arXiv research

Algorithm learns mixtures of Markov chains and MDPs from short trajectories.

DISCO predicts system states from short trajectories using an evolved operator.

Gaussian process model learns Hamiltonian systems from noisy data.

Optimal execution of portfolio transactions is the essential part of algorithmic trading. In this paper we present in simple analytical form the optimal trajectory for risk-averse trader with the assumption of exponential market recovery and short-time investment horizon.

Study learns dynamics of linear systems from multiple short trajectories.

Algorithm learns multiple LDS models from unlabeled short trajectories.

TRAKNN detects rare atmospheric trajectories efficiently.

Paper proposes a reinforcement learning method for trading using expert trajectories.

Improves neural relational inference for dynamic multi-agent trajectories.

DGFS improves sampling from complex densities by optimizing partial trajectories.

As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles. This paper presents an algorithm for long-horizon trajectory prediction of surrounding vehicles usi…

Algorithm recovers graph from Glauber dynamics trajectory without mixing.

Podcast recommendations improved by analyzing user listening paths.

A method for inferring motility models and heterogeneity from particle trajectories.

This research predicts vehicle movements by analyzing their intentions relative to road lanes.

Detecting inaccurate smart meters and targeting them for replacement can save significant resources. For this purpose, a novel deep-learning method was developed based on long short-term memory (LSTM) and a modified convolutional neural network (CNN) to predict electricity usage trajectories based on historical data. F…

Paper proposes a new descriptor for early trajectory characterization in matrix iterations.

New method for data assimilation using score-based models.

In this paper, we present a new location fingerprinting database comprised of Wi-Fi received signal strength (RSS) and geomagnetic field intensity measured with multiple devices at a multi-floor building in Xi'an Jiatong-Liverpool University, Suzhou, China. We also provide preliminary results of localization and trajec…

The tremendous growth of positioning technologies and GPS enabled devices has produced huge volumes of tracking data during the recent years. This source of information constitutes a rich input for data analytics processes, either offline (e.g. cluster analysis, hot motion discovery) or online (e.g. short-term forecast…

PODNet discovers plannable options from unstructured demonstrations.

Reliable 4D aircraft trajectory prediction, whether in a real-time setting or for analysis of counterfactuals, is important to the efficiency of the aviation system. Toward this end, we first propose a highly generalizable efficient tree-based matching algorithm to construct image-like feature maps from high-fidelity m…

In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural n…

Tracking large numbers of densely-arranged, interacting objects is challenging due to occlusions and the resulting complexity of possible trajectory combinations, as well as the sparsity of relevant, labeled datasets. Here we describe a novel technique of collective tracking in the model environment of a 2D honeybee hi…

We propose a minimal theory of non-linear price impact based on a linear (latent) order book approximation, inspired by diffusion-reaction models and general arguments. Our framework allows one to compute the average price trajectory in the presence of a meta-order, that consistently generalizes previously proposed pro…

Enhances RL by controlling policy stochasticity through trajectory entropy constraints.

We present effective numerical algorithms for locally recovering unknown governing differential equations from measurement data. We employ a set of standard basis functions, e.g., polynomials, to approximate the governing equation with high accuracy. Upon recasting the problem into a function approximation problem, we …

Method learns model for unknown stochastic system from data.

We present a generalization of the Simultaneous Long-Short (SLS) trading strategy described in recent control literature wherein we allow for different parameters across the short and long sides of the controller; we refer to this new strategy as Generalized SLS (GSLS). Furthermore, we investigate the conditions under …

Minimal hypersurfaces can't always be connected by mean curvature flow.

Bi-Mamba model predicts diffusion coefficients and exponents from short data.

Paper learns interaction laws from multiple trajectories of heterogeneous systems.

Deep learning approximates SPDE solutions from noise trajectories.

Markov state models (MSMs) and Master equation models are popular approaches to approximate molecular kinetics, equilibria, metastable states, and reaction coordinates in terms of a state space discretization usually obtained by clustering. Recently, a powerful generalization of MSMs has been introduced, the variationa…

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength i…

Proposes deep optimal feedback control for continuous-time systems with action constraints.

Deep learning predicts adhesive forces in soft viscoelastic contacts quickly and accurately.

Hybrid approach combines transformer and Bayesian filtering for robust multiple particle tracking.

Early SGD hyperparameters affect deep neural network training, showing a break-even point.

Neural network outperforms traditional methods in chaotic dynamics classification.

This paper proposes a new algorithm for learning guidance rewards in RL.

A common assumption in financial engineering is that the market price for any derivative coincides with an objectively defined risk-neutral price - a plausible assumption only if traders collectively possess objective knowledge about the price dynamics of the underlying security over short time scales. Here we assume t…

LLM trading agents show risk feedback can improve alignment without fine-tuning.

Optimizes liquidation strategies for assets with Levy process price dynamics.

Deep network solves maze path planning without training.

Generically learns movement control policies from exploration data.

Statistical fields model explains capital allocation and accumulation among firms and investors.

condLSTM-Q predicts COVID-19 deaths at county level with quantile forecasts.