In this article, we investigate when the set of primitive geodesic lengths on a Riemannian manifold have arbitrarily long arithmetic progressions. We prove that in the space of negatively curved metrics, a metric having such arithmetic progressions is quite rare. We introduce almost arithmetic progressions, a coarsific…
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48 results for “progressive”
Which factor dominates the industry evolution? A synergy analysis based on China's ICT industryphysics.soc-ph
Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. This paper plans to probe the industry synergetic evolution mechanism based on…
DPVis integrates HMMs into visualizations for disease progression analysis.
problem Challenges in interpreting HMMs for disease progression modeling.
method Design study with clinical experts, visualizations of HMM parameters and outcomes.
result DPVis successfully evaluates and summarizes disease progression models.
Paper introduces a new curriculum generation method for reinforcement learning.
problem Improving reinforcement learning performance and speed through curriculum learning.
method The paper proposes a novel curriculum generation paradigm based on progression and mapping functions.
result Empirical results show the new approach outperforms state-of-the-art algorithms.
Lie groups applied to tech progress in economic growth.
problem Understanding the impact of technical progress on economic growth.
method Application of Lie theory and economic modeling.
result Estimation of GDP function for Viet Nam, highlighting tech progress impact.
This paper improves disentanglement in VAEs by progressively learning hierarchical representations.
problem Compromised disentanglement in VAEs due to high-level abstraction extraction.
method Progressive learning of independent hierarchical representations from high to low levels.
result Improved disentanglement demonstrated on two benchmark datasets using new metrics.
Develops CSI for predicting disease progression from sparse data.
problem Lack of frequent clinical metrics for disease progression.
method Machine learning framework (CSI) for analyzing disease progression from sparse observations.
result CSI effectively predicts disease progression from sparse data.
Bayesian model identifies health disparities in disease progression.
problem Health disparities bias disease progression models.
method Interpretable Bayesian model accounting for three disparities.
result Model identifies and corrects for health disparities.
New algorithm outperforms existing ones by focusing on mastering rate.
problem Low sample efficiency in learning progress-based algorithms.
method Proposes a new algorithm based on mastering rate.
result Significantly outperforms learning progress-based algorithms.
The purpose of this paper relies on the study of long term yield curves modeling. Inspired by the economic litterature, it provides a financial interpretation of the Ramsey rule that links discount rate and marginal utility of aggregate optimal consumption. For such a long maturity modelization, the possibility of adju…
In this article, we prove that every arithmetic locally symmetric orbifold of classical type without Euclidean or compact factors has arbitrarily long arithmetic progressions in its primitive length spectrum. Moreover, we show the stronger property that every primitive length occurs in arbitrarily long arithmetic progr…
The concept of progress has characterized human society from millennia. However, this concept is elusive and too often given for certain. The goal of this paper is to suggest a general definition of human progress that satisfies, whenever possible the conditions of independence, generality, epistemological applicabilit…
This work designs an active world model learning system with progress-based curiosity.
problem Learning compact world models from visual exploration.
method Constructs a curious agent in a 3D environment, using -Progress as a curiosity signal.
result The -Progress-driven controller outperforms state-of-the-art exploration strategies.
Survey of recent progress in Sasakian geometry.
problem Topological properties and existence of Sasakian structures.
method Review of recent research findings.
result Recent progress in Sasakian geometry.
Study uses machine learning and survival analysis to predict CKD progression.
problem Early detection and management of CKD to reduce ESRD risk.
method Combines machine learning and classical statistical models to identify novel CKD progression predictors.
result Deep learning models outperform other methods in predicting CKD progression.
We introduce a novel approach for predicting the progression of adolescent idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray images. Recent progress in machine learning have allowed to improve classification and prognosis rates, but lack a probabilistic framework to measure uncertainty in the …
Study finds little progress in medical machine learning benchmarks over 3 years.
problem Lack of meaningful progress in medical machine learning benchmarks for structured healthcare data.
method Comprehensive review and meta-analysis of benchmarks in medical machine learning for structured data.
result Deep recurrent models perform only better than logistic regression on certain clinical prediction tasks.
Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming abnormal, the pathophysiology of which is very complex and largely unknown. Event-based modeling (EBM) is a data-driven technique to estimate the sequence in which biomarkers for a disease become abnormal based on cross-sectional data. It can…
Deep learning's success requires vast computing power, making future progress unsustainable.
problem Deep learning's success is heavily dependent on computing power, making future progress unsustainable.
method Cataloging and extrapolating the dependency on computing power for various deep learning applications.
result Continued progress in deep learning applications will require more computationally-efficient methods.
In clinical practice and biomedical research, measurements are often collected sparsely and irregularly in time while the data acquisition is expensive and inconvenient. Examples include measurements of spine bone mineral density, cancer growth through mammography or biopsy, a progression of defective vision, or assess…
New method interprets deep embeddings for diabetes patient clustering.
problem Interpreting deep embeddings for disease progression.
method Patient clustering approach using deep embeddings.
result Clinically meaningful insights into diabetes progression patterns.
We propose Progressive Structure-conditional Generative Adversarial Networks (PSGAN), a new framework that can generate full-body and high-resolution character images based on structural information. Recent progress in generative adversarial networks with progressive training has made it possible to generate high-resol…
We survey recent progress in the problem of recovering a tensor field from its integrals along geodesics. We also propose several open problems.
We give a short review of recent progress on the positive energy theorem in general relativity, especially for spacetimes with nonzero cosmological constant.
New progress on frame flow ergodicity for nearly pinched manifolds.
problem Ergodicity of frame flow on negatively-curved manifolds.
method New ideas leading to ergodicity for nearly 0.25-pinched manifolds.
result Achieved progress towards Brin's conjecture.
Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are characterized by heterogeneity in age, disease span, progression rate, impairment of memory and cognitive abilities. Due to these variabilities, …
DIM shows promise in predicting Alzheimer's progression.
problem Predicting Alzheimer's disease progression from brain imaging.
method Used variants of Deep InfoMax (DIM) for brain imaging analysis.
result DIM outperforms supervised AlexNet and ResNet in Alzheimer's progression prediction.
This paper begins with a study on the dual representations of risk and regret measures and their impact on modeling multistage decision making under uncertainty. A relationship between risk envelopes and regret envelopes is established by using the Lagrangian duality theory. Such a relationship opens a door to a decomp…
The Minimal Genus Problemmath.GT
Survey of minimal genus problem progress.
problem Finding the smallest genus surface in 3-manifolds.
method Survey and review of existing research.
result Summarizes recent advancements in the minimal genus problem.
This paper gives a survey of recent progress in isoparametric functions and isoparametric hypersurfaces, mainly in two directions. (1) Isoparametric functions on Riemannian manifolds, including exotic spheres. The existences and non-existences will be considered. (2) The Yau conjecture on the first eigenvalues of the e…
GAAN uses progressive margin folding for graph feature aggregation.
problem Insufficient aggregation methods between GCNN layers.
method Graph attribute aggregation network with progressive margin folding.
result GAAN outperforms existing GCNN models on molecule datasets.
Survey of recent austere submanifold results.
problem Understanding austere submanifolds.
method Review of existing papers [24,25].
result Recent progress in the field of austere submanifolds.
The purpose of this paper relies on the study of long term affine yield curves modeling. It is inspired by the Ramsey rule of the economic literature, that links discount rate and marginal utility of aggregate optimal consumption. For such a long maturity modelization, the possibility of adjusting preferences to new ec…
A random walk on a separable, geodesic hyperbolic metric space converges to the boundary with probability one when the step distribution supports two independent loxodromics. In particular, the random walk makes positive linear progress. Progress is known to be linear with exponential decay when …
We show that a random walk on the mapping class group of an orientable surface of finite type makes linear progress in the relative metric, which is quasi-isometric to the complex of curves.
AI progress measured by reduced compute needed to reach past performance.
problem Quantifying algorithmic progress in AI.
method Analysis of floating-point operations required to train neural networks.
result Algorithmic efficiency doubled every 16 months over 7 years.
Modeling disease progression using irregular time intervals in EHRs.
problem Challenges in analyzing temporal data from EHRs.
method Developed a Markovian generative model using EHR data.
result Model accurately recovers underlying disease progression patterns from irregular time intervals.
New method predicts AD progression using MEG brain networks.
problem Early diagnosis and prediction of Alzheimer's disease progression.
method MG2G, a deep learning method that maps brain networks into a latent space.
result MG2G detects subtle brain connectivity patterns and predicts AD progression.
Deep neural networks solve Raven's Progressive Matrices with high accuracy.
problem Testing relational reasoning in machine learning systems.
method Combining Wild Relation Networks with Multi-Layer Relation Networks and introducing Magnitude Encoding.
result Deep neural networks achieve 98.0 percent accuracy, significantly improving over previous methods.
Deep learning classifies keratoconus patients with high accuracy.
problem Accurately identifying keratoconus patients for early intervention.
method Unsupervised and semi-supervised machine learning models using corneal topography and clinical data.
result Unsupervised method with 29 variables shows better classification accuracy.
Progressive stochastic binarization improves deep network inference efficiency.
problem Efficient inference of deep networks with reduced memory and computational resources.
method A progressive stochastic binarization scheme for deep networks that uses small integers and fixed shifts.
result Matches the accuracy of previous binarized approaches and reduces inference costs by up to 33%.
Improves disease progression prediction using auxiliary surrogate labels and health markers.
problem Challenges in predicting disease progression due to unknown true disease states.
method Integrates hidden Markov model with time-varying discriminative classification model.
result Significant improvement in distinguishing LBD from AD using objective markers.
New equations reveal how cylinder power in progressive lenses depends on geodesic curvature.
problem Current understanding of cylinder power in progressive lenses is incomplete.
method Derived complete compatibility equations for spatially-varying curvature surfaces.
result Cylinder power depends on geodesic curvature, not just principal curvature.
Ricci solitons are natural generalizations of Einstein metrics. They are also special solutions to Hamilton's Ricci flow and play important roles in the singularity study of the Ricci flow. In this paper, we survey some of the recent progress on Ricci solitons.
In recent years, there are many progress made in Kähler geometry. In particular, the topics related to the problems of the existence and uniqueness of extremal Kähler metrics, as well as obstructions to the existence of such metrics in general Kähler manifold. In this talk, we will report some recent developments in th…
Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment. However, commonly used sepsis diagnostic criteria fail to account for significant underlying heterogeneity, both between patients as well as over time in a single patient. W…
From Gravity to Confinement: Wealth Redistribution as Optimal Drift Design in the Fokker-Planck Frameworkphysics.soc-ph
Wealth redistribution through Fokker-Planck equation controls preserves Gini coefficient.
problem Preserving Gini coefficient through proportional wealth tax.
method Formulating optimal redistribution as a control problem for Fokker-Planck equation.
result Progressive taxes redistribute within policy-relevant timescales.
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Datastat.ML
Modeling disease progression in brain images using monotonic Gaussian Processes.
problem Disentangling spatio-temporal disease trajectories from brain imaging data.
method Spatio-temporal matrix factorization with anatomically plausible priors, monotonic Gaussian Processes, and sparse codes.
result Monotonic Gaussian Processes model realistic disease trajectories in brain imaging data.