Social Media Skits: Reshaping the Entertainment Experience of Broadcast Audience

The emergence of social media has produced diverse changes for broadcast media in the discharge of their entertainment function. While the uses and gratification theory identifies entertainment as one of the needs that motivate the audience to use the media, the technological determinism theory argues that the nature and strength of interaction in the society change as new media technologies evolve. This study is a descriptive and predictive discourse on how social media skits are reshaping audience consumption, participation, expectation, and production of entertainment. As opposed to broadcast experience, the audience engage with social media skits, own them, many times produce them, form relationships around them, demand for new contents, and through their reactions, affect the sustainability of the content providers online. They also redefine entertainment for comedians who release skits to test new comedy materials. These interacting features together reshape the way the audience experience entertainment on social media.

Novel phylogenetic methods reveal that resource-use intensification drives the evolution of “complex” societies

Explaining the rise of large, sedentary populations, with attendant expansions of socio-political hierarchy and labor specialization (collectively referred to as “societal complexity”), is a central problem for social scientists and historians. Adoption of agriculture has often been invoked to explain the rise of complex societies, but archaeological and ethnographic records contradict simple agri-centric models. Rather than a unitary phenomenon, “complexity” may be better understood as a network of interacting features, which in turn have causal relationships with subsistence. Here we use novel comparative methods and a global sample of 186 nonindustrial societies to infer the role of subsistence practices in shaping complexity. We also introduce a phylogenetic method for causal inference that generalizes beyond two binary traits, lifting a major constraint on comparative research. We found that, rather than agriculture alone, a suite of resource-use intensification variables leads to broad increases in technological and social differentiation. Our study provides evidence that resource intensification is a leader, not a follower, in the rise of complex societies worldwide.

Empowering individual trait prediction using interactions for precision medicine

Background One component of precision medicine is to construct prediction models with their predicitve ability as high as possible, e.g. to enable individual risk prediction. In genetic epidemiology, complex diseases like coronary artery disease, rheumatoid arthritis, and type 2 diabetes, have a polygenic basis and a common assumption is that biological and genetic features affect the outcome under consideration via interactions. In the case of omics data, the use of standard approaches such as generalized linear models may be suboptimal and machine learning methods are appealing to make individual predictions. However, most of these algorithms focus mostly on main or marginal effects of the single features in a dataset. On the other hand, the detection of interacting features is an active area of research in the realm of genetic epidemiology. One big class of algorithms to detect interacting features is based on the multifactor dimensionality reduction (MDR). Here, we further develop the model-based MDR (MB-MDR), a powerful extension of the original MDR algorithm, to enable interaction empowered individual prediction. Results Using a comprehensive simulation study we show that our new algorithm (median AUC: 0.66) can use information hidden in interactions and outperforms two other state-of-the-art algorithms, namely the Random Forest (median AUC: 0.54) and Elastic Net (median AUC: 0.50), if interactions are present in a scenario of two pairs of two features having small effects. The performance of these algorithms is comparable if no interactions are present. Further, we show that our new algorithm is applicable to real data by comparing the performance of the three algorithms on a dataset of rheumatoid arthritis cases and healthy controls. As our new algorithm is not only applicable to biological/genetic data but to all datasets with discrete features, it may have practical implications in other research fields where interactions between features have to be considered as well, and we made our method available as an R package (https://github.com/imbs-hl/MBMDRClassifieR). Conclusions The explicit use of interactions between features can improve the prediction performance and thus should be included in further attempts to move precision medicine forward.

Signs Of The Objective Side Of Crime In The Theory Of Criminal Law Belonging To The Romano-Germanic Legal Family: Theoretical And Practical Problems

The objective side of the crime is one of the elements of the corpus delicti and consists of a number of interacting features that together characterize the process of external encroachment on the object of criminal law protection. In addition, proposals and recommendations for further improvement of the criminal legislation of the Republic of Uzbekistan.

Mapping the Imaginative Mind: Charting New Paths Forward

The fields of psychology and neuroscience are in the midst of an explosion of research aimed at illuminating the human imagination—the ability to form thoughts and mental images that stretch beyond what is currently available to the senses. Imaginative thought is proving to be remarkably diverse, capturing the capacity to recall past experiences, consider what lies ahead, and understand other people’s minds, in addition to other forms of creative and spontaneous thinking. In the first part of this article, we introduce an integrative framework that attempts to explain how components of a core brain network facilitate interacting features of imagination that we refer to as the mind’s eye and mind’s mind. We then highlight three emerging research directions that could inform our understanding of how imagination arises and unfolds in everyday life.

Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.

Cracks in Dents: How Can I Use an ILI Robot to Detect Them?

Cracks in dents or linear anomalies interacting with dents are a major pipeline threat. These combined anomalies represent challenges to the Mechanical Engineers that design ILI tools as they need to keep the sensor in an optimal position towards the inner pipe wall. Ultrasonic Crack (UC) tools consist in a sensor plate with a fixed incidence angle that depends on the coupling medium. This plate is then attached to the skids; these are in constant contact with the internal pipe wall. When the tool interacts with a dent, the incidence angle is not optimal; therefore, detection of any interacting feature is compromised. By not having the optimum angles in the pipe wall, the amplitudes from the reflections caused by cracks will be attenuated. Depending on the magnitude of the attenuation, these might be below analysis thresholds meaning that an algorithm and/or analyst will not consider them as relevant signals. Up to this point, detection of interacting features sounds like a “guess “ or “luck”. So, how can we use UC inspection to detect the interacting features? How can operators manage their assets knowing that they have dents but there is an uncertainty if there are interacting features? To answer these questions, a systematic approach had to be used. It consisted of multiple phases where 1.- The mechanical design of the tool was understood, 2.- Simulation campaigns to understand the ultrasonic pulse while interacting with the dent, 3.- Pump tests with artificial features, and 4.- Pump test with real features. All of the data gathered through the different phases allowed the authors to understand the attributes from the features and conditions that influence detection and identification of cracks in dents. This derived in a performance specification stating the truth capabilities to detect interacting features in a dent. These learnings were applied to commercial inspections where the feedback loop is closed with the field verifications.

Graph Representation of 3D CAD Models for Machining Feature Recognition With Deep Learning

In this paper, the application of deep learning methods to the task of machining feature recognition in CAD models is studied. Four contributions are made: 1. An automatic method to generate large datasets of 3D CAD models is proposed, where each model contains multiple machining features with face labels. 2. A concise and informative graph representation for 3D CAD models is presented. This is shown to be applicable to graph neural networks. 3. The graph representation is compared with voxels on their performance of training deep neural networks to segment 3D CAD models. 4. Experiments are also conducted to evaluate the effectiveness of graph-based deep learning for interacting feature recognition. Results show that the proposed graph representation is a more efficient representation of 3D CAD models than voxels for deep learning. It is also shown that graph neural networks can be used to recognize individual features on the model and also identify complex interacting features.