Temporal Probability Research Articles

A kinematic method for generating earthquake sequences

Computational earthquake sequence models provide generative estimates of the time, location, and size of synthetic seismic events that can be compared with observed earthquake histories and assessed as rupture forecasts. Here we describe a three-dimensional probabilistic earthquake sequence model that produces slip event time series constrained across geometrically complex non-planar fault systems. This model is kinematic in nature, integrating the time evolution of geometric moment accumulation and release with empirical earthquake scaling laws. The temporal probability of event occurrence is determined from the time history of geometric moment integrated with short-term Omori-style rate decay following each earthquake achieving long-term time-averaged moment balance. Similarly, the net geometric moment monotonically controls the probability of event localization, and seismic events release geometric moment with spatially heterogeneous slip on three-dimensional non-planar fault surfaces. We use this model to generate a synthetic earthquake sequence on the Nankai subduction zone over a 1,250-year-long interval, including 700+ MW=5.5−8.5 coseismic events, with decadal-to-centennial scale quiescent intervals quasi-periodic great earthquake clusters followed by aftershock sequences.

Fundamental Accuracy-Resolution Trade-Off for Timekeeping Devices.

From a thermodynamic point of view, all clocks are driven by irreversible processes. Additionally, one can use oscillatory systems to temporally modulate the thermodynamic flux towards equilibrium. Focusing on the most elementary thermalization events, this modulation can be thought of as a temporal probability concentration for these events. There are two fundamental factors limiting the performance of clocks: On the one level, the inevitable drifts of the oscillatory system, which are addressed by finding stable atomic or nuclear transitions that lead to astounding precision of today's clocks. On the other level, there is the intrinsically stochastic nature of the irreversible events upon which the clock's operation is based. This becomes relevant when seeking to maximize a clock's resolution at high accuracy, which is ultimately limited by the number of such stochastic events per reference time unit. We address this essential trade-off between clock accuracy and resolution, proving a universal bound for all clocks whose elementary thermalization events are memoryless.

Landslide hazard mapping using temporal probability analysis of rainfall thresholds in the city of Azazga and surrounding areas, northern Algeria

Landslide hazard mapping using temporal probability analysis of rainfall thresholds in the city of Azazga and surrounding areas, northern Algeria

Enhancing Diagnosis of Anterior and Inferior Myocardial Infarctions Using UWB Radar and AI-Driven Feature Fusion Approach.

Despite significant improvement in prognosis, myocardial infarction (MI) remains a major cause of morbidity and mortality around the globe. MI is a life-threatening cardiovascular condition that requires prompt diagnosis and appropriate treatment. The primary objective of this research is to identify instances of anterior and inferior myocardial infarction by utilizing data obtained from Ultra-wideband radar technology in a hospital for patients of anterior and inferior MI. The collected data is preprocessed to extract spectral features. A novel feature engineering approach is designed to fuse temporal features and class prediction probability features derived from the spectral feature dataset. Several well-known machine learning models are implemented and fine-tuned to obtain optimal performance in the detection of anterior and inferior MI. The results demonstrate that integration of the fused feature set with machine learning models results in a notable improvement in both the accuracy and precision of MI detection. Notably, random forest (RF) and k-nearest neighbor showed superb performance with an accuracy of 98.8%. For demonstrating the capacity of models to generalize, K-fold cross-validation is carried out, wherein RF exhibits a mean accuracy of 99.1%. Furthermore, the examination of computational complexity indicates a low computational complexity, thereby indicating computational efficiency.

Analysis Of Fire Risk For Forest And Land In West Kalimantan Using Logistic Regression Method With Generalized Extreme Value Approach

Forests in Indonesia have been reduced by half due to fires. Forest and land fires often occur during the long dry season in places such as the island of Borneo. West Kalimantan is an area passed by the equator which is directly above the Pontianak area. The main effect is to make West Kalimantan a tropical area with high air temperatures so that forest and land fires often occur. This study aims to obtain the results of the probability of land and forest fires in each district in West Kalimantan. The method used is binary logistic regression analysis with response variables in the form of data categories based on spatial data and analysis of extreme values with Generalized Extreme Value (GEV). Spatial analysis uses the help of ARCGIS software in processing raster data (grid cells). The data used is data on maximum temperature and maximum wind speed taken from October 7, 2021 to October 31, 2022 from the official NASA website. The spatial data used in this study is forest and land fire vulnerability data taken from the BNPB website in the form of raster data. The results of logistic regression analysis found that the maximum temperature variable has a negative relationship with the response variable, while the maximum speed of wind variable has a positive relationship with the response variable. The temporal probability of the resulting GEV is getting higher with a longer period of years ahead. The probability of forest and land fires is obtained by multiplying the log probability by the GEV temporal probability. In this study, it was found that the highest chance of forest and land fires occurring in Sanggau Regency was suspected to occur due to an increase in temperature every year.

Temporal distribution model and occurrence probability of M ≥ 6.5 earthquakes in North China Seismic Zone

Temporal distribution model and occurrence probability of M ≥ 6.5 earthquakes in North China Seismic Zone

Temporal prediction of shallow landslides exploiting soil saturation degree derived by ERA5-Land products

ERA5-Land service has been released recently as an integral and operational component of Copernicus Climate Change Service. Within its set of climatological and atmospheric parameters, it provides soil moisture estimates at different soil depths, represeting an important tool for retrieving saturation degree for predicting natural hazards as shallow landslides. This paper represents an innovative attempt aiming to exploit the use of saturation degree derived from ERA5-Land soil moisture products in a data-driven model to predict the daily probability of occurence of shallow landslides. The study was carried out by investigating a multi-temporal inventory of shallow landslides occurred in Oltrepò Pavese (northern Italy). The achieved results follow: (i) ERA5-Land-derived saturation degree reconstructs well field trends measured in the study area until 1 m from ground; (ii) in agreement with the typical sliding surfaces depth, saturation degree values obtained since ERA5-Land 28–100 cm layer represent a significant predictor for the estimation of temporal probability of occurrence of shallow landslides, able especially to reduce overestimation of triggering events; (iii) saturation degree estimated by ERA5-Land 28–100 cm layer allows to detect soil hydrological conditions leading to triggering in the study area, represented by saturation degree in this layer close to complete saturation. Even if other works of research are required in different geological and geomorphological settings, this study demonstrates that ERA5-Land-derived saturation degree could be implemented to identify triggering conditions and to develop prediction methods of shallow landslides, thanks also to its free availability and constantly updating with a delay of 5 days.

P3-like signatures of temporal predictions: a computational EEG study.

Many cognitive processes, ranging from perception to action, depend on the ability to predict the timing of forthcoming events. Yet, how the brain uses predictive models in the temporal domain is still an unsolved question. In previous work, we began to explore the neural correlates of temporal predictions by using a computational approach in which an ideal Bayesian observer learned the temporal probabilities of target onsets in a simple reaction time task. Because the task was specifically designed to disambiguate updating of predictive models and surprise, changes in temporal probabilities were explicitly cued. However, in the real world, we are usually incidentally exposed to changes in the statistics of the environment. Here, we thus aimed to further investigate the electroencephalographic (EEG) correlates of Bayesian belief updating and surprise associated with incidental learning of temporal probabilities. In line with our previous EEG study, results showed distinct P3-like modulations for updating and surprise. While surprise was indexed by an early fronto-central P3-like modulation, updating was associated with a later and more posterior P3 modulation. Moreover, updating was associated with a P2-like potential at centro-parietal electrodes, likely capturing integration processes between prior beliefs and likelihood of the observed event. These findings support previous evidence of trial-by-trial variability of P3 amplitudes as an index of dissociable inferential processes. Coupled with our previous findings, the present study strongly bolsters the view of the P3 as a key brain signature of temporal Bayesian inference. Data and scripts are shared on OSF: osf.io/sdy8j/.

Physical meaning of temperature and evaluation of distribution laws (in the area of the Lake Sevan basin)

Formulation of the problem. In the work, the most general definition of temperature was discussed and presented, the temporal probability distribution of air temperature was analyzed and evaluated. Temperature has no specific definition. Thus it will be difficult to study the nature of any geophysical phenomena, including the characteristics of temperature distribution, without understanding the modern scientific definition and adjustment of temperature. The aim of the work is to give the most modern reasonable definition of temperature or what the thermometer measures, the answer to which requires a more specific scientific justification, as well as to evaluate the patterns of possible spatiotemporal distribution of air temperature in the territory of Armenia and in the area of the Lake Sevan basin. Methods. To solve the set tasks, the authors used corresponding research and published works as a theoretical basis in the work. In the work, the average monthly data of actual observations of the temperature of the ground surface air layer and the amount of precipitation of the «Center for Hydrometeorology and Monitoring» SNCO of the Ministry of Environment of the Republic of Armenia were used. It was found that temperature is a quantity characterizing the thermal state and radiation of the terrestrial and celestial bodies and, in general, the environment, which is strongly related to the entropy change of the system; temperature is the main thermodynamic characteristic of thermal equilibrium; the thermodynamic and microscopic concepts of temperature coincide; the temperature-heat (energy) difference becomes known in the sense that a system can have high energy but low temperature. The authors applied the following research methods in the article: mathematical and statistical, extrapolation, analysis, analogy, correlation, cartographic. Results. Energy depends on the geometry (dimensions) of the system, but temperature does not. A trend of increasing air temperature is observed in the RA territory, which is also a result of the entropy change of the system. It can change very quickly, depending on the environmental factors in the given area (the growth rate of greenhouse farms, the artificial filling of valleys, which are wave carriers of air flow, the relentless use of green spaces for the purpose of public buildings, and other factors) in the process of disrupting the excessively permissible norms, which are currently separate needs serious research and prevention. The long-term variability of surface air temperature in the area of the Lake Sevan basin is analyzed. The analysis carried out made it possible to give a quantitative assessment characterizing the climate change in this region over the past 98 years. An analysis of the observational data showed that the trend of climate warming is confirmed by an increase in air temperature both in winter and over a long period of time. The change in surface air temperature occurs at a rate of 0.002 ºС/year to 0.012 ºС/year and is generally 0.008 ºС/year (or 0.08 ºС/10 years) for all analyzed stations. The results obtained confirm the presence of two periods of warming observed in 1927–1970 and 1971–2021. All the results obtained in the course of the work testify to the trend of climate mitigation in the area of the Lake Sevan basin at the end of the 20th - beginning of the 21st century.

Towards a Field-Theory based Relativistic Quantum Information

We present our program for the development of quantum informational concepts in relativistic systems in terms of the unequal-time correlation functions of quantum fields. We employ two formalisms that provide the basis for further developments. (i) The Quantum Temporal Probabilities (QTP) Method for quantum field measurements and (ii) the Closed-Time-Path (CTP) formalism for causal time evolutions. We present the main ideas of QTP and we show how it relates to the CTP formalism, allowing us to express concepts of measurement theory in terms of path-integrals. We also present many links of our program to non-equilibrium quantum field theories. Details can be found in a recent paper by the authors [1].

An artificial intelligence based framework to analyze the landside risk of a mountainous highway

The present study entails an artificial intelligence-based framework for landslide risk analysis of a highway infrastructure in the Himalayan region. In total, 241 landslide polygons that were inventoried for the study area. The spatial component of landslide susceptibility map was prepared by incorporating drainage density, TWI, geology, elevation and slope gradient as major contributing factors, in the certainty factor–random forest (CF-RF) hybrid model with accuracy of 0.928. The landslide hazard analysis was carried out by multiplying landslide spatial and temporal probabilities. The landslide vulnerability analysis of the highway stretch was carried out by integrating the elements at risk. The built-up area was extracted by using U-Net deep learning algorithm with an accuracy of 0.964. The landslide risk map of the highway stretch prepared by the multiplication of landslide hazard and vulnerability maps depicts that 16.78% and 6.25% of the study area falls in high and very high-risk zones, respectively.

Quantum field theory based quantum information: Measurements and correlations

This is the first in a series of papers aiming to develop a relativistic quantum information theory in terms of unequal-time correlation functions in quantum field theory. In this work, we highlight two formalisms which together can provide a useful theoretical platform suitable for further developments: (1) Quantum field measurements using the Quantum Temporal Probabilities (QTP) method; (2) Closed-Time-Path (CTP) formalism for causal time evolutions. QTP incorporates the detector into the quantum description, while emphasizing that the records of measurement are macroscopic and can be expressed in terms of classical spacetime coordinates. We first present a new, elementary derivation of the QTP formulas for the probabilities of n measurement events. We then demonstrate the relation of QTP with the Closed-Time-Path formalism, by writing an explicit formula that relates the associated generating functionals. We exploit the path integral representation of the CTP formalism, in order to express the measured probabilities in terms of path integrals. After this, we provide some simple applications of the QTP formalism. In particular, we show how Unruh–DeWitt detector models and Glauber’s photodetection theory appear as limiting cases. Finally, with quantum correlation being the pivotal notion in relativistic quantum information and measurements, we highlight the role played by the CTP two-particle irreducible effective action which enables one to tap into the resources of non-equilibrium quantum field theory for our stated purpose.

A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems

A deep learning approach is proposed to detect data and system anomalies using high-resolution continuous point-on-wave (CPOW) or phasor measurements. Both the anomaly and anomaly-free measurement models are assumed to have unknown temporal dependencies and probability distributions. Historical training samples are assumed for the anomaly-free model, while no training samples are available for the anomaly measurements. By transforming the anomaly-free observations into uniform independent and identically distributed sequences via a generative adversarial network, the proposed approach deploys a uniformity test for anomaly detection at the sensor level. A distributed detection scheme that combines sensor level detections at the control center is also proposed which combines local detections to form more reliable detections. Numerical results demonstrate significant improvement over the state-of-the-art solutions for various bad-data cases using real and synthetic CPOW and PMU data sets.

Comparison of quantized transfers of a given volume of gas through a pipe under its temporal and velocity probability distributions

The paper considers the quantization of gas volume transfers along a hypothetical pipe. The volume of gas is represented by two probability distributions in temporal and velocity form. The known model of optimal in the sense of filling information quantization is applied to quantization of material objects. There is a constant size gap between quanta. Technological aspects of formation of quanta are not considered. On the basis of the carried out minimum cost study the advantage of velocity quantization over time quantization for a number of selected quanta is determined. A normal probability distribution is taken as the initial distribution of the gas volume. The results of the article can be applied to any material and information processes. The main purpose in future research is recommended for any complex network processes, analysis and synthesis of their quality.

Not When But Whether: Modality and Future Time Reference in English and Dutch.

Previous research on linguistic relativity and economic decisions hypothesized that speakers of languages with obligatory tense marking of future time reference (FTR) should value future rewards less than speakers of languages which permit present tense FTR. This was hypothesized on the basis of obligatory linguistic marking (e.g., will) causing speakers to construe future events as more temporally distal and thereby to exhibit increased "temporal discounting": the subjective devaluation of outcomes as the delay until they will occur increases. However, several aspects of this hypothesis are incomplete. First, it overlooks the role of "modal" FTR structures which encode notions about the likelihood of future outcomes (e.g., might). This may influence "probability discounting": the subjective devaluation of outcomes as the probability of their occurrence decreases. Second, the extent to which linguistic structures are subjectively related to temporal or probability discounting differences is currently unknown. To address these, we elicited FTR language and subjective ratings of temporal distance and probability from speakers of English, which exhibits strongly grammaticized FTR, and Dutch, which does not. Several findings went against the predictions of the previous hypothesis: Framing an FTR statement in the present ("Ellie arrives later on") versus the future tense ("…will arrive…") did not affect ratings of temporal distance; English speakers rated future statements as relatively more temporally proximal than Dutch speakers; and English and Dutch speakers rated future tenses as encoding high certainty, which suggests that obligatory future tense marking might result in less discounting. Additionally, compared with Dutch speakers, English speakers used more low-certainty terms in general (e.g., may) and as a function of various experimental factors. We conclude that the prior cross-linguistic observations of the link between FTR and psychological discounting may be caused by the connection between low-certainty modal structures and probability discounting, rather than future tense andtemporality.

Rainfall Induced Shallow Landslide Temporal Probability Modelling and Early Warning Research in Mountains Areas: A Case Study of Qin-Ba Mountains, Western China

The rainfall-induced landslide early warning model (LEWM) is an important means to mitigate property loss and casualties, but the conventional discriminant matrix-based LEWM (DLEWM) leaves room for subjectivity and limits warning accuracy. Additionally, it is important to employ appropriate indicators to evaluate warning model performance. In this study, a new method for calculating the spatiotemporal probability of rainfall-induced landslides based on a Bayesian approach is proposed, and a probabilistic-based LEWM (PLEWM) at the regional scale is developed. The method involves four steps: landslide spatial probability modeling, landslide temporal probability modeling, coupling of spatial and temporal probability models, and the conversion method from the spatiotemporal probability index to warning levels. Each step follows the law of probability and is tested with real data. At the same time, we propose the idea of using economic indicators to evaluate the performance of the multilevel LEWM and reflect its significant and unique aspects. The proposed PLEWM and the conventional DLEWM are used to conduct simulate warnings for the study area day-by-day in the rainy season (July-September) from 2016 to 2020. The results show that the areas of the 2nd-, 3rd-, and 4th-level warning zones issued by the PLEWM account for 60.23%, 45.99%, and 43.98% of those of the DLEWM, respectively. The investment in issuing warning information and the losses caused by landslides account for 54.54% and 59.06% of those of the DLEWM, respectively. Moreover, under extreme rainfall conditions, the correct warning rate of the PLEWM is much higher than that of the DLEWM.

Fault chain risk expectation-based resilient transmission hardening planning against hurricane

Hardening planning is an effective way to enhance the ability of the transmission system against hurricane attacks and further improve the resilience of power systems. However, the existing hardening method gives little consideration to the risk of multiple failures under hurricanes which may underestimate the real post-disaster damage. To fill the research gap, this paper proposes a resilience index, i.e. fault chain risk expectation (FCRE), which can measure the risk of multiple failures under hurricanes. Then the FCRE-based resilient transmission hardening planning (RTHP) model is proposed to minimize hardening and load shedding penalty costs under different hurricane scenarios. In this paper, hurricane simulation is finished by critical parameters firstly, then the failure probability curve is adopted to obtain the spatial–temporal failure probability of different components, and finally, the corresponding contingency scenarios under simulated hurricanes are obtained by Monte Carlo Simulation (MCS) and k-means clustering algorithms. Meanwhile, the set of fault chains is constructed to calculate the FCRE for each line, and the corresponding constraint is formed to ensure that the system resilience reaches a target level. The proposed model is verified on the modified RTS 24-bus system and the results demonstrate the effectiveness of the proposed hardening planning method.

Stride-to-stride time intervals are independently affected by the temporal pattern and probability distribution of visual cues

The temporal structure of the variability of the stride-to-stride time intervals during paced walking is affected by the underlying autocorrelation function (ACF) of the pacing signal. This effect could be accounted for by differences in the underlying probability distribution function (PDF) of the pacing signal. We investigated the isolated and combined effect of the ACF and PDF of the pacing signals on the temporal structure of the stride-to-stride time intervals during visually guided paced overground walking. Ten young, healthy participants completed four walking trials while synchronizing their footstep to a visual pacing signal with a temporal pattern of either pink or white noise (different ACF) and either a Gaussian or normal probability distribution (different PDF). The scaling exponent from the Detrended Fluctuation Analysis was used to quantify the temporal structure of the stride-to-stride time intervals. The ACF and PDF of the pacing signals had independent effects on the scaling exponent of the stride-to-stride time intervals. The scaling exponent was higher during the pink noise pacing trials compared to the white noise pacing trials and higher during the trials with the Gaussian probability distribution compared to the uniform distribution. The results suggest that the sensorimotor system in healthy young individuals has an affinity towards external cues with a pink noise pattern and a Gaussian probability distribution during paced walking.

Discovering Spatio-Temporal Co-Occurrence Patterns of Crimes with Uncertain Occurrence Time

The discovery of spatio-temporal co-occurrence patterns (STCPs) among multiple types of crimes whose events frequently co-occur in neighboring space and time is crucial to the joint prevention of crimes. However, the crime event occurrence time is often uncertain due to a lack of witnesses. This occurrence time uncertainty further results in the uncertainty of the spatio-temporal neighborhood relationships and STCPs. Existing methods have mostly modeled the uncertainty of events under the independent and identically distributed assumption and utilized one-sided distance information to measure the distance between uncertain events. As a result, STCPs detected from a dataset with occurrence time uncertainty (USTCPs) are likely to be erroneously assessed. Therefore, this paper proposes a probabilistic-distance-based USTCP discovery method. First, the temporal probability density functions of crime events with uncertain occurrence times are estimated by considering the temporal dependence. Second, the spatio-temporal neighborhood relationships are constructed based on the spatial Euclidean distance and the proposed temporal probabilistic distance. Finally, the prevalent USTCPs are identified. Experimental comparisons performed on twelve types of crimes from X City Public Security Bureau in China demonstrate that the proposed method can more objectively express the occurrence time of crimes and more reliably identify USTCPs.

Temporal context modulates sensory attenuation magnitude

In studies with self-produced sensory events, sensitivity for these events has been found to be reduced. This phenomenon is called sensory attenuation, and it has been assumed that the crucial factor is the self-production of the event. However, this factor may be confounded with the temporal predictability of the event, as well as with attentional focus on the event. In this study, we wondered about the influence of temporal stimulus predictably on sensory attenuation. We asked observers to discriminate the orientation of Gabor patches that were presented randomly at various times around a button press. Despite the unpredictability of stimulus occurrence, attenuation for these stimuli was tuned to the time of the button press. However, temporal expectations determined attenuation magnitude. Sensory attenuation was stronger when stimuli were expected to occur more often before the button press. When stimuli were expected to occur more often after the button press, no sensory attenuation was found. Our results show first that sensory attenuation occurs mandatorily even if stimulus occurrence cannot be predicted temporally. Second, temporal attention, guided by temporal stimulus probabilities, modulates sensory attenuation magnitude.