantum dots (QDs) fully encapsulated by AlAs, grown on (100) GaAs by molecular beam epitaxy (MBE). The QDs were formed without a wetting layer using a high temperature, in-situ anneal. Subsequent AlAs overgrowth was free from threading dislocations and anti-phase domains. The straddling band alignment for Ge in AlAs promises strong and tunable confinement for both electrons and holes. The reflection high-energy electron diffraction (RHEED) pattern changed from 2x3 to 2x5 with anneal, which can be explained by surface reconstructions based on the electron-counting model.

cond-mat.mtrl-sci↗