Graphical Lasso algorithm segments occupants' energy usage behaviors in HC-CP systems.
problem Improving sustainability and energy efficiency in HC-CP systems.
method Introduced a gamification framework and applied Graphical Lasso for energy usage segmentation.
result Characterized different energy usage behaviors in HC-CP systems.
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…
The paper defines and analyzes Poissonian occupation times for negative Lévy processes.
problem Analyzing the time spent below zero for Lévy processes with interruptions.
method Introduces Poissonian occupation times for spectrally negative Lévy processes.
result Extends results on continuous observation to interrupted observation.
NDI learns from expert demonstrations by estimating occupancy measures.
problem Imitation Learning (IL) for complex systems.
method Density estimation of expert's occupancy measure followed by RL.
result NDI achieves state-of-the-art performance on control tasks.
Paper analyzes AI's impact on job tasks, predicting future demands.
problem AI's impact on job tasks and potential technological unemployment.
method Dynamic task shares analysis using ARIMA model on large job postings dataset.
result AI has risen in high wage occupations, predicting future task demands.
FORE evaluates occupancy ratios without requiring Bellman completeness.
problem Offline reinforcement learning occupancy ratio estimation.
method Fitted occupancy-ratio evaluation (FORE) using adjoint Bellman recursion.
result FORE achieves convergence in KL without Bellman completeness.
A new method learns continuous occupancy fields efficiently using the Ising model.
problem Efficiently learning continuous occupancy fields for robot navigation.
method Generalizes the Ising model to model continuous occupancy fields, using a unique kernel for range measurements.
result The method is computationally efficient and requires only a small number of hyperparameters.
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.
problem Fault detection in dynamical systems.
method Occupation kernel PCA for irregularly sampled data.
result Validation of reconstruction error approach for fault detection.
Paper calculates the distribution of time spent below zero in risk models.
problem Analyzing time spent below zero in risk models.
method Analytical expressions for the distribution of occupation times.
result Improved understanding of risk processes by providing distribution formulas.
Paper develops multilingual job classification for ISCO and KZiS.
problem Classifying job advertisements for accurate occupation coding.
method Transformer architecture for hierarchical multi-class classification.
result Hierarchical structure improves prediction accuracy by 1-2 percentage points.
Study measures gender bias in machine translation using multiple reference points.
problem Measuring and identifying gender bias in machine translation.
method Used an optimal non-biased translator, reference points from occupational statistics and survey.
result Found bias against both genders, but more against women, and found occupations have a greater effect than adjectives.
GANs improve building performance model accuracy by integrating occupant behaviors.
problem Discrepancies between design and operation performance in buildings.
method Generative Adversarial Networks (GANs) to learn mixture models combining existing BPMs with occupant behaviors.
result Augmented BPMs significantly outperform existing BPMs in achieving specified performance targets.
Study shows gender bias in occupation classification tasks.
problem Gender bias in machine learning for occupation classification.
method Analyzed impact of explicit gender indicators in semantic representations of biographies.
result True positive rates differ between genders, correlating with existing gender imbalances.
Operationalizes engagement as human behavior, linking theory and data.
problem Fuzziness of engagement concept.
method Formal framework, Melchoir Model, model comparison, theory-driven hypothesis.
result Engagement can be shaped and interpreted using data-driven methods.
Digital tools may hinder or facilitate multidisciplinary collaboration in occupational health.
problem Challenges in multidisciplinary collaboration in occupational health.
method Study of a precursor SPSTI's digital tools and their impacts.
result Digital tools can act as both brakes and levers for multidisciplinary dynamics.
A new method estimates SDEs using occupation kernels.
problem Learning multivariate stochastic differential equations (SDEs).
method Two-step procedure: estimate drift, then diffusion. Occupation kernels used in RKHS.
result Validated on simulated and real-world data.
ROCK method generalizes MOCK for learning dynamical systems efficiently.
problem Learning dynamical systems from data efficiently.
method Variational formulation in Reproducing Kernel Hilbert Spaces.
result ROCK method is more computationally efficient and performs better on benchmarks.
Spacematch matches office workers with suitable workspaces based on their preferences.
problem Matching workers with suitable workspaces in ABW environments.
method Developed a web-based mobile app to collect occupant preferences and IoT sensors for real-time environmental feedback.
result Occupant preferences can be segmented and matched to build a recommendation platform.
Detects student engagement states using unobtrusive appearance, context, and mouse data.
problem Detecting student engagement states in a classroom setting.
method Multimodal approach combining appearance, context, and mouse data; classifiers are fused at the decision level.
result Effective detection of student engagement states in a classroom setting.
Method learns SDEs from data snapshots.
problem Learning drift and diffusion of SDEs from data.
method Two-step process: learn drift by expected value, learn diffusion by SDP.
result Validated on examples and simulations.
Study predicts room occupancy using machine learning, achieving high accuracy.
problem Efficient room occupancy management for energy conservation.
method Leveraged diverse machine learning models, focusing on Random Forest.
result Random Forest model achieved highest predictive 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…
Improved student engagement detection using contextual and visual data.
problem Detecting students' behavioral engagement in real-world settings.
method Two-phase approach: contextual logs for active use, appearance information for engagement inference.
result Improved F1-scores from 0.77 to 0.82 with contextual information.
We introduce cylindrical projections to simulate infinite-dimensional occupation flows of diffusions.
problem Computational intractability of infinite-dimensional occupation flows of diffusions.
method Introduce cylindrical projections to approximate the occupation flow via a finite-dimensional system.
result Strong convergence of cylindrical projections to the initial process with derived rates.
Blockchain fan tokens boost sports fan engagement by 50%.
problem Low fan engagement in sports decisions.
method Analyzed 3,576 fan token polls to identify determinants of participation.
result Fan tokens engage 4,003 participants per poll, 50% of token holders.
Mathematical models reveal key factors for engaging Gen Z at work.
problem Understanding and improving engagement of Generation Z employees.
method Correlation and cluster analyses on engagement surveys.
result Clear responsibilities and challenging work are essential for Gen Z employees.
Paper proposes a method to aggregate customer engagement data for better ranking of e-commerce results.
problem Cold start problem and under-representation of new or under-impressed products in e-commerce search results.
method Aggregates customer engagements within a day for the same query as input training data for machine learning models.
result Training models on aggregated data leads to better ranking of new and under-impressed products.
The paper uses action graphs to predict user engagement in Snapchat.
problem Understanding what motivates users to engage with mobile social apps.
method Formalized in-app action transition patterns as action graphs, analyzing their characteristics to predict future engagement.
result Action graphs can characterize user behavior patterns and inform future engagement.
FATE predicts user engagement on social apps with explainable explanations.
problem Accurate user engagement prediction for social apps with explainability.
method FATE, a flexible neural framework incorporating friendships, actions, and temporal dynamics.
result FATE outperforms state-of-the-art approaches by 10% error and 20% runtime reduction.
The paper presents a method to score patient engagement in care programs and predicts their response.
problem Improving health outcomes of high-need patients through better patient engagement.
method Data-driven behavioral engagement scoring pipeline for two aspects of patient engagement.
result The scoring method successfully predicts patient engagement and provides interpretable insights.
Develops a new calculus for stochastic processes with occupation flows.
problem Analyzing the behavior of stochastic processes with occupation flows.
method Itô calculus for occupied processes, Feynman-Kac approach.
result Unified Markovian lifts for pricing financial derivatives.
Study shows impersonators generate fake engagement on Instagram.
problem Impersonators exploit Instagram for fake engagement.
method Examined three communities (Politician, News agency, Sports star) on Instagram, collected data on 4.8K comments and 2.6K likes from 3.8K impersonators over 7 months.
result Impersonators generate significant fake engagement on verified accounts.
Study improves engagement prediction in educational videos.
problem Addressing cold-start problem in educational recommenders.
method Introduced VLE dataset with content and engagement features, conducted experiments.
result VLE dataset leads to better engagement prediction models.
Investment strategies in occupational pension plans are optimized for non-tradable income risk.
problem Optimizing investment strategies for occupational pension plans in the presence of non-tradable income risk.
method Formulated as a stochastic optimization problem, analyzed in both constant and stochastic volatility environments.
result Random contributions induce the optimal glide path structure, influenced by initial wealth, contributions, and risk aversion.
The paper learns personalized thermal preferences using Bayesian active learning.
problem Learning personalized thermal preferences from occupant feedback.
method Bayesian active learning with unimodality constraints on Gaussian process.
result The method requires fewer observations to learn optimal temperature preferences.
Deep learning predicts real-time parking occupancy using multiple data sources.
problem Predicting real-time parking occupancy in spatio-temporal networks.
method Graph-Convolutional Neural Networks (GCNN) for spatial relations, Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) for temporal features, multiple data sources.
result The model outperforms other methods with an average testing MAPE of 10.6%.
A dataset for evaluating engagement with scientific video lectures.
problem Challenges in managing learning resources due to rapid creation of video lectures.
method Introduction of VLEngagement dataset with content-based and video-specific features, and metrics related to user engagement.
result The largest and most diverse publicly available dataset for understanding context-agnostic engagement in video lectures.
Topologically and geometrically engaging actions have proved to be useful to obtain rigidity results for semisimple Lie group actions. We show that the action of a simple noncompact Lie group on a compact manifold preserving a unimodular rigid geometric structure of algebraic type (e.g. a connection together with a vol…
Predict player lifetime value based on engagement metrics.
problem User profiling in video games.
method Survival curves, deep learning, long short-term memory.
result Deep learning predicts player lifetime value.
New framework optimizes for 'value' rather than engagement.
problem Gap between engagement signals and desired notion of 'value'.
method Measurement theory framework, latent variable model, qualitative evaluation.
result Operationalizes and optimizes for a desired notion of 'value'.
UAVs use RL to outperform a greedy shooter in 2D engagements.
problem Optimizing UAV pursuit-evasion strategies in 2D environments.
method Reinforcement learning applied to a greedy shooter strategy in a 2D aerial engagement.
result Deep Q-learning UAV outperforms greedy shooter UAV in simulations.
Proposes a game-theoretic framework to motivate energy-efficient behavior in smart buildings.
problem Improving energy efficiency in smart building infrastructure through occupant behavior.
method Introduces a novel game-theoretic framework with human interaction, incorporating utility learning and deep neural networks.
result Demonstrates highly accurate prediction of occupant energy resource usage and explainable decision-making.
D2KLab's approach predicts tweet engagement using two stages.
problem Predicting user engagement with tweets.
method Two-stage approach: feature learning and ensemble XGBoost.
result Ranked 22 in the 2020 RecSys Challenge leaderboard.
Paper uses user engagement signals to automatically label training data for AI assistants.
problem Lack of annotated training data for AI assistants.
method Leverages user engagement signals for unsupervised entity labeling and data augmentation.
result Significant accuracy gains in sequence labeling tasks and user-facing results.
Model predicts user engagement and survival time in games.
problem Building data-efficient engagement models for diverse games.
method Data-driven approach using minimal metrics.
result Joint estimates of survival time and churn probability.
Study analyzes factors influencing healthcare providers' engagement with SMS campaigns.
problem Understanding what drives healthcare providers to engage with SMS campaigns.
method Used logistic regression, random forest, and neural network models to analyze data.
result Identified key factors influencing engagement with SMS campaigns.
D2SRM solves complex PDEs using deep learning.
problem High-dimensional, Hessian-dependent fully nonlinear parabolic PDEs.
method Single scalar space-time network generating derivative-consistent approximations trained through residuals and penalties.
result Well-posedness and convergence theory established for globally Lipschitz equations.