Phosphorus Budget of the Hyrum Reservoir - Little Bear River System

Phosphorus concentrations in the water of the Hyrum Reservoir - Little Dear River Watershed were determined by collecting 12 samples every two weeks over a seven month period and analyzing them for dissolved orthophosphate, total dissolved phosphorus, and total unfiltered phosphorus. The concentrations obtained were used in conjunction with a water budget to determine a phosphorus budget. Apparent major inputs of phosphorus to the reservoir included a trout farm and runoff from agricultural land in the watershed. Statistical analyses of the data were made to determine what effect precipitation, streamflow, and mileage downstream had on the concentrations of phosphorus in the watershed

Using the Responsible Suicide Reporting Model to increase adherence to global media reporting guidelines

Numerous guidelines on responsible reporting of suicide are available to journalists globally, offering advice on best practice regarding approaches and suitability of content. Whilst their advice is compelling and legitimate, their use is uneven at best. With a suicide death every 40 seconds worldwide, it is imperative journalists understand and recognise the best ethical practices in order to report suicide responsibly. To address these shortcomings, the authors present a model for responsible suicide reporting (RSR) that is grounded in news-work and embeds media reporting guidelines within journalistic storytelling practices. The RSR model consists of a typology of suicide narratives and ‘othering’, ethical rules and a standard of moderation. Methodologically, these typologies emerged from analysis of 159 suicide news stories published in 2018–19, with particular focus on adherence and non-adherence to global media reporting guidelines. We posit through the process of producing stories using the RSR model, journalists should interact more effectively with critical risk factors for example, stigmatisation, copycat effects, harmful speculation, highlighted by media reporting guidelines

The Russian Bride’s Attire: A Journey Through Art and Consciousness

This paper addresses the triad of, creativity, consciousness and culture. How the viewer “sees” the painting is influenced by consciousness and the surrounding culture – creativity is the creative process of the artist. Art is fluid in the sense that viewers see it differently at different times in their lives. The creative process experienced by the artist is the genesis of the painting. However, the art takes on a life of it’s own for every viewer. The definition of consciousness in this paper is based on the awareness and environment of the viewer

Representation of Lexical Form: Evidence From Studies of Sublexical Ambiguity

The authors examined the role of intermediate, sublexical representations in spoken word perception. In particular, they tested whether flaps, which are neutralized allophones of intervocalic /t/s and /d/s, map onto their underlying phonemic counterparts. In 2 shadowing tasks, the authors found that flaps primed their carefully articulated counterparts, and vice versa. Because none of the flapped stimuli were lexically ambiguous (e.g., between rater and raider), these results provide evidence that such priming is sublexically mediated. Therefore, the current study provides further insights into when underlying form-based representations are activated during spoken word processing. In particular, the authors argue that phonological ambiguity, inherent in their flapped stimuli, is one of the conditions leading to the activation of underlying representations

Extracting topological features from dynamical measures in networks of Kuramoto oscillators

The Kuramoto model for an ensemble of coupled oscillators provides a paradigmatic example of non-equilibrium transitions between an incoherent and a synchronized state. Here we analyze populations of almost identical oscillators in arbitrary interaction networks. Our aim is to extract topological features of the connectivity pattern from purely dynamical measures, based on the fact that in a heterogeneous network the global dynamics is not only affected by the distribution of the natural frequencies, but also by the location of the different values. In order to perform a quantitative study we focused on a very simple frequency distribution considering that all the frequencies are equal but one, that of the pacemaker node. We then analyze the dynamical behavior of the system at the transition point and slightly above it, as well as very far from the critical point, when it is in a highly incoherent state. The gathered topological information ranges from local features, such as the single node connectivity, to the hierarchical structure of functional clusters, and even to the entire adjacency matrix.Comment: 11 pages, 10 figure

Sequence effects in the categorization of tones varying in frequency

In contrast to exemplar and decision-bound categorization models, the memory and contrast models described here do not assume that long-term representations of stimulus magnitudes are available. Instead, stimuli are assumed to be categorized using only their differences from a few recent stimuli. To test this alternative, the authors examined sequential effects in a binary categorization of 10 tones varying in frequency. Stimuli up to 2 trials back in the sequence had a significant effect on the response to the current stimulus. The effects of previous stimuli interacted with one another. A memory and contrast model, according to which only ordinal information about the differences between the current stimulus and recent preceding stimuli is used, best accounted for these dat

Measurement Invariance, Entropy, and Probability

We show that the natural scaling of measurement for a particular problem defines the most likely probability distribution of observations taken from that measurement scale. Our approach extends the method of maximum entropy to use measurement scale as a type of information constraint. We argue that a very common measurement scale is linear at small magnitudes grading into logarithmic at large magnitudes, leading to observations that often follow Student's probability distribution which has a Gaussian shape for small fluctuations from the mean and a power law shape for large fluctuations from the mean. An inverse scaling often arises in which measures naturally grade from logarithmic to linear as one moves from small to large magnitudes, leading to observations that often follow a gamma probability distribution. A gamma distribution has a power law shape for small magnitudes and an exponential shape for large magnitudes. The two measurement scales are natural inverses connected by the Laplace integral transform. This inversion connects the two major scaling patterns commonly found in nature. We also show that superstatistics is a special case of an integral transform, and thus can be understood as a particular way in which to change the scale of measurement. Incorporating information about measurement scale into maximum entropy provides a general approach to the relations between measurement, information and probability

Investigation of inverse design of multilayer thin-films with conditional invertible Neural Networks

The task of designing optical multilayer thin-films regarding a given target is currently solved using gradient-based optimization in conjunction with methods that can introduce additional thin-film layers. Recently, Deep Learning and Reinforcement Learning have been been introduced to the task of designing thin-films with great success, however a trained network is usually only able to become proficient for a single target and must be retrained if the optical targets are varied. In this work, we apply conditional Invertible Neural Networks (cINN) to inversely designing multilayer thin-films given an optical target. Since the cINN learns the energy landscape of all thin-film configurations within the training dataset, we show that cINNs can generate a stochastic ensemble of proposals for thin-film configurations that that are reasonably close to the desired target depending only on random variables. By refining the proposed configurations further by a local optimization, we show that the generated thin-films reach the target with significantly greater precision than comparable state-of-the art approaches. Furthermore, we tested the generative capabilities on samples which are outside the training data distribution and found that the cINN was able to predict thin-films for out-of-distribution targets, too. The results suggest that in order to improve the generative design of thin-films, it is instructive to use established and new machine learning methods in conjunction in order to obtain the most favorable results