arXiv subjects

Wenlong Wang

Publications and source records attributed to Wenlong Wang.

At least 37 records · Page 2Linked to original sources

arXiv2025

BLI: A High-performance Bucket-based Learned Index with Concurrency Support

Learned indexes are promising to replace traditional tree-based indexes. They typically employ machine learning models to efficiently predict target positions in strictly sorted linear arrays. However, the strict sorted order 1) significantly increases insertion overhead, 2) makes it challenging to support lock-free concurrency, and 3) harms in-node lookup/insertion efficiency due to model inaccuracy.\ In this paper, we introduce a \textit{Bucket-based Learned Index (BLI)}, which is an updatable in-memory learned index that adopts a "globally sorted, locally unsorted" approach by replacing linear sorted arrays with \textit{Buckets}. BLI optimizes the insertion throughput by only sorting Buckets, not the key-value pairs within a Bucket. BLI strategically balances three critical performance metrics: tree fanouts, lookup/insert latency for inner nodes, lookup/insert latency for leaf nodes, and memory consumption. To minimize maintenance costs, BLI performs lightweight bulk loading, insert, node scaling, node split, model retraining, and node merging adaptively. BLI supports lock-free concurrency thanks to the unsorted design with Buckets. Our results show that BLI achieves up to 2.21x better throughput than state-of-the-art learned indexes, with up to 3.91x gains under multi-threaded conditions.

cs.DB↗

arXiv2024

Applying Neural Monte Carlo Tree Search to Unsignalized Multi-intersection Scheduling for Autonomous Vehicles

Dynamic scheduling of access to shared resources by autonomous systems is a challenging problem, characterized as being NP-hard. The complexity of this task leads to a combinatorial explosion of possibilities in highly dynamic systems where arriving requests must be continuously scheduled subject to strong safety and time constraints. An example of such a system is an unsignalized intersection, where automated vehicles' access to potential conflict zones must be dynamically scheduled. In this paper, we apply Neural Monte Carlo Tree Search (NMCTS) to the challenging task of scheduling platoons of vehicles crossing unsignalized intersections. Crucially, we introduce a transformation model that maps successive sequences of potentially conflicting road-space reservation requests from platoons of vehicles into a series of board-game-like problems and use NMCTS to search for solutions representing optimal road-space allocation schedules in the context of past allocations. To optimize search, we incorporate a prioritized re-sampling method with parallel NMCTS (PNMCTS) to improve the quality of training data. To optimize training, a curriculum learning strategy is used to train the agent to schedule progressively more complex boards culminating in overlapping boards that represent busy intersections. In a busy single four-way unsignalized intersection simulation, PNMCTS solved 95\% of unseen scenarios, reducing crossing time by 43\% in light and 52\% in heavy traffic versus first-in, first-out control. In a 3x3 multi-intersection network, the proposed method maintained free-flow in light traffic when all intersections are under control of PNMCTS and outperformed state-of-the-art RL-based traffic-light controllers in average travel time by 74.5\% and total throughput by 16\% in heavy traffic.

cs.AI↗

arXiv2024

Singular Solutions to the Complex Monge-Ampère Equation

We present an explicit pluripotential and viscosity solution to the complex Monge-Ampère equation with constant right-hand side on $\mathbb D\times\mathbb C^{n-1}\,(n\geq 2)$, which lies merely in $W^{1,2}_{loc}\cap W^{2,1}_{loc}$ and is not even Dini continuous. Additionally, we exhibit two families of explicit entire toric solutions on $\mathbb C^n$ with continuous Hölder exponent $α\in(0,1)$ and $W^{2,p}_{loc}$ exponent $p\in (1,2)$.

math.AP↗

arXiv2024

Fast Explicit Machine Learning-Based Model Predictive Control of Nonlinear Processes Using Input Convex Neural Networks

Explicit machine learning-based model predictive control (explicit ML-MPC) has been developed to reduce the real-time computational demands of traditional ML-MPC. However, the evaluation of candidate control actions in explicit ML-MPC can be time-consuming due to the non-convex nature of machine learning models. To address this issue, we leverage Input Convex Neural Networks (ICNN) to develop explicit ICNN-MPC, which is formulated as a convex optimization problem. Specifically, ICNN is employed to capture nonlinear system dynamics and incorporated into MPC, with sufficient conditions provided to ensure the convexity of ICNN-based MPC. We then formulate mixed-integer quadratic programming (MIQP) problems based on the candidate control actions derived from the solutions of multi-parametric quadratic programming (mpQP) problems within the explicit ML-MPC framework. Optimal control actions are obtained by solving real-time convex MIQP problems. The effectiveness of the proposed method is demonstrated through two case studies, including a chemical reactor example, and a chemical process network simulated by Aspen Plus Dynamics, where explicit ML-MPC written in Python is integrated with Aspen dynamic simulation through a programmable interface.

math.OC↗

arXiv2024

VL-DNA: Enhance DNA Storage Capacity with Variable Payload (Strand) Lengths

DNA storage is a promising archival data storage solution to today's big data problem. A DNA storage system encodes and stores digital data with synthetic DNA sequences and decodes DNA sequences back to digital data via sequencing. For efficient target data retrieving, existing Polymerase Chain Reaction PCR based DNA storage systems apply primers as specific identifier to tag different set of DNA strands. However, the PCR based DNA storage system suffers from primer-payload collisions, causing a significant reduction of storage capacity. This paper proposes using variable strand length, which takes advantage of the inherent payload-cutting process, to split collisions and recover primers. The executing time of our scheme is linear to the number of primer-payload collisions. The scheme serves as a post-processing method to any DNA encoding scheme. The evaluation of three state-of-the-art encoding schemes shows that the scheme can recover thousands of usable primers and improve tube capacity ranging from 18.27% to 19x.

cs.ET↗

arXiv2024

Watt-level all polarization-maintaining femtosecond fiber laser source at 1100 nm for multicolor two-photon fluorescence excitation of fluorescent proteins

We demonstrate a compact watt-level all polarization-maintaining (PM) femtosecond fiber laser source at 1100 nm. The fiber laser source is seeded by an all PM fiber mode-locked laser employing a nonlinear amplifying loop mirror. The seed laser can generate stable pulses at a fundamental repetition rate of 40.71 MHz with a signal-to-noise rate of >100 dB and an integrated relative intensity noise of only ~0.061%. After two-stage external amplification and pulse compression, an output power of ~1.47 W (corresponding to a pulse energy of ~36.1 nJ) and a pulse duration of ~251 fs are obtained. The 1100 nm femtosecond fiber laser is then employed as the excitation light source for multicolor multi-photon fluorescence microscopy of Chinese hamster ovary (CHO) cells stably expressing red fluorescent proteins.

physics.optics↗

arXiv2024

A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography

Synthetic aperture radar (SAR) tomography (TomoSAR) retrieves three-dimensional (3-D) information from multiple SAR images, effectively addresses the layover problem, and has become pivotal in urban mapping. Unmanned aerial vehicle (UAV) has gained popularity as a TomoSAR platform, offering distinct advantages such as the ability to achieve 3-D imaging in a single flight, cost-effectiveness, rapid deployment, and flexible trajectory planning. The evolution of compressed sensing (CS) has led to the widespread adoption of sparse reconstruction techniques in TomoSAR signal processing, with a focus on $\ell _1$ norm regularization and other grid-based CS methods. However, the discretization of illuminated scene along elevation introduces modeling errors, resulting in reduced reconstruction accuracy, known as the "off-grid" effect. Recent advancements have introduced gridless CS algorithms to mitigate this issue. This paper presents an innovative gridless 3-D imaging framework tailored for UAV-borne TomoSAR. Capitalizing on the pulse repetition frequency (PRF) redundancy inherent in slow UAV platforms, a multiple measurement vectors (MMV) model is constructed to enhance noise immunity without compromising azimuth-range resolution. Given the sparsely placed array elements due to mounting platform constraints, an atomic norm soft thresholding algorithm is proposed for partially observed MMV, offering gridless reconstruction capability and super-resolution. An efficient alternative optimization algorithm is also employed to enhance computational efficiency. Validation of the proposed framework is achieved through computer simulations and flight experiments, affirming its efficacy in UAV-borne TomoSAR applications.

eess.SP↗

arXiv2023

Linear limit continuation: Theory and an application to two-dimensional Bose-Einstein condensates

We present a coherent and effective theoretical framework to systematically construct numerically exact nonlinear solitary waves from their respective linear limits. First, all possible linear degenerate sets are classified for a harmonic potential using lattice planes. For a generic linear degenerate set, distinct wave patterns are identified in the near-linear regime using a random searching algorithm by suitably mixing the linear degenerate states, followed by a numerical continuation in the chemical potential extending the waves into the Thomas-Fermi regime. The method is applied to the two-dimensional, one-component Bose-Einstein condensates, yielding a spectacular set of waveforms. Our method opens a remarkably large program, and many more solitary waves are expected. Finally, the method can be readily generalized to three dimensions, and also multi-component condensates, providing a highly powerful technique for investigating solitary waves in future works.

cond-mat.quant-gas↗

arXiv2023

Non-Hermitian topolectrical circuit sensor with high sensitivity

Electronic sensors play important roles in various applications, such as industry and environmental monitoring, biomedical sample ingredient analysis, wireless networks and so on. However, the sensitivity and robustness of current schemes are often limited by the low quality-factors of resonators and fabrication disorders. Hence, exploring new mechanisms of the electronic sensor with a high-level sensitivity and a strong robustness is of great significance. Here, we propose a new way to design electronic sensors with superior performances based on exotic properties of non-Hermitian topological physics. Owing to the extreme boundary-sensitivity of non-Hermitian topological zero modes, the frequency shift induced by boundary perturbations can show an exponential growth trend with respect to the size of non-Hermitian topolectrical circuit sensors. Moreover, such an exponential growth sensitivity is also robust against disorders of circuit elements. Using designed non-Hermitian topolectrical circuit sensors, we further experimentally verify the ultra-sensitive identification of the distance, rotation angle, and liquid level with the designed capacitive devices. Our proposed non-Hermitian topolectrical circuit sensors can possess a wide range of applications in ultra-sensitive environmental monitoring and show an exciting prospect for nextgeneration sensing technologies.

cond-mat.mes-hall↗

arXiv2023

Proxy Forecasting to Avoid Stochastic Decision Rules in Decision Markets

Information that is of relevance for decision-making is often distributed, and held by self-interested agents. Decision markets are well-suited mechanisms to elicit such information and aggregate it into conditional forecasts that can be used for decision-making. However, for incentive-compatible elicitation, decision markets rely on stochastic decision rules which entails that sometimes actions have to be taken that have been predicted to be sub-optimal. In this work, we propose three closely related mechanisms that elicit and aggregate information similar to a decision market, but are incentive compatible despite using a deterministic decision rule. Following ideas from peer prediction mechanisms, proxies rather than observed future outcomes are used to score predictions. The first mechanism requires the principal to have her own signal, which is then used as a proxy to elicit information from a group of self-interested agents. The principal then deterministically maps the aggregated forecasts and the proxy to the best possible decision. The second and third mechanisms expand the first to cover a scenario where the principal does not have access to her own signal. The principal offers a partial profit to align the interest of one agent and retrieve its signal as a proxy; or alternatively uses a proper peer prediction mechanism to elicit signals from two agents. Aggregation and decision-making then follow the first mechanism. We evaluate our first mechanism using a multi-agent bandit learning system. The result suggests that the mechanism can train agents to achieve a performance similar to a Bayesian inference model with access to all information held by the agents.

cs.GT↗

arXiv2022

Ultrashort dissipative Raman solitons in Kerr resonators driven with phase-coherent optical pulses

External driving of passive, nonlinear optical resonators has emerged over the past decade as a novel route for the generation of ultrashort optical pulses and corresponding broadband frequency combs. Whilst the pulse formation dynamics in such systems differ dramatically from those manifesting themselves in conventional mode-locked lasers, the demarcation between the two traditionally distinct paradigms has recently begun to blur, with demonstrations of hybrid systems incorporating both external driving and active media shown to offer specific advantages. Here we explore a new pathway for ultrashort pulse generation at the interface of externally-driven passive resonators and lasers. By leveraging the nonlinear Raman gain inherent to fused silica, we achieve deterministic generation of low-noise dissipative solitons with durations well below 100 fs via phase-coherent pulsed driving of resonators made of standard, commercially-available optical fibre. We explore and explain the physics of the new dissipative Raman soliton states, identifying scaling laws that govern the pulses' characteristics and that allow output repetition rates to be scaled at will without influencing the soliton duration. The scheme explored in our work enables the shortest ever pulses generated in resonators (active or passive) made from a single commercially-available optical fibre, and it has the potential to be transferred into a chip-scale format by using existing dispersion-engineered silica microresonators.

physics.optics↗

arXiv2022

Decision Market Based Learning For Multi-agent Contextual Bandit Problems

Information is often stored in a distributed and proprietary form, and agents who own information are often self-interested and require incentives to reveal their information. Suitable mechanisms are required to elicit and aggregate such distributed information for decision making. In this paper, we use simulations to investigate the use of decision markets as mechanisms in a multi-agent learning system to aggregate distributed information for decision-making in a contextual bandit problem. The system utilises strictly proper decision scoring rules to assess the accuracy of probabilistic reports from agents, which allows agents to learn to solve the contextual bandit problem jointly. Our simulations show that our multi-agent system with distributed information can be trained as efficiently as a centralised counterpart with a single agent that receives all information. Moreover, we use our system to investigate scenarios with deterministic decision scoring rules which are not incentive compatible. We observe the emergence of more complex dynamics with manipulative behaviour, which agrees with existing theoretical analyses.

cs.MA↗

arXiv2022

Ground state interface exponents of the diluted Sherrington-Kirkpatrick spin glass

We present a large-scale simulation of the ground state interface properties of the diluted Sherrington-Kirkpatrick spin glass of Gaussian disorder for a broad range of the bond occupation probability $p$ using the strong disorder renormalization group and the population annealing Monte Carlo methods. We find that the interface is space-filling independent of $p$, i.e., the fractal dimension $d_s=1$. The stiffness exponent $θ$ is likely also independent of $p$, despite that the energy finite-size correction exponent $ω$ varies with $p$ as recently found. The energy finite-size scaling is also analyzed and compared with that of the $\pm J$ disorder, finding that the thermodynamic energy is universal in both $p$ and the disorder, and the exponent $ω$ varies with $p$ but is universal in the disorder.

cond-mat.dis-nn↗

arXiv2022

Systematic solitary waves from their linear limits in two-component Bose-Einstein condensates with unequal dispersion coefficients

We systematically construct vector solitary waves in harmonically trapped one-dimensional two-component Bose-Einstein condensates with unequal dispersion coefficients by a numerical continuation in chemical potentials from the respective analytic low-density linear limits to the high-density nonlinear Thomas-Fermi regime. The main feature of the linear states herein is that the component with the larger quantum number has instead a smaller linear eigenenergy, enabled by suitable unequal dispersion coefficients, leading to new series of solutions compared with the states similarly obtained in the equal dispersion setting. Particularly, the lowest-lying series gives the well-known dark-anti-dark waves, and the second series yields the dark-multi-dark states, and the following series become progressively more complex in their wave structures. The Bogoliubov-de Gennes spectra analysis shows that most of these states are typically unstable, but they can be long-lived and most of them can be fully stabilized in suitable parameter regimes.

cond-mat.quant-gas↗

arXiv2022

An effective introduction to the Markov Chain Monte Carlo method

We present an intuitive, conceptual, but semi-rigorous introduction to the celebrated Markov Chain Monte Carlo method using a simple model of population dynamics as our motivation and focusing on a few elementary distributions. Conceptually, the population flow between cities closely resembles the random walk of a single walker in a state space. We start from two states, then three states, and finally the setup is fully generalized to many states of both discrete and continuous distributions. Despite the mathematical simplicity, the setup remarkably includes all the essential concepts of Markov Chain Monte Carlo without loss of generality, e.g., ergodicity, global balance and detailed balance, proposal or selection probability, acceptance probability, up to the underlying stochastic matrix, and error analysis. Our teaching experience suggests that most senior undergraduate students in physics can closely follow these materials without much difficulty.

physics.comp-ph↗

arXiv2021

CNN-tuned spatial filters for P- and S-wave decomposition and applications in elastic imaging

P- and S-wave decomposition is essential for imaging multi-component seismic data in elastic media. A data-driven workflow is proposed to obtain a set of spatial filters that are highly accurate and artifact-free in decomposing the P- and S-waves in 2D isotropic elastic wavefields. The filters are formulated initially by inverse Fourier transforms of the wavenumber-domain operators, and then are tuned in a convolutional neural network to improve accuracy using synthetic snapshots. Spatial filters are flexible for decomposing P-and S-waves at any time step without performing Fourier transforms over the entire wavefield snapshots, and thus are suitable for target-oriented imaging. Snapshots from synthetic data show that the network-tuned spatial filters can decompose P- and S-waves with improved decomposition accuracy compared with other space-domain PS decomposition methods. Elastic reverse-time migration using P- and S-waves decomposed from the proposed algorithm shows reduced artifacts in the presence of a high velocity contrast.

physics.geo-ph↗

arXiv2021

Semi-supervised Impedance Inversion by Bayesian Neural Network Based on 2-d CNN Pre-training

Seismic impedance inversion can be performed with a semi-supervised learning algorithm, which only needs a few logs as labels and is less likely to get overfitted. However, classical semi-supervised learning algorithm usually leads to artifacts on the predicted impedance image. In this artical, we improve the semi-supervised learning from two aspects. First, by replacing 1-d convolutional neural network (CNN) layers in deep learning structure with 2-d CNN layers and 2-d maxpooling layers, the prediction accuracy is improved. Second, prediction uncertainty can also be estimated by embedding the network into a Bayesian inference framework. Local reparameterization trick is used during forward propagation of the network to reduce sampling cost. Tests with Marmousi2 model and SEAM model validate the feasibility of the proposed strategy.

eess.SP↗

arXiv2021

Securities Based Decision Markets

Decision markets are mechanisms for selecting one among a set of actions based on forecasts about their consequences. Decision markets that are based on scoring rules have been proven to offer incentive compatibility analogous to properly incentivised prediction markets. However, in contrast to prediction markets, it is unclear how to implement decision markets such that forecasting is done through the trading of securities. We here propose such a securities based implementation, and show that it offers the same expected payoff as the corresponding scoring rules based decision market. The distribution of realised payoffs, however, might differ. Our analysis expands the knowledge on forecasting based decision making and provides novel insights for intuitive and easy-to-use decision market implementations.

cs.GT↗