Over several years of recent efforts to make sense and detect online hate speech, we still know relatively little about how hateful expressions enter online platforms and whether there are patterns ...and features characterizing the corpus of hateful speech.
In this research, we introduce a new conceptual framework suitable for better capturing the overall scope and dynamics of the current forms of online hateful speech.
We adopt several Python-based crawlers to collect a comprehensive data set covering a variety of subjects from a multiplicity of online communities in South Korea. We apply the notions of marginalization and polarization in identifying patterns and dynamics of online hateful speech.
Our analyses suggest that polarization driven by political orientation and age difference predominates in the hateful speech in most communities, while marginalization of social minority groups is also salient in other communities. Furthermore, we identify a temporal shift in the trends of online hate from gender to age based, reflecting the changing sociopolitical conditions within the polarization dynamics in South Korea.
By expanding our understanding of how hatred shifts and evolves in online communities, our study provides theoretical and practical implications for both researchers and policy-makers.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Neuroscience research has become a national priority for the Korean government. Korean scholars have dedicated interest in the societal ramifications of neurotechnologies; neuroethics is an integral ...component of the Korea Brain Initiative and to the formation of its growing neuroscience community.
Neuroscience research has become a national priority for the Korean government. Korean scholars have dedicated interest in the societal ramifications of neurotechnologies; neuroethics is an integral component of the Korea Brain Initiative and to the formation of its growing neuroscience community.
To develop a novel, non-contrast-enhanced magnetic resonance angiography (MRA) exploiting cardiac-gated chemical-shift-encoded single-slab 3D gradient- and spin-echo (GRASE) imaging for robust ...background suppression.
The proposed single-slab 3D GRASE employs variable-flip-angles (VFA) in the refocusing radio-frequency (RF) pulse train to promote sensitivity to blood flow as well as imaging encoding efficiency. Phase encoding blips were inserted between adjacent lobes of the switching readout gradients, so chemical shift-induced phase information was encoded into different locations in k-space. Based on the assumption that most background signals in the angiogram come from the fatty tissues, the proposed method directly decomposed angiograms and fatty background tissue signals from the subtracted k-space data by solving a constrained optimization problem with subtraction-induced sparsity prior. Simulations and experiments are performed to validate the effectiveness of the proposed method compared with the conventional FBI.
Compared with conventional fresh blood imaging (FBI), the proposed method enhances the delineation of small arteries without apparent noise amplification while successfully eliminating fatty background artifacts.
We have successfully demonstrated the feasibility of the proposed method compared with the conventional FBI for the generation of peripheral angiograms with robust background suppression and clear delineation of small arteries.
The motor cortex not only executes but also prepares movement, as motor cortical neurons exhibit preparatory activity that predicts upcoming movements. In movement preparation, animals adopt ...different strategies in response to uncertainties existing in nature such as the unknown timing of when a predator will attack-an environmental cue informing "go." However, how motor cortical neurons cope with such uncertainties is less understood. In this study, we aim to investigate whether and how preparatory activity is altered depending on the predictability of "go" timing. We analyze firing activities of the anterior lateral motor cortex in male mice during two auditory delayed-response tasks each with predictable or unpredictable go timing. When go timing is unpredictable, preparatory activities immediately reach and stay in a neural state capable of producing movement anytime to a sudden go cue. When go timing is predictable, preparation activity reaches the movement-producible state more gradually, to secure more accurate decisions. Surprisingly, this preparation process entails a longer reaction time. We find that as preparatory activity increases in accuracy, it takes longer for a neural state to transition from the end of preparation to the start of movement. Our results suggest that the motor cortex fine-tunes preparatory activity for more accurate movement using the predictability of go timing.
High‐energy and long cycle lithium–sulfur (Li–S) pouch cells are limited by the insufficient capacities and stabilities of their cathodes under practical electrolyte/sulfur (E/S), ...electrolyte/capacity (E/C), and negative/positive (N/P) ratios. Herein, an advanced cathode comprising highly active Fe single‐atom catalysts (SACs) is reported to form 320.2 W h kg−1 multistacked Li–S pouch cells with total capacity of ≈1 A h level, satisfying low E/S (3.0), E/C (2.8), and N/P (2.3) ratios and high sulfur loadings (8.4 mg cm−2). The high‐activity Fe SAC is designed by manipulating its local environments using electron‐exchangeable binding (EEB) sites. Introducing EEB sites comprising two different types of S species, namely, thiophene‐like‐S (–S) and oxidized‐S (–SO2), adjacent to Fe SACs promotes the kinetics of the Li2S redox reaction by providing additional binding sites and modulating the Fe d‐orbital levels via electron exchange with Fe. The –S donates the electrons to the Fe SACs, whereas –SO2 withdraws electrons from the Fe SACs. Thus, the Fe d‐orbital energy level can be modulated by the different –SO2/–S ratios of the EEB site, controlling the electron donating/withdrawing characteristics. This desirable electrocatalysis is maximized by the intimate contact of the Fe SACs with the S species, which are confined together in porous carbon.
The introduction of electron exchangeable binding sites comprising thiophene S and oxidized S, adjacent to Fe single atom electrocatalysts, improves Li2S redox kinetics by modulating d‐orbital level of Fe single atom via electron exchange. These specially designed electrocatalysts enable realization of a 1 A h level Li–S pouch cell with high gravimetric energy density and long cycle stability.
The utilization of ferromagnetic (FM) materials in thermoelectric devices allows one to have a simpler structure and/or independent control of electric and thermal conductivities, which may further ...remove obstacles for this technology to be realized. The thermoelectricity in FM/non-magnet (NM) heterostructures using an optical heating source is studied as a function of NM materials and a number of multilayers. It is observed that the overall thermoelectric signal in those structures which is contributed by spin Seebeck effect and anomalous Nernst effect (ANE) is enhanced by a proper selection of NM materials with a spin Hall angle that matches to the sign of the ANE. Moreover, by an increase of the number of multilayer, the thermoelectric voltage is enlarged further and the device resistance is reduced, simultaneously. The experimental observation of the improvement of thermoelectric properties may pave the way for the realization of magnetic-(or spin-) based thermoelectric devices.
Humans often attempt to predict what others prefer based on a narrow slice of experience, called thin-slicing. According to the theoretical bases for how humans can predict the preference of others, ...one tends to estimate the other's preference using a perceived difference between the other and self. Previous neuroimaging studies have revealed that the network of dorsal medial prefrontal cortex (dmPFC) and right temporoparietal junction (rTPJ) is related to the ability of predicting others' preference. However, it still remains unknown about the temporal patterns of neural activities for others' preference prediction through thin-slicing. To investigate such temporal aspects of neural activities, we investigated human electroencephalography (EEG) recorded during the task of predicting the preference of others while only a facial picture of others was provided. Twenty participants (all female, average age: 21.86) participated in the study. In each trial of the task, participants were shown a picture of either a target person or self for 3 s, followed by the presentation of a movie poster over which participants predicted the target person's preference as liking or disliking. The time-frequency EEG analysis was employed to analyze temporal changes in the amplitudes of brain oscillations. Participants could predict others' preference for movies with accuracy of 56.89 ± 3.16% and 10 out of 20 participants exhibited prediction accuracy higher than a chance level (95% interval). There was a significant difference in the power of the parietal alpha (10~13 Hz) oscillation 0.6~0.8 s after the onset of poster presentation between the cases when participants predicted others' preference and when they reported self-preference (
< 0.05). The power of brain oscillations at any frequency band and time period during the trial did not show a significant correlation with individual prediction accuracy. However, when we measured differences of the power between the trials of predicting other's preference and reporting self-preference, the right temporal beta oscillations 1.6~1.8 s after the onset of facial picture presentation exhibited a significant correlation with individual accuracy. Our results suggest that right temporoparietal beta oscillations may be correlated with one's ability to predict what others prefer with minimal information.
In systems neuroscience, advances in simultaneous recording technology have helped reveal the population dynamics that underlie the complex neural correlates of animal behavior and cognitive ...processes. To investigate these correlates, neural interactions are typically abstracted from spike trains of pairs of neurons accumulated over the course of many trials. However, the resultant averaged values do not lead to understanding of neural computation in which the responses of populations are highly variable even under identical external conditions. Accordingly, neural interactions within the population also show strong fluctuations. In the present study, we introduce an analysis method reflecting the temporal variation of neural interactions, in which cross-correlograms on rate estimates are applied via a latent dynamical systems model. Using this method, we were able to predict time-varying neural interactions within a single trial. In addition, the pairwise connections estimated in our analysis increased along behavioral epochs among neurons categorized within similar functional groups. Thus, our analysis method revealed that neurons in the same groups communicate more as the population gets involved in the assigned task. We also showed that the characteristics of neural interaction from our model differ from the results of a typical model employing cross-correlation coefficients. This suggests that our model can extract nonoverlapping information about network topology, unlike the typical model.
Completing everyday tasks often requires the execution of action sequences matched to a particular problem. To study the neural processes underlying these behaviors, we trained monkeys to produce a ...series of eye movements according to a sequence that changed unpredictably from one block of trials to the next. We then applied a decoding algorithm to estimate which sequence was being represented by the ensemble activity in prefrontal cortex. We found that the sequence predicted by this analysis changed gradually from the sequence that had been correct in the previous block to the sequence that was correct in the current block, closely following the fraction of executed movements that were consistent with the corresponding sequence. Thus, the neural activity dynamically tracked the monkeys' uncertainty about the correct sequence of actions. These results are consistent with prefrontal involvement in representing subjective knowledge of the correct action sequence.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The control of arm movements through intracortical brain–machine interfaces (BMIs) mainly relies on the activities of the primary motor cortex (M1) neurons and mathematical models that decode their ...activities. Recent research on decoding process attempts to not only improve the performance but also simultaneously understand neural and behavioral relationships. In this study, we propose an efficient decoding algorithm using a deep canonical correlation analysis (DCCA), which maximizes correlations between canonical variables with the nonlinear approximation of mappings from neuronal to canonical variables via deep learning. We investigate the effectiveness of using DCCA for finding a relationship between M1 activities and kinematic information when non-human primates performed a reaching task with one arm. Then, we examine whether using neural activity representations from DCCA improves the decoding performance through linear and nonlinear decoders: a linear Kalman filter (LKF) and a long short-term memory in recurrent neural networks (LSTM-RNN). We found that neural representations of M1 activities estimated by DCCA resulted in more accurate decoding of velocity than those estimated by linear canonical correlation analysis, principal component analysis, factor analysis, and linear dynamical system. Decoding with DCCA yielded better performance than decoding the original FRs using LSTM-RNN (6.6 % and 16.0 % improvement on average for each velocity and position, respectively; Wilcoxon rank sum test, p < 0.05). Thus, DCCA can identify the kinematics-related canonical variables of M1 activities, thus improving the decoding performance. Our results may help advance the design of decoding models for intracortical BMIs.
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