arXiv research

Novel hybrid modeling combines ML and physics for real-time diagnosis.

Invariants derived for rail knotoids based on associated knots.

We work on the notions of rail arcs and rail isotopy in $\mathbb{R}^3$, and we introduce the notions of rail knotoid diagrams and their equivalence. Our main result is that two rail arcs in $\mathbb{R}^3$ are rail isotopic if and only if their knotoid diagram projections onto the plane of the two lines which we call ra…

This paper calculates stick numbers for rail arcs and knot classes.

Data integration methods that analyze multiple sources of data simultaneously can often provide more holistic insights than can separate inquiries of each data source. Motivated by the advantages of data integration in the era of "big data", we investigate feature selection for high-dimensional multi-view data with mix…

Risk, including economic risk, is increasingly a concern for public policy and management. The possibility of dealing effectively with risk is hampered, however, by lack of a sound empirical basis for risk assessment and management. The paper demonstrates the general point for cost and demand risks in urban rail projec…

HSR reduces analyst earnings forecast errors by lowering travel friction.

Because of the prominent position of urban rail in reducing urban transport-related problems, such as congestion and air pollution, insights into the costs of possible new urban rail projects is very relevant for those involved with cost estimations, policy makers, cost-benefit analysts, and other target groups. Knowle…

This article presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$59 billion. The study shows with very high statistical significance that forecasters gener…

Survival strategy for crypto firms in bear markets using BTC-to-sats payments rail.

Mixture model-based clustering, usually applied to multidimensional data, has become a popular approach in many data analysis problems, both for its good statistical properties and for the simplicity of implementation of the Expectation-Maximization (EM) algorithm. Within the context of a railway application, this pape…

New neural network enforces mass conservation for better ice flow predictions.

GeMA learns latent manifolds to benchmark complex systems.

Factory machinery is prone to failure or breakdown, resulting in significant expenses for companies. Hence, there is a rising interest in machine monitoring using different sensors including microphones. In the scientific community, the emergence of public datasets has led to advancements in acoustic detection and clas…

Transformer model predicts train axle vibrations for safer maintenance.

Bayesian model predicts crack evolution on rails with uncertainties.

This paper examines three independent explanatory variables and their relation with cost overrun in order to decide whether this is different for Dutch infrastructure projects compared to worldwide findings. The three independent variables are project type (road, rail, and fixed link projects), project size (measured i…

New algorithm reduces switching costs in multinomial logit bandit problems.

New polynomial invariants derived from birack and switch structures.

In this paper, we study optimal switching problems under ambiguity. To characterize the optimal switching under ambiguity in the finite horizon, we use multidimensional reflected backward stochastic differential equations (multidimensional RBSDEs) and show that a value function of the optimal switching under ambiguity …

This work extends identifiability analysis to sequential latent variable models, focusing on Switching Dynamical Systems.

The problem of optimal switching between nonlinear autonomous subsystems is investigated in this study where the objective is not only bringing the states to close to the desired point, but also adjusting the switching pattern, in the sense of penalizing switching occurrences and assigning different preferences to util…

Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world. This survey reviews computational approaches for code-switched Speech and Natural Language Processing. We motivate why processing code-switched …

New algorithms improve sampling from complex distributions.

Developing countries suffer from traffic congestion, poorly planned road/rail networks, and lack of access to public transportation facilities. This context results in an increase in fuel consumption, pollution level, monetary losses, massive delays, and less productivity. On the other hand, it has a negative impact on…

Squirrel switches between optimizers for better performance.

Study approximates financial market with discrete-time models.

This paper studies deep learning methodologies for portfolio optimization in the US equities market. We present a novel residual switching network that can automatically sense changes in market regimes and switch between momentum and reversal predictors accordingly. The residual switching network architecture combines …

Optimizes control of hybrid systems with multiple switching processes.

Study tackles balancing policy switching costs in offline RL.

New algorithm learns switching dynamics from multiple neural signals.

Paper tackles utility maximization with job-switching and retirement constraints.

New RL algorithm reduces policy switching cost to loglog(T) with similar regret.

Paper presents an efficient algorithm for linear MDP with low switching cost.

This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributio…

Study strategic competition in commodity markets using impulse-switching controls.

One type of switch simplifies operations on lattice knots.

Optimal switching regret for all segmentations in online convex optimisation.

As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear regret has been provided in many previous researches, we still have little knowledge about the relation between the dynamic regret and the s…

This paper tackles near-optimal adversarial RL with switching costs, providing algorithms and matching lower bounds.

This paper addresses parameter estimation for wave equations with Markovian switching.

Algorithm for bandits with switching costs achieves optimal regret bounds.

In this paper, we derive the family switching formula of -n two-sphere fiber bundle embedded in a smooth four-manifold fiber bundle. In the smooth category, it is a partial generalization of Fintushel-Stern's argument for four-manifolds. We also derive an algebraic analogue of the family switching formula, allowing the…

Regime switching volatility models provide a tractable method of modelling stochastic volatility. Currently the most popular method of regime switching calibration is the Hamilton filter. We propose using the Baum-Welch algorithm, an established technique from Engineering, to calibrate regime switching models instead. …

Audit fees change based on company and economic factors during auditor switching.

We study the problem of switching-constrained online convex optimization (OCO), where the player has a limited number of opportunities to change her action. While the discrete analog of this online learning task has been studied extensively, previous work in the continuous setting has neither established the minimax ra…

LaMBO optimizes modular systems with switching costs, achieving better results than existing methods.

New algorithm reduces switching costs in RL beyond linear MDPs.