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.
Study of influenza A virus spread using mathematical equations.
Avian Influenza breakouts cause millions of dollars in damage each year globally, especially in Asian countries such as China and South Korea. The impact magnitude of a breakout directly correlates to time required to fully understand the influenza virus, particularly the interspecies pathogenicity. The procedure requi…
MutaGAN predicts mutations of evolving protein populations using GANs.
Modeling influenza spread using feature engineering and international flow deconvolution.
GRUs predict flu at state and city levels with lower error.
Seasonal influenza infects between 10 and 50 million people in the United States every year, overburdening hospitals during weeks of peak incidence. Named by the CDC as an important tool to fight the damaging effects of these epidemics, accurate forecasts of influenza and influenza-like illness (ILI) forewarn public he…
Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistica…
Flusion combines multiple data sources to improve flu forecasts.
Emergenet predicts animal influenza strain emergence, outperforming current methods.
Accurate real-time monitoring systems of influenza outbreaks help public health officials make informed decisions that may help save lives. We show that information extracted from cloud-based electronic health records databases, in combination with machine learning techniques and historical epidemiological information,…
New method uses Transformers for flu forecasting.
Enhanced Zika spread forecasting using topological data analysis.
Study assesses sugar beet yields under EU's neonicotinoids ban and climate change.
Bayesian consensus improves accuracy of forecasts from miscalibrated sources.
Multi-scanner Antivirus systems provide insightful information on the nature of a suspect application; however there is often a lack of consensus and consistency between different Anti-Virus engines. In this article, we analyze more than 250 thousand malware signatures generated by 61 different Anti-Virus engines after…
Realistic evolutionary fitness landscapes are notoriously difficult to construct. A recent cutting-edge model of virus assembly consists of a dodecahedral capsid with corresponding packaging signals in three affinity bands. This whole genome/phenotype space consisting of genomes has been explored via comp…
Paper proposes a graph neural network for accurate long-term ILI prediction.
Optimizes lockdown strategies to balance economic activities and virus spread.
Conventional surveillance systems for monitoring infectious diseases, such as influenza, face challenges due to shortage of skilled healthcare professionals, remoteness of communities and absence of communication infrastructures. Internet-based approaches for surveillance are appealing logistically as well as economica…
The application of machine learning to bioinformatics problems is well established. Less well understood is the application of bioinformatics techniques to machine learning and, in particular, the representation of non-biological data as biosequences. The aim of this paper is to explore the effects of giving amino acid…
We analyse the importance of international relations between countries on the financial stability. The contagion effect in the network is tested by implementing an epidemiological model, comprising a number of European countries and using bilateral data on foreign claims between them. Banking statistics of consolidated…
As machine-learning (ML) based systems for malware detection become more prevalent, it becomes necessary to quantify the benefits compared to the more traditional anti-virus (AV) systems widely used today. It is not practical to build an agreed upon test set to benchmark malware detection systems on pure classification…
Noisy Pooled PCR tests large groups more efficiently.
While machine learning is rapidly being developed and deployed in health settings such as influenza prediction, there are critical challenges in using data from one environment in another due to variability in features; even within disease labels there can be differences (e.g. "fever" may mean something different repor…
Accurate and reliable predictions of infectious disease dynamics can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task, using different model structures, covariates, and targets for prediction. Expe…
DeepVir uses deep matrix factorization to predict antivirals for COVID-19.
This paper automates mining of COVID-19 scholarly articles using machine learning.
This study analyzes the effects of lifting lockdowns on Brazil's COVID-19 spread.
This paper tackles efficient testing strategies for COVID-19 by using a partially observable MDP approach.
The aim of this paper is to construct a natural Riemann-Lagrange differential geometry on 1-jet spaces, in the sense of nonlinear connections, generalized Cartan connections, d-torsions, d-curvatures, jet electromagnetic fields and jet electromagnetic Yang-Mills energies, starting from some given nonlinear evolution OD…
Machine learning identifies potential drugs for COVID-19.
DeCom predicts post-COVID RSV timing and intensity with NPI consideration.
Viral sequence classification is an important task in pathogen detection, epidemiological surveys and evolutionary studies. Statistical learning methods are widely used to classify and identify viral sequences in samples from environments. These methods face several challenges associated with the nature and properties …
Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or adopt the information, observing individual transmissions (i.e., who infects whom, or who influences whom) is typically very difficult…
Mathematician summarizes protein geometry and mutation effects.
Bayesian networks and ML improve COVID-19 symptom classification and severity analysis.
Framework uses machine learning to distinguish major COVID-19 variants.
This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Correlation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phra…
Estimates effect sizes and power from a pilot experiment.
New lattice path method for statistical inference of persistent diagrams.
WeatherFormer learns robust weather features from small datasets.
TinyBayes detects crop diseases from images on edge devices with high accuracy and minimal resources.
Study uses machine learning to analyze Twitter sentiments about COVID-19.
Paper proposes a hybrid AI method to optimize pandemic actions.
Novel method for estimating SIRD model parameters and forecasting COVID-19 deaths in Poland.
This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models. The main idea is to iteratively refine a knockoff sampling mechanism until a criterion measuring the validity of the produced knockoffs is optimized; this criterion i…
Icosahedral virus capsids are composed of symmetrons, organized arrangements of capsomers. There are three types of symmetrons: disymmetrons, trisymmetrons, and pentasymmetrons, which have different shapes and are centered on the icosahedral 2-fold, 3-fold and 5-fold axes of symmetry, respectively. In 2010 [Sinkovits &…
We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high dimensional space. Unlike …