arXiv research

Challenge forecasts EV charging station usage accurately.

HCNAF models complex conditional distributions for probabilistic occupancy forecasting.

Proposes a game-theoretic framework to motivate energy-efficient behavior in smart buildings.

Mamba improves time series forecasting by quantifying uncertainty.

We present a queuing model of parking dynamics and a model-based prediction method to provide real-time probabilistic forecasts of future parking occupancy. The queuing model has a non-homogeneous arrival rate and time-varying service time distribution. All statistical assumptions of the model are verified using data f…

Structured subsampling improves privacy in deep time series forecasting.

In this paper, we introduce the concept of \emph{Poissonian occupation times} below level $0$ of spectrally negative Lévy processes. In this case, occupation time is accumulated only when the process is observed to be negative at arrival epochs of an independent Poisson process. Our results extend some well known conti…

Study improves forecasting of ED crowding using advanced ML models.

NDI learns from expert demonstrations by estimating occupancy measures.

Paper analyzes AI's impact on job tasks, predicting future demands.

FORE evaluates occupancy ratios without requiring Bellman completeness.

PIML uses physics equations in machine learning for better forecasting.

CAST predicts distribution-valued time series by stabilizing and transporting simplex-supported successors.

A new method learns continuous occupancy fields efficiently using the Ising model.

In this short paper, in order to price occupation-time options, such as (double-barrier) step options and quantile options, we derive various joint distributions of a mixed-exponential jump-diffusion process and its occupation times of intervals.

New PCA method detects faults using occupation kernels.

Paper develops multilingual job classification for ISCO and KZiS.

Study measures gender bias in machine translation using multiple reference points.

In this paper, we obtain analytical expression for the distribution of the occupation time in the red (below level $0$) up to an (independent) exponential horizon for spectrally negative Lévy risk processes and refracted spectrally negative Lévy risk processes. This result improves the existing literature in which only…

Digital tools may hinder or facilitate multidisciplinary collaboration in occupational health.

A new method estimates SDEs using occupation kernels.

ROCK method generalizes MOCK for learning dynamical systems efficiently.

The implementation of smart building technology in the form of smart infrastructure applications has great potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. However, human preference in regard to living conditions is usually unknown and heterogeneous in its manifestati…

Spacematch matches office workers with suitable workspaces based on their preferences.

Method learns SDEs from data snapshots.

Study predicts room occupancy using machine learning, achieving high accuracy.

We find the optimal investment strategy to minimize the expected time that an individual's wealth stays below zero, the so-called {\it occupation time}. The individual consumes at a constant rate and invests in a Black-Scholes financial market consisting of one riskless and one risky asset, with the risky asset's price…

We introduce cylindrical projections to simulate infinite-dimensional occupation flows of diffusions.

A generalized gamification framework is introduced as a form of smart infrastructure with potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. The proposed framework enables a Human-Centric Cyber-Physical System using an interface to allow building managers to interact wi…

Building performance discrepancies between building design and operation are one of the causes that lead many new designs fail to achieve their goals and objectives. One of main factors contributing to the discrepancy is occupant behaviors. Occupants responding to a new design are influenced by several factors. Existin…

Develops a new calculus for stochastic processes with occupation flows.

Investment strategies in occupational pension plans are optimized for non-tradable income risk.

Thermal preferences vary from person to person and may change over time. The main objective of this paper is to sequentially pose intelligent queries to occupants in order to optimally learn the indoor air temperature values which maximize their satisfaction. Our central hypothesis is that an occupant's preference rela…

We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives. We analyze the potential allocation harms that can result from semantic representation bias. To do so, we study the impact on occupation classification of…

D2SRM solves complex PDEs using deep learning.

DFKI Cabin Simulator tests visual monitoring functions in vehicles.

Occupant behavior (OB) and in particular window openings need to be considered in building performance simulation (BPS), in order to realistically model the indoor climate and energy consumption for heating ventilation and air conditioning (HVAC). However, the proposed OB window opening models are often biased towards …

Paper uses Sinkhorn distances to improve imitation learning effectiveness.

New conditions prevent gaps in optimal control problems.

This work explores efficient reinforcement learning with density features in low-rank MDPs.

MOCK learns complex systems from trajectories efficiently.

Study shows visual feedback and monetary incentives reduce plugload energy consumption in commercial buildings.

A deep learning model is applied for predicting block-level parking occupancy in real time. The model leverages Graph-Convolutional Neural Networks (GCNN) to extract the spatial relations of traffic flow in large-scale networks, and utilizes Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) to capture …

New algorithm reduces dynamic regret for MDPs with unknown transition and adversarial rewards.

Paper tackles dynamic pricing in a geometrically decaying environment, achieving better occupancy with lower rates.

Energy consumption for hot water production is a major draw in high efficiency buildings. Optimizing this has typically been approached from a thermodynamics perspective, decoupled from occupant influence. Furthermore, optimization usually presupposes existence of a detailed dynamics model for the hot water system. The…

Bayesian framework predicts post-disruption travel times in metro networks.

How do regions acquire the knowledge they need to diversify their economic activities? How does the migration of workers among firms and industries contribute to the diffusion of that knowledge? Here we measure the industry, occupation, and location-specific knowledge carried by workers from one establishment to the ne…