Approach to Data Science with Multiscale Information Theory

Data Science is a multidisciplinary field that plays a crucial role in extracting valuable insights and knowledge from large and intricate datasets. Within the realm of Data Science, two fundamental components are Information Theory (IT) and Statistical Mechanics (SM), which provide a theoretical framework for understanding dataset properties. IT enables efficient storage and transmission of information, while SM focuses on the behavior of systems comprising numerous interacting components. In the context of data science, SM allows us to model complex interactions among variables within a dataset. By leveraging these tools, data scientists can gain a profound understanding of data properties, leading to the development of advanced models and algorithms for analysis and interpretation. Consequently, data science has the potential to drive accurate predictions and enhance decision-making across various domains, including finance, marketing, healthcare, and scientific research. In this paper, we apply this data science framework to a large and intricate quantum mechanical system composed of particles. Our research demonstrates that the dynamic and probabilistic nature of such systems can be effectively addressed using a Multiscale Entropic Dynamics (MED) approach, derived from the Boltzmann methods of SM. Through the MED approach, we can describe the system's dynamics by formulating a general form of the Nonlinear Schr\"odinger equation and how it can be applied to various systems with particles and quasi-particles, such as electrons, plasmons, polarons, and solitons. By employing this innovative approach, we pave the way for a deeper understanding of quantum mechanical systems and their behaviors within complex materials.Comment: 12 page

Inter-observer variability in diagnosing radiological features of aneurysmal subarachnoid hemorrhage; a preliminary single centre study comparing observers from different specialties and levels of training

BACKGROUND: A noncontrast computed tomography (CT) scan remains the initial radiological investigation of choice for a patient with suspected aneurysmal subarachnoid hemorrhage (aSAH). This initial scan may be used to derive key information about the underlying aneurysm which may aid in further management. The interpretation, however, is subject to the skill and experience of the interpreting individual. The authors here evaluate the interpretation of such CT scans by different individuals at different levels of training, and in two different specialties (Radiology and Neurosurgery). METHODS: Initial nonontrast CT scan of 35 patients with aSAH was evaluated independently by four different observers. The observers selected for the study included two from Radiology and two from Neurosurgery at different levels of training; a resident currently in mid training and a resident who had recently graduated from training of each specialty. Measured variables included interpreter's suspicion of presence of subarachnoid blood, side of the subarachnoid hemorrhage, location of the aneurysm, the aneurysm's proximity to vessel bifurcation, number of aneurysm(s), contour of aneurysm(s), presence of intraventricular hemorrhage (IVH), intracerebral hemorrhage (ICH), infarction, hydrocephalus and midline shift. To determine the inter-observer variability (IOV), weighted kappa values were calculated. RESULTS: There was moderate agreement on most of the CT scan findings among all observers. Substantial agreement was found amongst all observers for hydrocephalus, IVH, and ICH. Lowest agreement rates were seen in the location of aneurysm being supra or infra tentorial. There were, however, some noteworthy exceptions. There was substantial to almost perfect agreement between the radiology graduate and radiology resident on most CT findings. The lowest agreement was found between the neurosurgery graduate and the radiology graduate. CONCLUSION: Our study suggests that although agreements were seen in the interpretation of some of the radiological features of aSAH, there is still considerable IOV in the interpretation of most features among physicians belonging to different levels of training and different specialties. Whether these might affect management or outcome is unclear

LSST Science Book, Version 2.0

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at http://www.lsst.org/lsst/sciboo

The interplay of landscape composition and configuration: new pathways to manage functional biodiversity and agroecosystem services across Europe

Managing agricultural landscapes to support biodiversity and ecosystem services is a key aim of a sustainable agriculture. However, how the spatial arrangement of crop fields and other habitats in landscapes impacts arthropods and their functions is poorly known. Synthesising data from 49 studies (1515 landscapes) across Europe, we examined effects of landscape composition (% habitats) and configuration (edge density) on arthropods in fields and their margins, pest control, pollination and yields. Configuration effects interacted with the proportions of crop and non‐crop habitats, and species’ dietary, dispersal and overwintering traits led to contrasting responses to landscape variables. Overall, however, in landscapes with high edge density, 70% of pollinator and 44% of natural enemy species reached highest abundances and pollination and pest control improved 1.7‐ and 1.4‐fold respectively. Arable‐dominated landscapes with high edge densities achieved high yields. This suggests that enhancing edge density in European agroecosystems can promote functional biodiversity and yield‐enhancing ecosystem services

Crop pests and predators exhibit inconsistent responses to surrounding landscape composition

The idea that noncrop habitat enhances pest control and represents a win–win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win–win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies