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.
Automated malaria diagnosis from field slides achieves accurate results.
Mobile apps and machine learning improve malaria prevention and treatment.
LEMPS predicts malaria prevalence with high accuracy in Ibadan, West Africa.
Anomaly detection identifies unusual malaria transmission patterns in Ghana.
Crowd-sourced mosquito audio dataset for malaria research.
Deep neural network improves malaria detection from red blood cells.
Mosquitoes are a major vector for malaria, causing hundreds of thousands of deaths in the developing world each year. Not only is the prevention of mosquito bites of paramount importance to the reduction of malaria transmission cases, but understanding in more forensic detail the interplay between malaria, mosquito vec…
Researchers tackle malaria control as a reinforcement learning problem with limited data.
In tropical regions, populations continue to suffer morbidity and mortality from malaria and arboviral diseases. In Kedougou (Senegal), these illnesses are all endemic due to the climate and its geographical position. The co-circulation of malaria parasites and arboviruses can explain the observation of coinfected case…
We build a deep reinforcement learning (RL) agent that can predict the likelihood of an individual testing positive for malaria by asking questions about their household. The RL agent learns to determine which survey question to ask next and when to stop to make a prediction about their likelihood of malaria based on t…
Malaria is a serious infectious disease that is responsible for over half million deaths yearly worldwide. The major cause of these mortalities is late or inaccurate diagnosis. Manual microscopy is currently considered as the dominant diagnostic method for malaria. However, it is time consuming and prone to human error…
Paper introduces a method to interpret complex epidemiology simulators.
New model maps malaria prevalence across Kenya's changing administrative boundaries.
This paper deals with prediction of anopheles number, the main vector of malaria risk, using environmental and climate variables. The variables selection is based on an automatic machine learning method using regression trees, and random forests combined with stratified two levels cross validation. The minimum threshol…
Kernel interpolation speeds up online Gaussian process updates.
In this study, we propose an automatic learning method for variables selection based on Lasso in epidemiology context. One of the aim of this approach is to overcome the pretreatment of experts in medicine and epidemiology on collected data. These pretreatment consist in recoding some variables and to choose some inter…
The paper investigates deep neural networks for medical imaging applications, providing interpretable results.
New method disentangles hidden data structures using HSIC and supervision.
Extends diffusion models to handle exponential family distributions for inverse problems.
Hybrid model integrates GATv2 and geostatistics for better spatial prediction and uncertainty.
We present the first fully variational Bayesian inference scheme for continuous Gaussian-process-modulated Poisson processes. Such point processes are used in a variety of domains, including neuroscience, geo-statistics and astronomy, but their use is hindered by the computational cost of existing inference schemes. Ou…
In life sciences, the experts generally use empirical knowledge to recode variables, choose interactions and perform selection by classical approach. The aim of this work is to perform automatic learning algorithm for variables selection which can lead to know if experts can be help in they decision or simply replaced …
While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs. Aggregation of outputs makes generalization to new in…
Simulation-based inference aids in predicting disease dynamics for health policy.
TWIST algorithm detects communities in multi-layer networks with tensor decomposition.
In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language process…
A scalable online method for Gaussian processes that improves decision-making in various applications.
DIVA learns domain-invariant latent subspaces for domain generalization.
BCCNet combines biased crowd labels to train classifiers for disaster response.
Bipartite data is common in data engineering and brings unique challenges, particularly when it comes to clustering tasks that impose on strong structural assumptions. This work presents an unsupervised method for assessing similarity in bipartite data. Similar to some co-clustering methods, the method is based on regu…