How emotion is experienced and expressed in multiple cultures: a large-scale experiment

Core to understanding emotion are subjective experiences and their embodiment in facial behavior. Past studies have focused on six emotions and prototypical facial poses, reflecting limitations in scale and narrow assumptions about emotion. We examine 45,231 reactions to 2,185 evocative videos, largely in North America, Europe, and Japan, collecting participants’ self-reported experiences in English or Japanese and manual/automated annotations of facial movement. We uncover 21 dimensions of emotion underlying experiences reported across languages. Facial expressions predict at least 12 dimensions of experience, despite individual variability. We also identify culture-specific display tendencies—many facial movements differ in intensity in Japan compared to the U.S./Canada and Europe, but represent similar experiences. These results reveal how people actually experience and express emotion: in high-dimensional, categorical, and complex fashion.

Download Full-text

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2009.00859 ◽

2009 ◽

Vol 35 (7) ◽

pp. 859-866

Author(s):

Ming LIU ◽

Xiao-Long WANG ◽

Yuan-Chao LIU

Keyword(s):

Large Scale ◽

Clustering Algorithm ◽

High Dimensional Data ◽

High Dimensional

Download Full-text

Public Health Workforce Burnout in the COVID-19 Response in the U.S.

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18084369 ◽

2021 ◽

Vol 18 (8) ◽

pp. 4369

Author(s):

Kahler W. Stone ◽

Kristina W. Kintziger ◽

Meredith A. Jagger ◽

Jennifer A. Horney

Keyword(s):

Public Health ◽

Health Workforce ◽

Work Experience ◽

Large Scale ◽

Health Workers ◽

Public Health Workforce ◽

Cross Sectional Survey ◽

Public Health Response ◽

Cross Sectional ◽

The U.S

While the health impacts of the COVID-19 pandemic on frontline health care workers have been well described, the effects of the COVID-19 response on the U.S. public health workforce, which has been impacted by the prolonged public health response to the pandemic, has not been adequately characterized. A cross-sectional survey of public health professionals was conducted to assess mental and physical health, risk and protective factors for burnout, and short- and long-term career decisions during the pandemic response. The survey was completed online using the Qualtrics survey platform. Descriptive statistics and prevalence ratios (95% confidence intervals) were calculated. Among responses received from 23 August and 11 September 2020, 66.2% of public health workers reported burnout. Those with more work experience (1–4 vs. <1 years: prevalence ratio (PR) = 1.90, 95% confidence interval (CI) = 1.08−3.36; 5–9 vs. <1 years: PR = 1.89, CI = 1.07−3.34) or working in academic settings (vs. practice: PR = 1.31, CI = 1.08–1.58) were most likely to report burnout. As of September 2020, 23.6% fewer respondents planned to remain in the U.S. public health workforce for three or more years compared to their retrospectively reported January 2020 plans. A large-scale public health emergency response places unsustainable burdens on an already underfunded and understaffed public health workforce. Pandemic-related burnout threatens the U.S. public health workforce’s future when many challenges related to the ongoing COVID-19 response remain unaddressed.

Download Full-text

Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

Applied Sciences ◽

10.3390/app11020472 ◽

2021 ◽

Vol 11 (2) ◽

pp. 472

Author(s):

Hyeongmin Cho ◽

Sangkyun Lee

Keyword(s):

Machine Learning ◽

Data Quality ◽

Large Scale ◽

High Dimensional Data ◽

Quality Measures ◽

Training Data ◽

Measure Data ◽

High Dimensional ◽

Small Scale ◽

Class Separability

Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being disclosed and published online. From a data consumer or manager point of view, measuring data quality is an important first step in the learning process. We need to determine which datasets to use, update, and maintain. However, not many practical ways to measure data quality are available today, especially when it comes to large-scale high-dimensional data, such as images and videos. This paper proposes two data quality measures that can compute class separability and in-class variability, the two important aspects of data quality, for a given dataset. Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor. We provide efficient algorithms to compute our quality measures based on random projections and bootstrapping with statistical benefits on large-scale high-dimensional data. In experiments, we show that our measures are compatible with classical measures on small-scale data and can be computed much more efficiently on large-scale high-dimensional datasets.

Download Full-text

Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽

10.3390/a14050146 ◽

2021 ◽

Vol 14 (5) ◽

pp. 146

Author(s):

Aleksei Vakhnin ◽

Evgenii Sopov

Keyword(s):

Global Optimization ◽

Evolutionary Algorithms ◽

Numerical Experiments ◽

Large Scale ◽

Optimization Problems ◽

State Of The Art ◽

Fixed Number ◽

High Dimensional ◽

Cooperative Coevolution ◽

Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

Download Full-text

High-dimensional optimization of large-scale steel truss structures using guided stochastic search

Structures ◽

10.1016/j.istruc.2021.05.035 ◽

2021 ◽

Vol 33 ◽

pp. 1439-1456

Author(s):

Saeid Kazemzadeh Azad ◽

Saman Aminbakhsh

Keyword(s):

Large Scale ◽

Stochastic Search ◽

Truss Structures ◽

High Dimensional ◽

Steel Truss ◽

Dimensional Optimization

Download Full-text

Marsh rabbit mortalities tie pythons to the precipitous decline of mammals in the Everglades

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2015.0120 ◽

2015 ◽

Vol 282 (1805) ◽

pp. 20150120 ◽

Cited By ~ 39

Author(s):

Robert A. McCleery ◽

Adia Sovie ◽

Robert N. Reed ◽

Mark W. Cunningham ◽

Margaret E. Hunter ◽

...

Keyword(s):

National Park ◽

Large Scale ◽

Ongoing Debate ◽

Faunal Communities ◽

Python Molurus ◽

Scale Experiment ◽

Precipitous Decline ◽

Python Molurus Bivittatus ◽

The Impact ◽

Ecological Functioning

To address the ongoing debate over the impact of invasive species on native terrestrial wildlife, we conducted a large-scale experiment to test the hypothesis that invasive Burmese pythons ( Python molurus bivittatus ) were a cause of the precipitous decline of mammals in Everglades National Park (ENP). Evidence linking pythons to mammal declines has been indirect and there are reasons to question whether pythons, or any predator, could have caused the precipitous declines seen across a range of mammalian functional groups. Experimentally manipulating marsh rabbits, we found that pythons accounted for 77% of rabbit mortalities within 11 months of their translocation to ENP and that python predation appeared to preclude the persistence of rabbit populations in ENP. On control sites, outside of the park, no rabbits were killed by pythons and 71% of attributable marsh rabbit mortalities were classified as mammal predations. Burmese pythons pose a serious threat to the faunal communities and ecological functioning of the Greater Everglades Ecosystem, which will probably spread as python populations expand their range.

Download Full-text

Fundamental Dynamical Modes Underlying Human Brain Synchronization

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/912729 ◽

2012 ◽

Vol 2012 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Catalina Alvarado-Rojas ◽

Michel Le Van Quyen

Keyword(s):

Large Scale ◽

Brain Regions ◽

Electrode Placement ◽

High Dimensional ◽

Sleep Cycle ◽

Epileptic Patients ◽

Brain States ◽

Intracranial Recordings ◽

Characteristic Dynamic

Little is known about the long-term dynamics of widely interacting cortical and subcortical networks during the wake-sleep cycle. Using large-scale intracranial recordings of epileptic patients during seizure-free periods, we investigated local- and long-range synchronization between multiple brain regions over several days. For such high-dimensional data, summary information is required for understanding and modelling the underlying dynamics. Here, we suggest that a compact yet useful representation is given by a state space based on the first principal components. Using this representation, we report, with a remarkable similarity across the patients with different locations of electrode placement, that the seemingly complex patterns of brain synchrony during the wake-sleep cycle can be represented by a small number of characteristic dynamic modes. In this space, transitions between behavioral states occur through specific trajectories from one mode to another. These findings suggest that, at a coarse level of temporal resolution, the different brain states are correlated with several dominant synchrony patterns which are successively activated across wake-sleep states.

Download Full-text

Parallel Framework for Dimensionality Reduction of Large-Scale Datasets

Scientific Programming ◽

10.1155/2015/180214 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Sai Kiranmayee Samudrala ◽

Jaroslaw Zola ◽

Srinivas Aluru ◽

Baskar Ganapathysubramanian

Keyword(s):

Dimensionality Reduction ◽

Organic Solar Cells ◽

Large Scale ◽

Parallel Implementation ◽

High Dimensional Data ◽

Real Life ◽

Processing Parameters ◽

High Dimensional ◽

Morphology Evolution ◽

Reduction Techniques

Dimensionality reduction refers to a set of mathematical techniques used to reduce complexity of the original high-dimensional data, while preserving its selected properties. Improvements in simulation strategies and experimental data collection methods are resulting in a deluge of heterogeneous and high-dimensional data, which often makes dimensionality reduction the only viable way to gain qualitative and quantitative understanding of the data. However, existing dimensionality reduction software often does not scale to datasets arising in real-life applications, which may consist of thousands of points with millions of dimensions. In this paper, we propose a parallel framework for dimensionality reduction of large-scale data. We identify key components underlying the spectral dimensionality reduction techniques, and propose their efficient parallel implementation. We show that the resulting framework can be used to process datasets consisting of millions of points when executed on a 16,000-core cluster, which is beyond the reach of currently available methods. To further demonstrate applicability of our framework we perform dimensionality reduction of 75,000 images representing morphology evolution during manufacturing of organic solar cells in order to identify how processing parameters affect morphology evolution.

Download Full-text