arXiv research

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

In this paper, we propose a gamification approach as a novel framework for smart building infrastructure with the goal of motivating human occupants to reconsider personal energy usage and to have positive effects on their environment. Human interaction in the context of cyber-physical systems is a core component and c…

The paper optimizes air conditioning setpoints using machine learning.

EUREKA builds classifiers that use surprising features.

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…

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.

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…

We present a new method of learning a continuous occupancy field for use in robot navigation. Occupancy grid maps, or variants of, are possibly the most widely used and accepted method of building a map of a robot's environment. Various methods have been developed to learn continuous occupancy maps and have successfull…

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.

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

Mobile context determination is an important step for many context aware services such as location-based services, enterprise policy enforcement, building or room occupancy detection for power or HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the…

Method learns SDEs from data snapshots.

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.

We tune one of the most common heating, ventilation, and air conditioning (HVAC) control loops, namely the temperature control of a room. For economical and environmental reasons, it is of prime importance to optimize the performance of this system. Buildings account from 20 to 40% of a country energy consumption, and …

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…

Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging becaus…

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 method considers subjectivity in text analysis using 'Room Theory'.

HCNAF models complex conditional distributions for probabilistic occupancy forecasting.

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.

Autoencoder neural networks reconstruct missing indoor environment data.

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

Challenge forecasts EV charging station usage accurately.

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…

Paper tackles robust offline RL with heavy-tailed rewards.

CSAC enables cooperative reinforcement learning for multi-stage tasks.