Modeling Temporal Data as Continuous Functions with Process Diffusion

Temporal data like time series are often observed at irregular intervals which is a challenging setting for existing machine learning methods. To tackle this problem, we view such data as samples from some underlying continuous function. We then define a diffusion-based generative model that adds noise from a predefined stochastic process while preserving the continuity of the resulting underlying function. A neural network is trained to reverse this process which allows us to sample new realizations from the learned distribution. We define suitable stochastic processes as noise sources and introduce novel denoising and score-matching models on processes. Further, we show how to apply this approach to the multivariate probabilistic forecasting and imputation tasks. Through our extensive experiments, we demonstrate that our method outperforms previous models on synthetic and real-world datasets

Epidemiological Data of Neurological Disorders in Pakistan and Neighboring Countries: A Review

Neurological disorders are the impairments of nervous system and are an important and growing cause of morbidity, mortality, and disability. In addition to health costs, those suffering from these conditions are also frequently victimized of stigmatization and discrimination. Stigmatization further minimizes the patients\u27 access to treatment and social activities. These disorders, therefore, require special attention particularly in developing countries where unfortunately, the burden of these disorders remains largely unrecognized. Moreover, the burden imposed by such chronic neurological conditions in general can be expected to be particularly devastating in poor populations. These conditions are emerging as severe public health concerns in the developing countries due to the facts such as unawareness, Illiteracy, large numbers of people who are untreated, and unavailability of inexpensive but effective interventions. Regrettably, reliable population-based data from developing countries including Pakistan on the epidemiology of neurological disorders are extremely limited. Although, some information on epidemiological aspects of neurological diseases are available from some developing countries (Pakistan, Iran, India, Sri Lanka, Saudi Arabia and China) but disease prevalence and pattern are based on geographical, social, cultural, religious, and ethnic factors. In this review, w e critically analyzed data of 209 studies regarding the burden and prevalence of hypertension, depression, Stroke, Alzheimer\u27s disease (AD), epilepsy, and Parkinson\u27s disease (PD) in Pakistan and neighboring countries

Dengue Fever: A General Perspective

Dengue Fever or commonly known as Dengue, a mosquito-borne arboviral infection has emerged as havoc around the globe. Annually, about 50 million infections are reported, resulting in 22,000 deaths and almost 2.5 billion people are reported living at risk. Dengue infection is caused by Dengue Virus (DENV), which is a member of genus Flavivirus and comprised of ten proteins; three proteins, capsid (C), membrane (M), and envelope (E), play structural role and seven are identified as non-structural that direct DENV replication. Four distinct serotypes: DENV-1, DENV-2, DENV-3 and DENV-4 are transmitted via Aedes mosquitoes. Clinically, Dengue patients can be categorized into three groups according to WHO 2009 revised classification. Typical symptoms of dengue include: extreme fatigue; sudden fever (from 3-7 days), headache, joint, muscle, and back pain; vomiting and diarrhea, appetite loss; skin rash along minor bleeding. Aedes aegypti is geographically distributed in tropical areas and breeds in artificially filled water containers i.e. drums, tyres, flower vases plastic food containers, tin cans, etc. Due to four viral serotypes and non-availability of the model animal for dengue, producing vaccines is a challenging task. Thus, Dengue can be managed using various vector control strategies through physical, chemical and biological means