Time-resolved smoothness of distributed brain activity tracks conscious states and unifies emergent neural phenomena

Much of systems neuroscience posits that emergent neural phenomena underpin important aspects of brain function. Studies in the field variously emphasize the importance of distinct emergent phenomena, including weakly stable dynamics, arrhythmic 1/f activity, long-range temporal correlations, and scale-free avalanche statistics. Few studies, however, have sought to reconcile these often abstract phenomena with interpretable properties of neural activity. Here, we developed a method to efficiently and unbiasedly generate model data constrained by interpretable empirical features in long neurophysiological recordings. We used this method to ground several major emergent neural phenomena to time-resolved smoothness, the correlation of distributed brain activity between adjacent timepoints. We first found that in electrocorticography recordings, time-resolved smoothness closely tracked transitions between conscious and anesthetized states. We then showed that a minimal model constrained by time-resolved smoothness, variance, and mean, captured dynamical and statistical emergent neural phenomena across modalities and species. Our results thus decouple major emergent neural phenomena from network mechanisms of brain function, and instead couple these phenomena to spatially nonspecific, time-resolved changes of brain activity. These results anchor several theoretical frameworks to a single interpretable property of the neurophysiological signal and, in this way, ultimately help bridge abstract theories of brain function with observed properties of brain activity.

SIMULATING FLOOD RECOVERY MANOEUVRES USING A FREE-RUNNING SUBMARINE MODEL

Managing submarine safety, effectively, requires an understanding of many areas of platform performance, including its ability to manoeuvre. QinetiQ’s free-running submarine model (FRM) capability, the second generation Submarine Research Model (SRMII), forms a key part of the UK’s predictive manoeuvring capability that supports the MoD’s ability to conduct hydrodynamic assessment of the manoeuvring and control performance of the Royal Navy’s current and future submarines. Uniquely for an FRM, the SRMII has a large and capable ballast system. This is able to emulate a flooding incident within a submarine compartment and the subsequent emergency recovery procedures, which may include blowing the submarine’s main ballast tanks. This paper discusses how the SRMII’s ballast system was used to generate model-scale trajectories, which are not obtainable with many other FRMs. The experimental data were used to successfully validate the mathematical model, which predicts the maximum pitch angle response of a full-scale submarine to a compartment flood, to within an average accuracy of 1% at model-scale. However, the range of the non- dimensional flow angles the FRM exhibited was shown to be within that for a full-scale flood trajectory. Therefore, further tests have been proposed to increase the extent of the data in the future.

The GAMBIT Universal Model Machine: from Lagrangians to likelihoods

We introduce the Universal Model Machine (), a tool for automatically generating code for the global fitting software framework , based on Lagrangian-level inputs. accepts models written symbolically in and formats, and can use either tool along with and to generate model, collider, dark matter, decay and spectrum code, as well as interfaces to corresponding versions of , , and (C "Image missing"). In this paper we describe the features, methods, usage, pathways, assumptions and current limitations of . We also give a fully worked example, consisting of the addition of a Majorana fermion simplified dark matter model with a scalar mediator to via , and carry out a corresponding fit.

Effectiveness of Budgetary Controls on Budget Execution in Zanzibar

This study aims at examining the effectiveness of budgetary controls on budget execution in Zanzibar. The proper linkages between public resources and its management through service delivery are the key to success of any government. The study adopted descriptive research design in which primary data was collected by the use of questionnaire. More over the study used a stepwise approach to generate model, where regression model was used to analyze the degree of the relationship between budgetary control and the extent to which it contribute to the performance of budget in Zanzibar. The results reveals that a budgetary control as perceived by various respondents is effective on the basis of planning and budgeting, monitoring and control, analyzing and feedback and thus concluded to have a positive relationship with budget performance. Based on the study, it is recommended that management should be ready to participate in budget execution process in order to remove the existing challenges.

lcsm: An R package and tutorial on latent change score modelling

Latent change score models (LCSMs) are used across disciplines in behavioural sciences to study how constructs change over time. LCSMs can be used to estimate the trajectory of one construct (univariate) and allow the investigation of how changes between two constructs (bivariate) are associated with each other over time. This paper introduces the R package lcsm, a tool that aims to help users understand, analyse, and visualise different latent change score models. The lcsm package provides functions to generate model syntax for basic univariate and bivariate latent change score models with different model specifications. It is also possible to visualise different model specifications in simplified path diagrams. An interactive application illustrates the main functions of the package and demonstrates how the model syntax and path diagrams change based on different model specifications. This R package aims to increase the transparency of reporting analyses and to provide an additional resource to learn latent change score modelling.

An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3

Physical processes within geoscientific models are sometimes described by simplified schemes known as parameterisations. The values of the parameters within these schemes can be poorly constrained by theory or observation. Uncertainty in the parameter values translates into uncertainty in the outputs of the models. Proper quantification of the uncertainty in model predictions therefore requires a systematic approach for sampling parameter space. In this study, we develop a simple and efficient approach to identify regions of multi-dimensional parameter space that are consistent with observations. Using the Parallel Ice Sheet Model to simulate the present-day state of the Antarctic Ice Sheet, we find that co-dependencies between parameters preclude any simple identification of a single optimal set of parameter values. Approaches such as large ensemble modelling are therefore required in order to generate model predictions that incorporate proper quantification of the uncertainty arising from the parameterisation of physical processes.

Detection of OH at the evening terminator of the ultra-hot Jupiter WASP-76b

<p>Ultra-hot Jupiters have dayside temperatures similar to those of M-dwarfs. While molecular absorption from the hydroxyl radical (OH) is easily observed in near-infrared spectra of M-dwarfs, it is often not considered when studying the atmospheres of (ultra-)hot Jupiters. We use high-resolution spectroscopic near-infrared observations of a transit of WASP-76b obtained using CARMENES to assess the presence of OH. After validating the OH line list, we generate model transit spectra of WASP-76b with petitRADTRANS. The data are corrected for telluric contamination and cross-correlated with the model spectra. After combining all cross-correlation functions from the transit, a detection map is constructed. OH is detected in the atmosphere of WASP-76b with a signal-to-noise ratio of 6.1. From a Markov Chain Monte Carlo retrieval we obtain Kp=234 km/s and a blueshift of 13.9 km/s. Considering the fast spin-rotation of the planet, the OH signal is best explained with the signal mainly originating from the evening terminator and the presence of a strong day- to nightside wind. The signal appears to be broad, with a full width at half maximum of 16.2 km/s. The retrieval results in a weak constraint on the temperature of 2420-3150 K at the pressure of the OH signal. Our results demonstrate that OH is readily observable in the transit spectra of ultra-hot Jupiters. Studying this molecule can give new insights in the molecular dissociation processes in the atmospheres of such planets.</p>

Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting

The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced when the model is encouraged to remember the evidence for previously made decisions. As a first step towards exploring this hypothesis, we propose a simple novel training paradigm, called Remembering for the Right Reasons (RRR), that additionally stores visual model explanations for each example in the buffer and ensures the model has “the right reasons” for its predictions by encouraging its explanations to remain consistent with those used to make decisions at training time. Without this constraint, there is a drift in explanations and increase in forgetting as conventional continual learning algorithms learn new tasks. We demonstrate how RRR can be easily added to any memory or regularization-based approach and results in reduced forgetting, and more importantly, improved model explanations. We have evaluated our approach in the standard and few-shot settings and observed a consistent improvement across various CL approaches using different architectures and techniques to generate model explanations and demonstrated our approach showing a promising connection between explainability and continual learning. Our code is available at \url{https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons}

An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3

Physical processes within geoscientific models are sometimes described by simplified schemes known as parameterisations. The values of the parameters within these schemes can be poorly constrained by theory or observation. Uncertainty in the parameter values translates into uncertainty in the outputs of the models. Proper quantification of the uncertainty in model predictions therefore requires a systematic approach for sampling parameter space. In this study, we develop a simple and efficient approach to identify regions of multi-dimensional parameter space that are consistent with observations. Using the Parallel Ice Sheet Model to simulate the present-day state of the Antarctic Ice Sheet, we find that co-dependencies between parameters preclude the identification of a single optimal set of parameter values. Approaches such as large ensemble modelling are therefore required in order to generate model predictions that incorporate proper quantification of the uncertainty arising from the parameterisation of physical processes.

Simulating Flood Recovery Manoeuvres Using a Free-Running Submarine Model

Managing submarine safety, effectively, requires an understanding of many areas of platform performance, including its ability to manoeuvre. QinetiQ’s free-running submarine model (FRM) capability, the second generation Submarine Research Model (SRMII), forms a key part of the UK’s predictive manoeuvring capability that supports the MoD’s ability to conduct hydrodynamic assessment of the manoeuvring and control performance of the Royal Navy’s current and future submarines. Uniquely for an FRM, the SRMII has a large and capable ballast system. This is able to emulate a flooding incident within a submarine compartment and the subsequent emergency recovery procedures, which may include blowing the submarine’s main ballast tanks. This paper discusses how the SRMII’s ballast system was used to generate model-scale trajectories, which are not obtainable with many other FRMs. The experimental data were used to successfully validate the mathematical model, which predicts the maximum pitch angle response of a full-scale submarine to a compartment flood, to within an average accuracy of 1% at model-scale. However, the range of the non-dimensional flow angles the FRM exhibited was shown to be within that for a full-scale flood trajectory. Therefore, further tests have been proposed to increase the extent of the data in the future.