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Evidence accumulates that the cerebellum's role in the brain is not restricted to motor functions. Rather, cerebellar activity seems to be crucial for a variety of tasks that rely on precise event timing and prediction. Due to its complex structure and importance in communication, human speech requires a particularly precise and predictive coordination of neural processes to be successfully comprehended. Recent studies proposed that the cerebellum is indeed a major contributor to speech processing, but how this contribution is achieved mechanistically remains poorly understood. The current study aimed to reveal a mechanism underlying cortico-cerebellar coordination and demonstrate its speech-specificity. In a reanalysis of magnetoencephalography data, we found that activity in the cerebellum aligned to rhythmic sequences of noise-vocoded speech, irrespective of its intelligibility. We then tested whether these "entrained" responses persist, and how they interact with other brain regions, when a rhythmic stimulus stopped and temporal predictions had to be updated. We found that only intelligible speech produced sustained rhythmic responses in the cerebellum. During this "entrainment echo," but not during rhythmic speech itself, cerebellar activity was coupled with that in the left inferior frontal gyrus, and specifically at rates corresponding to the preceding stimulus rhythm. This finding represents evidence for specific cerebellum-driven temporal predictions in speech processing and their relay to cortical regions. [ABSTRACT FROM AUTHOR]

The purpose of the present study was to examine whether spatial or temporal prediction of the tactile stimulus contributes to tactile sensitivity. To investigate the effect of spatial prediction on tactile sensitivity, electrical stimuli were provided for the digit nerve in one of five fingers, and advanced notice of the stimulating finger was provided before the stimulus in some trials but not in others. There was no significant effect of spatial prediction on the intensity at the perceptual threshold of the digit nerve stimulus. This indicates that spatial prediction of the tactile stimulus does not influence tactile sensitivity. To examine the effect of temporal prediction, an auditory warning cue was provided 0, 1, or 10 s before the electrical stimulus to the digit nerve. The stimulus intensity at the perceptual threshold in the trials with the 1 s warning cue was lower than those with the 0 s warning cue. This indicates that temporal prediction enhances tactile sensitivity. The stimulus intensity at the perceptual threshold in the trials with the 1 s warning cue was lower than those with the 10 s warning cue. This means that the contribution of temporal prediction to the tactile sensitivity is greater as the warning cue is closer to the time of the stimulus. This finding may be explained by a defense mechanism activated when humans predict that a tactile stimulus is coming soon. [ABSTRACT FROM AUTHOR]

Temporal prediction (TP) influences our perception and cognition. The cerebellum could mediate this multi-level ability in a context-dependent manner. We tested whether a modulation of the cerebellar neural activity, induced by transcranial Direct Current Stimulation (tDCS), changed the TP ability according to the temporal features of the context and the duration of target interval. Fifteen healthy participants received anodal, cathodal, and sham tDCS (15 min × 2 mA intensity) over the right cerebellar hemisphere during a TP task. We recorded reaction times (RTs) to a target during the task in two contextual conditions of temporal anticipation: rhythmic (i.e., interstimulus intervals (ISIs) were constant) and single-interval condition (i.e., the estimation of the timing of the target was based on the prior exposure of the train of stimuli). Two ISIs durations were explored: 600 ms (short trials) and 900 ms (long trials). Cathodal tDCS improved the performance during the TP task (shorter RTs) specifically in the rhythmic condition only for the short trials and in the single-interval condition only for the long trials. Our results suggest that the inhibition of cerebellar activity induced a different improvement in the TP ability according to the temporal features of the context. In the rhythmic context, the cerebellum could integrate the temporal estimation with the anticipatory motor responses critically for the short target interval. In the single-interval context, for the long trials, the cerebellum could play a main role in integrating representation of time interval in memory with the elapsed time providing an accurate temporal prediction. [ABSTRACT FROM AUTHOR]

Landslide disasters frequently result in significant casualties and property losses, underscoring the critical importance of research on landslide displacement prediction. This paper introduces an approach combining improved empirical mode decomposition (ICEEMDAN) and singular entropy-enhanced singular spectrum analysis (SSA) to predict landslide displacement using a time series short-duration memory network (LSTM). Initially, ICEEMDAN decomposes the landslide displacement time series into trend and periodic terms. SSA is then employed to denoise these components before fitting the trend term with LSTM. Pearson correlation analysis is utilized to identify characteristic factors within the LSTM model, followed by predictions using a multivariate LSTM model. The empirical results from the Baijiabao landslide in the Three Gorges Reservoir area demonstrate that the joint ICEEMDAN-SSA approach, when combined with LSTM modeling, outperforms the separate applications of SSA and ICEEMDAN, as well as other models such as RNN and SVM. Specifically, the ICEEMDAN-SSA-LSTM model achieves an RMSE of 6.472 mm and an MAE of 4.992 mm, which are considerably lower than those of the RNN model (19.945 mm and 15.343 mm, respectively) and the SVM model (16.584 mm and 11.748 mm, respectively). Additionally, the R2 value for the ICEEMDAN-SSA-LSTM model is 97.5%, significantly higher than the RNN model's 72.3% and the SVM model's 92.8%. By summing the predictions of the trend and periodic terms, the cumulative displacement prediction is obtained, indicating the superior accuracy of the ICEEMDAN-SSA-LSTM model. This model provides a new benchmark for precise landslide displacement prediction and contributes valuable insights to related research. [ABSTRACT FROM AUTHOR]

Music, like spoken language, is often characterized by hierarchically organized structure. Previous experiments have shown neural tracking of notes and beats, but little work touches on the more abstract question: how does the brain establish high-level musical structures in real time? We presented Bach chorales to participants (20 females and 9 males) undergoing electroencephalogram (EEG) recording to investigate how the brain tracks musical phrases. We removed the main temporal cues to phrasal structures, so that listeners could only rely on harmonic information to parse a continuous musical stream. Phrasal structures were disrupted by locally or globally reversing the harmonic progression, so that our observations on the original music could be controlled and compared. We first replicated the findings on neural tracking of musical notes and beats, substantiating the positive correlation between musical training and neural tracking. Critically, we discovered a neural signature in the frequency range ~0.1 Hz (modulations of EEG power) that reliably tracks musical phrasal structure. Next, we developed an approach to quantify the phrasal phase precession of the EEG power, revealing that phrase tracking is indeed an operation of active segmentation involving predictive processes. We demonstrate that the brain establishes complex musical structures online over long timescales (>5 s) and actively segments continuous music streams in a manner comparable to language processing. These two neural signatures, phrase tracking and phrasal phase precession, provide new conceptual and technical tools to study the processes underpinning high-level structure building using noninvasive recording techniques. [ABSTRACT FROM AUTHOR]

Predicting the timing of incoming information allows brain to optimize information processing in dynamic environments. However, the effects of temporal predictions on tactile perception are not well established. In this study, two experiments were conducted to determine how temporal predictions interact with conditional probabilities in tactile perceptual processing. In Experiment 1, we explored the range of the interval between preceding ready cues and imperative targets in which temporal prediction effects can be observed. This prediction effect was observed for intervals of 500 and 1,000 ms. In Experiment 2, we investigated the benefits of temporal predictions on tactile perception while manipulating the conditional probability (setting the stimulus onset earlier or later than the predicted moment in short and long intervals). Our results revealed that this effect became stronger as the probability of the stimulus at the predicted time point increased under short-interval conditions. Together, our results show that the difficulty of transferring processing resources increases in temporally dynamic environments, suggesting a greater subjective cost associated with maladaptive responses to temporally uncertain events. [ABSTRACT FROM AUTHOR]

The ability to accurately encode the temporal information of sensory events and hence to make prompt action is fundamental to humans’ prompt behavioral decision-making. Here we examined the ability of ensemble coding (averaging multiple inter-intervals in a sound sequence) and subsequent immediate reproduction of target duration at half, equal, or double that of the perceived mean interval in a sensorimotor loop. With magnetoencephalography (MEG), we found that the contingent magnetic variation (CMV) in the central scalp varied as a function of the averaging tasks, with a faster rate for buildup amplitudes and shorter peak latencies in the “half” condition as compared to the “double” condition. ERD (event-related desynchronization) -to-ERS (event-related synchronization) latency was shorter in the ”half” condition. A robust beta band (15–23 Hz) power suppression and recovery between the final tone and the action of key pressing was found for time reproduction. The beta modulation depth (i.e., the ERD-to-ERS power difference) was larger in motor areas than in primary auditory areas. Moreover, results of phase slope index (PSI) indicated that beta oscillations in the left supplementary motor area (SMA) led those in the right superior temporal gyrus (STG), showing SMA to STG directionality for the processing of sequential (temporal) auditory interval information. Our findings provide the first evidence to show that CMV and beta oscillations predict the coupling between perception and action in time averaging.

Definitions of syncopation share two characteristics: the presence of a meter or analogous hierarchical rhythmic structure and a displacement or contradiction of that structure. These attributes are translated in terms of a Bayesian theory of syncopation, where the syncopation of a rhythm is inferred based on a hierarchical structure that is, in turn, learned from the ongoing musical stimulus. Several experiments tested its simplest possible implementation, with equally weighted priors associated with different meters and independence of auditory events, which can be decomposed into two terms representing note density and deviation from a metric hierarchy. A computational simulation demonstrated that extant measures of syncopation fall into two distinct factors analogous to the terms in the simple Bayesian model. Next, a series of behavioral experiments found that perceived syncopation is significantly related to both terms, offering support for the general Bayesian construction of syncopation. However, we also found that the prior expectations associated with different metric structures are not equal across meters and that there is an interaction between density and hierarchical deviation, implying that auditory events are not independent from each other. Together, these findings provide evidence that syncopation is a manifestation of a form of temporal expectation that can be directly represented in Bayesian terms and offer a complementary, feature‐driven approach to recent Bayesian models of temporal prediction. [ABSTRACT FROM AUTHOR]

Temporal processing can be divided into explicit timing and implicit timing. Explicit timing tasks require participants to attend to the temporal aspects of the task, whereas in implicit timing tasks, temporal information affects performance without explicit instruction to process time. Compared to younger adults, older adults have been shown to exhibit greater variability in explicit timing, while in implicit timing, they have been shown to rely more on temporal predictions formed by the hazard function. However, the relationship between explicit and implicit timing and its age-related changes have yet to be explored. To address this issue, we collected data in which younger and older adults performed a time bisection task (i.e., explicit timing task) and a foreperiod task (i.e., implicit timing task) in a within-subjects design. Based on a Bayesian optimization framework, we hypothesized that individuals with higher variability in explicit timing would show a stronger foreperiod effect, which is an index of the degree of reliance on temporal predictions. Results showed a different relation between explicit and implicit timing in younger and older adults. In older adults, results were consistent with the hypothesis that increased variability in explicit timing was associated with a stronger foreperiod effect. In contrast, bias in the temporal representation but not variability was associated with the foreperiod effect in younger adults. Implications for the age-related difference in the relation between explicit and implicit timing are discussed. [ABSTRACT FROM AUTHOR]

The purpose of the present study was to examine whether spatial or temporal prediction of the tactile stimulus contributes to tactile sensitivity. To investigate the effect of spatial prediction on tactile sensitivity, electrical stimuli were provided for the digit nerve in one of five fingers, and advanced notice of the stimulating finger was provided before the stimulus in some trials but not in others. There was no significant effect of spatial prediction on the intensity at the perceptual threshold of the digit nerve stimulus. This indicates that spatial prediction of the tactile stimulus does not influence tactile sensitivity. To examine the effect of temporal prediction, an auditory warning cue was provided 0, 1, or 10 s before the electrical stimulus to the digit nerve. The stimulus intensity at the perceptual threshold in the trials with the 1 s warning cue was lower than those with the 0 s warning cue. This indicates that temporal prediction enhances tactile sensitivity. The stimulus intensity at the perceptual threshold in the trials with the 1 s warning cue was lower than those with the 10 s warning cue. This means that the contribution of temporal prediction to the tactile sensitivity is greater as the warning cue is closer to the time of the stimulus. This finding may be explained by a defense mechanism activated when humans predict that a tactile stimulus is coming soon.

Landslide disasters frequently result in significant casualties and property losses, underscoring the critical importance of research on landslide displacement prediction. This paper introduces an approach combining improved empirical mode decomposition (ICEEMDAN) and singular entropy-enhanced singular spectrum analysis (SSA) to predict landslide displacement using a time series short-duration memory network (LSTM). Initially, ICEEMDAN decomposes the landslide displacement time series into trend and periodic terms. SSA is then employed to denoise these components before fitting the trend term with LSTM. Pearson correlation analysis is utilized to identify characteristic factors within the LSTM model, followed by predictions using a multivariate LSTM model. The empirical results from the Baijiabao landslide in the Three Gorges Reservoir area demonstrate that the joint ICEEMDAN-SSA approach, when combined with LSTM modeling, outperforms the separate applications of SSA and ICEEMDAN, as well as other models such as RNN and SVM. Specifically, the ICEEMDAN-SSA-LSTM model achieves an RMSE of 6.472 mm and an MAE of 4.992 mm, which are considerably lower than those of the RNN model (19.945 mm and 15.343 mm, respectively) and the SVM model (16.584 mm and 11.748 mm, respectively). Additionally, the R2 value for the ICEEMDAN-SSA-LSTM model is 97.5%, significantly higher than the RNN model’s 72.3% and the SVM model’s 92.8%. By summing the predictions of the trend and periodic terms, the cumulative displacement prediction is obtained, indicating the superior accuracy of the ICEEMDAN-SSA-LSTM model. This model provides a new benchmark for precise landslide displacement prediction and contributes valuable insights to related research.

Bagmati river is biologically, geologically, religiously and historically significant among the river systems of the Kathmandu Valley. The river is affected by five major tributaries, including Manohara, Dhobi Khola, Tukucha, Bishnumati, and Balkhu Khola, which significantly impact the water chemistry inside the Kathmandu Valley. The data of water quality parameters pH, dissolved oxygen, turbidity, temperature, oxygen reduction potential, conductivity, total dissolved solids, salinity among others was collected using fixed sensors (in period of 5 seconds) and mobile sensors (with latitude and longitude) along the river. The observation is important for two reasons, one because it was collected in real-time and fine scale, which is not normally possible with traditional ways, and next such observation was done for the first time in Bagmati River. The aim of this study was to predict water quality parameters of the Bagmati River using machine learning time series models, specifically ARIMA and LSTM. The LSTM model was designed with one input layer, one encoder layer, one repeat layer, one decoder layer, and one output dense layer to separate the output into temporal slices. Additionally, a DNN model was employed for location-based prediction, utilizing two input layers for latitude and longitude and seven output layers for the seven water quality parameters considered for study. The models demonstrated promising performance, but further data collection and parameter variation are recommended for continued optimization.

Rhythmic entrainment echoes—rhythmic brain responses that outlast rhythmic stimulation—can demonstrate endogenous neural oscillations entrained by the stimulus rhythm. Here, we tested for such echoes in auditory perception. Participants detected a pure tone target, presented at a variable delay after another pure tone that was rhythmically modulated in amplitude. In four experiments involving 154 human (female and male) participants, we tested (1) which stimulus rate produces the strongest entrainment echo and, inspired by the tonotopical organization of the auditory system and findings in nonhuman primates, (2) whether these are organized according to sound frequency. We found the strongest entrainment echoes after 6 and 8 Hz stimulation, respectively. The best moments for target detection (in phase or antiphase with the preceding rhythm) depended on whether sound frequencies of entraining and target stimuli matched, which is in line with a tonotopical organization. However, for the same experimental condition, best moments were not always consistent across experiments. We provide a speculative explanation for these differences that relies on the notion that neural entrainment and repetition-related adaptation might exercise competing opposite influences on perception. Together, we find rhythmic echoes in auditory perception that seem more complex than those predicted from initial theories of neural entrainment. [ABSTRACT FROM AUTHOR]

Representation learning based on temporal knowledge graphs (TKGs) has attracted widespread interest, and temporal knowledge graph embedding (TKGE) expresses time entity and relation tokens and exhibit strong dynamics. Despite the significance of the dynamics and the persistent updates in TKGs, most studies have been devoted to static knowledge graphs. Moreover, previous temporal works ignored the semantic hierarchies observed in knowledge modelling cases, which are common in real-world applications. Inaccurate semantic expressions caused by incomplete projections might not capture complex topological structures very well. To solve this problem, a novel hierarchicaltime-surfaceembedding (HTSE) model is proposed for the representation learning of entities, relations and time. Specifically, a unified relation-oriented hierarchical space aims to distinguish relations at different semantic levels of a hierarchy, and entities can naturally reflect the corresponding hierarchy. Then, a time surface aims to enhance the temporal characteristics, and quadruples are learned through exponential mapping and tangent planes in the time surface. According to extensive experiments, HTSE can achieve remarkable performance on five benchmark datasets, outperforming baseline models for time scope prediction, temporal link prediction and hierarchical relation embedding tasks.Furthermore, the qualitative analysis is used to demonstrate the explainable strategy for hierarchical embeddings and their significance in TKGs. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for transmitting and reconstructing the estimated point cloud by temporally estimating a dynamic point cloud sequence. When a non-rigid 3D point cloud sequence (PCS) is input, the sequence is divided into groups of point cloud frames (PCFs), and a key PCF is selected. The 3D skeleton is predicted through 3D pose estimation, and the motion of the skeleton is estimated by analyzing the joints and bones of the 3D skeleton. For the deformation of the non-rigid human PC, the 3D PC model is transformed into a mesh model, and the key PCF is rigged using the 3D skeleton. After deforming the key PCF into the target PCF utilizing the motion vector of the estimated skeleton, the residual PC between the motion compensation PCF and the target PCF is generated. If there is a key PCF, the motion vector of the target PCF, and a residual PC, the target PCF can be reconstructed. Just as compression is performed using pixel correlation between frames in a 2D video, this paper compresses 3D PCFs by estimating the non-rigid 3D motion of a 3D object in a 3D PC. The proposed algorithm can be regarded as an extension of the 2D motion estimation of a rigid local region in a 2D plane to the 3D motion estimation of a non-rigid object (human) in 3D space. Experimental results show that the proposed method can successfully compress 3D PC sequences. If it is used together with a PC compression technique such as MPEG PCC (point cloud compression) in the future, a system with high compression efficiency may be configured. [ABSTRACT FROM AUTHOR]

Both the cerebellum and basal ganglia are involved in rhythm processing, but their specific roles remain unclear. During rhythm perception, these areas may be processing purely sensory information, or they may be involved in motor preparation, as periodic stimuli often induce synchronized movements. Previous studies have shown that neurons in the cerebellar dentate nucleus and the caudate nucleus exhibit periodic activity when the animals prepare to respond to the random omission of regularly repeated visual stimuli. To detect stimulus omission, the animals need to learn the stimulus tempo and predict the timing of the next stimulus. The present study demonstrates that neuronal activity in the cerebellum is modulated by the location of the repeated stimulus and that in the striatum (STR) by the direction of planned movement. However, in both brain regions, neuronal activity during movement and the effect of electrical stimulation immediately before stimulus omission were largely dependent on the direction of movement. These results suggest that, during rhythm processing, the cerebellum is involved in multiple stages from sensory prediction to motor control, while the STR consistently plays a role in motor preparation. Thus, internalized rhythms without movement are maintained as periodic neuronal activity, with the cerebellum and STR preferring sensory and motor representations, respectively. [ABSTRACT FROM AUTHOR]

For the prediction of landslide-generated waves, previous studies have developed numerous empirical equations to express the maximums of wave characteristics as functions of slide parameters upon impact. In this study, we built the temporal relationship between the wave characteristics and slide features. We gave specific insights into impulse waves generated by snow avalanches and mimicked them using a buoyant material called Carbopol whose density is close to that of water. Using the particle image velocimetry (PIV) technique, the slide's temporal velocity field and thickness, as well as the temporal free water surface fluctuation, were determined experimentally. Using a statistical method denoted as panel data analysis, we quantified the temporal wave amplitude from the time series data of the thickness and depth-averaged velocity of the sliding mass at the shoreline. Then, the slide's temporal thickness and velocity at the shoreline were estimated from the parameters of the stationary slide at the initial position, based on the viscoplastic theory. Combining the panel data analysis and the viscoplastic theory, the temporal wave amplitudes were estimated from the initial slide parameters. In the end, we validated the proposed theoretical–statistical combined predictive method with the support of experimental data. [ABSTRACT FROM AUTHOR]

Data-driven Remaining Useful Life (RUL) prediction is one of the core technologies of Prognostics and Health Management (PHM). Committed to improving the accuracy of RUL prediction for aero-engines, this paper proposes a model that is entirely based on the attention mechanism. The attention model is divided into the multi-head self-attention and timing feature enhancement attention models. The multi-head self-attention model employs scaled dot-product attention to extract dependencies between time series; the timing feature enhancement attention model is used to accelerate and enhance the feature selection process. This paper utilises Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbofan engine simulation data obtained from NASA Ames' Prognostics Center of Excellence and compares the proposed algorithm to other models. The experiments conducted validate the superiority of our model's approach. [ABSTRACT FROM AUTHOR]

One hypothesis for why humans enjoy musical rhythms relates to their prediction of when each beat should occur. The ability to predict the timing of an event is important from an evolutionary perspective. Therefore, our brains have evolved internal mechanisms for processing the progression of time. However, due to inherent noise in neural signals, this prediction is not always accurate. Theoretical considerations of optimal estimates suggest the occurrence of certain systematic errors made by the brain when estimating the timing of beats in rhythms. Here, we tested psychophysically whether these systematic errors exist and if so, how they depend on stimulus parameters. Our experimental data revealed two main types of systematic errors. First, observers perceived the time of the last beat of a rhythmic pattern as happening earlier than actual when the inter-beat interval was short. Second, the perceived time of the last beat was later than the actual when the inter-beat interval was long. The magnitude of these systematic errors fell as the number of beats increased. However, with many beats, the errors due to long inter-beat intervals became more apparent. We propose a Bayesian model for these systematic errors. The model fits these data well, allowing us to offer possible explanations for how these errors occurred. For instance, neural processes possibly contributing to the errors include noisy and temporally asymmetric impulse responses, priors preferring certain time intervals, and better-early-than-late loss functions. We finish this article with brief discussions of both the implications of systematic errors for the appreciation of rhythm and the possible compensation by the brain's motor system during a musical performance. [ABSTRACT FROM AUTHOR]

The ability to accurately encode the temporal information of sensory events and hence to make prompt action is fundamental to humans' prompt behavioral decision-making. Here we examined the ability of ensemble coding (averaging multiple inter-intervals in a sound sequence) and subsequent immediate reproduction of target duration at half, equal, or double that of the perceived mean interval in a sensorimotor loop. With magnetoencephalography (MEG), we found that the contingent magnetic variation (CMV) in the central scalp varied as a function of the averaging tasks, with a faster rate for buildup amplitudes and shorter peak latencies in the "half" condition as compared to the "double" condition. ERD (event-related desynchronization) -to-ERS (event-related synchronization) latency was shorter in the "half" condition. A robust beta band (15–23 Hz) power suppression and recovery between the final tone and the action of key pressing was found for time reproduction. The beta modulation depth (i.e., the ERD-to-ERS power difference) was larger in motor areas than in primary auditory areas. Moreover, results of phase slope index (PSI) indicated that beta oscillations in the left supplementary motor area (SMA) led those in the right superior temporal gyrus (STG), showing SMA to STG directionality for the processing of sequential (temporal) auditory interval information. Our findings provide the first evidence to show that CMV and beta oscillations predict the coupling between perception and action in time averaging. • Human observers ensemble encode the mean auditory intervals efficiently. • CMV varied as a function of the interval averaging tasks. • Beta modulation depth was larger in motor areas than in primary auditory areas. [ABSTRACT FROM AUTHOR]

One hypothesis for why humans enjoy musical rhythms relates to their prediction of when each beat should occur. The ability to predict the timing of an event is important from an evolutionary perspective. Therefore, our brains have evolved internal mechanisms for processing the progression of time. However, due to inherent noise in neural signals, this prediction is not always accurate. Theoretical considerations of optimal estimates suggest the occurrence of certain systematic errors made by the brain when estimating the timing of beats in rhythms. Here, we tested psychophysically whether these systematic errors exist and if so, how they depend on stimulus parameters. Our experimental data revealed two main types of systematic errors. First, observers perceived the time of the last beat of a rhythmic pattern as happening earlier than actual when the inter-beat interval was short. Second, the perceived time of the last beat was later than the actual when the inter-beat interval was long. The magnitude of these systematic errors fell as the number of beats increased. However, with many beats, the errors due to long inter-beat intervals became more apparent. We propose a Bayesian model for these systematic errors. The model fits these data well, allowing us to offer possible explanations for how these errors occurred. For instance, neural processes possibly contributing to the errors include noisy and temporally asymmetric impulse responses, priors preferring certain time intervals, and better-early-than-late loss functions. We finish this article with brief discussions of both the implications of systematic errors for the appreciation of rhythm and the possible compensation by the brain’s motor system during a musical performance.

Abstract Planning forest management relies on predicting insect outbreaks such as mountain pine beetle, particularly in the intermediate‐term future, e.g., 5‐year. Machine‐learning algorithms are potential solutions to this challenging problem due to their many successes across a variety of prediction tasks. However, there are many subtle challenges in applying them: identifying the best learning models and the best subset of available covariates (including time lags) and properly evaluating the models to avoid misleading performance‐measures. We systematically address these issues in predicting the chance of a mountain pine beetle outbreak in the Cypress Hills area and seek models with the best performance at predicting future 1‐, 3‐, 5‐ and 7‐year infestations. We train nine machine‐learning models, including two generalized boosted regression trees (GBM) that predict future 1‐ and 3‐year infestations with 92% and 88% AUC, and two novel mixed models that predict future 5‐ and 7‐year infestations with 86% and 84% AUC, respectively. We also consider forming the train and test datasets by splitting the original dataset randomly rather than using the appropriate year‐based approach and show that this may obtain models that score high on the test dataset but low in practice, resulting in inaccurate performance evaluations. For example, a k‐nearest neighbor model with the actual performance of 68% AUC, scores the misleadingly high 78% on a test dataset obtained from a random split, but the more accurate 66% on a year‐based split. We then investigate how the prediction accuracy varies with respect to the provided history length of the covariates and find that neural network and naive Bayes, predict more accurately as history‐length increases, particularly for future 1‐ and 3‐year predictions, and roughly the same holds with GBM. Our approach is applicable to other invasive species. The resulting predictors can be used in planning forest and pest management and planning sampling locations in field studies.

For the prediction of landslide-generated waves, previous studies have developed numerous empirical equations to express the maximums of wave characteristics as functions of slide parameters upon impact. In this study, we built the temporal relationship between the wave characteristics and slide features. We gave specific insights into impulse waves generated by snow avalanches and mimicked them using a buoyant material called Carbopol whose density is close to that of water. Using the particle image velocimetry (PIV) technique, the slide’s temporal velocity field and thickness, as well as the temporal free water surface fluctuation, were determined experimentally. Using a statistical method denoted as panel data analysis, we quantified the temporal wave amplitude from the time series data of the thickness and depth-averaged velocity of the sliding mass at the shoreline. Then, the slide’s temporal thickness and velocity at the shoreline were estimated from the parameters of the stationary slide at the initial position, based on the viscoplastic theory. Combining the panel data analysis and the viscoplastic theory, the temporal wave amplitudes were estimated from the initial slide parameters. In the end, we validated the proposed theoretical–statistical combined predictive method with the support of experimental data.

A regular rhythmic stimulation increases people's ability to anticipate future events in time and to move their body in space. Temporal concepts are usually prescribed to spatial locations through a past-behind and future-ahead mapping. In this study, we tested the hypothesis that a regular rhythmic stimulation could promote the forward-body (i.e., toward the future) projections in the peri-personal space. In a Visual Approach/Avoidance by the Self Task (VAAST), participants (N = 24) observed a visual scene on the screen (i.e., a music studio with a metronome in the middle). They were exposed to 3 s of auditory isochronous or non-isochronous rhythms, after which they were asked to make as quickly as possible a perceptual judgment on the visual scene (i.e., whether the metronome pendulum was pointing to the right or left). The responses could trigger a forward or backward visual flow, i.e., approaching or moving them away from the scene. Results showed a significant interaction between the rhythmic stimulation and the movement projections (p < 0.001): participants were faster for responses triggering forward-body projections (but not backward-body projections) after the exposure to isochronous (but not non-isochronous) rhythm. By highlighting the strong link between isochronous rhythms and forward-body projections, these findings support the idea that temporal predictions driven by a regular auditory stimulation are grounded in a perception-action system integrating temporal and spatial information. [ABSTRACT FROM AUTHOR]

Frost events incur substantial economic losses to farmers. These events could induce damage to plants and crops by damaging the cells. In this article, a recurrent neural network-based method, automating the frost prediction process, is proposed. The recurrent neural network-based models leveraged in this article include the standard recurrent neural network, long short-term memory, and gated recurrent unit. The proposed method aims to increase the prediction frequency from once per 12–24 h for the next day or night events to minute-wise predictions for the next hour events. To achieve this goal, datasets from NSW and ACT of Australia are obtained. The experiments are designed considering the scene of deploying the model to the Internet of Things systems. Factors such as model processing speed, long-term error and data availability are reviewed. After model construction, there are three experiments. The first experiment tests the errors between different model types. The second and third experiments test the effect of sequence length on error and performance for recurrent neural network-based models. All tests introduce artificial neural network models as the baseline. Also, all tests for model error are conducted in two rounds with testing datasets from the current year (2016) and next year (2017). As a result, recurrent neural network-based models are more suitable for short-term deployment with a smaller sequence length. In contrast, artificial neural network models demonstrate a lower error over the long term with faster processing time. With the results presented, the limitations of the proposed method are discussed.

A regular rhythmic stimulation increases people’s ability to anticipate future events in time and to move their body in space. Temporal concepts are usually prescribed to spatial locations through a past-behind and future-ahead mapping. In this study, we tested the hypothesis that a regular rhythmic stimulation could promote the forward-body (i.e., toward the future) projections in the peri-personal space. In a Visual Approach/Avoidance by the Self Task (VAAST), participants (N = 24) observed a visual scene on the screen (i.e., a music studio with a metronome in the middle). They were exposed to 3 s of auditory isochronous or non-isochronous rhythms, after which they were asked to make as quickly as possible a perceptual judgment on the visual scene (i.e., whether the metronome pendulum was pointing to the right or left). The responses could trigger a forward or backward visual flow, i.e., approaching or moving them away from the scene. Results showed a significant interaction between the rhythmic stimulation and the movement projections (p < 0.001): participants were faster for responses triggering forward-body projections (but not backward-body projections) after the exposure to isochronous (but not non-isochronous) rhythm. By highlighting the strong link between isochronous rhythms and forward-body projections, these findings support the idea that temporal predictions driven by a regular auditory stimulation are grounded in a perception-action system integrating temporal and spatial information.

Temporal prediction ability is vital for movement synchronization with external rhythmic stimuli (sensorimotor synchronization); however, little is known regarding individual variations in temporal prediction ability and its neural correlates. We determined the underlying neural correlates of temporal prediction and individual variations during auditory-motor synchronization. We hypothesized that the non-primary motor cortices, such as the premotor cortex and supplementary motor area, are the key brain regions that correlate individual variations in prediction ability. Functional magnetic resonance imaging (7T) was performed for 18 healthy volunteers who tapped to 3 types of auditory metronome beats: isochronous, tempo change, and random. The prediction ability was evaluated using prediction/tracking ratios that were computed based on cross-correlations between tap timing and pacing events. Participants with a higher prediction/tracking ratio (i.e. stronger predictive tendency) tapped to metronome beats more accurately and precisely. The prediction/tracking ratio was positively correlated with the activity in the bilateral dorsal premotor cortex (PMd), suggesting that the bilateral PMd explains the individual variation in prediction ability. These results indicate that the PMd is involved in generating a model for temporal prediction of auditory rhythm patterns and its activity would reflect model accuracy, which is critical for accurate and precise sensorimotor synchronization. [ABSTRACT FROM AUTHOR]

Prior research on the relation between empathy and burnout in social professions appears to be inconsistent. Whereas some studies have claimed that empathy functions as a buffer against burnout, others have argued that empathy contributes to the development of burnout. One explanation for this disparity may be methodological as most studies have relied on cross-sectional single-measure designs. To examine their cross-sectional and longitudinal associations, the present study analyzed cross-lagged data of several measures of empathy and burnout. Data from two longitudinal studies with three to twelve months between measurements were analyzed. The samples contained 172 nursing students and 186 nurses, respectively. Empathy was assessed using the Interpersonal Reactivity Index, the Toronto Empathy Questionnaire, the Geneva Emotion Recognition Test, and the Social Mindfulness Test Paradigm. Burnout was assessed using the Maslach Burnout Inventory, the Irritation Scale, and the Copenhagen Burnout Inventory. Results showed significant cross-sectional correlations between the questionnaire measures of empathy and burnout. However, there was insufficient support for the cross-lagged associations. Associations between empathy and burnout may be attributed to the methodological limitations of cross-sectional designs. However, the influence of potential moderators is discussed as a promising direction of future research. [ABSTRACT FROM AUTHOR]

Self-generated auditory input is perceived less loudly than the same sounds generated externally. The existence of this phenomenon, called Sensory Attenuation (SA), has been studied for decades and is often explained by motor-based forward models. Recent developments in the research of SA, however, challenge these models. We review the current state of knowledge regarding theoretical implications about the significance of Sensory Attenuation and its role in human behavior and functioning. Focusing on behavioral and electrophysiological results in the auditory domain, we provide an overview of the characteristics and limitations of existing SA paradigms and highlight the problem of isolating SA from other predictive mechanisms. Finally, we explore different hypotheses attempting to explain heterogeneous empirical findings, and the impact of the Predictive Coding Framework in this research area. [ABSTRACT FROM AUTHOR]

Data-driven Remaining Useful Life (RUL) prediction is one of the core technologies of Prognostics and Health Management (PHM). Committed to improving the accuracy of RUL prediction for aero-engines, this paper proposes a model that is entirely based on the attention mechanism. The attention model is divided into the multi-head self-attention and timing feature enhancement attention models. The multi-head self-attention model employs scaled dot-product attention to extract dependencies between time series; the timing feature enhancement attention model is used to accelerate and enhance the feature selection process. This paper utilises Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbofan engine simulation data obtained from NASA Ames’ Prognostics Center of Excellence and compares the proposed algorithm to other models. The experiments conducted validate the superiority of our model’s approach.

Self-generated auditory input is perceived less loudly than the same sounds generated externally. The existence of this phenomenon, called Sensory Attenuation (SA), has been studied for decades and is often explained by motor-based forward models. Recent developments in the research of SA, however, challenge these models. We review the current state of knowledge regarding theoretical implications about the significance of Sensory Attenuation and its role in human behavior and functioning. Focusing on behavioral and electrophysiological results in the auditory domain, we provide an overview of the characteristics and limitations of existing SA paradigms and highlight the problem of isolating SA from other predictive mechanisms. Finally, we explore different hypotheses attempting to explain heterogeneous empirical findings, and the impact of the Predictive Coding Framework in this research area.

Music, like spoken language, is often characterized by hierarchically organized structure. Previous experiments have shown neural tracking of notes and beats, but little work touches on the more abstract question: how does the brain establish high-level musical structures in real time? We presented Bach chorales to participants (20 females and 9 males) undergoing electroencephalogram (EEG) recording to investigate how the brain tracks musical phrases. We removed the main temporal cues to phrasal structures, so that listeners could only rely on harmonic information to parse a continuous musical stream. Phrasal structures were disrupted by locally or globally reversing the harmonic progression, so that our observations on the original music could be controlled and compared. We first replicated the findings on neural tracking of musical notes and beats, substantiating the positive correlation between musical training and neural tracking. Critically, we discovered a neural signature in the frequency range ∼0.1 Hz (modulations of EEG power) that reliably tracks musical phrasal structure. Next, we developed an approach to quantify the phrasal phase precession of the EEG power, revealing that phrase tracking is indeed an operation of active segmentation involving predictive processes. We demonstrate that the brain establishes complex musical structures online over long timescales (>5 s) and actively segments continuous music streams in a manner comparable to language processing. These two neural signatures, phrase tracking and phrasal phase precession, provide new conceptual and technical tools to study the processes underpinning high-level structure building using noninvasive recording techniques., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 the authors.)

Planning forest management relies on predicting insect outbreaks such as mountain pine beetle, particularly in the intermediate‐term future, e.g., 5‐year. Machine‐learning algorithms are potential solutions to this challenging problem due to their many successes across a variety of prediction tasks. However, there are many subtle challenges in applying them: identifying the best learning models and the best subset of available covariates (including time lags) and properly evaluating the models to avoid misleading performance‐measures. We systematically address these issues in predicting the chance of a mountain pine beetle outbreak in the Cypress Hills area and seek models with the best performance at predicting future 1‐, 3‐, 5‐ and 7‐year infestations. We train nine machine‐learning models, including two generalized boosted regression trees (GBM) that predict future 1‐ and 3‐year infestations with 92% and 88% AUC, and two novel mixed models that predict future 5‐ and 7‐year infestations with 86% and 84% AUC, respectively. We also consider forming the train and test datasets by splitting the original dataset randomly rather than using the appropriate year‐based approach and show that this may obtain models that score high on the test dataset but low in practice, resulting in inaccurate performance evaluations. For example, a k‐nearest neighbor model with the actual performance of 68% AUC, scores the misleadingly high 78% on a test dataset obtained from a random split, but the more accurate 66% on a year‐based split. We then investigate how the prediction accuracy varies with respect to the provided history length of the covariates and find that neural network and naive Bayes, predict more accurately as history‐length increases, particularly for future 1‐ and 3‐year predictions, and roughly the same holds with GBM. Our approach is applicable to other invasive species. The resulting predictors can be used in planning forest and pest management and planning sampling locations in field studies. [ABSTRACT FROM AUTHOR]

• Functions of the cerebellum are more than online coordination of muscle activity during movements. • Neurons in the deep cerebellar nucleus exhibit firing modulation during motor preparation. • These signals enable deliberate motor control, adjustment of self-timing, prediction of rhythms and motor synchronization. • The generation mechanism of these signals and their effects on downstream pathways need to be elucidated in future studies. The cerebellum is thought to have a variety of functions because it developed with the evolution of the cerebrum and connects with different areas in the frontoparietal cortices. Like neurons in the cerebral cortex, those in the cerebellum also exhibit strong activity during planning in addition to the execution of movements. However, their specific roles remain elusive. In this article, we review recent findings focusing on preparatory activities found in the primate deep cerebellar nuclei during tasks requiring deliberate motor control and temporal prediction. Neurons in the cerebellum are active during anti-saccade preparation and their inactivation impairs proactive inhibitory control for saccades. Experiments using a self-timing task show that there are mechanisms for tracking elapsed time and regulating trial-by-trial variation in timing, and that the cerebellum is involved in the latter. When predicting the timing of periodic events, the cerebellum provides more accurate temporal information than the striatum. During a recently developed synchronized eye movement task, cerebellar nuclear neurons exhibited periodic preparatory activity for predictive synchronization. In all cases, the cerebellum generated preparatory activity lasting for several hundred milliseconds. These signals may regulate neuronal activity in the cerebral cortex that adjusts movement timing and predicts the timing of rhythmic events. [ABSTRACT FROM AUTHOR]

Prediction of periodic event timing is an important function for everyday activities, while the exact neural mechanism remains unclear. Previous studies in nonhuman primates have demonstrated that neurons in the cerebellar dentate nucleus and those in the caudate nucleus exhibit periodic firing modulation when the animals attempt to detect a single omission of isochronous repetitive audiovisual stimuli. To understand how these subcortical signals are sent and processed through the thalamocortical pathways, we examined single-neuron activities in the central thalamus of two macaque monkeys (one female and one male). We found that three types of neurons responded to each stimulus in the sequence in the absence of movements. Reactive-type neurons showed sensory adaptation and gradually waned the transient response to each stimulus. Predictive-type neurons steadily increased the magnitude of the suppressive response, similar to neurons previously reported in the cerebellum. Switch-type neurons initially showed a transient response, but after several cycles, the direction of firing modulation reversed and the activity decreased for each repetitive stimulus. The time course of Switch-type activity was well explained by the weighted sum of activities of the other types of neurons. Furthermore, for only Switch-type neurons the activity just before stimulus omission significantly correlated with behavioral latency, indicating that this type of neuron may carry a more advanced signal in the system detecting stimulus omission. These results suggest that the central thalamus may transmit integrated signals to the cerebral cortex for temporal information processing, which are necessary to accurately predict rhythmic event timing. [ABSTRACT FROM AUTHOR]

WE EXPLORE THE EFFECTS OF TRAINED MUSICAL movements on sensorimotor interactions in order to clarify the interpretation of previously observed expertise differences. Pianists and non-pianists listened to an auditory sequence and identified whether the final event occurred in time with the sequence. In half the trials participants listened without moving, and in half they synchronized keystrokes while listening. Pianists and non-pianists were better able to identify the timing of the final tone after synchronizing keystrokes compared to listening only. Curiously, this effect of movement did not differ between pianists and non-pianists despite substantial training differences with respect to finger movements. We also found few group differences in the ability to align keystrokes with events in the auditory sequence; however, movements were less variable (lower coefficient of variation) in pianists compared to nonpianists. Consistent with the idea that the benefits of synchronization on rhythm perception are constrained by motor effector kinematics, this work helps clarify previous findings in this paradigm. We discuss these outcomes in light of training and the kinematics involved in pianist keystrokes compared to musicians synchronizing movements in other studies. We also overview how these differences across motor effector synchronization and training must be accounted for in models of perception and action. [ABSTRACT FROM AUTHOR]

• Temporal predictability often benefits behaviour but can induce impulsive responding. • Erroneous but subthreshold EMG activations can be used to index impulsive behaviour. • Temporal cueing increased the number and speed of such impulsive responses. • Yet temporal cueing did not impair the ability to then correct these responses. • Motor impulsivity can explain both costs and benefits of temporal predictability. Temporal predictability optimises behaviour when a simple response is required, as demonstrated by faster reaction times (RTs) and higher accuracy. However, its beneficial effects come at a cost under situations of response conflict. Here, we investigated the motor underpinnings of behaviour to temporally predictable events in the Simon conflict task. We compared motor responses to lateralised targets whose position conflicted (incompatible condition) or not (compatible condition) with the hand of response. Importantly, electromyographic (EMG) recordings allowed us to study "partial errors", defined as subthreshold muscle activity in the incorrect response agonist preceding a correct response. Advanced distributional analyses coupled with EMG data revealed that temporal predictability induced impulsive premature responding, as indexed by increased likelihood of fast incorrect EMG activations (both partial errors and errors) to incompatible targets. In parallel, responding to temporally predictable targets speeded the latency of partial errors, further indicating that temporal predictability increased the tendency to act prematurely. There was, however, no effect of temporal predictability on subsequent suppression of partial errors. Our results provide direct evidence that temporal predictability acts by increasing the urge to initiate a fast, yet potentially erroneous, response. This mechanism parsimoniously explains both beneficial effects of temporal predictability when no conflict in the environment is present, as well as its costs when more complex motor behaviour is required. [ABSTRACT FROM AUTHOR]

Digital ecosystems reinforce the commercial achievements of digital innovations, providing organizations with platforms to implement digital products by sharing, co-developing, and using application programming interfaces (APIs) as digital resources. The use of APIs in digital ecosystems formulates dynamic API networks that evolve with the emergence of APIs and their updates. API network dynamics are associated with disruptive technology, heterogeneous networks, product and service innovation, and entrepreneurial success. However, methods for measuring and predicting API network dynamics have not been developed. We developed a framework for measuring and predicting the API network dynamics generated by APIs. To develop the abovementioned framework, we invented three network embeddings that could represent and measure API network dynamics and a prediction model based on a deep learning approach that could forecast API network dynamics. We conducted multiple experiments to assess the performance and usability of our method framework, and the results consistently demonstrate that our developed approach surpasses existing methods. [ABSTRACT FROM AUTHOR]

Rhythmic events recruit neuronal activity in the basal ganglia and cerebellum, but their roles remain elusive. In monkeys attempting to detect a single omission of isochronous visual stimulus, we found that neurons in the caudate nucleus showed increased activity for each stimulus in sequence, while those in the cerebellar dentate nucleus showed decreased activity. Firing modulation in the majority of caudate neurons and all cerebellar neurons was proportional to the stimulus interval, but a quarter of caudate neurons displayed a clear duration tuning. Furthermore, the time course of population activity in the cerebellum well predicted stimulus timing, whereas that in the caudate reflected stochastic variation of response latency. Electrical stimulation to the respective recording sites confirmed a causal role in the detection of stimulus omission. These results suggest that striatal neurons might represent periodic response preparation while cerebellar nuclear neurons may play a role in temporal prediction of periodic events.

Objective. Detecting movement intention is a typical use of brain-computer interfaces (BCI). However, as an endogenous electroencephalography (EEG) feature, the neural representation of movement is insufficient for improving motor-based BCI. This study aimed to develop a new movement augmentation BCI encoding paradigm by incorporating the cognitive function of rhythmic temporal prediction, and test the feasibility of this new paradigm in optimizing detections of movement intention. Methods. A visual-motion synchronization task was designed with two movement intentions (left vs. right) and three rhythmic temporal prediction conditions (1000 ms vs. 1500 ms vs. no temporal prediction). Behavioural and EEG data of 24 healthy participants were recorded. Event-related potentials (ERPs), event-related spectral perturbation induced by left- and right-finger movements, the common spatial pattern (CSP) and support vector machine, Riemann tangent space algorithm and logistic regression were used and compared across the three temporal prediction conditions, aiming to test the impact of temporal prediction on movement detection. Results. Behavioural results showed significantly smaller deviation time for 1000 ms and 1500 ms conditions. ERP analyses revealed 1000 ms and 1500 ms conditions led to rhythmic oscillations with a time lag in contralateral and ipsilateral areas of movement. Compared with no temporal prediction, 1000 ms condition exhibited greater beta event-related desynchronization (ERD) lateralization in motor area ( P < 0.001) and larger beta ERD in frontal area ( P < 0.001). 1000 ms condition achieved an averaged left-right decoding accuracy of 89.71% using CSP and 97.30% using Riemann tangent space, both significantly higher than no temporal prediction. Moreover, movement and temporal information can be decoded simultaneously, achieving 88.51% four-classification accuracy. Significance. The results not only confirm the effectiveness of rhythmic temporal prediction in enhancing detection ability of motor-based BCI, but also highlight the dual encodings of movement and temporal information within a single BCI paradigm, which is promising to expand the range of intentions that can be decoded by the BCI., (© 2023 IOP Publishing Ltd.)

Assessing human health risk using spatiotemporal migration and geochemical evolution concurrently in an area where the groundwater is contaminated with heavy metals can provide more instructive information to protect specific potential negative impacts on human health. In this research, we established a model of long-term assessment of human health risk for metal contaminated groundwater by coupling two models: the geochemical (based on the law of chemical mass balance) model and the hydrodynamics module. The hydrodynamics module is used to initially identify the total temporal concentration of various elements, and the chemical mass balance module is used to gain the concentration and ionic activity of various toxic elements according to the range of environmental pH. Effective concentrations calculated using activity weight (based on speciation and ionic activity) were introduced into the formula for risk analysis. The results of the study show that, with the exploitation and recharge of groundwater, the non-carcinogenic and carcinogenic health risks cannot be reduced to acceptable levels until 18 and 22 years, respectively. The calculated risk values of using the coupling model are lower than that of statistics or single hydrokinetics. The sensitivity analysis results show that this model is reliable. The recharge, pH and the permeability coefficient are defined as the most sensitive factors. Image 1 • Predicting groundwater metal content by chemical equilibrium and hydrodynamic model. • Assessing human health risks associated with metal species. • Forecasting spatiotemporal trends of heavy metal concentrations. • Analysis of sensitive factors (rainfall and pH) affecting future risk prediction. [ABSTRACT FROM AUTHOR]