Responses to terrorism scenarios: Event features, individual characteristics, and subjective evaluations

The extensive research into responses to terrorism has focused on the effects of individual characteristics on reactions to past terrorism events. This literature has largely omitted two issues: the impact of terrorism event features, and reactions to possible future terrorism events. The first purpose of this dissertation was to account for the effects of event features as well as subjective evaluations on responses to terrorism events. The second purpose of this dissertation was to compare reactions to past and future terrorism scenarios. A series of actual and hypothetical written scenarios were presented to undergraduate psychology students, and various responses measured. A number of individual characteristics were also measured. Studies 1 and 2 served to identify type of weapon, number of victims, type of target, and level of disruption as specific features of terrorism events or threats that are salient to observers. Study 3 through 5 manipulated these features to examine their impact on responses. Study 3 found that weapon independently affected some responses to terrorism, and affected others in conjunction with the type of target. Study 3 also found that some individual characteristics were important after controlling for event features. Study 4 found that type of weapon interacted with the presence of an actual attack to impact responses to terrorism. Study 5 incorporated a series of subjective evaluations of each scenario, and found that these evaluations were not related to responses after accounting for event features and individual characteristics. Differences between Studies 3 and 5 also suggest differing responses to threats and attacks. This dissertation reviews the relevant literature for responses to terrorism and perceptions of risk. Also, the results are discussed in relation to previous research, and several implications are outlined for emergency preparedness and response agencies. Implications for future studies and empirical extensions of this work are also discussed

Conversation Derailment Forecasting with Graph Convolutional Networks

Online conversations are particularly susceptible to derailment, which can manifest itself in the form of toxic communication patterns like disrespectful comments or verbal abuse. Forecasting conversation derailment predicts signs of derailment in advance enabling proactive moderation of conversations. Current state-of-the-art approaches to address this problem rely on sequence models that treat dialogues as text streams. We propose a novel model based on a graph convolutional neural network that considers dialogue user dynamics and the influence of public perception on conversation utterances. Through empirical evaluation, we show that our model effectively captures conversation dynamics and outperforms the state-of-the-art models on the CGA and CMV benchmark datasets by 1.5\% and 1.7\%, respectively.Comment: WOAH, AC

Predicting Evoked Emotions in Conversations

Understanding and predicting the emotional trajectory in multi-party multi-turn conversations is of great significance. Such information can be used, for example, to generate empathetic response in human-machine interaction or to inform models of pre-emptive toxicity detection. In this work, we introduce the novel problem of Predicting Emotions in Conversations (PEC) for the next turn (n+1), given combinations of textual and/or emotion input up to turn n. We systematically approach the problem by modeling three dimensions inherently connected to evoked emotions in dialogues, including (i) sequence modeling, (ii) self-dependency modeling, and (iii) recency modeling. These modeling dimensions are then incorporated into two deep neural network architectures, a sequence model and a graph convolutional network model. The former is designed to capture the sequence of utterances in a dialogue, while the latter captures the sequence of utterances and the network formation of multi-party dialogues. We perform a comprehensive empirical evaluation of the various proposed models for addressing the PEC problem. The results indicate (i) the importance of the self-dependency and recency model dimensions for the prediction task, (ii) the quality of simpler sequence models in short dialogues, (iii) the importance of the graph neural models in improving the predictions in long dialogues

Asymmetrical representation of body orientation

The perceived orientation of objects, gravity, and the body are biased to the left. Whether this leftward bias is attributable to biases in sensing or processing vestibular, visual, and body sense cues has never been assessed directly. The orientation in which characters are most easily recognized-the perceived upright (PU)-can be well predicted from a weighted vector sum of these sensory cues. A simple form of this model assumes that the directions of the contributing inputs are coded accurately and as a consequence participants tilted leftor right-side-down relative to gravity should exhibit mirror symmetric patterns of responses. If a left/right asymmetry were present then varying these sensory cues could be used to assess in which sensory modality or modalities a PU bias may have arisen. Participants completed the Oriented Character Recognition Test (OCHART) while manipulating body posture and visual orientation cues relative to gravity. The response patterns showed systematic differences depending on which side they were tilted. An asymmetry of the PU was found to be best modeled by adding a leftward bias of 5.68 to the perceived orientation of the body relative to its actual orientation relative to the head. The asymmetry in the effect of body orientation is reminiscent of the body-defined left-leaning asymmetry in the perceived direction of light coming from above and reports that people tend to adopt a right-leaning posture

Suspended sediment load and bedload flux from the Glacier d'Otemma proglacial forefield (summers 2020 and 2021)

The Glacier d’Otemma proglacial margin, located in the Swiss Alps at an altitude of about 2450 m a.s.l. (45.93423 N, 7.41160 E), is characterized by a ca. 1 km long by 200 m wide active braided forefield. In this setting we installed two gauging stations for the monitoring of both suspended sediment and bedload transport within the proglacial margin: GS1 at about 350 m from the glacier terminus and GS2 at the forefield outlet. Monitoring stations were equipped with water pressure sensors (CS451 from Cambell Scientific), turbidity probes (OBS300+ from Cambell Scientific) and geophones (3-components PE-6/B from Sensor Nederland connected to a DiGOS DATA-CUBE type 2 logger). Water discharge were determined following modalities described in Müller and Miesen (2022). Suspended loads were quantified using a conventional turbidity-suspended sediment concentration relationship, while bedload transport was derived seismically using the geophysical Fluvial model inversion (FMI) algorithm developed in Dietze et al. (2018). The dataset covers summers 2020 and 2021. Further details on data aquisition and post-processing techniques are available in Mancini et al. (2023)

Estimation of rate coefficients and branching ratios for gas-phase reactions of OH with aliphatic organic compounds for use in automated mechanism construction

Reaction with the hydroxyl (OH) radical is the dominant removal process for volatile organic compounds (VOCs) in the atmosphere. Rate coefficients for reactions of OH with VOCs are therefore essential parameters for chemical mechanisms used in chemistry transport models, and are required more generally for impact assessments involving the estimation of atmospheric lifetimes or oxidation rates for VOCs. Updated and extended structure–activity relationship (SAR) methods are presented for the reactions of OH with aliphatic organic compounds, with the reactions of aromatic organic compounds considered in a companion paper. The methods are optimized using a preferred set of data including reactions of OH with 489 aliphatic hydrocarbons and oxygenated organic compounds. In each case, the rate coefficient is defined in terms of a summation of partial rate coefficients for H abstraction or OH addition at each relevant site in the given organic compound, so that the attack distribution is defined. The information can therefore guide the representation of the OH reactions in the next generation of explicit detailed chemical mechanisms. Rules governing the representation of the subsequent reactions of the product radicals under tropospheric conditions are also summarized, specifically their reactions with O2 and competing processes

Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching

Energy saving in wireless networks is growing in importance due to increasing demand for evolving new-gen cellular networks, environmental and regulatory concerns, and potential energy crises arising from geopolitical tensions. In this work, we propose an approximate dynamic programming (ADP)-based method coupled with online optimization to switch on/off the cells of base stations to reduce network power consumption while maintaining adequate Quality of Service (QoS) metrics. We use a multilayer perceptron (MLP) given each state-action pair to predict the power consumption to approximate the value function in ADP for selecting the action with optimal expected power saved. To save the largest possible power consumption without deteriorating QoS, we include another MLP to predict QoS and a long short-term memory (LSTM) for predicting handovers, incorporated into an online optimization algorithm producing an adaptive QoS threshold for filtering cell switching actions based on the overall QoS history. The performance of the method is evaluated using a practical network simulator with various real-world scenarios with dynamic traffic patterns