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.
Enhanced Zika spread forecasting using topological data analysis.
Study of influenza A virus spread using mathematical equations.
Study assesses sugar beet yields under EU's neonicotinoids ban and climate change.
MutaGAN predicts mutations of evolving protein populations using GANs.
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…
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…
Optimizes lockdown strategies to balance economic activities and virus spread.
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…
Paper tackles pandemic resource allocation challenges.
Noisy Pooled PCR tests large groups more efficiently.
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.
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…
This paper tackles efficient testing strategies for COVID-19 by using a partially observable MDP approach.
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…
New lattice path method for statistical inference of persistent diagrams.
Mosquitoes are vectors of many human diseases. In particular, Aedes ægypti (Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a global threat. Public health policies that aim at combating this vector require dependable and timely information, which is usually expe…
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 …
Unsupervised learning identifies key factors for NYC COVID-19 spread.
ESOP uses Bayesian optimization to find optimal lock-down schedules.
Lung segmentation from abnormal CXRs using data imputation.
Study reveals hidden infections and infection dynamics from early data.
Neural networks model COVID-19 spread with partial isolation data.
The global crisis of 2008 provoked a heightened interest among scientists to study the phenomenon, its propagation and negative consequences. The process of modelling the spread of a virus is commonly used in epidemiology. Conceptually, the spread of a disease among a population is similar to the contagion process in e…
Motivation: HIV is difficult to treat because its virus mutates at a high rate and mutated viruses easily develop resistance to existing drugs. If the relationships between mutations and drug resistances can be determined from historical data, patients can be provided personalized treatment according to their own mutat…
Researchers use clustering to differentiate COVID-19 lung scans.
Develops algorithm to find subgroups with different treatment effects in HIV patients.
Paper introduces a new method to model epidemic dynamics with varying parameters.
Study predicts future hospitalizations to manage COVID-19 patient surge.
Machine learning models have been widely used in security applications such as intrusion detection, spam filtering, and virus or malware detection. However, it is well-known that adversaries are always trying to adapt their attacks to evade detection. For example, an email spammer may guess what features spam detection…
Koopman-PINN framework improves epidemic model parameter inference and forecasting