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Website measures of engagement captured from millions of users, such as in-page scrolling and viewport position, can provide deeper understanding of attention than possible with simpler measures, such as dwell time. Using data from 1.2M news reading sessions, we examine and evaluate three increasingly sophisticated models of sub-document attention computed from viewport time, the time a page component is visible on the user display. Our modeling incorporates prior eye-tracking knowledge about onscreen reading, and we validate it by showing how, when used to estimate user reading rate, it aligns with known empirical measures. We then show how our models reveal an interaction between article topic and attention to page elements. Our approach supports refined large-scale measurement of user engagement at a level previously available only from lab-based eye-tracking studies.

Gene interactions are acknowledged to be a likely source of missing heritability in large-scale genetic studies of complex neurological phenotypes. However, involvement of rare variants, de novo mutations, genetic lesions that are not easily detected with commonly used methods and epigenetic factors also are possible explanations. We used a laboratory evolution study to investigate the modulatory effects of background genetic variation on the phenotypic effect size of a null mutation with known impact on olfactory learning. To accomplish this, we first established a population that contained variation at just 23 loci and used selection to evolve suppression of the learning defect seen with null mutations in the rutabaga adenylyl cyclase. We thus biased the system to favor relatively simplified outcomes by choosing a Mendelian trait and by restricting the genetic variation segregating in the population. This experimental design also assures that the causal effects are among the known 23 segregating loci. We observe a robust response to selection that requires the presence of the 23 variants. Analyses of the underlying genotypes showed that interactions between more than two loci are likely to be involved in explaining the selection response, with implications for the missing heritability problem.

The discovery of shared behavioral processes across phyla is a significant step in the establishment of a comparative study of behavior. We use immobility as an origin and reference for the measurement of fly locomotor behavior; speed, walking direction and trunk orientation as the degrees of freedom shaping this behavior; and cocaine as the parameter inducing progressive transitions in and out of immobility. We characterize and quantify the generative rules that shape Drosophila locomotor behavior, bringing about a gradual buildup of kinematic degrees of freedom during the transition from immobility to normal behavior, and the opposite narrowing down into immobility. Transitions into immobility unfold via sequential enhancement and then elimination of translation, curvature and finally rotation. Transitions out of immobility unfold by progressive addition of these degrees of freedom in the opposite order. The same generative rules have been found in vertebrate locomotor behavior in several contexts (pharmacological manipulations, ontogeny, social interactions) involving transitions in-and-out of immobility. Recent claims for deep homology between arthropod central complex and vertebrate basal ganglia provide an opportunity to examine whether the rules we report also share common descent. Our approach prompts the discovery of behavioral homologies, contributing to the elusive problem of behavioral evolution., This study was funded by the Portuguese Foundation for Science and Technology (FCT grant No SFRH/BPD/97544/2013 to AGM) and by the Israel Science Foundation (grant No 760/08 to IG and YB).

A common set of signal features measurable by a basic sound level meter are analyzed, and the quality of information carried in subsets of these features are examined for their ability to discriminate military blast and non-blast sounds. The analysis is based on over 120 000 human classified signals compiled from seven different datasets. The study implements linear and Gaussian radial basis function (RBF) support vector machines (SVM) to classify blast sounds. Using the orthogonal centroid dimension reduction technique, intuition is developed about the distribution of blast and non-blast feature vectors in high dimensional space. Recursive feature elimination (SVM-RFE) is then used to eliminate features containing redundant information and rank features according to their ability to separate blasts from non-blasts. Finally, the accuracy of the linear and RBF SVM classifiers is listed for each of the experiments in the dataset, and the weights are given for the linear SVM classifier.

Variability in received sound levels were investigated at distances ranging from 4 m to 16 km from a typical blast source in two locations with different climates and terrain. Four experiments were conducted, two in a temperate climate with a hilly terrain and two in a desert climate with a flat terrain, under a variety of meteorological conditions. Sound levels were recorded in three different directions around the source during the summer and winter seasons in each location. Testing occurred over the course of several days for each experiment during all 24 h of the day, and meteorological data were gathered throughout each experiment. The peak levels (L(Pk)), C-weighted sound exposure levels (CSEL), and spectral characteristics of the received sound pressure levels were analyzed. The results show high variability in L(Pk) and CSEL at distances beyond 2 km from the source for each experiment, which was not clearly explained by the time of day the blasts occurred. Also, as expected, higher frequency energy is attenuated more drastically than the lower frequency energy as the distance from the source increases. These data serve as a reference for long-distance blast sound propagation.

We developed a novel assay to examine social interactions in Drosophila and, as a first attempt, apply it here at examining the behavior of Drosophila Fragile X Mental Retardation gene (dfmr1) mutants. Fragile X syndrome is the most common cause of single gene intellectual disability (ID) and is frequently associated with autism. Our results suggest that dfmr1 mutants are less active than wild-type flies and interact with each other less often. In addition, mutants for one allele of dfmr1, dfmr1(B55), are more likely to come in close contact with a wild-type fly than another dfmr1(B55) mutant. Our results raise the possibility of defective social expression with preserved receptive abilities. We further suggest that the assay may be applied in a general strategy of examining endophenoypes of complex human neurological disorders in Drosophila, and specifically in order to understand the genetic basis of social interaction defects linked with ID.

This article presents two instances in which multimedia systems and processing have elucidated animal behavior and have been central in developing quantitative descriptions. These examples demonstrate multimedia systems' utility and necessity in developing a complete phenotypic description. We hope that this article will spur interest in this subject in the multimedia community, so more advanced processing techniques will enter the field of quantitative neuroethology. You might have noticed that in our two examples, there was nothing very multimodal about the media techniques used. Both of these systems are transparently unimodal. This speaks to the limited crossover between the multimedia community and the behavioral neuroscientists (or neuroethologists). These examples did show, however, that the neuroscientific community can benefit greatly from incorporating multimedia techniques into their experiments and data analysis. As the walls between these disciplines begin to fall, experimental setups that are truly multimedia will likely appear. Such systems will allow complete phenotypic descriptions of animals in ethologically relevant settings, along with methods for analyzing, manipulating, annotating, and storing the resulting data. Combining these phenotypic descriptions with the corresponding genetic and neural network properties will facilitate the connection of these organization levels and lead to a more thorough understanding of brain functioning.

The discovery of shared behavioral processes across phyla is an essential step in the establishment of a comparative study of behavior. We use immobility as an origin and reference for the measurement of locomotor behavior; speed, direction of walking and direction of facing as the three degrees of freedom shaping fly locomotor behavior; and cocaine as the parameter inducing a progressive transition in and out of immobility. In this way we expose and quantify the generative rules that shape part of fruit fly locomotor behavior, bringing about a gradual buildup of freedom during the transition from immobility to normal behavior and a precisely opposite narrowing down during the transition into immobility. During buildup the fly exhibits enhancement and then reduction to normal values of movement along each degree of freedom: first, body rotation in the horizontal plane, then path curvature and then speed of translation. Transition into immobility unfolds by narrowing down of the repertoire in the opposite sequential order, showing reciprocal relations during both buildup and narrowing down. The same generative rules apply to vertebrate locomotor behavior in a variety of contexts involving transition out and into immobility. Recent claims for deep homology between the arthropod central complex and the vertebrate basal ganglia provide an opportunity to examine whether the generative rules we discovered also share common descent. Neurochemical processes mediating the buildup of locomotor behavior in vertebrates could guide the search for equivalent processes in arthropods. The measurement methodology we use prompts the discovery of candidate behavioral homologies.

In this study we characterize the coordination between the direction a fruit-fly walks and the direction it faces, as well as offer a methodology for isolating and validating key variables with which we phenotype fly locomotor behavior. Our fundamental finding is that the angular interval between the direction a fly walks and the direction it faces is actively managed in intact animals and modulated in a patterned way with drugs. This interval is small in intact flies, larger with alcohol and much larger with cocaine. The dynamics of this interval generates six coordinative modes that flow smoothly into each other. Under alcohol and much more so under cocaine, straight path modes dwindle and modes involving rotation proliferate. To obtain these results we perform high content analysis of video-tracked open field locomotor behavior. Presently there is a gap between the quality of descriptions of insect behaviors that unfold in circumscribed situations, and descriptions that unfold in extended time and space. While the first describe the coordination between low-level kinematic variables, the second quantify cumulative measures and subjectively defined behavior patterns. Here we reduce this gap by phenotyping extended locomotor behavior in terms of the coordination between low-level kinematic variables, which we quantify, combining into a single field two disparate fields, that of high content phenotyping and that of locomotor coordination. This will allow the study of the genes/brain/locomotor coordination interface in genetically engineered and pharmacologically manipulated animal models of human diseases.

These days, anyone with a laptop, Python, and a free afternoon can build an intelligent machine. In fact, if you have a bunch of data, go ahead and throw it into a deep neural net: your worries will be over! Machine learning will save us all! Unfortunately, those of us who spend our days fighting with these systems know that this is far from the truth. As anything created by humans, these algorithms have limitations, and there is no algorithmic substitute for careful thought. The gory details of techniques like Support Vector Machines, Neural Networks, and K-means clustering can be found in innumerable blog posts, texts, and online courses; we won’t discuss the details here. Instead, this talk will focus on the conceptual and mathematical framework under which all machine learning algorithms fall. We’ll briefly discuss the workings of a few of the most popular modern techniques (#DeepLearning, anyone?) and how they fit into this framework. More importantly, we’ll discuss some often-overlooked consideratio...

Blast noise from military installations often has a negative impact on the quality of life of residents living in nearby communities. This, in turn, negatively impacts the military’s testing & training capabilities due to restrictions, curfews, or range closures enacted to address noise complaints. In order to more directly manage noise around military installations, accurate noise monitoring around bases has become a necessity. Although most noise monitors are simple sound level meters, more recent ones are capable of discerning blasts from ambient noise with some success. Investigators at the University of Pittsburgh (Pitt) developed a more advanced noise classifier that can discern between wind, aircraft, and blast noise, while simultaneously lowering the measurement threshold. Here, more recent work between Pitt and the US Army Engineer Research and Development Center will be presented from the development of a more advanced classifier that identifies additional classes of noise such as machine gun fire, vehicles, and electronic noise. Additional signal metrics were explored given the increased complexity of the classifier. By broadening the types of noise the system can accurately classify and increasing the number of metrics, a new system was developed with increased blast noise accuracy, decreased number of missed events, and significantly fewer false positives.

Current blast noise assessment procedures at military installations in the United States do not fully meet the military s noise management needs; military blast noise sometimes disturbs surrounding communities, resulting in legal actions against US military installations. Specifically, current procedures do not accurately capture the way humans respond to blast events, and do not adequately account for the level, number, timing, and spatial variability of blast noise events. This work constructed and administered the General Community Survey (GCS) within SERDP Project WP-1546 at the first of three military installations to determine how blast noise levels affect general community annoyance and how the community reaction changes over time in response to a dynamic blast noise environment. The results indicate that, while blast noise was the most annoying noise source around this installation, current blast noise assessment metrics are weakly correlated with community annoyance, and a large percentage of the study population were highly annoyed at relatively low C-weighted Day-Night blast noise levels. Current findings highlight the importance of capturing temporal and spatial variation of the both stimulus and response, and also of non-acoustical factors such as habituation and vibration.

BACKGROUND: Obtaining a complete phenotypic characterization of a freely moving organism is a difficult task, yet such a description is desired in many neuroethological studies. Many metrics currently used in the literature to describe locomotor and exploratory behavior are typically based on average quantities or subjectively chosen spatial and temporal thresholds. All of these measures are relatively coarse-grained in the time domain. It is advantageous, however, to employ metrics based on the entire trajectory that an organism takes while exploring its environment. METHODOLOGY/PRINCIPAL FINDINGS: To characterize the locomotor behavior of Drosophila melanogaster, we used a video tracking system to record the trajectory of a single fly walking in a circular open field arena. The fly was tracked for two hours. Here, we present techniques with which to analyze the motion of the fly in this paradigm, and we discuss the methods of calculation. The measures we introduce are based on spatial and temporal probability distributions and utilize the entire time-series trajectory of the fly, thus emphasizing the dynamic nature of locomotor behavior. Marginal and joint probability distributions of speed, position, segment duration, path curvature, and reorientation angle are examined and related to the observed behavior. CONCLUSIONS/SIGNIFICANCE: The measures discussed in this paper provide a detailed profile of the behavior of a single fly and highlight the interaction of the fly with the environment. Such measures may serve as useful tools in any behavioral study in which the movement of a fly is an important variable and can be incorporated easily into many setups, facilitating high-throughput phenotypic characterization.

Unattended and continuously running environmental noise monitoring systems can capture an intractable amount of data. The signals captured can include a multitude of sources (e.g., wind noise and anthropogenic noise sources) in addition to the environmental noise sources of interest (e.g., aircraft, vehicles, trains, and military weapons). In this presentation, we explore the use of machine-learning methods to effectively isolate and identify environmental noise sources captured on such a noise monitoring system. Specifically, we consider the use of both unsupervised (e.g., principle components analysis, clustering methods, and deep belief networks) and supervised (e.g., logistic regression, support vector machines, and neural networks) pattern-learning methods to derive the features of interest and classify the signals based on the obtained features. The generalization performance of each method is assessed using a dataset of over 120,000 human classified signals, and the strengths and weaknesses of each approach are discussed.

Evaluating the effectiveness of outdoor sound propagation assessment schemes using experimentally collected data is a complex task. Specifically, when the acoustic data collected assume non-Gaussian distributions with differing sample sizes per assigned class, traditional parametric statistical techniques may not be used. As an alternative, this research introduces an original cost function to evaluate and compare various methods aimed at defining acoustic propagation classes based on meteorological measurements. Class assignments in each scheme are based on the atmospheric stability, the strength of the vertical effective sound speed gradient, and the vertical effective sound speed profile. The acoustic data included in this analysis were generated by a high-energy impulsive source and gathered at source-to-receiver distances of 1, 2, 4, and 8 km. The results indicate that the assessment scheme based on the strength of the effective sound speed gradient most effectively classifies the peak level sound pr...

Outdoor sound propagation is highly dependent upon meteorological conditions. While this, of course, is a trivial statement, predicting sound levels based on meteorology is not. This is especially true for signals that propagate many kilometers, as is the case for those generated by high-energy impulsive sources such as explosions and heavy weaponry; waves have ample opportunity for refraction by and scattering from local atmospheric features along the entire propagation path. The range of received blast levels at distances greater than 2 km can span nearly 50 dB, depending on weather conditions. Using a statistical learning method known as a Random Forest, we demonstrate the prediction of levels from simple meteorological measurements in the face of this extreme variability. With simple, spatially sparse meteorological data, the model can predict levels to within 3 dB at 2 km and 5 dB at 15 km. The results presented here suggest that as more data are acquired through continuous noise monitoring programs, physics-blind, data-driven statistical models have the potential to supplant computationally intensive propagation models for noise prediction. Caveats and cautions when using these types of machine learning methods will also be discussed.

Periodic acoustic waveguides allow the propagation of only certain bands of frequencies, thus making them very effective acoustic filters. The particular distribution of these bands depends critically on the waveguide geometry. If the variation of the waveguide walls happens to be sinusoidal, then the waveguide preferentially reflects only the frequency which matches the Bragg condition for complete backscatter. The reflection of a single frequency in the sinusoidal waveguide seems to contrast the case of a more general periodic structure, where numerous bands of frequencies are disallowed. In this work, the relationship between the sinusoidal waveguide and an arbitrary periodic waveguide is studied using a transmission line model. Inspection of the input reflection coefficient as a function of frequency yields striking similarities to the Fourier transform of the wall variation, implying that a waveguide whose walls are defined by an arbitrary periodic function independently Bragg reflects the individual...

It is common for residents living on and around military installations to be exposed to a significant amount of high amplitude impulsive noise, primarily from large weaponry and other blast noise producing sources. Yet in comparison to transportation noise, there have been relatively few studies of how communities and individuals respond to this type of noise. This presentation will report the latest findings from recent human response to blast noise studies conducted at three military installations. Across all sites, blast noise has been found to be the most annoying noise source, despite the fact that a large percentage of respondents reported that their neighborhood was a good or excellent place to live. It has also been found that each community and individual has a unique tolerance to blast noise. Furthermore, individuals use a different and finite portion of the response scale, suggesting that the current methodology of fixating on the percent of the population that is highly annoyed may inadvertently be discarding useful response information. Comparisons between respondents living on- and off-post within and between study sites will be made, with special emphasis placed on differences between the community tolerance level and the community tolerance spread for each site.

Temperature and wind effects on outdoor sound propagation have been well-studied in numerous theoretical investigations, leading to a number of commonly held beliefs about how weather affects outdoor propagation. For example, louder sound levels are expected when downwind propagation and temperature inversions are present, whereas lower levels are expected when upwind propagation and temperature lapses exist. However, the validity of such relationships has not been rigorously tested in the field for various terrains at long distances from an outdoor source. One is justified in questioning the validity of these relationships due to the difficulty of adequately modeling the dynamic atmosphere along the entire propagation path. This study examines some commonly held notions of outdoor sound propagation by experimentally investigating the effects of temperature gradients and wind speed/direction on sound propagation at long distances from a typical impulsive source. Temperature and wind conditions measured ne...

Utilizing explosives to destroy questionable munitions is a standard procedure in the military. It is critical to have teams trained to perform these activities safely. Often the training involves practice in setting up and detonating relatively small charges of explosives, but these activities can cause annoyance in surrounding civilian populations. A study was performed at one military installation to determine best practices for managing the noise generated by these activities. Three methods were investigated: burying charges with sandbags, covering charges with a rubber blast mat, and spraying water over the charges during detonation. Acoustic and seismic measurements were performed at several distances between 4 and 2500 m in two directions to investigate the relative effectiveness of each method. The study found that covering the charges with sandbags provided a reduction in noise levels of as much as 15 dB in the far field with minimal impact on the training. Use of sandbags was therefore superior ...

As a result of suburban sprawl, the number of people living near military installations is drastically increasing. Coupled with an escalation of military activities and preparedness, the potential for noise generated by an installation to impact the surrounding communities has grown, especially for large amplitude impulsive events such as those generated during artillery training exercises. To assess the effect of blast noise on individuals living near installations, a large scale in situ study has been performed. The homes of study participants were instrumented and outdoor/indoor blast signature pairs of routine installation activities were captured over the course of 1 year. Participants filled out short questionnaires whenever they heard blast noise events. Measurements of single events at subjects’ homes along with their responses present unique data with which to investigate the human response to blast noise on an event‐by‐event basis. In this presentation, the characteristics of the noise typically...

Quantifiying animal behavior is a challenge at the forefront of research in neuroscience. This type of objective description is essential for proper interpretation of results, ensures reproducibility of experiments across laboratories, and presents the researcher with a more complete picture of the genotype-phenotype relationship in the context of the nervous system. Because high-throughput experimental setups increasingly require automated acquisition and analysis of long series of behavioral data, multimedia systems are becoming a vital part of the behavioral neuroscientist’s toolkit. In this article we present two examples in which audio and video acquisition and processing have been used to quantify animal behavior, ranging from mammal locomotor activity to birdsong.

The unique characteristics of wave propagation through periodic acoustic structures have been well known for many years. These structures allow only certain bands of frequencies to propagate through the system, resulting in a distinct filter response. The purpose of this work is to investigate the properties of a periodic waveguide whose walls vary sinusoidally along its length. The waveguide is modeled using a transmission line/transfer matrix approach to obtain the power reflection and transmission coefficients at the waveguide input. Typically in the literature, waveguides with distinct periodic scatterers or abrupt area changes have been examined, and a distinct band structure is evident. It is shown that for small amplitude wall variations of the sinusoidal waveguide, the system denies the transmission of only one frequency. This frequency fits the condition for Bragg backscatter, a phenomenon that is analogous to what is observed in the acousto‐optic (AO) effect. As the amplitude of the wall variations increase, however, a band structure similar to what is expected for periodic systems begins to arise. [Work supported by FRA.]