A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Generative model for SSc disease trajectories using deep learning.
Multiple Sclerosis is a degenerative condition of the central nervous system that affects nearly 2.5 million of individuals in terms of their physical, cognitive, psychological and social capabilities. Researchers are currently investigating on the use of patient reported outcome measures for the assessment of impact a…
The paper proposes a deep generative model for complex disease trajectories.
CRBM generates digital twins for MS patients, aiding in disease progression analysis.
In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease patients, to verify their performance in challenging conditions, mo…
Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS) that results in focal injury to the grey and white matter. The presence of white matter lesions biases morphometric analyses such as registration, individual longitudinal measurements and tissue segmentation for brain v…
Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessments and tests to rule out …
Automated detection of MS lesions improves to 67% with 7T MRI.
Model uses smartphone data to assess MS trajectories.
Graph Attention Networks predict disease state from single-cell data.
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by a rapid motor decline, leading to respiratory failure and subsequently to death. In this context, researchers have sought for models to automatically predict disease progression to assisted ventilation in ALS patients. However, the clin…
Study predicts when ALS patients will lose speech, swallowing, etc. based on covariates.
Enhancing spectral embedding for low-dimensional embeddings in rare disease cohorts
In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. Here we propose novelty detection methods based on training variational autoencoders…
While sample sizes in randomized clinical trials are large enough to estimate the average treatment effect well, they are often insufficient for estimation of treatment-covariate interactions critical to studying data-driven precision medicine. Observational data from real world practice may play an important role in a…
Proposes modifications to model-based forests for HTE estimation in observational data.
Digital twins improve single-arm trials by providing robust treatment effect estimates.
The study develops a logic reasoner to verify MS case management specifications.
We investigate the effect of the proportional hazards assumption on prognostic and predictive models of the survival time of patients suffering from amyotrophic lateral sclerosis (ALS). We theoretically compare the underlying model formulations of several variants of survival forests and implementations thereof, includ…
We consider high-dimensional regression over subgroups of observations. Our work is motivated by biomedical problems, where disease subtypes, for example, may differ with respect to underlying regression models, but sample sizes at the subgroup-level may be limited. We focus on the case in which subgroup-specific model…
Develops a machine learning model to predict ALS progression and assistive device use.
Motivation: Single cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct coun…
Optimizes treatment duration to maximize quality-adjusted lifetime.
With the increased need for multi-center magnetic resonance imaging studies, problems arise related to differences in hardware and software between centers. Namely, current algorithms for brain volume quantification are unreliable for the longitudinal assessment of volume changes in this type of setting. Currently most…
Bayesian model improves BCI performance for ALS users.
Developed a multiway classification method for sparse data.
Simultaneous Latent Budget Trees for stratified classification
High-dimensional linear classifiers, such as the support vector machine (SVM) and distance weighted discrimination (DWD), are commonly used in biomedical research to distinguish groups of subjects based on a large number of features. However, their use is limited to applications where a single vector of features is mea…
The biological processes involved in a drug's mechanisms of action are oftentimes dynamic, complex and difficult to discern. Time-course gene expression data is a rich source of information that can be used to unravel these complex processes, identify biomarkers of drug sensitivity and predict the response to a drug. H…
MISTR improves HTE estimation in survival data with heavy censoring and instrumental variables.
In-vivo examination of the physical connectivity of axonal projections through the white matter of the human brain is made possible by diffusion weighted magnetic resonance imaging (dMRI) Analysis of dMRI commonly considers derived scalar metrics such as fractional anisotrophy as proxies for "white matter integrity," a…
A cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on conventional and white-matter-nulled (WMn) magnetization prepared rapid gradient echo (MPRAGE) data. A single network was optimized to work with images from healthy controls and patients with multiple scler…
Extends Shifts dataset for MS lesion segmentation and marine vessel power estimation.
Overview of integrable systems with symmetries, focusing on toric and semitoric systems.
New method to derive integrable systems from existing Lax systems.
Learning to control linear systems is statistically hard, especially for underactuated systems.
Discrete-time systems can be characterized by simple flat coordinates and their shifts.
The paper explores when linear system identification is hard or easy, especially for under-actuated systems.
This paper improves system identification by reducing sample complexity for high-dimensional linear dynamical systems.
In integrable hydrodynamic systems, coordinates exist where generators and symmetries are simple.
This paper studies nonholonomic constraints in Hamiltonian systems, deriving equations and theorems.
New method models unknown systems with hidden parameters using neural networks.
Study absolute equivalence for Pfaffian systems, applying to control systems.
Solves selecting the best optimizing system problems.
This paper considers control systems defined on Lie algebroids. After deriving basic controllability tests for general control systems, we specialize our discussion to the class of mechanical control systems on Lie algebroids. This class of systems includes mechanical systems subject to holonomic and nonholonomic const…
Superintegrable systems on curved manifolds found to have Hessian structures.
Systemic risk refers to the risk that the financial system is susceptible to failures due to the characteristics of the system itself. The tremendous cost of systemic risk requires the design and implementation of tools for the efficient macroprudential regulation of financial institutions. The current paper proposes a…
Researchers solve boundary and scattering rigidity problems for magnetic systems.