A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Finite-time queue peaks in stochastic networks have logarithmic scaling after geometric thresholds.
A deep-learning inference accelerator is synthesized from a C-language software program parallelized with Pthreads. The software implementation uses the well-known producer/consumer model with parallel threads interconnected by FIFO queues. The LegUp high-level synthesis (HLS) tool synthesizes threads into parallel FPG…
The paper develops algorithms to minimize queue length regret in a communication system.
Study on queues with Hawkes arrivals, proving steady-state behavior and developing an efficient algorithm.
Algorithm stabilizes queues in asymmetric systems with unknown service rates.
The paper analyzes transaction fees on blockchains using a priority queue model.
Designs for allocating resources to prioritize needy applicants while estimating treatment effects.
Improved queue-reactive model considers order sizes for better market simulation.
MERLIN tackles multi-objective task scheduling with hierarchical DRL, outperforming existing methods.
Enhances queue-reactive model for realistic limit order book simulation.
Modern Internet services, such as those at Google, Yahoo!, and Amazon, handle billions of requests per day on clusters of thousands of computers. Because these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. Such services are model…
The paper models blockchain queues and trading dynamics, finding conditions for transaction priority and price impact.
Order positions are key variables in algorithmic trading. This paper studies the limiting behavior of order positions and related queues in a limit order book. In addition to the fluid and diffusion limits for the processes, fluctuations of order positions and related queues around their fluid limits are analyzed. As a…
Study improves queue length estimation from connected vehicles by filtering parameters.
The paper tackles ride-hailing fleet repositioning with a calibrated demand approach.
In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilisti…
Motivated by empirical data, we develop a statistical description of the queue dynamics for large tick assets based on a two-dimensional Fokker-Planck (diffusion) equation, that explicitly includes state dependence, i.e. the fact that the drift and diffusion depends on the volume present on both sides of the spread. "J…
Paper uses queue theory to model financial signals with relativistic delay.
In this work we introduce two variants of multivariate Hawkes models with an explicit dependency on various queue sizes aimed at modeling the stochastic time evolution of a limit order book. The models we propose thus integrate the influence of both the current book state and the past order flow. The first variant cons…
The paper optimizes LLM inference systems through queueing theory.
Unified model for market dynamics, linking price and order flow.
In a financial market, for agents with long investment horizons or at times of severe market stress, it is often changes in the asset price that act as the trigger for transactions or shifts in investment position. This suggests the use of price thresholds to simulate agent behavior over much longer timescales than are…
Study develops hybrid model to mitigate stablecoin liquidity risk.
This paper considers a cross-layer adaptive modulation system that is modeled as a Markov decision process (MDP). We study how to utilize the monotonicity of the optimal transmission policy to relieve the computational complexity of dynamic programming (DP). In this system, a scheduler controls the bit rate of the m-qu…
An online learning framework optimizes pricing and capacity in service systems.
Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift. Class imbalance deals with data streams that have very skewed distributions while concept drift deals with changes in the class imbalance s…
This paper attempts to find out numerically the distribution of the queue-length ratio in the context of a model of preferential attachment. Here we consider two restaurants only and a large number of customers (agents) who come to these restaurants. Each day the same number of agents sequentially arrives and decides w…
The paper improves regret bounds for admission control in queueing systems.
We investigate whether the bid/ask queue imbalance in a limit order book (LOB) provides significant predictive power for the direction of the next mid-price movement. We consider this question both in the context of a simple binary classifier, which seeks to predict the direction of the next mid-price movement, and a p…
Model for cross-border markets with limited transmission capacities.
We introduce Delay Pruning, a simple yet powerful technique to regularize dynamic Boltzmann machines (DyBM). The recently introduced DyBM provides a particularly structured Boltzmann machine, as a generative model of a multi-dimensional time-series. This Boltzmann machine can have infinitely many layers of units but al…
A robust method for decomposing spectral peaks robust to distortion and interference.
SPADE improves demand forecasting accuracy by 4.5% for post-promotion periods.
We find empirically a characteristic sharp peak-flat trough pattern in a large set of commodity prices. We argue that the sharp peak structure reflects an endogenous inter-market organization, and that peaks may be seen as local ``singularities'' resulting from imitation and herding. These findings impose a novel strin…
Order submission and cancellation are two constituent actions of stock trading behaviors in order-driven markets. Order submission dynamics has been extensively studied for different markets, while order cancellation dynamics is less understood. There are two positions associated with a cancellation, that is, the price…
Paper improves volatility estimation using a Queue-Reactive model.
RL optimizes meta-order execution by adapting to market conditions.
We examine the dynamics of the bid and ask queues of a limit order book and their relationship with the intensity of trade arrivals. In particular, we study the probability of price movements and trade arrivals as a function of the quote imbalance at the top of the limit order book. We propose a stochastic model in an …
The study examines how backrun auctions can protect traders from price manipulation.
Joint peak detection is a central problem when comparing samples in genomic data analysis, but current algorithms for this task are unsupervised and limited to at most 2 sample types. We propose PeakSegJoint, a new constrained maximum likelihood segmentation model for any number of sample types. To select the number of…
Mass spectrometry (MS) is an important technique for chemical profiling which calculates for a sample a high dimensional histogram-like spectrum. A crucial step of MS data processing is the peak picking which selects peaks containing information about molecules with high concentrations which are of interest in an MS in…
Gittins index optimizes decision-making under uncertainty, even in complex scenarios.
Study on-chain peak shaving to reduce Ethereum transaction costs.
The paper explains two distinct peaks in generalization error for neural networks and simpler models, each governed by different factors.
Study proves fluid limits of fragmented limit-order markets.
The heuristic identification of peaks from noisy complex spectra often leads to misunderstanding of the physical and chemical properties of matter. In this paper, we propose a framework based on Bayesian inference, which enables us to separate multipeak spectra into single peaks statistically and consists of two steps.…
Silent abandonment reduces contact center efficiency by 5%-15%.
Bayesian framework integrates spectral deconvolution with expert reasoning for robust peak estimation.