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The ability to predict a dynamic crowd response during Mass Gatherings may improve medical access and egress in a mass casualty incident. Validated models that accurately anticipate the chaos and crowd flow are not readily available. Recent advances in machine learning for crowd simulation offer an under-explored opportunity to improve emergency response strategies.
Objectives:
Train a crowd simulation model on video data and prove validity by comparing its predictions to an actual crowd-egress event.
Method/Description:
A physics-based Social Force Model was used to simulate crowd movement. The model considers obstacles and other pedestrians in trajectory prediction, seeking best to estimate crowd density rather than individuals’ positions. Four parameters– maximum speed multiplier, motivation factor, social force factor, and obstacle repulsion factor –were found to be meaningful when comparing simulations to known pedestrian video. These parameters were optimized using an evolutionary algorithm to predict crowd response to an actual bomb scare in Times Square. A convolutional neural network model, CSRNet, was used to analyze crowd density frame by frame from an actual video for comparison.
Results/Outcomes:
Predicted density heat maps were compared to the video, demonstrating a realistic simulation of crowd egress. The pedestrians filter similarly in the prediction model and the ground-truth video. Divergence is mainly noted in the upper portion of the image, accounted for by the fact that the model currently does not adjust for additional population to enter the frame.
Conclusion:
This study marks a significant stride in demonstrating the potential of machine learning in crowd-egress prediction using video data.
Despite their use in clinical practice, there is little evidence to support the use of therapist written goodbye letters as therapeutic tools. However, preliminary evidence suggests that goodbye letters may have benefits in the treatment of anorexia nervosa (AN).
Aims:
This study aimed to examine whether therapist written goodbye letters were associated with improvements in body mass index (BMI) and eating disorder symptomology in patients with AN after treatment.
Method:
Participants were adults with AN (n = 41) who received The Maudsley Model of Anorexia Treatment for Adults (MANTRA) in a clinical trial evaluating two AN out-patient treatments. As part of MANTRA, therapists wrote goodbye letters to patients. A rating scheme was developed to rate letters for structure and quality. Linear regression analyses were used to examine associations between goodbye letter scores and outcomes after treatment.
Results:
Higher quality letters and letters that adopted a more affirming stance were associated with greater improvements in BMI at 12 months. Neither the overall quality nor the style of goodbye letters were associated with improvements in BMI at 24 months or reductions in eating disorder symptomology at either 12 or 24 months.
Conclusions:
The results highlight the potential importance of paying attention to the overall quality of therapist written goodbye letters in the treatment of AN, and adopting an affirming stance.
A domain exchange map (DEM) is a dynamical system defined on a smooth Jordan domain which is a piecewise translation. We explain how to use cut-and-project sets to construct minimal DEMs. Specializing to the case in which the domain is a square and the cut-and-project set is associated to a Galois lattice, we construct an infinite family of DEMs in which each map is associated to a Pisot–Vijayaraghavan (PV) number. We develop a renormalization scheme for these DEMs. Certain DEMs in the family can be composed to create multistage, renormalizable DEMs.
The unconstrained exponential family of random graphs assumes no prior knowledge of the graph before sampling, but it is natural to consider situations where partial information about the graph is known, for example the total number of edges. What does a typical random graph look like, if drawn from an exponential model subject to such constraints? Will there be a similar phase transition phenomenon (as one varies the parameters) as that which occurs in the unconstrained exponential model? We present some general results for this constrained model and then apply them to obtain concrete answers in the edge-triangle model with fixed density of edges.
We report a study of resistive switching in a silicon-based memristor/resistive RAM (RRAM) device in which the active layer is silicon-rich silica. The resistive switching phenomenon is an intrinsic property of the silicon-rich oxide layer and does not depend on the diffusion of metallic ions to form conductive paths. Both unipolar and bipolar programming is demonstrated.
Switching exhibits the pinched hysteresis I/V loop characteristic of RRAM/memristive systems, and on/off resistance ratios of 104:1 or higher can be easily achieved. Scanning Tunnelling Microscopy suggests that switchable conductive pathways are 10nm in diameter or smaller.
We consider subsets of the (symbolic) sequence space that are invariant under the action of the semigroup of multiplicative integers. A representative example is the collection of all 0–1 sequences (xk) such that xkx2k=0 for all k. We compute the Hausdorff and Minkowski dimensions of these sets and show that they are typically different. The proof proceeds via a variational principle for multiplicative subshifts.
We investigate topological mixing for $\mathbb Z$ and $\mathbb R$ actions associated with primitive substitutions on two letters. The characterization is complete if the second eigenvalue $\theta_2$ of the substitution matrix satisfies $|\theta_2|\ne 1$. If $|\theta_2|<1$, then (as is well known) the substitution system is not topologically weak mixing, so it is not topologically mixing. We prove that if $|\theta_2|> 1$, then topological mixing is equivalent to topological weak mixing, which has an explicit arithmetic characterization. The case $|\theta_2|=1$ is more delicate, and we only obtain some partial results.
For each irreducible hyperbolic automorphism $A$ of the $n$-torus we construct a sofic system $(\Sigma,\sigma)$ and a bounded-to-one continuous semiconjugacy from $(\Sigma,\sigma)$ to $({\Bbb T}^n,A)$. This construction is natural in the sense that it depends only on the characteristic polynomial of $A$ and, furthermore, it has an arithmetic interpretation.
Hyperbolic geometry was created in the first half of the nineteenth century in the midst of attempts to understand Euclid's axiomatic basis for geometry. It is one type of non-Euclidean geometry, that is, a geometry that discards one of Euclid's axioms. Einstein and Minkowski found in non-Euclidean geometry a geometric basis for the understanding of physical time and space. In the early part of the twentieth century every serious student of mathematics and physics studied non-Euclidean geometry. This has not been true of the mathematicians and physicists of our generation. Nevertheless with the passage of time it has become more and more apparent that the negatively curved geometries, of which hyperbolic non-Euclidean geometry is the prototype, are the generic forms of geometry. They have profound applications to the study of complex variables, to the topology of two- and three-dimensional manifolds, to the study of finitely presented infinite groups, to physics, and to other disparate fields of mathematics. A working knowledge of hyperbolic geometry has become a prerequisite for workers in these fields.
These notes are intended as a relatively quick introduction to hyperbolic geometry. They review the wonderful history of non-Euclidean geometry. They give five different analytic models for and several combinatorial approximations to non-Euclidean geometry by means of which the reader can develop an intuition for the behavior of this geometry. They develop a number of the properties of this geometry that are particularly important in topology and group theory. They indicate some of the fundamental problems being approached by means of non-Euclidean geometry in topology and group theory.
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