Temporal Relations Research Articles

Temporal relations in archaeology: a survey and a new typology

AbstractThis article presents a new typology of temporal relations suited for archaeological use. It discusses the properties and advantages of the proposed system and compares it with three other typologies of temporal relations: Allen's relations, Holst's relation, and the CIDOC‐CRM. It is argued that a more detailed typology of temporal relations in archaeology than currently available is called for, such as the one proposed in this paper. A final synoptic table is provided to help users navigate among the different typologies.

The importance of intrinsic reading motivation goes beyond the reading domain: Relations to school performance and motivation in the language domain

AbstractBackgroundIntrinsic reading motivation has often been investigated regarding its relations to reading‐related variables (e.g. reading achievement). Research has paid little attention to the relations between intrinsic reading motivation and variables related to the overarching language domain. We investigated the temporal relations between intrinsic reading motivation, reading achievement, language school performance, language self‐concept, and language intrinsic value.MethodsThe constructs were measured at three annual measurement waves covering Grades 7–9 with a sample of German secondary school students (N = 1271). The relations among constructs were analysed using cross‐lagged panel models in structural equation modelling.ResultsPrevious intrinsic reading motivation was found to be positively related to later reading achievement, language school performance, and language self‐concept. The patterns of relations were found to be stable across the two time lags and remained when including students' gender, secondary school track, and socio‐economic status as covariates.ConclusionsThis study underscores the importance of intrinsic reading motivation which does not only address other reading‐related variables but also motivational and achievement variables of the language domain.

An upper temporal limit of action-effect integration as reflected by motor adaptation

Motor parameters of simple, repetitive actions like tapping, pinching, or pushing a button differ as a function of their action effects – adding a consistent, immediate sound-effect to such actions leads to a decrease in applied force. This action-effect related motor adaptation occurs only, however, when the sound-effect follows actions within about 200 ms, which has been hypothesized to reflect a temporal limit of action-effect integration. Using a university student sample, the present study replicated the effect of action-sound effect delays on force application. Furthermore, given that the perception of action-effect contingencies, and that of temporal relations are deteriorated in schizophrenia, we explored the relationship between the schizotypy trait and the duration of the action-effect related motor optimization window. Participants pinched a force sensitive device every 3 s on their own volition, which elicited a tone with a delay increasing from block to block in 70 ms steps from 0 to 560 ms. The applied force gradually increased with action-effect delay, with an estimated force optimization window size of 290 ms, confirming the importance of temporal contiguity in action-effect related motor adaptation. A Bayes-factor based analysis provided evidence for no correlation between the motor optimization window size and schizotypy.

Postpositional locative expressions in Dogri: a syntactic-semantic analysis of spatial, directional, and temporal relations

Abstract India’s linguistic diversity encompasses twenty-two constitutionally recognized languages, each characterized by rich morphology, extensive inflectional properties, and relatively free word order. Among these, Dogri, an Indo-Aryan language with a Subject–Object–Verb (SOV) structure, stands out for its use of postpositions instead of prepositions to convey spatial relations, a feature that sets it apart from languages like English and Russian. This study explores the syntactic and semantic structures of postpositional locative expressions in Dogri, a language that remains significantly underexplored and underdocumented. The research focuses on two primary objectives: (1) to identify and classify various one-word and two-word locative case markers, both free and bound, that exhibit polysemous relationships in Dogri; and (2) to analyze how these markers semantically and syntactically link nouns or pronouns with locative expressions, enriching sentence meaning. Data were collected through a random survey of 90 native Dogri speakers from Kathua, Jammu, and Reasi districts in Jammu and Kashmir, with 45 participants responding to pictorial questionnaires and 45 participating in interviews. The analysis examines spatial, directional, and temporal locative relations, differentiating static positional references from directional shifts. The findings reveal that Dogri locative markers are predominantly postpositional, with their functions marked through inflectional affixes. This study offers valuable insights into Dogri’s unique linguistic features and contributes to understanding its typological placement within the Indo-Aryan language family.

Work Stressors and Asthma in Female and Male US Workers: Findings From the National Health Interview Survey.

Prior work has linked work stressors to asthma. However, research related to gender-specific associations remains sparse and yielded mixed results. We aimed to address this gap. We drew on cross-sectional data from the 2015 National Health Interview Survey (individual-level response rate = 79.7%). Included were participants in employment who were aged 18-70 (n = 18,701). Work-to-family conflict, workplace bullying, and job insecurity were assessed as work stressors. Asthma was defined based on self-reports of a lifetime diagnosis by a doctor or other health professional. To account for the complex sampling design, variance estimation was used to compute weighted descriptive statistics and odds ratios (ORs) as well as corresponding 95% confidence intervals (CIs) using multivariable logistic regression. To test for interaction, interaction terms for work stressors and gender were included in additional models. In the full sample, work-to-family conflict, workplace bullying and job insecurity showed positive associations with asthma (OR = 1.20, 95%CI = 1.03-1.40; OR = 1.45, 95%CI = 1.17-1.80; and OR = 1.20, 95%CI = 0.99-1.45, respectively). We did not observe meaningful gender differences in the magnitudes of the ORs. All interaction terms were not statistically significant. Work stressors were positively associated with asthma, but there was no evidence of gender differences. Prospective studies are needed to determine the potential temporal relation of these associations.

CognTKE: A Cognitive Temporal Knowledge Extrapolation Framework

Reasoning future unknowable facts on temporal knowledge graphs (TKGs) is a challenging task, holding significant academic and practical values for various fields. Existing studies exploring explainable reasoning concentrate on modeling comprehensible temporal paths relevant to the query. Yet, these path-based methods primarily focus on local temporal paths appearing in recent times, failing to capture the complex temporal paths in TKG and resulting in the loss of longer historical relations related to the query. Motivated by the Dual Process Theory in cognitive science, we propose a Cognitive Temporal Knowledge Extrapolation framework (CognTKE), which introduces a novel temporal cognitive relation directed graph (TCR-Digraph) and performs interpretable global shallow reasoning and local deep reasoning over the TCR-Digraph. Specifically, the proposed TCR-Digraph is constituted by retrieving significant local and global historical temporal relation paths associated with the query. In addition, CognTKE presents the global shallow reasoner and the local deep reasoner to perform global one-hop temporal relation reasoning (System 1) and local complex multi-hop path reasoning (System 2) over the TCR-Digraph, respectively. The experimental results on four benchmark datasets demonstrate that CognTKE achieves significant improvement in accuracy compared to the state-of-the-art baselines and delivers excellent zero-shot reasoning ability.

Robust Tracking via Mamba-based Context-aware Token Learning

How to make a good trade-off between performance and computational cost is crucial for a tracker. However, current famous methods typically focus on complicated and time-consuming learning that combining temporal and appearance information by input more and more images (or features). Consequently, these methods not only increase the model's computational source and learning burden but also introduce much useless and potentially interfering information. To alleviate the above issues, we propose a simple yet robust tracker that separates temporal information learning from appearance modeling and extracts temporal relations from a set of representative tokens rather than several images (or features). Specifically, we introduce one track token for each frame to collect the target's appearance information in the backbone. Then, we design a mamba-based Temporal Module for track tokens to be aware of context by interacting with other track tokens within a sliding window. This module consists of a mamba layer with autoregressive characteristic and a cross-attention layer with strong global perception ability, ensuring sufficient interaction for track tokens to perceive the appearance changes and movement trends of the target. Finally, track tokens serve as a guidance to adjust the appearance feature for the final prediction in the head. Experiments show our method is effective and achieves competitive performance on multiple benchmarks at a real-time speed.

Multi-Modal Deep Learning for Crop Yield Prediction Network: Static and Temporal Feature Space

In agriculture, crop yield prediction is the process of making yield estimates using data from crops, soil, and weather. Although ML models have been utilized before, they frequently depend on features that were manually created. So, a DL model like a 1D Convolutional Neural Network (1DCNN) can be employed. However, it struggles to learn temporal relations amongtime-series data. Therefore, a Deep learning-based Crop Yield prediction Network (DeepCropYNet) was designed using Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN). However, this model struggles to learn significant features from complex datasets that involve multimodal inputs like time-series and image data.Thus, this paper proposes a Deep learning-based Multi-Modal CropYNet (DeepMMCropYNet) for crop yield prediction, which utilizes both time-series and image data related to crop yields. First, the dataset is pre-processed using a normalization technique to remove missing values and outliers. Then, the DeepMMCropYNet is trained using the pre-processed data to predict crop yields. This model comprises two branches: (i) LSTM-TCN for time-series data and (ii) multi-dimensional CNN for soil image data. This multi-dimensional CNN model comprises static and temporal feature extraction modules. The static module learns the static features from the soil images using 18 parallel 1DCNNs. The temporal module employs 16 parallel 2-dimensional CNNs (2DCNNs) to extract temporal features from soil images. The outputs of these modules are fused by the lateral connections. Moreover, each branch applies an attention strategy to assign the feature weights and find significant features. The features of each branch are then merged and given to a Fully Connected (FC) layer followed by an output layer to get a final prediction result of different crop yields.By comparing the DeepMMCropYNet model to previous models, the experimental findings demonstrate that it outperforms them in terms of Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Correlation Coefficient (R2). when it comes to predicting various crop yields.

Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation

To equip artificial intelligence with a comprehensive understanding towards a temporal world, video and 4D panoptic scene graph generation abstracts visual data into nodes to represent entities and edges to capture temporal relations. Existing methods encode entity masks tracked across temporal dimensions (mask tubes), then predict their relations with temporal pooling operation, which does not fully utilize the motion indicative of the entities' relation. To overcome this limitation, we introduce a contrastive representation learning framework that focuses on motion pattern for temporal scene graph generation. Firstly, our framework encourages the model to learn close representations for mask tubes of similar subject-relation-object triplets. Secondly, we seek to push apart mask tubes from their temporally shuffled versions. Moreover, we also learn distant representations for mask tubes belonging to the same video but different triplets. Extensive experiments show that our motion-aware contrastive framework significantly improves state-of-the-art methods on both video and 4D datasets.

Thinking With Post‐Birth Bodies: Articulating Sociological Care for Bodies That Function Differently After Birth

ABSTRACTThis paper articulates a sociological approach to bodies that function differently after birth. We suggest that post‐birth bodies are distributed across a variety of areas of existing scholarship and that this can make it difficult to grapple with experiences that encompass gestation, altered functioning/injury, parenting and medical knowledge. We review and synthesise this rich literature to illustrate how it can be mobilised to sociologically theorise and explore physical recovery from birth, characterising this as the development of sociological care for such bodies. Our analysis draws on autoethnographic reflection on the post‐birth body of a cis/queer/neurodivergent/white/middle‐class mother alongside four pilot interviews concerning experiences with post‐birth bodies in England. By placing these lived experiences into thematic dialogue with existing feminist/STS and sociological scholarship we illustrate why bodies altered through birth are good for sociologists to think with and outline potential avenues for future research in this field. We suggest that a focus on care for post‐birth bodies enables critical exploration of assumptions about temporal relations between pregnancy, birth and mothering/parenting, as well as how these forms of labour are socially distributed and supported.

Temporal Enhancement of Top-N Recommendation on Heterogeneous Graphs

Heterogeneous information networks (HINs) have seen rapid development and have attracted extensive attention because of their effectiveness in recommender systems. Although many existing models based on HINs for recommender systems have obtained good recommendation performance on account of their superior ability to process heterogeneous data and capture rich semantic information, there are still several problems. Firstly, the temporal relations among different nodes in meta-paths, which include users and items, are rarely considered in HINs. Secondly, the interactions among meta-paths, users, and items are similarly often overlooked. Thirdly, their ability to learn the heterogeneous information of users and items is limited. In view of the above problems, we propose a system for the temporal enhancement of top-N recommendations on HINs called TMRec. Specifically, we first adopted long short-term memory (LSTM) and residual self-attention (RSA) to process users and items and enhance the network’s ability to both learn the heterogeneous information in them and capture the temporal relations among them. Second, we designed a novel method for processing meta-paths, including deep perception self-attention (DPSA), max pooling, and L2-normalization, that can effectively obtain the temporal relations among different nodes in meta-paths. Third, we used collaborative attention to process meta-paths, users, and items to obtain their interactions. Finally, extensive experiments were conducted on four public datasets of recommender systems to verify the superiority of our method compared with state-of-the-art top-N recommendation models.

Pediatric headache attributed to brain tumor.

Pediatric headache attributed to brain tumor.

Dependent taxis constructions with elements of conditionality semantics in modern Russian (to help the teacher)

The article examines the constructions of the adverbial dependent taxis, the semantics of which is complicated by the logical and semantic relations between the actions of the main and secondary predicates. The relevance is due to the significant dynamics of the use of these figures of speech, the variety of means of expressing taxis meanings, as well as the contradictory interpretation of taxis in linguistic literature. The purpose of the article is to study the constructions of dependent taxis, in which the temporal relations between the actions of the main and secondary predicates are complicated by the meanings of conditionality, as well as to identify the influence of these meanings on the temporal relationship between the verbal and adverbial participial actions. The work used descriptive, comparative-contrastive and transformational methods. It was found that various adverbial meanings are superimposed on the temporal relations between the actions of the main and secondary predicates, often modifying these temporal relations. It was revealed that in the presence of additional conditional, causal and concessive meanings, perfective participles denote an action preceding the action of the verb-predicate, and imperfective participles denote an action simultaneous with the action of the verb-predicate. In the case of expressing other logical-semantic relations, perfective participles can express an action simultaneous with the action of the main predicate or following it, and imperfective participles, accordingly, can denote a preceding or subsequent action. The novelty of the study is expressed in the fact that it identifies and analyzes typical cases of modification of the temporal relationship between the actions of predicates in the constructions of dependent adverbial taxis.

Acute symptomatic seizure: structural brain lesions, metabolic disorders, anti-seizure medications

Seizures that occur in close temporal relation to brain lesions and do not recur after resolution of the pathological condition or elimination of the factor that caused it should be considered as acute symptomatic seizures. They differ from unprovoked seizures and epilepsy because the risk of recurrence is significantly lower and there is no long-term predisposition to further seizures. The most important causes of acute symptomatic seizures in adults are both diseases that lead to structural brain damage, such as ischemic stroke, cerebral hemorrhage, brain trauma, or encephalitis, and factors that do not affect the structural integrity of the brain, such as metabolic disorders and intoxications. Patients with acute symptomatic seizures have a high risk of mortality in the first weeks after the event. They should be treated with anti-seizure medications during the acute phase of the underlying disease, as this may prevent subsequent acute symptomatic seizure. Treatment should focus on managing the underlying disease and correcting or eliminating the conditions or factors that provoke seizure. Long-term treatment with anti-seizure medications is usually not necessary, and anticonvulsants should be gradually withdrawn over several weeks or months following the acute symptomatic seizure.

A Reinforcement Learning-Based Generative Approach for Event Temporal Relation Extraction.

Event temporal relation extraction is a crucial task in natural language processing, aimed at recognizing the temporal relations between event triggers in a text. Despite extensive efforts in this area, the existing methods face two main issues. Firstly, the previous models for event temporal relation extraction mainly rely on a classification framework, which fails to output the crucial contextual words necessary for predicting the temporal relations between two event triggers. Secondly, the prior research that formulated natural language processing tasks as text generation problems usually trained the generative models by maximum likelihood estimation. However, this approach encounters potential difficulties when the optimization objective is misaligned with the task performance metrics. To resolve these limitations, we introduce a reinforcement learning-based generative framework for event temporal relation extraction. Specifically, to output the important contextual words from the input sentence for temporal relation identification, we introduce dependency path generation as an auxiliary task to complement event temporal relation extraction. This task is solved alongside temporal relation prediction to enhance model performance. To achieve this, we reformulate the event temporal relation extraction task as a text generation problem, aiming to generate both event temporal relation labels and dependency path words based on the input sentence. To bridge the gap between the optimization objective and task performance metrics, we employ the REINFORCE algorithm to optimize our generative model, designing a novel reward function to simultaneously capture the accuracy of temporal prediction and the quality of generation. Lastly, to mitigate the high variance issue encountered when using the REINFORCE algorithm in multi-task generative model training, we propose a baseline policy gradient algorithm to improve the stability and efficiency of the training process. Experimental results on two widely used datasets, MATRES and TB-DENSE, show that our approach exhibits competitive performance.

Food Insecurity Predicts Excessive Exercise, Dietary Restriction, Cognitive Restraint, and Purging, but NotBinge Eating, in College Students Across 3 Months.

College students are at elevated risk for both food insecurity and eating disorder (ED) symptoms. Prior literature supports cross-sectional associations between food insecurity and ED symptoms, including binge eating, purging (e.g., diuretic and laxative misuse, self-induced vomiting), and dietary restriction. However, less is known about the temporal relation, particularly among college students. We tested associations between food insecurity and cognitive restraint, binge eating, dietary restriction, purging, and excessive exercise across one college semester (three months). College students [N = 259; mean (SD) age = 19.22 (1.23)] were recruited to complete the Eating Pathology Symptoms Inventory (EPSI) and the 30-day version of the United States Department of Agriculture Adult Food Security Survey Module in August (baseline) and November (follow-up). We conducted five multiple regression models to examine baseline food insecurity as a predictor of each EPSI subscale score of interest, adjusting for baseline EPSI score, sociodemographic characteristics, and body mass index. Baseline food insecurity significantly predicted greater cognitive restraint (β = 0.12, p < 0.05), dietary restriction (β = 0.18, p < 0.001), excessive exercise (β = 0.15, p < 0.01), and purging (β = 0.14, p < 0.05) at follow-up, adjusting for baseline levels, sociodemographic characteristics, and body mass index. Baseline food insecurity did not predict binge eating at follow-up when the baseline level, body mass index, and sociodemographic characteristics were considered. Experiencing food insecurity may contribute to the development or exacerbation of excessive exercise, dietary restriction, cognitive restraint, and purging among college students. Findings highlight the potential need for food insecurity interventions to include support for disordered eating.

A Survey on Seasonal Symptoms in Subjects with and Without Allergic Rhinitis Diagnosis

In Switzerland, only scarce data are available on the prevalence and treatment of allergic rhinitis. Although the presence of AR symptoms in temporal relation to the respective aeroallergen is indicative, still a substantial number of affected individuals are deemed underdiagnosed and potentially undertreated. A national online survey was conducted for consecutive participants with AR symptoms in medical practices irrespective of diagnosis, therapy, or the reason for the visit. Univariate and multivariate regression analyses were performed, as well as multiple correspondence analysis for participants with allergic rhinitis diagnosis (ARwD) and without diagnosis (ARwoD). A total of 392 of 637 participants with rhinitic symptoms self-reported an AR diagnosis with a symptom onset more than 5 years ago in 74%. Despite treatment, up to one-third of participants with ARwD had persistent severe symptoms. Asthma was reported more frequently in participants with ARwD (148/392) than with ARwoD (26/245), (42% vs. 12%, p &lt; 0.001, q &lt; 0.001). Allergologists were consulted more often by participants with ARwD (106/392; 30% vs. 3/245; 2%), while more participants with ARwoD visited pharmacies for treatment advice (40/392; 11% vs. 57/245; 40%). The coexistence of AR and asthma with severe symptoms is a specific phenotype with difficult to treat nasal symptoms, amongst others. Hence, appropriate diagnosis and treatment of suspected and diagnosed AR should be prioritized, especially, but not limited to, patients with AR and asthma.

Contribution of a post-secondary academic enrichment program on cognitive abilities of adults with severe intellectual disability using an e-book.

Contribution of a post-secondary academic enrichment program on cognitive abilities of adults with severe intellectual disability using an e-book.

Taming "hanger" and falling prey to boredom-emotional and stress-eating in 801 healthy individuals using ecological momentary assessment.

Emotional eating and Stress Eating concepts hold that affective experiences can instill the desire for palatable foods. The empirical evidence for such couplings between affective and appetitive systems, however, is mixed and it remains unclear which one precedes the other or whether interindividual differences in such relationships exist. To study the temporal relations between a range of negative and positive emotions and stress on the one hand and snacking behavior on the other, we analyzed over 40.000 questionnaire entries obtained through ecological momentary assessment from 801 participants across nine different studies. Several trait-level eating style questionnaire scores were modelled as moderators for the emotion/stress - snacking relationships. Results showed that stronger boredom was followed by more snacking. Only irritation showed the pattern of reduction following snacking that would be predicted by emotion regulation accounts of emotional eating. Restrained eaters showed larger increases in boredom after snacking (compared to not snacking) than unrestrained eaters. Eating style questionnaires did not significantly moderate any other emotion - snacking - emotion relationships. Together with other recent findings from this dataset (Aulbach et al., n.d.) the present results suggest that eating style questionnaires capture tendencies to experience food cravings, but not snacking, as the latter might be 'gated' by several internal and external conditions that our EMA data and the trait questionnaire do not capture well. Accordingly, we suggest a novel terminology for affect-eating relationships that increases precision on the temporal (affects before or after eating/craving) and the phenomenological (snacking, craving) level.

Document-Level Causal Event Extraction Enhanced by Temporal Relations Using Dual-Channel Neural Network

Event–event causal relation extraction (ECRE) represents a critical yet challenging task in natural language processing. Existing studies primarily focus on extracting causal sentences and events, despite the use of joint extraction methods for both tasks. However, both pipeline methods and joint extraction approaches often overlook the impact of document-level event temporal sequences on causal relations. To address this limitation, we propose a model that incorporates document-level event temporal order information to enhance the extraction of implicit causal relations between events. The proposed model comprises two channels: an event–event causal relation extraction channel (ECC) and an event–event temporal relation extraction channel (ETC). Temporal features provide critical support for modeling node weights in the causal graph, thereby improving the accuracy of causal reasoning. An Association Link Network (ALN) is introduced to construct an Event Causality Graph (ECG), incorporating an innovative design that computes node weights using Kullback–Leibler divergence and Gaussian kernels. The experimental results indicate that our model significantly outperforms baseline models in terms of accuracy and weighted average F1 scores.