The study predicts acute hypotensive episodes using unsupervised learning and hashing.
problem Early detection of acute hypotensive episodes in ICU.
method Unsupervised representation learning and stratified locality sensitive hashing applied to multivariate time-series data.
result The method accurately predicts upcoming acute hypotensive episodes.
SODA-RL learns diverse treatment options for hypotension from data.
problem Identifying the best treatment for acute hypotension from observational data.
method SODA-RL: Safely Optimized, Diverse, and Accurate Reinforcement Learning.
result SODA-RL identifies distinct, plausible treatment options from observational data.
Method uses semi-supervised learning to estimate optimal treatment regimes from medical records.
problem Estimating optimal treatment regimes from electronic medical records.
method Imputation-based semi-supervised method using unlabeled data.
result Proposed method yields more efficient estimators of optimal treatment regimes.
Study predicts hearing recovery in MD patients using TEOAE signals.
problem Predicting hearing recovery in MD patients during acute episodes.
method Applied machine learning to TEOAE signals from MD patients, using SVM for classification.
result Baseline TEOAE parameters can predict hearing recovery in MD patients.
Study predicts blood pressure response to fluid bolus therapy with high accuracy.
problem Predicting successful response to fluid bolus therapy in hypotensive ICU patients.
method Used attention-based LSTM and GRU neural networks on a large ICU database.
result Stacked LSTM with attention mechanism achieved highest accuracy of 0.852.
A new CA-GAN architecture improves minority class data generation in health datasets.
problem Algorithmic bias due to health data poverty and underrepresentation of minority groups.
method Proposes CA-GAN architecture to address shortcomings of resampling and GAN-based approaches.
result CA-GAN outperforms SMOTE and WGAN-GP* in generating authentic minority class data and maintaining original distribution.
Proof of Gromov's theorem on convex polytopes with acute angles.
problem Gromov's conjecture on extremal scalar curvature of convex polytopes.
method Smoothing construction using Dirac operator techniques.
result Detailed proof of Gromov's theorem.
Study on Teichmüller space of acute triangles using explicit calculations.
problem Analogue of Thurston's metric on acute triangles.
method Direct calculation of distance function and Finsler structure.
result Explicit expressions and properties of the metric on acute triangles.
AI system predicts acute critical illness from EHRs with explainability.
problem Lack of clinical interpretability in AI predictions for acute critical illness.
method Developed an explainable AI early warning score (xAI-EWS) system.
result System provides clinicians with insights into EHR data explaining predictions.
Let C ( L ) C(L) C ( L ) be the right-angled Coxeter group defined by an abstract triangulation L L L of S 2 \mathbb{S}^2 S 2 . We show that C ( L ) C(L) C ( L ) is isomorphic to a hyperbolic right-angled reflection group if and only if L L L can be realized as an acute triangulation. The proof relies on the theory of CAT(-1) spaces. A corollary is that an …
Maps between acute triangles with minimal stretch found and studied.
problem Finding the minimal stretch between acute triangles.
method Formula for the smallest Lipschitz constant and analysis of the metric space.
result Metric space of pairs of acute triangles with fixed area is Finsler and geodesics determined.
The paper explores Kähler and anti-Kähler structures on quasi-statistical manifolds.
problem Investigating Kähler and anti-Kähler structures on quasi-statistical manifolds.
method Analyzing conditions for integrability of almost complex structures and defining Kähler and anti-Kähler manifolds.
result Conditions for ( N ˊ , h , a b l a , L ) (\acute{N},h,
abla ,L) ( N ˊ , h , ab l a , L ) to be an anti-Kähler manifold are identified. Improved rigidity of Delaunay triangulated plane.
problem Rigidity of Delaunay triangulated plane under discrete conformality.
method Modifying Wu's proof to weaken the uniformly acute condition to the uniformly Delaunay condition.
result Improved rigidity result for Delaunay triangulated plane.
Bayesian optimization improves classifier selection for acute infection and mortality.
problem Improving accuracy of acute infection and mortality prediction.
method Comparison of hyperparameter optimization methods (grid search, random sampling, Bayesian optimization).
result Bayesian optimization outperforms grid search or random sampling for in-hospital mortality classifiers.
In this paper, the development of a probabilistic network for the diagnosis of acute cardiopulmonary diseases is presented. This paper is a draft version of the article published after peer review in 2018 (https://doi.org/10.1002/bimj.201600206). A panel of expert physicians collaborated to specify the qualitative part…
Study developed phenotypes for ICU patients' brain dysfunction states.
problem Underdiagnosis of acute brain dysfunction in ICU patients.
method Created algorithms to quantify and cluster brain dysfunction states.
result Developed three phenotypes of ICU patients' brain dysfunction states.
Study proposes a self-correcting deep learning model for ICU patient condition prediction.
problem Challenging task of continuously monitoring high-dimensional vital signs and lab measurements in critical care.
method Utilized accumulative ICU data, self-correcting mechanism, and regularization method.
result Outperformed conventional deep learning models in predicting acute kidney injury.
The paper classifies Poincaré complexes as topological manifolds.
problem Classifying Poincaré complexes as topological manifolds.
method Using spherical fibrations and CW-complexes, the paper proves stability and homotopy equivalence.
result A sufficient condition for Poincaré complexes to be homotopy types of topological manifolds.
It has been shown that the Alvarez-Gaum e ˊ \mathrm{\acute{e}} e ˊ -Witten miraculous anomaly cancellation formula in type IIB superstring theory and its various generalizations can be derived from modularity of certain characteristic forms. In this paper, we show that the Green-Schwarz formula and the Schwarz-Witten formula i…
Natural language processing predicts AKI onset in ICU patients.
problem Early detection of AKI in ICU patients to improve outcomes.
method Clinical notes were processed to generate word and concept embeddings. Five classifiers and a deep learning model were used to predict AKI.
result The best model achieved an AUC of 0.779 for predicting AKI onset.
Bayesian method estimates dynamics from near-optimal trajectories.
problem Estimating dynamics from near-optimal expert trajectories in reinforcement learning.
method Constraint-based Bayesian approach integrating expert near-optimality.
result Significant improvements in decision-making and transfer success.
Study preference-based reinforcement learning in episodic kernel MDPs.
problem Learning from episodic human preferences in reinforcement learning.
method Developed preference-based value estimation and confidence sets for kernel-based MDPs.
result Proved high-probability regret bounds that converge to optimal policy value.
Algorithm tackles constrained reinforcement learning with concave-convex and knapsack constraints.
problem Constrained episodic reinforcement learning with concave rewards and convex constraints.
method Modular analysis with strong theoretical guarantees for concave-convex and knapsack settings.
result Significantly outperforms existing approaches in constrained episodic environments.
Algorithm improves multi-armed bandit performance by transferring reward samples.
problem Sequential multi-armed bandit problem with changing reward distributions.
method UCB algorithm with reward sample transfer.
result Significant improvement in cumulative regret over standard UCB.
Plane triangulations remain rigid under discrete conformal changes.
problem Rigidity of acute triangulations under discrete conformal changes.
method Maximum principles, discrete Liouville theorem, extremal lengths, Euclidean to hyperbolic discrete conformality.
result Uniformly acute triangulations are rigid under Luo's discrete conformal change.
BerlinUCB learns from episodic rewards in nonstationary contexts.
problem Online learning with episodic rewards in nonstationary environments.
method BerlinUCB integrates clustering for self-supervision.
result BerlinUCB outperforms standard contextual bandits in various scenarios.
We construct discrete and faithful representations into the isometry group of a hyperbolic space of the fundamental groups of acute negatively curved even-sided polygons of finite groups.
Paper develops a model to identify LVO in stroke patients.
problem Early identification of LVO in stroke patients to prevent severe outcomes.
method Used demographic, clinical, and CT scan data to build three hierarchical models.
result Level-3 model with clinical and imaging features achieved best performance.
Improved regret bound for online learning in unknown MDPs.
problem Online learning in unknown episodic MDPs with changing loss functions.
method Adapts adversarial MDP model to convex performance criteria using entropic regularization.
result Achieved i l d e O ( L ∣ X ∣ ∣ A ∣ T ) ilde{O}(L|X|\sqrt{|A|T}) i l d e O ( L ∣ X ∣ ∣ A ∣ T ) regret bound. Algorithm reduces episode count for CMDPs with constraints.
problem Online decision-making with constraints in episodic CMDPs.
method Optimistic planning using linear programming for PAC guarantee.
result Probably approximately correct (PAC) guarantee on episode count.
Many interesting real world domains involve reinforcement learning (RL) in partially observable environments. Efficient learning in such domains is important, but existing sample complexity bounds for partially observable RL are at least exponential in the episode length. We give, to our knowledge, the first partially …
Algorithm improves online learning in adversarial bandits.
problem Online learning in adversarial multi-armed bandits with non-uniform best arm distribution.
method Online-within-online setup, inner and outer learners, leveraging non-uniform empirical distribution of best arms.
result Improves regret bounds for non-uniform best arm distributions.
New algorithm learns reward signals from episodic returns for better reinforcement learning.
problem Difficulty in designing reward functions for real-world reinforcement learning tasks.
method Introduces a new algorithm that decomposes episodic returns into time-step rewards using deep neural networks.
result Learning reward signals from episodic returns improves reinforcement learning efficiency.
Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). Despite the success, deep RL algorithms are known to be sample inefficient, often requiring many rounds of interaction with the environments to obtain satisfactory performance. Recently,…
Tiny episodic memory significantly improves continual learning performance.
problem Transfer knowledge between tasks in continual learning.
method Store and replay a small number of examples from previous tasks.
result A simple baseline outperforms CL approaches with episodic memory.
Paper develops a statistical model for summarizing event sequences.
problem Discovering frequent serial episodes from sequential data.
method Minimum Description Length (MDL) principle with modifications.
result Reduces dictionary size by more than four-fold without losing accuracy.
We introduce a new class of reinforcement learning methods referred to as {\em episodic multi-armed bandits} (eMAB). In eMAB the learner proceeds in {\em episodes}, each composed of several {\em steps}, in which it chooses an action and observes a feedback signal. Moreover, in each step, it can take a special action, c…
Model learns continuously from text without forgetting.
problem Catastrophic forgetting in lifelong language learning.
method Episodic memory with sparse experience replay and local adaptation.
result Model can continuously learn from new datasets.
Paper proposes DAC-ML, a cognitive architecture that learns quickly from few episodes.
problem Sample inefficiency in AI learning action policies.
method Incorporates hippocampus-inspired sequential memory system into DAC theory of mind.
result DAC-ML rapidly converges to effective action policies maximizing reward.
The paper examines properties of self-affine Sierpiński sponges using metric invariants.
problem Investigating properties of self-affine Sierpiński sponges using metric invariants.
method Examined through maximal power law property and perfectly disconnectedness.
result Characterized self-affine Sierpiński sponges by their metric properties.
Enhanced image recognition models learn from human-like memory and shape biases.
problem Improving robustness of image recognition models against various perturbations.
method Integrating human-like episodic memory and shape bias features into image recognition models.
result Combining human-like features improves robustness against both adversarial and natural perturbations.
We propose Episodic Backward Update (EBU) - a novel deep reinforcement learning algorithm with a direct value propagation. In contrast to the conventional use of the experience replay with uniform random sampling, our agent samples a whole episode and successively propagates the value of a state to its previous states.…
New rule reduces exploration regret to logarithmic, improving bad episode handling.
problem Improving exploration regret in average reward MDPs.
method Replacing Doubling Trick with Vanishing Multiplicative rule in EVI-based algorithms.
result Regret is logarithmic under the new rule, significantly better than linear.
Recurrent major mood episodes and subsyndromal mood instability cause substantial disability in patients with bipolar disorder. Early identification of mood episodes enabling timely mood stabilisation is an important clinical goal. Recent technological advances allow the prospective reporting of mood in real time enabl…
Proposes a framework for personalized treatment recommendations using observational data.
problem Estimating patient-level treatment effects from observational data.
method Integrates existing methods for learning patient-level causal models.
result Improves patient outcomes in heart failure patients with acute kidney injury.
J.J.L. Vel a ˊ \acute{a} a ˊ zquez in 1994 used the degree theory to show that there is a perturbation of Simons' cone, starting from which the mean curvature flow develops a type I I \mathrm{II} II singularity at the origin. He also showed that under a proper time-dependent rescaling of the solution around the origin, the rescaled…
New algorithms reduce dynamic regret in online MDPs with changing losses.
problem Online MDPs with adversarial loss changes and known transitions.
method Dynamic regret measure, novel ensemble algorithms for three models.
result Provably optimal dynamic regret bounds for episodic SSP, improved bounds for predictable environments.
The study finds minimal hypersurfaces in wedge-shaped manifolds with boundary.
problem Finding minimal hypersurfaces in wedge-shaped manifolds with boundary.
method Developed a min-max theory for locally wedge-shaped manifolds with boundary.
result Proved existence of smooth free boundary minimal hypersurfaces in wedge-shaped manifolds.