Study improves forecasting of ED crowding using advanced ML models.
problem Improving forecasting of emergency department crowding.
method Advanced machine learning models (N-BEATS, LightGBM, DeepAR) using multivariable input data.
result N-BEATS and LightGBM outperform benchmarks in forecasting ED occupancy.
SONMF reduces ED crowding by predicting patient dispositions from triage notes.
problem Crowded Emergency Departments and delayed patient admissions.
method Semi-orthogonal Non-negative Matrix Factorization (SONMF) for text mining.
result SONMF improves classification accuracy and interpretability of patient notes.
Proposes proactive bed requests to reduce ED boarding and patient wait times.
problem Reduces ED boarding and patient wait times by proactively requesting inpatient beds.
method Formulates as a Markov decision process, uses predictions of admission probability and time to disposition to guide bed requests.
result Proactive aggregate bed requests can reduce boarding times by 30-70% and length of stay by 6-15%.
ED-Filter improves eating disorder classification on Twitter.
problem High dimensionality and extensive feature sets in Twitter data for ED classification.
method Informed branch and bound search technique with hybrid greedy-based deep learning.
result Significant improvements in classification accuracy and efficiency.
Combining forecasts of 16 ED causes improves accuracy and stability.
problem Forecasting accuracy and stability for ED admissions is poor due to model uncertainty and limited data.
method High-dimensional forecast combinations of 16 cause-specific ED forecasts using extensive covariates.
result Forecast combinations yield forecast accuracies of 3.81%-23.54% across causes, outperforming individual models in 50% of scenarios.
New insights show Medicaid impacts on ED use vary widely, with some groups seeing significant increases.
problem Understanding the varied impacts of Medicaid on emergency department use.
method Causal machine learning methods to identify heterogeneous impacts.
result Meaningful heterogeneity in the effect of Medicaid on ED use, with a small group driving the overall effect.
Study robustness of polynomial neural networks using algebraic geometry.
problem Certify robustness radius of polynomial neural networks.
method Metric algebraic geometry, Euclidean distance degree, symbolic elimination, homotopy-continuation methods.
result Found decision boundaries with lower ED degree than generic cubic hypersurfaces.
I describe work in progress with Baryshnikov and Zharnitsky on periodic billiard orbits that leads one to an exterior differential system (EDS). I then give a brief introduction to EDS illustrated by several examples.
MRCNet tackles crowd counting and density mapping in aerial imagery.
problem Accurate crowd counting and density estimation in aerial imagery.
method MRCNet is a novel encoder-decoder CNN that combines VGG-16 with FPN-inspired lateral connections.
result MRCNet outperforms state-of-the-art methods in aerial and CCTV-based crowd counting.
A new loss function ED simplifies training energy-based models without scores.
problem Training energy-based models is computationally expensive.
method Energy Discrepancy (ED) loss function that does not rely on scores or MCMC.
result ED effectively interpolates between score matching and negative log-likelihood.
New clustering method improves climate data analysis in Lesser Antilles.
problem Inducing undesirable effects in clustering algorithms using Euclidean distance.
method Replacing Euclidean distance with Expert Deviation (ED) based on symmetrized Kullback-Leibler divergence.
result KMS-ED produces more interpretable clusters with better discrimination of daily situations.
ED-VAE improves VAEs by explicitly including entropy components in ELBO.
problem Limitations of traditional VAEs with ELBO in generating high-quality samples and interpreting latent spaces.
method Introduces ED-VAE, a re-formulation of ELBO that includes entropy and cross-entropy components.
result Significantly enhances model flexibility and improves interpretability and generative performance.
This paper is a survey of the subject of variations of Hodge structure (VHS) considered as exterior differential systems (EDS). We review developments over the last twenty-six years, with an emphasis on some key examples. In the penultimate section we present some new results on the characteristic cohomology of a homog…
We describe a bicategory (RedOrb) of reduced orbifolds in the framework of classical differential geometry (i.e. without any explicit reference to notions of Lie groupoids or differentiable stacks, but only using orbifold atlases, local lifts and changes of charts). In order to construct such a …
Crowd wisdom improves decision-making by integrating multiple responses.
problem Improving collective decision-making accuracy from multiple responses.
method Analogous to unsupervised dimension reduction, using techniques like PCA and Isomap.
result Crowd wisdom methods outperform existing solutions, including supervised learning.
A stable approach to eigendecomposition for deep learning networks.
problem Numerical instability in backpropagation of eigendecomposition results.
method A numerically stable and differentiable approach to eigendecomposition.
result Better robustness of the new approach over standard methods for ZCA whitening and PCA denoising.
It is common for CCTV operators to overlook inter- esting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveil…
Max-MIG tackles crowdsourced label learning without knowing crowd information structure.
problem Learning from crowds without knowing the information structure among crowds.
method Max-MIG is an information theoretic approach that simultaneously aggregates crowdsourced labels and learns a data classifier.
result Max-MIG achieves state-of-the-art results in most settings, including real-world data.
We found that factors decay over time, with momentum fitting best.
problem Understanding how factors decay over time and their impact on performance.
method Derived a hyperbolic decay model for factors, tested against linear and exponential alternatives.
result Momentum exhibits hyperbolic decay, outperforming linear and exponential models.
Study shows how 'crowding' in equity trading affects performance and costs.
problem Deterioration of strategy performance, increased trading costs, and systemic risk due to equity factor crowding.
method Direct metrics of crowding based on imbalances of trades executed on the market, analyzing U.S. equity market data.
result Significant signs of crowding in well-known equity signals, especially Momentum, affecting order flow and portfolio rebalancing.
MTCNet uses MTL to estimate crowd density and count.
problem Crowd count estimation challenges due to scale variations and perspective.
method MTL deep neural network architecture with two tasks: density estimation and count classification.
result Achieves lower MAE than state-of-the-art methods on multiple datasets.
Crowd opinions in microblogs can predict event outcomes, matching with expert opinions.
problem Utilizing crowd wisdom for event outcome prediction in microblogs.
method Multi-label sentiment classification of tweets to gauge crowd opinion and compare with expert predictions.
result Crowd opinions in microblogs often match with expert opinions, especially in non-debate events.
A new neural network method improves interpretability and detection of outliers.
problem Improving interpretability and detection of outliers in neural networks.
method Prototype-based learning (PbL) using a winner-take-all (WTA) network with two prototypes: positive and negative.
result The negative prototype is similar to the positive one, aligning with the BCM theory.
ACFM predicts crowd flow adaptively integrating various factors.
problem Adaptive integration of factors affecting crowd flow changes.
method Unified neural network module with attention mechanism.
result Significant improvements over state-of-the-art methods.
Binomial tree methods (BTM) and explicit difference schemes (EDS) for the variational inequality model of American options with time dependent coefficients are studied. When volatility is time dependent, it is not reasonable to assume that the dynamics of the underlying asset's price forms a binomial tree if a partitio…
New method improves crowd counting accuracy using inverse k-NN maps and multiscale upsampling.
problem Improving accuracy of crowd density maps for high-density gatherings.
method Developed MUD-ikNN architecture using inverse k-NN maps and multiscale upsampling. result New network architecture outperforms state-of-the-art crowd counting.
Crowded trades cluster investors, affecting stock price stability.
problem Crowded trades lead to price instability and systemic risk.
method Market clustering measure using granular trading data.
result Market clustering has a causal effect on stock return distribution tails, especially positive tail.
This paper introduces a noise-robust clustering method using distribution distances.
problem Reducing noise impact on clustering results.
method Introduces expectation distance (ED) for distribution clustering, extending K-means and K-medoids.
result Improved clustering accuracy and reduced computation time.
System detects social interactions in crowds using mobile phone sensors.
problem Detecting social interactions in crowded settings.
method Multi-modal mobile sensing (BLE, accelerometer, gyroscope) and machine learning.
result 77.8% precision and 86.5% recall for predicting social interactions.
Machine learning outperforms crowd investors in predicting loan defaults and investment returns.
problem Determining if machine learning can outperform human decision-making in crowd lending.
method Using data from Prosper.com, a sophisticated ML algorithm was trained to predict loan defaults and investment returns.
result The ML algorithm outperforms crowd investors in predicting loan defaults and investment returns, especially for risky loans.
The paper interprets financial markets as crowds during booms and busts.
problem Understanding market irrationality during booms and busts.
method Integrates crowd psychology into behavioural finance.
result Markets behave like psychological crowds during booms and busts.
A model-free hedging method using stock crowding scores.
problem Designing costless portfolio strategies to hedge market risk.
method Network analysis of fund holdings to compute crowding scores, constructing long-short portfolios without numerical optimization.
result Long-short portfolios provide protection against both small and large market price fluctuations.
We present two families of exterior differential systems (EDS) for non-isometric embeddings of orthonormal frame bundles over Riemannian spaces of dimension q = 2, 3, 4, 5.... into orthonormal frame bundles over flat spaces of sufficiently higher dimension. We have calculated Cartan characters showing that these EDS sa…
Crowdsourced wisdom improves causal learning.
problem Improving causal learning through collective intelligence.
method Crowdsourcing, expert knowledge elicitation, aggregation techniques, and LLMs.
result Collective contributions enhance global causal structure.
These are expository notes from the 2008 Srni Winter School. They have two purposes: (1) to give a quick introduction to exterior differential systems (EDS), which is a collection of techniques for determining local existence to systems of partial differential equations, and (2) to give an exposition of recent work (jo…
PIED optimizes experimental design for inverse problems using physics-informed neural networks.
problem Optimizing experimental design for inverse problems with limited budget and constraints.
method PIED uses physics-informed neural networks (PINNs) for continuous optimization of design parameters in one-shot deployments.
result PIED significantly outperforms existing ED methods in solving inverse problems, including unknown functions.
Investment herding can reduce household consumption, a phenomenon called crowding-out effect.
problem Investment herding's impact on household consumption.
method Optimal control theory to model and solve for household investment and consumption decisions.
result Existence of crowding-out effect due to investment herding.
The average portfolio structure of institutional investors is shown to have properties which account for transaction costs in an optimal way. This implies that financial institutions unknowingly display collective rationality, or Wisdom of the Crowd. Individual deviations from the rational benchmark are ample, which il…
This paper proposes a general model for synchronized crowding behavior. An order parameter is introduced to quantify the level of synchronization which is shown a function of percentage of agents in reactive state. Further, synchronization is shown to be driven by the most active agents with the highest volatility. A t…
CENs learn interpretable embeddings from images considering worker biases and visual context.
problem Learning interpretable embeddings from noisy crowd annotations.
method Context Embedding Networks (CENs) model worker biases and visual context.
result CENs produce more interpretable embeddings than existing approaches.
Framework identifies comorbidities for frequent ED and inpatient visits.
problem Reducing resource usage and costs in frequent patients.
method Developed MSAR algorithm to identify comorbidities.
result MSAR identifies conditions most associated with reoccurring ED and inpatient visits.
New method addresses crowding in high-dimensional data visualization.
problem Crowding issue in visualizing high-dimensional data.
method Adjusting capacity of high-dimensional balls and estimating correlation dimension.
result Mitigates crowding in various distance metrics.
Crowdsourcing can improve scientific investigation by enabling reproducibility and transparency.
problem Current research methods lack reproducibility and transparency, leading to unreliable decisions.
method Next-generation investigative approach leveraging human diversity, micro-specialized crowds, and computer-assisted control methods.
result The Theory of Enablers provides specific cognitive and non-cognitive enablers for crowd-based scientific investigation.
Enhances crowd safety through AI and data-driven models.
problem Improving crowd safety during events.
method Innovative data collection, AI, and machine learning.
result Accurate multi-day forecasts for event planning.
Submitted to F. Schweitzer (ed.), Microscopic Models for Economic Dynamics, Lecture notes in physics, Springer, Berlin-Heidelberg 2002.kiel.tex
ED-NeRF efficiently edits 3D scenes using latent space NeRF and improved loss functions.
problem Slow training speeds and inadequate editing loss functions in existing NeRF editing techniques.
method Embedding real-world scenes into latent space of LDM, using a unique refinement layer and an improved loss function.
result ED-NeRF achieves faster editing speed and improved output quality compared to state-of-the-art models.
Bayesian model improves truth inference from highly redundant crowd annotations.
problem Inferring true annotations from highly redundant crowd annotations.
method Bayesian graphical model with conjugate priors and iterative expectation-maximisation inference.
result Our technique significantly outperforms majority vote heuristic at one-sided level 0.025.
A local deformation property for uniform embeddings in metric manifolds (LD) is formulated and its behaviour is studied in a formal view point. It is shown that any metric manifold with a geometric group action, typical metric spaces (Euclidean space, hyperbolic space and cylinders) and for κ\leq 0 the κ-cone ends over…