scholarly journals Temporal microstructure of dyadic social behavior during relationship formation in mice

2019 ◽  
Author(s):  
Won Lee ◽  
Jiayi Fu ◽  
Neal Bouwman ◽  
Pam Farago ◽  
James P. Curley
Keyword(s):  
Temporal Dynamics ◽  
Time Window ◽  
Detection Algorithm ◽  
Social Contexts ◽  
Social Partner ◽  
Male Mice ◽  
Behavioral Repertoire ◽  
Relationship Formation ◽  
Quiescence Period ◽  
Phi Coefficient

AbstractUnderstanding the temporal dynamics of how unfamiliar animals establish dominant-subordinate relationships and learn to modify their behavior in response to their social partner in context-appropriate manners is critical in biomedical research concerning social competence. Here we observe and analyze the microstructure of social and non-social behaviors as 21 pairs of outbred CD-1 male mice (Mus Musculus) establish dominant-subordinate relationships during daily 20-minute interaction for five consecutive days. Using Kleinberg burst detection algorithm, we demonstrate aggressive and subordinate interactions occur in bursting patterns followed by quiescence period rather than in uniformly distributed across social interactions. Further, we identify three phases of dominant-subordinate relationship development (pre-, middle-, and post-resolution) by combining phi-coefficient and difference methods used to determine at which bursting event mice resolve dominant-subordinate relationships. Using First Order Markov Chains within individuals we show dominant and subordinate animals establish significantly different behavioral repertoire once they resolve the relationships. In both dominant and subordinate mice, the transitions between investigative and agonistic behavior states are not common. Lastly, we introduce Forward Spike Time Tiling Coefficient, the strength of association between the given behavior of one individual with the target behavior of the other individual within a specified time window. With this method, we describe the likelihood of a mouse responding to a behavior with another behavior differ in pre- and post-resolution phases. The data suggest that subordinate mice learn to exhibit subordinate behavior in response to dominant partner’s behaviors while dominant mice become less likely to show subordinate behaviors in response to their partners’ action. Overall, with the tool we present in this study, the data suggest CD-1 male mice are able to establish dominance relationships and modify their behaviors even to the same social cues under different social contexts competently.

scholarly journals Organizational Practices and Workplace Relationships in Precarious Work: New Survey Evidence

2021 ◽  
Author(s):  
Hana Shepherd
Keyword(s):  
Collective Action ◽  
The United States ◽  
Social Contexts ◽  
Organizational Practices ◽  
Workplace Relationships ◽  
Relationship Formation ◽  
The Social ◽  
Regular Contact ◽  
Retail Workers

Organizational practices are important dimensions of the social contexts that shape relationship formation. In workplaces, the formation of relationships among coworkers are resources for personal outcomes, and they can be channels through which workers might identify common grievances, form workplace solidarity, and engage in collective action. Using a unique dataset of retail workers across the United States, The Shift Project, this paper examines two potential pathways by which organizational practices common in precarious jobs in the retail industry in the U.S. might shape the formation of workplace relationships. I find evidence of the role of both pathways: practices that limit the opportunities for regular contact and practices that negatively impact the conditions of contact among employees are both associated with fewer workplace ties. I discuss the implications of these findings for the study of collective action, and network ecology.

scholarly journals A New Method for Automated Clearcut Disturbance Detection in Mediterranean Coppice Forests Using Landsat Time Series

2020 ◽  
Vol 12 (22) ◽  
pp. 3720 ◽  
Author(s):  
Francesca Giannetti ◽  
Raffaello Pegna ◽  
Saverio Francini ◽  
Ronald E. McRoberts ◽  
Davide Travaglini ◽  
...  
Keyword(s):  
Time Series ◽  
Sustainable Forest Management ◽  
Time Window ◽  
Detection Algorithm ◽  
Forest Disturbances ◽  
Forest Change ◽  
Short Time Window ◽  
Short Time ◽  
Automated Procedures ◽  
Disturbance Detection

A Landsat time series has been recognized as a viable source of information for monitoring and assessing forest disturbances and for continuous reporting on forest dynamics. This study focused on developing automated procedures for detecting disturbances in Mediterranean coppice forests which are characterized by rapid regrowth after a cut. Specifically, new methods specific to Mediterranean coppice forests are needed for mapping clearcut disturbances over time and for estimating related indicators in the context of Sustainable Forest Management and Biodiversity International monitoring frameworks. The aim of this work was to develop a new change detection algorithm for mapping clearcut disturbances in Mediterranean coppice forests with Landsat time series (LTS) using a short time window. Accuracy for the new algorithm, characterized as the Two Thresholds Method (TTM), was evaluated using an independent clearcut reference dataset over a temporal period of the 13 years between 2001 and 2013. TTM was also evaluated against two benchmark approaches: (i) LandTrendr, and (ii) the forest loss category of the Global Forest Change Map. Overall Accuracy for LandTrendr and TTM were greater than 0.94. Meanwhile, smaller accuracies were always obtained for the GFC. In particular, Producer’s Accuracy ranged between 0.45 and 0.84 for TTM and between 0.49 and 0.83 for LT, while for the GFC, PA ranged between 0 and 0.38. User’s Accuracy ranged between 0.86 and 0.96 for TTM and between 0.73 and 0.91 for LT, while for the GFC UA ranged between 0.19 and 1.00. Moreover, to illustrate the utility of TTM for mapping clearcut disturbances in Mediterranean coppice forests, we applied TTM to a Landsat scene that covered almost the entirety of the Tuscany region in Italy.

Dynamic Changes in the Mental Rotation Network Revealed by Pattern Recognition Analysis of fMRI Data

2009 ◽  
Vol 21 (5) ◽  
pp. 890-904 ◽  
Author(s):  
Janaina Mourao-Miranda ◽  
Christine Ecker ◽  
Joao R. Sato ◽  
Michael Brammer
Keyword(s):  
Pattern Recognition ◽  
Functional Connectivity ◽  
Mental Rotation ◽  
Temporal Dynamics ◽  
Parietal Lobe ◽  
Time Window ◽  
Support Vector ◽  
Dynamic Changes ◽  
Angular Disparity ◽  
Spatio Temporal

We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88–99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0° vs. 20°, 0° vs. 60°, and 0° vs. 100°), and the discrimination accuracy is correlated with the difference in angular disparity between the conditions. For the comparison with highest accuracy (0° vs. 100°), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100° condition in relation to the 0° condition (posterior cingulate, frontal, and superior temporal gyrus). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100° condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100° condition.

scholarly journals Rough Estimator Based Asynchronous Distributed Super Points Detection on High Speed Network Edge

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 277
Author(s):  
Jie Xu ◽  
Wei Ding
Keyword(s):  
High Speed ◽  
Time Window ◽  
Detection Algorithm ◽  
Research Topic ◽  
Linear Estimator ◽  
Communication Overhead ◽  
Distributed Environment ◽  
Single Node ◽  
Ip Address ◽  
High Speed Network

Super points detection plays an important role in network research and application. With the increase of network scale, distributed super points detection has become a hot research topic. The key point of super points detection in a multi-node distributed environment is how to reduce communication overhead. Therefore, this paper proposes a three-stage communication algorithm to detect super points in a distributed environment, Rough Estimator based Asynchronous Distributed super points detection algorithm (READ). READ uses a lightweight estimator, the Rough Estimator (RE), which is fast in computation and takes less memory to generate candidate super points. Meanwhile, the famous Linear Estimator (LE) is applied to accurately estimate the cardinality of each candidate super point, so as to detect the super point correctly. In READ, each node scans IP address pairs asynchronously. When reaching the time window boundary, READ starts three-stage communication to detect the super point. This paper proves that the accuracy of READ in a distributed environment is no less than that in the single-node environment. Four groups of 10 Gb/s and 40 Gb/s real-world high-speed network traffic are used to test READ. The experimental results show that READ not only has high accuracy in a distributed environment, but also has less than 5% of communication burden compared with existing algorithms.

scholarly journals An Application of ARIMA Model to Forecast the Dynamics of COVID-19 Epidemic in India

2021 ◽  
pp. 097215092098865
Author(s):  
Rupinder Katoch ◽  
Arpit Sidhu
Keyword(s):  
Temporal Dynamics ◽  
Time Window ◽  
Moving Average ◽  
Arima Model ◽  
National Level ◽  
Epidemiological Data ◽  
Final Size ◽  
The Past ◽  
The Future ◽  
Near Future

The swiftly growing and overwhelming epidemic in India has intensified the question: What will the trend and magnitude of impact of the novel coronavirus disease 2019 (COVID-19) be in India in the near future? To answer the present question, the study requires ample historical data to make an accurate forecast of the blowout of expected confirmed cases. All at once, no prediction can be certain as the past seldom reiterates itself in the future likewise. Besides, forecasts are influenced by a number of factors like reliability of the data and psychological factors like perception and reaction of the people to the hazards arising from the epidemic. The present study presents a simple but powerful and objective, that is, autoregressive integrated moving average (ARIMA) approach, to analyse the temporal dynamics of the COVID-19 outbreak in India in the time window 30 January 2020 to 16 September 2020 and to predict the final size and trend of the epidemic over the period after 16 September 2020 with Indian epidemiological data at national and state levels. With the assumption that the data that have been used are reliable and that the future will continue to track the same outline as in the past, underlying forecasts based on ARIMA model suggest an unending increase in the number of confirmed COVID-19 cases in India in the near future. The present article suggests varying epidemic’s inflection point and final size for underlying states and for the mainland, India. The final size at national level is expected to reach 25,669,294 in the next 230 days, with infection point that can be expected to be projected only on 23 April 2021. The study has enormous potential to plan and make decisions to control the further spread of epidemic in India and provides objective forecasts for the confirmed cases of COVID-19 in the coming days corresponding to the respective COVID periods of the underlying regions.

Anxiety Impedes Adaptive Social Learning Under Uncertainty

2020 ◽  
Vol 31 (5) ◽  
pp. 592-603 ◽  
Author(s):  
Amrita Lamba ◽  
Michael J. Frank ◽  
Oriel FeldmanHall
Keyword(s):  
Social Learning ◽  
Healthy Subjects ◽  
Selective Reduction ◽  
Social Contexts ◽  
Trust Game ◽  
Social Partner ◽  
Social Events ◽  
Dynamic Trust ◽  
Test Population ◽  
Social Uncertainty

Very little is known about how individuals learn under uncertainty when other people are involved. We propose that humans are particularly tuned to social uncertainty, which is especially noisy and ambiguous. Individuals exhibiting less tolerance for uncertainty, such as those with anxiety, may have greater difficulty learning in uncertain social contexts and therefore provide an ideal test population to probe learning dynamics under uncertainty. Using a dynamic trust game and a matched nonsocial task, we found that healthy subjects ( n = 257) were particularly good at learning under negative social uncertainty, swiftly figuring out when to stop investing in an exploitative social partner. In contrast, subjects with anxiety ( n = 97) overinvested in exploitative partners. Computational modeling attributed this pattern to a selective reduction in learning from negative social events and a failure to enhance learning as uncertainty rises—two mechanisms that likely facilitate adaptive social choice.

A Speech Signal Logarithmic Spectrum Amplitude Envelope Algorithm Analysis

2013 ◽  
Vol 756-759 ◽  
pp. 2072-2075
Author(s):  
Ying Ma ◽  
Feng Wei
Keyword(s):  
Speech Signal ◽  
Time Window ◽  
Characteristic Parameter ◽  
Detection Algorithm ◽  
Algorithm Analysis ◽  
Amplitude Envelope ◽  
Speech Signal Processing ◽  
Short Time Window ◽  
Pitch Information ◽  
Spectrum Amplitude

Cepstrum method is a traditional characteristic parameter detection algorithm in the modern in the speech signal processing . it is one of the speech signal using the cepstrum features, to detect the representation glottis incentive cycle pitch information.In order to eliminate the influence of the fundamental frequency harmonic, we utilize the homomorphism solution volume technology, getting smooth spectral envelope.While the artical analyses cepstrum from out of the signal deleted to the short time window , detect speech signal pitch information;Then the signal of Fourier transform, take the logarithmic spectrum amplitude envelope, to analyze the speech signal formant parameters. The speech signal characteristic parameters have higher accuracy than ones from the traditional cepstrum method.

scholarly journals Male mice song syntax depends on social contexts and influences female preferences

2015 ◽  
Vol 9 ◽  
Author(s):  
Jonathan Chabout ◽  
Abhra Sarkar ◽  
David B. Dunson ◽  
Erich D. Jarvis
Keyword(s):  
Social Contexts ◽  
Male Mice ◽  
Female Preferences ◽  
Song Syntax

scholarly journals Detecting Dynamic States of Temporal Networks Using Connection Series Tensors

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Shun Cao ◽  
Hiroki Sayama
Keyword(s):  
Temporal Dynamics ◽  
Time Window ◽  
Time Windows ◽  
Social Contact ◽  
Network Data ◽  
Temporal Network ◽  
Temporal Networks ◽  
Network Aggregation ◽  
System States ◽  
Dynamic States

Many temporal networks exhibit multiple system states, such as weekday and weekend patterns in social contact networks. The detection of such distinct states in temporal network data has recently been studied as it helps reveal underlying dynamical processes. A commonly used method is network aggregation over a time window, which aggregates a subsequence of multiple network snapshots into one static network. This method, however, necessarily discards temporal dynamics within the time window. Here we propose a new method for detecting dynamic states in temporal networks using connection series (i.e., time series of connection status) between nodes. Our method consists of the construction of connection series tensors over nonoverlapping time windows, similarity measurement between these tensors, and community detection in the similarity network of those time windows. Experiments with empirical temporal network data demonstrated that our method outperformed the conventional approach using simple network aggregation in revealing interpretable system states. In addition, our method allows users to analyze hierarchical temporal structures and to uncover dynamic states at different spatial/temporal resolutions.

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