Event Structure Research Articles

Commentary on the fourth Transatlantic G Protein-Coupled Receptor Symposium for early-career investigators.

Commentary on the fourth Transatlantic G Protein-Coupled Receptor Symposium for early-career investigators.

Perceptual event boundaries cause mnemonic trade-offs between temporal order memory and source Memory: The role of semantic relatedness among items.

Perceptual event boundaries cause mnemonic trade-offs between temporal order memory and source Memory: The role of semantic relatedness among items.

Event structure sculpts neural population dynamics in the lateral entorhinal cortex.

Our experience of the world is a continuous stream of events that must be segmented and organized at multiple timescales. The neural mechanisms underlying this process remain unknown. In this work, we simultaneously recorded hundreds to thousands of neurons in the lateral entorhinal cortex of freely behaving rats. Neural population activity drifted continuously along a one-dimensional manifold during all behaviors and behavioral states, including sleep, which points to an intrinsic origin of the drift. In awake animals, boundaries between events were associated with discrete shifts in population dynamics, which segmented the neural activity into temporal units. During tasks with recurring temporal structure, activity traveled additionally in directions orthogonal to the drift, encoding event information across multiple timescales. The results identify a hierarchical coding scheme for organizing events in time.

Advancing a temporal science of behavior.

Advancing a temporal science of behavior.

Combining top-down syllabic duration prediction with bottom-up envelope processing for syllabic segmentation in speech perception: a computational Modeling study with the COSMO-Onset model

ABSTRACT Recent neurocomputational speech perception models include a segmentation process delimiting speech segments, typically syllable-long, before decoding. This process is conceived as purely bottom-up, detecting temporally salient events in the speech signal. Beyond the scope of current models, the COSMO-Onset model incorporates in the segmentation process top-down predictions based on linguistic knowledge of speech rhythms. We present an adaptation of the model, previously studied on artificial stimuli, to process real speech signals. The model is used to simulate syllable-type recognition in noise, with two main results. Firstly, bottom-up segmentation based on resonant processes favours isochrony, since sentences with a more regular structure of syllabic events provide better segmentation and recognition scores. Secondly, top-down lexical predictions of syllable duration make detection and syllable recognition more robust in noise. The combination of bottom-up resonant and top-down predictive processes yields simulations in line with a recent behavioural experiment on speech comprehension in noise.

Relating Reversible Petri Nets and Reversible Event Structures, categorically

Causal nets (CNs) are Petri nets where causal dependencies are modelled via inhibitor arcs. They play the role of occurrence nets when representing the behaviour of a concurrent and distributed system, even when reversibility is considered. In this paper we extend CNs to account also for asymmetric conflicts and study (i) how this kind of nets, and their reversible versions, can be turned into a category; and (ii) their relation with the categories of reversible asymmetric event structures.

Synoptic structure of severe Zonda downslope windstorms in Argentina

The Zonda wind is a characteristic Argentinian downslope windstorm that occurs on the eastern slopes of the Andes Mountains, creating extremely windy, warm and dry conditions with substantial socioeconomic impacts. This study aims to characterise and understand the synoptic-scale atmospheric dynamics, as well as the vertical structure of the atmosphere on both sides of the Andes, associated with severe and long-duration Zonda events compared with non-severe events. Severe events are most frequent between August and October, comprising 64.8% of occurrences. These events exhibit earlier onset and later cessation, resulting in more late-night and morning Zonda hours compared with non-severe events. The synoptic structure for severe and long Zonda events is associated with a more baroclinic structure, featuring a deep trough at both low and mid-levels and shifted west with altitude. Lower levels present higher anomalies and move faster than mid-level anomalies when the wind crosses the Andes. This is accompanied by a deep trough east of the Andes with a tilted axis and a slower eastward movement. The associated upper-level dynamic shows a stronger convergence and divergence pattern on the windward and lee sides of the Andes respectively, along with a stronger, elongated and persistent jet streak. The precipitation pattern is in agreement with the more intense winds impinging on the Andes, resulting in more intense rising motions and more precipitation on the windward slopes. Analysis of the vertical structure in severe events shows a more humid windward profile, especially at low and mid-levels, with stronger winds. The leeward side presents a more unstable temperature profile, warmer at low levels and colder at upper levels, and extreme dryness between low and mid-levels, indicating the presence of the Zonda at this altitude before the event starts. Given the limited studies on Zonda downslope windstorms in South America, this research represents a major step in our understanding of these severe events and provides valuable insights for weather forecasters.

Implicit narrativizing and communicative understanding

Abstract This article argues that the intersubjective and self-constitutive function of narratives can be traced back to the coexistence and interaction of explicit and implicit narrative levels. Explicit narratives are visible and/or audible story constructions that take on concrete textual form in the process of communication. The deeper level of implicit narratives, on the one hand, is represented by specific models of meaning-making that represent event structures drawn from the personal experiences of individuals embedded in their cultural and social relations, which guide their behavior unconsciously, without reflection; and, on the other hand, represents the propositional attitudes of the individual who chooses among the possible actions in a given situation. The study will deal with the level of implicit narratives representing the relationship of the individual to the communication situation. The research would like to highlight that the hypothesis of this level of implicit narratives representing propositional attitudes can help understand why intersubjective narratives, used as tools in communicative processes to interpret the actions of others, can be applied to the description of the embodied self’s bodily experience of action. This level of narratives, in addition to being a condition for reproducing the intentional reasons behind intersubjective narratives, also provides the individual with a basis for choosing the most appropriate intersubjective narrative as an analogy for structuring his or her bodily experiences of action.

Winter Storm Surge Event Long‐Term Variability in the North Atlantic

Abstract Changes in storm surge events are investigated using the Event CHARacterization method, which identifies and quantifies the various dynamical structures of a typical storm surge event. This method was applied yearly, using a 20 year sliding window, to a set of 41 long‐term tide gauges in the North Atlantic. Storm surge events were investigated on the basis of four key parameters: the amplitude and duration of two structures related to atmospheric pressure (Gaussian structure) and wind stress (Laplace structure). The analysis reveals large changes in 71% of the tide gauges, located in the eastern North Sea, the Baltic Sea, and in specific regions of North America. Changes are more important for the duration of the events than for the amplitude . In Europe, the pressure‐induced structure (Gaussian) is more affected by changes than the wind‐stress‐induced structure (Laplace). In addition, the changes in North America show patterns with significant linear trends that lacked discernible geographical coherence, while in Europe, the changes exhibit cyclic patterns with more obvious geographical coherence.

EXAMINATION OF TELICITY IN LANGUAGE ACQUISITION AND TRANSLATION

This paper examines the phenomenon of telicity, or goal-orientedness of events, in language acquisition and translation, focusing on the cross-linguistic differences between Ukrainian and English. The study explores how these distinctions influence first (L1) and second (L2) language acquisition and affect translation accuracy and processing. The research highlights two primary methods of telicity marking: English employs a compositional system dependent on direct object properties, whereas Ukrainian, like other Slavic languages, uses morphological markers such as perfective prefixes. Findings from first language acquisition studies suggest that predicate-based telicity systems, as found in Ukrainian, facilitate earlier and more accurate acquisition due to their explicit morphological encoding. Conversely, languages that mark telicity compositionally, such as English, require learners to integrate multiple linguistic components, resulting in a more protracted acquisition process. Second language acquisition research reveals that L2 learners of English from languages without a determiner system often struggle with compositional telicity but show improvement with proficiency. Similarly, English-speaking Ukrainian learners initially rely on their L1 mechanisms but eventually acquire predicate telicity marking as their proficiency increases. These findings hold significant implications for translation studies. The inherent structural differences in telicity marking between English and Ukrainian can lead to translation challenges, particularly regarding aspectual nuances. Professional translators must be aware of these linguistic contrasts to ensure accurate cross-linguistic interpretation of event structures. Ultimately, this study underscores the role of telicity in shaping cognitive and linguistic processes, highlighting its relevance for both theoretical linguistics and applied translation studies.

Predicting the Event Types in the Human Brain: A Modeling Study Based on Embedding Vectors and Large-Scale Situation Type Datasets in Mandarin Chinese

Event types classify Chinese verbs based on the internal temporal structure of events. The categorization of verb event types is the most fundamental classification of concept types represented by verbs in the human brain. Meanwhile, event types exhibit strong predictive capabilities for exploring collocational patterns between words, making them crucial for Chinese teaching. This work focuses on constructing a statistically validated gold-standard dataset, forming the foundation for achieving high accuracy in recognizing verb event types. Utilizing a manually annotated dataset of verbs and aspectual markers’ co-occurrence features, the research conducts hierarchical clustering of Chinese verbs. The resulting dendrogram indicates that verbs can be categorized into three event types—state, activity and transition—based on semantic distance. Two approaches are employed to construct vector matrices: a supervised method that derives word vectors based on linguistic features, and an unsupervised method that uses four models to extract embedding vectors, including Word2Vec, FastText, BERT and ChatGPT. The classification of verb event types is performed using three classifiers: multinomial logistic regression, support vector machines and artificial neural networks. Experimental results demonstrate the superior performance of embedding vectors. Employing the pre-trained FastText model in conjunction with an artificial neural network classifier, the model achieves an accuracy of 98.37% in predicting 3133 verbs, thereby enabling the automatic identification of event types at the level of Chinese verbs and validating the high accuracy and practical value of embedding vectors in addressing complex semantic relationships and classification tasks. This work constructs datasets of considerable semantic complexity, comprising a substantial volume of verbs along with their feature vectors and situation type labels, which can be used for evaluating large language models in the future.

Helping Students Make Meaningful Connections: A Cross-Sectional Survey of College Student Loneliness

ABSTRACT Background Young adults are experiencing high levels of anxiety, depression, and loneliness. At colleges and universities across the United States, programs exist to address student mental health needs exacerbated by the COVID-19 pandemic. Purpose Current levels of loneliness among students (n = 487) at a Midwest university were researched to compare with previous trends and develop clearer recommendations for purposeful interventions. Methods This study utilized a cross-sectional study design with the UCLA Loneliness Scale during the fall 2022 semester. Results Higher levels of loneliness were linked to the demographics of identifying as cis-males, non-binary, and/or students of color. Students who had greater than 4+ h of social media usage per week and first year students also displayed the highest levels of loneliness. Qualitative data from student participants highlighted the importance of having a peer attend events with them, familiarity of event structures, and expanding access to counseling services and mentorship programs. Discussion Intentional development of programs needs to be prioritized to address student loneliness on campus. Translation to Health Education Practice With the increase in loneliness across the United States, there existopportunities for mentoring programs for struggling students and to build community to normalize the promotion of mental and emotional health resources.

No evidence for culmination inferences based on Hindi ergative marking

Prediction, both on the syntactic and the semantic level, is a central process in language comprehension. For instance, people predict aspects of event structure based on morphosyntactic markers on verbs: hearing has peeled directs one's attention towards a culminated event, as opposed to an ongoing event. Here, we ask how general this prediction process is, and specifically, whether it extends to cues outside the predicate, using the Hindi split-ergative system as case study. Ergativity allows properties of an event to be predicted on the subject, notably a constituent outside the Verb Phrase. In four studies, we map out the role subject marking plays for prediction of event properties in comprehension. Our results show that in some offline judgments, ergativity is a strong predictor of culminated events; but the cue provided by ergative marking is not taken into account during incremental comprehension, questioning accounts of automatically triggered culmination inferences in ergative constructions as well as providing evidence for a limit of predictive processing.

Toward understanding the differences between mesoscale and large-eddy simulations of tropical cyclones

Abstract In this work, we investigate the ability of mesoscale and large-eddy simulation (LES) model configurations to predict the mean wind speed profile within the boundary layer of tropical cyclones (TCs). To this end, we perform idealized simulations of five hypothetical intense storms ranging from Category 1 to 5 on the Saffir–Simpson scale, and extract time-averaged quantities near the eyewall region. We compare the model-generated data against mean wind speed profiles compiled from dropsondes launched from reconnaissance aircraft operating in the North Atlantic basin. Our analysis shows that mesoscale- and LES-generated mean wind fields display important differences in the boundary layer, including the magnitude of shear as well as the height where their low-level wind speed maxima are located. In addition, a comparison between the two model configurations with the dropsonde data shows that both modeling approaches are unable to capture the typical structure of mean winds in the lower part of the TC boundary layer (10m to 500 m), calling into question the use of simulations of near-axisymmetric storms for investigating the wind structure of past events. To better understand these differences, we conduct a momentum-budget analysis and show that modeled turbulent fluxes are underestimated in the mesoscale boundary-layer parameterization compared to the LES model. Based on the analysis of the horizontal turbulent fluxes and their potential impact on mean flow quantities, a TC-specific boundary-layer parameterization may be needed.

Dopaminergic processes predict temporal distortions in event memory

Our memories do not simply keep time — they warp it, bending the past to fit the structure of our experiences. For example, people tend to remember items as occurring farther apart in time if they spanned a change in context, or ‘event boundary,’ compared to the same context. While these distortions can sacrifice precise timing, they may serve to help separate temporally adjacent memories. However, the neural bases of this phenomenon are poorly understood. Here, we combined functional magnetic resonance imaging (fMRI; n = 32) with eye-tracking (n = 28) to test whether the dopaminergic system, known to influence encoding and time perception, predicts time dilation between adjacent events in memory. Participants encoded item sequences while listening to tones that mostly repeated over time, forming a stable auditory context, but occasionally switched, creating an event boundary. We found that boundaries predicted greater retrospective estimates of time between item pairs. Critically, tone switches significantly activated the ventral tegmental area (VTA), a key midbrain dopaminergic region, and these responses in turn predicted greater time dilation between item pairs spanning those switches. Boundaries furthermore predicted a momentary increase in blinks. VTA activation also predicted blinking in general, consistent with the idea that blink behavior is a potential marker of dopaminergic activity. On a larger timescale, higher blink rates predicted greater time dilation in memory, but only for boundary-spanning pairs. Together, these findings suggest that dopaminergic processes are sensitive to event structure and may drive temporal distortions that help disjoin memories of distinct events.

Characterization and stability of helical density snake structures in Madison Symmetric Torus tokamak plasmas

Abstract Steady, helical perturbations known as ‘density snakes’ with poloidal and toroidal mode numbers $m=1$ , $n=1$ have been studied in several tokamak experiments. These three-dimensional, helical states are interesting due to their stability and persistence, including their coexistence with the sawtooth cycle. Presented here are studies of density snakes in tokamak plasmas in the Madison Symmetric Torus (MST) device. They are diagnosed using an 11-chord interferometer, internal and edge magnetic coils and impurity ion spectroscopy. Compared with observations in other tokamak plasmas, snakes in MST form with relatively high resistivity and low edge safety factor, $ q(a) \geqslant 2.2$ , which moves the $q=1$ resonant surface outward in radius and probably forms a large magnetic island. As a result, the density perturbation associated with the snake is larger, the structure occupies a broader span of minor radius and the snakes are somewhat less stable. The helical structure and distribution of snake events are characterized, including whether they are best described as ideal or resistive kink modes. Finally, an analysis of their perturbation or destruction during sawtooth crashes is given.

Simulating Mixed‐Phase Clouds Over Coastal Antarctica During a Significant Snowfall Event in a High‐Resolution Regional Model

Abstract Global climate models and reanalysis products have revealed large, persistent downwelling shortwave radiation biases over the Southern Ocean and coastal Antarctica, likely caused by the incapability of models to accurately simulate frequent low‐level mixed‐phase clouds in these regions. In this study, we use the ground‐based observations collected at Davis, Antarctica during the Precipitation over Land and The Southern Ocean field campaign in austral summer of 2019 to assess the capability of the high‐resolution regional Unified Model (UM) to reproduce precipitating clouds off coastal Antarctica. We test the new UM RAL3 (Regional Atmosphere and Land 3) configuration with double‐moment Cloud AeroSol Interacting Microphysics scheme and bimodal cloud fraction scheme, running at the spatial resolution of 1.5‐km. We compare it to the previous RA2M configuration with a single‐moment cloud microphysics scheme and unimodal cloud fraction scheme. The RAL3 exhibits marginally degraded meteorological conditions relative to RA2M compared with observations. For cloud properties, the UM regional models can generally simulate the phase, vertical structure and timing of events during the sublimation and precipitation periods. Nevertheless, overestimated ice water path and potentially underestimated liquid water path (LWP) contribute to positive surface shortwave biases and negative longwave biases. The RA2M simulates more LWP, though we suggest for the wrong reasons due to its ice nucleating parameterization. Our results suggest that the new double‐moment cloud microphysics combined with bimodal cloud fraction parameterizations, while having reduced performance in some respects, has large potential to better represent low‐level mixed phase clouds for this region.

Lyrical Plot Composition: to the Practice of Analysis and Interpretation

The main purpose of the article is revealed in the discussion of the problem of terminological attribution of the concept of lyrical eventfulness. Researchers do not find a single basis for determining the nature of lyrical eventfulness. Questions remain debatable: can terms that are conceptually associated with a narrative prose text be «adapted» to a lyrical text? Does their volume / content change in the case of instrumental use in the analysis and interpretation of a lyrical text? What methods should be used to study the structure of a lyrical event if the subject of study itself is a matter that is difficult to describe? The article notes that in the practice of analysis and interpretation, the terms lyrical plot, lyrical eventfulness, lyrical plot acquire a content content that is different from prosaic eventfulness, associated with the lyrical nature of the text. The nature of lyrical eventfulness is revealed through eidetic expression, which entails a different axiomatics in the formation of its semantic meaning. Theoretical principles applied in the aspect of a phenomenological description of lyrical eventfulness are revealed using the example of a poem by A. Akhmatova.

Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents

Event extraction aims to identify and structure event information from unstructured text, playing a critical role in real-world applications such as news analysis, public opinion discovery, and intelligence gathering. Traditional approaches, however, struggle with event co-occurrence and long-distance dependencies. To address these challenges, we introduce the Semantic-aware Prompt-based Argument Extractor (SPARE) model, which integrates entity extraction, heterogeneous graph construction, event type detection, and argument filling. By constructing a document–sentence–entity heterogeneous graph and employing graph convolutional networks (GCNs), the model effectively captures global semantic associations and interactions between cross-sentence triggers and arguments. Additionally, a position-aware semantic role (SRL) attention mechanism is proposed to enhance the association between semantic and positional information, improving argument extraction accuracy in the context of event co-occurrence. The experimental outcomes on the Richly Annotated Multilingual Schema-guided Event Structure (RAMS) and WikiEvents datasets display considerable F1 score improvements, which confirms the model’s effectiveness.

From scarcity to insight: extreme events analysis with a partially linear single-index varying-coefficient model in high-dimensional settings

Analysing the causes of extreme events holds paramount importance across diverse domains. However, conventional models with asymptotic theory often exhibit limitations in flexibly capturing the intricate dependence structure in extreme events. Additionally, the scarcity of data on extreme events further complicates the nonparametric estimation of high-dimensional covariates, giving rise to the challenge known as the ‘dimensional curse.’ In order to address these challenges, we propose a flexible model that combines a partially linear single-index varying-coefficient model with extreme value theory, and it effectively avoids the curse of dimensionality while providing a strong interpretative ability and high flexibility. Consistency and oracle properties of estimators are established. The Monte Carlo simulation results confirm the finite sample properties of the estimators. Furthermore, the application to a real-world example involving network analysis of sector index risk in financial markets and valuable insights into risk drivers are obtained.