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Adapting to extremes: an integrative approach to exploring adaption in a model species along an aridity gradient
Get PDFIndividual Rationality in Topological Distance Games is Surprisingly Hard
Get PDFIn the recently introduced topological distance games, strategic agents need to be assigned to a subset of vertices of a topology. In the assignment, the utility of an agent depends on both the agent's inherent utilities for other agents and its distance from them on the topology. We study the computational complexity of finding individually rational outcomes; this notion is widely assumed to be the very minimal stability requirement and requires that the utility of every agent in a solution is non-negative. We perform a comprehensive study of the problem's complexity, and we prove that even in very basic cases, deciding whether an individually rational solution exists is intractable. To reach at least some tractability, one needs to combine multiple restrictions of the input instance, including the number of agents and the topology and the influence of distant agents on the utility
Truthful Interval Covering
Get PDFWe initiate the study of a novel problem in mechanism design without money, which we term Truthful Interval Covering (TIC). An instance of TIC consists of a set of agents each associated with an individual interval on a line, and the objective is to decide where to place a covering interval to minimize the total social or egalitarian cost of the agents, which is determined by the intersection of this interval with their individual ones. This fundamental problem can model situations of provisioning a public good, such as the use of power generators to prevent or mitigate load shedding in developing countries. In the strategic version of the problem, the agents wish to minimize their individual costs, and might misreport the position and/or length of their intervals to achieve that. Our goal is to design truthful mechanisms to prevent such strategic misreports and achieve good approximations to the best possible social or egalitarian cost. We consider the fundamental setting of known intervals with equal lengths and provide tight bounds on the approximation ratios achieved by truthful deterministic mechanisms. For the social cost, we also design a randomized truthful mechanism that outperforms all possible deterministic ones. Finally, we highlight a plethora of natural extensions of our model for future work, as well as some natural limitations of those settings
Measuring user response to attention guidance using the Integrated Cognitive User assistance system
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Kinematic Evolution of the Huincul High, Neuquén Basin (Argentina) - sequential restoration and analysis of inversion structures
Get PDFPsychological therapy for eating disorders: developing a grounded theory model of partners' experiences
Get PDFThe systematic review explores the lived experience of caring for someone with an eating disorder. The empirical study presents a grounded theory model of people's experiences of supporting a partner through psychological therapy for an eating disorder, based on interview data. The impact of the work is discussed, as well as plans for its dissemination
Speech Emotion Recognition Using Convolutional Recurrent Neural Networks
Get PDFResearch suggests that various machine learning and deep learning models can be used for implementation of speech emotion recognition (SER) using different acoustic properties, such as voice, pitch, loudness, intensity, Mel-frequency cepstral coefficients, and spectral characteristics. This chapter conducts speech emotion recognition using deep neural networks, such as long short-term memory, gated recurrent units, and convolutional recurrent neural network. These different acoustic features are investigated in our studies owing to their great efficiency in representing key events in audio representations. A cross-validation evaluation has been conducted with the data from different actors for model evaluation to check the robustness of each proposed network. The proposed models show impressive performances in comparison with those of existing state-of-the-art methods for evaluating several speech emotion datasets
“Unveiling the Invisible”:Deep Learning-based Semantic Segmentation for Analyzing Activity Patterns
Get PDFThe ubiquity of internet-enabled devices has led to a rapid increase in the use of connected cameras for real-time monitoring, creating a high demand for (automated) visual data analytics across various industries. The prospect of automating visual data analysis to drive positive change involves extracting actionable insights from data that will inform decision-making processes, improving efficiency, and contributing to evidence-based strategies across diverse applications and industries. This research explores and compares well-known semantic segmentation models such as DeepLabV3+ and UNet, determining the best-suited for use in a visual analytics and scene understanding, culminating in a proof of concept program capable of automating video analysis, plotting detections, average trajectories, and identifying outliers
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