Representation Of Time Research Articles

Integration and competition between space and time in the hippocampus

Episodic memory is organized in both spatial and temporal contexts. The hippocampus is crucial for episodic memory and has been demonstrated to encode spatial and temporal information. However, how the representations of space and time interact in the hippocampal memory system is still unclear. Here, we recorded the activity of hippocampal CA1 neurons in mice in a variety of one-dimensional navigation tasks while systematically varying the speed of the animals. For all tasks, we found neurons simultaneously represented space and elapsed time. There was a negative correlation between the preferred space and lap duration, e.g., the preferred spatial position shifted more toward the origin when the lap duration became longer. A similar relationship between the preferred time and traveled distance was also observed. The results strongly suggest a competitive and integrated representation of space-time by single hippocampal neurons, which may provide the neural basis for spatiotemporal contexts.

ACDRL: A constraint-guided dynamic reinforcement learning for index selection

Index selection aims to find the optimal index configuration for a certain workload. In recent years, the database committee has attempted to develop an intelligent index advisor for replacing database administrators (DBAs). Although some researches show that applying reinforcement learning (RL) to index selection is a significant and promising direction, many problems still exist in applying the RL agent to the index selection i.e. long training time, unstable performance, and limited state representation. To address these issues, we propose a constraint-guided dynamic RL for index selection. Specifically, our method has three major contributions: (i) To balance the space consumption and index configuration quality, we design a dynamic index state structure to avoid missing effective indexes in limited space consumption. (ii) To accelerate agent training, we design a constraint-guided method to largely reduce useless explorations. (iii) To keep the stability of the RL, we propose the trade-off strategy for value function design instead of using Q_network mindlessly. We test our ACDRL with two open-source benchmarks, TPC-H and TPC-DS. The experimental results show that our method outperforms state-of-the-art methods and could find the optimal index configurations with limited time consumption.

Frequency vs. Time Representation of a Signal with a Smartphone and a Slide Whistle

Frequency vs. Time Representation of a Signal with a Smartphone and a Slide Whistle

Waiting time representation of discrete distributions

We discuss a representation of any probability distribution on the set of non-negative integers as a waiting time distribution in a sequence of independent Bernoulli trials. Several associated results are derived and illustrated by examples. Multivariate extensions are briefly treated as well.

From Lévy walks to fractional material derivative: Pointwise representation and a numerical scheme

The fractional material derivative appears as the fractional operator that governs the dynamics of the scaling limits of Lévy walks - a stochastic process that originates from the famous continuous-time random walks. It is usually defined as the Fourier–Laplace multiplier, therefore, it can be thought of as a pseudo-differential operator. In this paper, we show that there exists a local representation in time and space, pointwise, of the fractional material derivative. This allows us to define it on a space of locally integrable functions which is larger than the original one in which Fourier and Laplace transform exist as functions.We consider several typical differential equations involving the fractional material derivative and provide conditions for their solutions to exist. In some cases, the analytical solution can be found. For the general initial value problem, we devise a finite volume method and prove its stability, convergence, and conservation of probability. Numerical illustrations verify our analytical findings. Moreover, our numerical experiments show superiority in the computation time of the proposed numerical scheme over a Monte Carlo method applied to the problem of probability density function’s derivation.

ChronoVectors: Mapping Moments through Enhanced Temporal Representation

Time-series data are prevalent across various fields and present unique challenges for deep learning models due to irregular time intervals and missing records, which hinder the ability to capture temporal information effectively. This study proposes ChronoVectors, a novel temporal representation method that addresses these challenges by enabling a more specialized encoding of temporal relationships through the use of learnable parameters tailored to the dataset’s dynamics while maintaining consistent time intervals post-scaling. The theoretical demonstration shows that ChronoVectors allow the transformed encoding tensors to map moments in time to continuous spaces, accommodating potentially infinite extensions of the sequence and preserving temporal consistency. Experimental validation using the Parking Birmingham and Metro Interstate Traffic Volume datasets reveals that ChronoVectors enhanced the predictive capabilities of deep learning models by reducing prediction error for regression tasks compared to conventional time representations, such as vanilla timestamp encoding and Time2Vec. These findings underscore the potential of ChronoVectors in handling irregular time-series data and showcase its ability to improve deep learning model performance in understanding temporal dynamics.

Taking the temperature of time: The influence of temperature on people's metaphorical perspectives on time through emotion

AbstractSpace provides the conceptual scaffolding for the understanding of time. Customarily, the movement of events in time is metaphorically conceptualized from two spatial perspectives: the ego‐moving perspective that envisions an observer moving towards a stationary future and the time‐moving perspective that envisages the future moving towards a stationary observer. Extensive work has shown that emotions of opposite valence can yield contrary temporal perspective preferences. Relatedly, much evidence suggests that physical warmth and coldness prompt antithetical emotional evaluations. Threading together these two strands of insights, the current research explored whether ambient temperatures would influence people's metaphorical perspectives on time through a common link to emotion. Study 1 suggested that when deciphering two temporally ambiguous statements, people in warmer spring preferred the ego‐moving perspective, whilst those in colder winter favoured the time‐moving perspective. To determine a causal effect and test the hypothesized underlying mechanism of emotion, Study 2 examined the priming effect of temperature on temporal perspective preferences and emotional states. The results showed that differential temperatures induced opposite emotional responses, which in turn prompted diametric perspectives on time. These findings were replicated in Study 3 using an alternative measure of perspectives on time. Study 4 further validated the mediating role of emotion in the temperature–time relationship. Taken together, the current findings evince a unidirectional relationship between the incidental environmental factor of temperature and the metaphorical representation of time and accentuate the roles of sensory perception and emotional experience in temporal cognition.

Time matters: a critical multimodal study of an English learning app for children in China

ABSTRACT Time and its management, as evident in the scheduling, sequencing and structuring of activities, is important in early childhood education (ECE). While ECE apps have been changing teaching and learning practices for over a decade, research has yet to examine how they represent and manage time. To address this gap, we analysed the representation and management of time in iHuman ABC, a popular English learning app for children in China. Adopting Djonov and Van Leeuwen's (2018) model for analysing semiotic software and Van Leeuwen’s (2008) critical discourse studies framework, we examined temporal features in the app's design and references to these features in promotional materials and user reviews. Findings reveal that iHuman ABC reflects an instructivist ideology which considerably limits children’s ability to organise their time and contradicts research-based recommendations for teaching English. This study contributes to critical multimodal studies of the potential of software to transform educational practices.

Video Art and Collective Memory in Turkey

The aim of this article is to present and study contemporary video practices since the 2000s in Turkey through the representation of time and collective memory. The article features the four video artists who have structured the video art scene in Turkey: Ali Kazma, Halil Altındere, Erkan Özgen and Seza Paker. This research addresses two interconnected issues: firstly, the purpose is to examine how the distinct technical parameters of video production shape a synthesized temporality. This inquiry naturally gives rise to a secondary purpose: in what manner does this constructed temporality facilitate the exploration of memory and historical discourse? The hypothesis is that the video image, both as a technical artifact and a conceptual construct, inherently possesses its unique framework for temporal construction, thereby actively contributing to novel reinterpretations of collective memory and historiography The methodology involves conducting a formal analysis of the video content. This approach focuses on examining the visual elements, composition, use of color, sound, editing techniques, and overall aesthetics of the video to gain deeper insights into its artistic significance and meaning. By employing formal analysis, the aim of this article is to decipher the visual language employed by the video artists and uncover nuanced layers of interpretation.

POSTMODERNIST VIEWS OF TIME AND SPACE IN LITERATURE

Rejecting canonical narrative forms and ideas, postmodern literature presents a fresh perspective on the passage of time and geographical location. In contrast to the linear and cohesive representations of time and space expected in modernist literature, postmodernist works often embrace dislocation, fragmentation, and distorted timelines. Through metafiction, intertextuality, and non-linear tales, this study investigates how postmodern writers question traditional ideas of space and time. These methods show time as cyclical, irrelevant, or fluid, which disrupts the reader's perception of a steady, chronological development. Similarly, postmodern literature frequently portrays space as hyperreal, fragmented, or disjointed, reflecting a world where the lines between the tangible and the abstract are increasingly blurred. This study analyses seminal postmodern works to see how these changed views of space and time fit into a more extensive critique of postmodern reality, identity, and meaning stability. According to the results, postmodern literature does more than make us rethink the passage of time; it also prompts us to wonder what it means to exist.

Representations of calendars and time at Göbekli Tepe and Karahan Tepe support an astronomical interpretation of their symbolism

ABSTRACT Göbekli Tepe, an archaeological site in southern Turkey, features several temple-like enclosures adorned with many intricately carved symbols. It is located centrally among a group of Taş Tepeler pre-pottery Neolithic sites which include Karahan Tepe and Sayburç. Here, an earlier astronomical interpretation for Gobekli Tepe’s symbolism is supported and extended by showing how V-symbols on Pillar 43 in Enclosure D can be interpreted in terms of a lunisolar calendar system with 11 epagomenal days, which would make it the oldest known example of its type. Furthermore, it is shown how Göbekli Tepe’s 11-pillar enclosures and a megalithic 11-pillar pool structure at nearby Karahan Tepe can also be interpreted in terms of the same lunisolar calendar system. Other V-symbols at Göbekli Tepe are also interpreted in astronomical terms, and it is shown how the Urfa Man statue, a wall carving at Sayburç and a statue at Karahan Tepe that display V-symbol necklaces can be interpreted as time-controlling or creator deities. Symbolic links with later cultures from the Fertile Crescent are explored. Throughout, links are made with the Younger Dryas impact and Cauvin’s theory for the origin of the Neolithic revolution in the Fertile Crescent.

Short-term solar irradiance forecasting under data transmission constraints

We report a data-parsimonious machine learning model for short-term forecasting of solar irradiance. The model follows the convolutional neural network – long-short term memory architecture. Its inputs include sky camera images that are reduced to scalar features to meet data transmission constraints. The model focuses on predicting the deviation of irradiance from the persistence of cloudiness (POC) model. Inspired by control theory, a noise signal input is used to capture the presence of unknown and/or unmeasured input variables and is shown to improve model predictions, often considerably. Five years of data from the NREL Solar Radiation Research Laboratory were used to create three rolling train-validate sets and determine the best representations for time, the optimal span of input measurements, and the most impactful model input data (features). For the chosen validation data, the model achieves a mean absolute error of 74.29 W/m2over a time horizon of up to two hours, compared to a baseline 134.35 W/m2 using the POC model.

Learning to express reward prediction error-like dopaminergic activity requires plastic representations of time

The dominant theoretical framework to account for reinforcement learning in the brain is temporal difference learning (TD) learning, whereby certain units signal reward prediction errors (RPE). The TD algorithm has been traditionally mapped onto the dopaminergic system, as firing properties of dopamine neurons can resemble RPEs. However, certain predictions of TD learning are inconsistent with experimental results, and previous implementations of the algorithm have made unscalable assumptions regarding stimulus-specific fixed temporal bases. We propose an alternate framework to describe dopamine signaling in the brain, FLEX (Flexibly Learned Errors in Expected Reward). In FLEX, dopamine release is similar, but not identical to RPE, leading to predictions that contrast to those of TD. While FLEX itself is a general theoretical framework, we describe a specific, biophysically plausible implementation, the results of which are consistent with a preponderance of both existing and reanalyzed experimental data.

Kernelized Bures metric: A framework for effective domain adaptation in sensor data analysis

Unsupervised Domain Adaptation (UDA) plays a crucial role in enabling the transfer of models trained on labeled source domains to unlabeled target domains. However, this transferability encounters significant challenges when dealing with intricate time series models due to the complex temporal dynamics that differ between domains. These divergent dynamics result in disparities in time and frequency representations, leading to misalignments and gaps. Furthermore, the absence of localized information capture in previous approaches undermines the performance potential of the models. We introduce the KBSDA (Kernel Bures Sub-Domain Adaptation) approach to tackle the issues above. Our methodology employs the fast Fourier transform to extract frequency features while encompassing the time features. Our approach’s key focus is capturing intricate attributes containing local information, a task accomplished by applying the Local Maximum Mean Discrepancy (LMMD) metric. Furthermore, we address the distribution gaps between the source and target domains, elevating the alignment process by incorporating the kernel Bures metric. This metric, which encompasses higher-order moments and effectively manages intricate non-linear relationships, significantly manages complex distribution shifts. To validate the effectiveness of our approach, we conducted comprehensive experiments on well-established time series domain adaptation datasets such as HAR, HHAR, WISDM, and SSC. Our approach shows average accuracy of 95.10%, 78.04%, 78.69%, and 74.25% for HAR, HHAR, WISDM, and SSC, respectively.

Comparing the Representation of a Simple Visual Stimulus across the Cerebellar Network.

The cerebellum is a conserved structure of the vertebrate brain involved in the timing and calibration of movements. Its function is supported by the convergence of fibers from granule cells (GCs) and inferior olive neurons (IONs) onto Purkinje cells (PCs). Theories of cerebellar function postulate that IONs convey error signals to PCs that, paired with the contextual information provided by GCs, can instruct motor learning. Here, we use the larval zebrafish to investigate (1) how sensory representations of the same stimulus vary across GCs and IONs and (2) how PC activity reflects these two different input streams. We use population calcium imaging to measure ION and GC responses to flashes of diverse luminance and duration. First, we observe that GCs show tonic and graded responses, as opposed to IONs, whose activity peaks mostly at luminance transitions, consistently with the notion that GCs and IONs encode context and error information, respectively. Second, we show that GC activity is patterned over time: some neurons exhibit sustained responses for the entire duration of the stimulus, while in others activity ramps up with slow time constants. This activity could provide a substrate for time representation in the cerebellum. Together, our observations give support to the notion of an error signal coming from IONs and provide the first experimental evidence for a temporal patterning of GC activity over many seconds.

INP_ESM: Neuropeptide Identification Based on Evolutionary Scale Modeling and Unified Representation Embedding Features.

Neuropeptides are biomolecules with crucial physiological functions. Accurate identification of neuropeptides is essential for understanding nervous system regulatory mechanisms. However, traditional analysis methods are expensive and laborious, and the development of effective machine learning models continues to be a subject of current research. Hence, in this research, we constructed an SVM-based machine learning neuropeptide predictor, iNP_ESM, by integrating protein language models Evolutionary Scale Modeling (ESM) and Unified Representation (UniRep) for the first time. Our model utilized feature fusion and feature selection strategies to improve prediction accuracy during optimization. In addition, we validated the effectiveness of the optimization strategy with UMAP (Uniform Manifold Approximation and Projection) visualization. iNP_ESM outperforms existing models on a variety of machine learning evaluation metrics, with an accuracy of up to 0.937 in cross-validation and 0.928 in independent testing, demonstrating optimal neuropeptide recognition capabilities. We anticipate improved neuropeptide data in the future, and we believe that the iNP_ESM model will have broader applications in the research and clinical treatment of neurological diseases.

Wind power forecasting based on a novel gated recurrent neural network model

Accurate wind power forecasting is crucial for upgrading the operation and maintenance of wind turbines and the effectiveness in wind power dispatching. In this work, density-based spatial clustering of applications with noise (DBSCAN) is adopted to exclude outliers, and is examined to yield top-quality data filtering for neural networks when implemented on supervisory control and data acquisition (SCADA) data from a 7-MW wind turbine. Apart from features, such as wind and rotor speeds, time is a vital feature involved with synchronous or asynchronous events. Thus, a model-agnostic vector representation for time, Time2Vec (T2V), is used. This can be embedded with neural network architectures, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The novel neural network model, T2V-GRU, concatenates T2V into vanilla GRU, replacing the notion of time vector by T2V. Forecasting results are evaluated by mean absolute error (MAE), root mean squared error (RMSE), and accuracy. In comparison to T2V-LSTM, LSTM and GRU, the proposed T2V-GRU model in combination with DBSCAN refinement has superior forecasting results for the 7-MW turbine, yielding maximum forecasting accuracy (93.12 %) and minimal prediction errors in terms of MAE (286.10 kW) and RMSE (489.54 kW), while simultaneously reducing the computational cost and time.

Spatio-temporal knowledge embedding method considering the lifecycle of geographical entities

With the emergence of substantial amounts of data featuring spatio-temporal characteristics, research on spatio-temporal knowledge has garnered widespread attention. However, the existing knowledge graph embedding methods are difficult to embed spatio-temporal knowledge in continuous time, failing to meet the unique embedding requirements of geographic entities for uninterrupted, constantly changing, and smoothly continuous embeddings. To address this issue, we begin by outlining a version-based representation of spatio-temporal knowledge. Building on this foundation, we propose a novel and effective knowledge graph embedding model called Version Embedding (VerE), which embeds continuous time into vector space, transforms geographic entities into corresponding versions using an attention mechanism, and calculates spatio-temporal knowledge scores under the constraint of version similarity regularization. Subsequently, we introduce a method for link prediction based on unknown time with the aim of assessing the model’s generalization capabilities in time representation. Finally, experiments conducted on two real-world datasets demonstrate significant performance improvement of VerE compared to most existing models, and its ability to maintain stability and continuity in link prediction under unknown time. These experiments confirmed the effectiveness of the proposed model and provided new perspectives and methods for spatio-temporal knowledge embedding.

Telling Time: Patient Experiences of Temporality in Brain Tumor Comics.

This article explores three different comics by creators with brain tumors: Rick, written and drawn by Gordon Shaw; Going Remote, written by Adam Bessie and drawn by Peter Glanting; and Parenthesis, written and drawn by Élodie Durand. It examines how the affordances of the comics medium enables the creators to present an experience of subjective time that is multiple, diffuse, and contradictory, in contrast to the regular apportioning of time via calendars, schedules, and pathways essential to institutional neuro-oncology. The question of time here is significant because the side effects of brain tumors can include blackouts, seizures, and periods of extreme fatigue, during which the experience of time can be significantly disrupted. The title of the article therefore evokes a temporal duality: on the one hand, it refers to the common phrase used to describe what clocks do, as well as our ability to read them; on the other hand, it speaks to one of the most important qualities of graphic medicine, which is that it allows patients dealing with medical or health issues to tell time differently. The article explores the representation of personal time in Rick, social time in Going Remote, and lost time in Parenthesis.

Decoupling representation contrastive learning for carbon emission prediction and analysis based on time series

Carbon emissions have become particularly urgent with the acceleration of industrialization. Currently, AI-based carbon emission modeling provides essential support for solving the problem of predicting, managing, and optimizing carbon emissions. However, there are still challenges in accurately predicting carbon dioxide emissions and establishing an appropriate carbon emission regulatory model to manage carbon dioxide emissions effectively. Firstly, this paper introduces a framework for learning seasonal and trend representations called DeTF (Decoupling representation for Time and Frequency domain of time series). The framework is designed for time series data of CO2 emissions used for power generation from 1973 to 2023. Secondly, we use various data enhancement techniques to improve the robustness of the learned representations. Finally, the prediction results establish a tripartite evolutionary game relationship between the state, regulatory agencies, and enterprises. We use MSE and MAE as evaluation metrics, and our model overall outperforms current state-of-the-art end-to-end algorithms. It outperforms the best performing end-to-end prediction method by 27.08% (MSE) in the prediction results. Besides, the time series prediction results are innovatively combined with the tripartite game model to analyze the main factors determining the optimal strategy.