Temporal Estimation Research Articles

Drivers of uncertainty in precipitation frequency under current and future climate – application to Maryland, USA

The intensity duration frequency (IDF) curves that inform engineering design must reflect the effects of changing climate on extreme precipitation events. These changes can be addressed, in part, by statistically assessing synthetic data/outputs from high-resolution climate model projections to develop IDF curves. The estimation of IDF curves is associated with multiple sources of uncertainty. Most studies that characterize uncertainty in IDF curves under climate change only address the uncertainty due to the choice and processing of climate model outputs, but neglect uncertainty from statistical modeling choices. This study assesses the uncertainty introduced by developing IDF curves for Maryland, USA from model simulations of current and future climate, using statistical methods that are used in U.S. practice. The study analyzes output time series from the North American Regional Climate Change Assessment Program (NARCCAP) suite, which consists of 6 Atmosphere-Ocean General Circulation Models, each spatially downscaled with two different Regional Climate Models. In a separate study, the 3-hour NARCCAP model output was temporally downscaled to 15 min using two different machine learning models. Uncertainty is assessed both across and within models. Across-model uncertainty arises from the differences among synthetic time-series for precipitation and other meteorological variables produced by the 12 NARCCAP climate model projections. Within-model uncertainty arises from the modeling choices used to develop statistical IDF curves from a single climate model time series. These choices include: temporal downscaling method, time-series type, distribution, and parameter estimation method. The choice of climate model (across-model uncertainty) is the dominant contributor under both current and future climate conditions. On the within-model level, the other sources of uncertainty contribute differently for different climate models. The development and application of future-climate IDF curves should acknowledge the uncertainty introduced by statistical modeling choices, as well as by variation among climate model projections.

Development of Data Processing and Analysis Pipeline for the Ricochet Experiment

Achieving a percentage-level precision measurement of the Coherent Elastic Neutrino Nucleus Scattering (CE{\nu}NS) spectrum requires a robust data processing pipeline which can be characterised with great precision. To fulfil this goal we present hereafter a new Python-based data processing pipeline specifically designed for temporal data analysis and pulse amplitude estimation. This pipeline features a data generator allowing to accurately simulate the expected data stream from the RICOCHET experiment at the Institut Laue Langevin (ILL) nuclear reactor, including both background and CE{\nu}NS signals. This data generator is pivotal to fully understand and characterise the data processing overall efficiency, its reconstruction biases, and to properly optimise its configuration parameters. We show that thanks to this optimized data processing pipeline, the CryoCube detector array will be able to achieve a 70 eV energy threshold combined with electronic/nuclear recoil discrimination down to {\sim}100 eV, hence fulfilling the RICOCHET targeted performance.

Human behavior determinants of exposure to Anopheles vectors of malaria in Sumba, Indonesia.

Malaria vector control interventions in Sumba, Indonesia, have not been able to eliminate malaria. Human drivers of exposure to Anopheles bites were investigated as part of a larger clinical trial evaluating the impact of a spatial repellent product on malaria incidence. Human behavioral observations (HBOs) evaluating temporal and spatial presence, sleeping behaviors, and insecticide treated net (ITN) use, were collected parallel to entomological collections-indoor and outdoor human landing catches (HLCs), and house hold surveys. Data demonstrates that mosquito access to humans, enabled by structurally open houses, is evident by the similar entomological landing rates both inside and outside households. The presence of animals inside houses was associated with increased mosquito entry-however, the number of humans present inside houses was not related to increased mosquito landing. Analyzing mosquito landing rates with human behavior data enables the spatial and temporal estimation of exposure to Anopheles bites, accounting for intervention (ITN) presence and usage. Human behavior adjusted exposure to Anopheles bites was found to be highest in the early in the evening, but continued at lower levels throughout the night. Over the night, most exposure (53%) occurred when people were indoors and not under the protection of nets (asleep or awake) followed by exposure outside (44%). Characterized gaps in protection are outdoor exposure as well as exposure indoors-when awake, and when asleep and not using ITNs. Interestingly, in the primary trial, even though there was not a significant impact of the spatial repellent on vector biting rates by themselves (16%), when factoring in human behavior, there was approximately 28% less exposure in the intervention arm than in the placebo arm. The treated arm had less human behavior adjusted bites in all spaces evaluated though there was proportionally higher exposure indoors. This analysis points to the importance of using HBOs both towards understanding gaps in protection as well as how interventions are evaluated. To mitigate ongoing transmission, understanding context specific spatial and temporal exposure based on the interactions of vectors, humans and interventions would be vital for a directed evidence-based control or elimination strategy.

Spatiotemporal monitoring of rice crops in the covid-19 pandemic period for local food security using sentinel 2b imagery case ctudy: tasikmalaya city

Rice crops (Oryza sativa L) is a very important food crop in Indonesia. According to data from the FAO (Food and Agriculture Organization), in 2015 Indonesia was in third place after India and China as rice producing countries in the world with a total production of 75.6 million tons. The depletion of land due to land conversion (conversion) is one of the factors causing the downturn in the agricultural sector in Indonesia today. The narrowing of rice fields does not have an impact on decreasing rice production but also at the stage of Indonesian society whose livelihoods are farmers. Data and information on the development of rice plants are very important as a database for the formulation of sustainable food crop agricultural policies mandated in the Law of the Republic of Indonesia Number 41 of 2009. In addition, the covid-19 pandemic which is now sweeping the world, requires the government to adopt large-scale social rights policies. It also needs to be assessed whether it has an effect on agricultural production. The use of remote sensing technology is an effective and efficient alternative for spatial and temporal estimation and monitoring in obtaining information on land cover for rice plants. Through multispectral image classification using guided classification (supervised classification), with the maximum possible possibility (maximum likelihood), it is known that local food security in Tasikmalaya City during the COVID-19 pandemic is still guaranteed. This is with the current population of not less than 808,506 people, while the land cover area for rice crops consecutively from 2017 - 2020 are 454 ha; 606, 94 ha; 463, 23 ha; and 673.8 ha. So it can be denied that large-scale rebels have no effect on the productivity of agricultural land in Tasikamalaya City.

Spatial number estimation has a higher linear range than temporal number estimation; differential affordances for subdivision might help to explain why.

Estimation of visuospatial number typically has a limited linear range that goes well beyond the subitizing range but typically not beyond 20 items without calibration procedures. Three experiments involving a total of 104 undergraduate students, each tested once, sought to determine if the limit on the linear range represented a capacity limitation of a linear accumulator or might be the result of a strategy based on subdividing spatial displays into potentially subitizable subsets. For visual and auditory temporal numbers for a large range of numbers (2-58; Experiment 1), the (unbiased) linear range was found to be quite restricted (three or four items). Using matched linear spatial number stimuli (Experiment 2), the linear range observed extended to about nine or 10 items. Experiment 3 compared estimates when simultaneous two-dimensional spatial number displays were presented briefly, with estimates for identical displays that accumulated over time. The linear range of estimates for accumulating spatial displays reached only 11 items, whereas that for briefly presented displays extended to about 20 items. These results suggest that the limit on the linear range is not simply a capacity limitation in a linear accumulator. Rather, they support the idea that linear spatial number estimation for the range from five to 20 may be based on subdividing the display into a subitizable number of (potentially) subitizable groups, even if those groups are not outwardly marked.

Understanding the effects of subsidence on unconfined aquifer parameters by integration of Lattice Boltzmann Method (LBM) and Genetic Algorithm (GA)

Excessive exploitation of groundwater has hitherto led to a significant land subsidence in a considerable number of plains in Iran. The compaction of aquifer layers ends up with changes in aquifer parameters, including hydraulic conductivity (Kx), specific yield (Sy), and compressibility (α). Accordingly, a precise estimation of aquifer parameters, Kx, Sy, and α seems essential for future water resources planning and management. In this study, an innovative inversion solution based on the combination of lattice Boltzmann method (LBM) and genetic algorithm (GA) was developed to determine the aquifer parameters, Kx, Sy, and α in Darab plain (in Fars province, Iran), which is highly subject to land subsidence. Herein, a newly developed LBM for unconfined groundwater flow was employed by incorporating the amount of subsidence measured by synthetic aperture radar interferometry (InSAR) spanning from 2010 to 2016. In order to optimize the aquifer parameters, the whole process of inverse modeling is replicated on the annual basis from 2010 to 2016 which leads to the temporal estimation of the aquifer parameters. Due to the compaction occurring in the aquifer system, a declining temporal trend is observed in the aquifer parameters in most parts of the plain. By fitting a function to time-dependent aquifer parameters, Kx, Sy, and α, their corresponding values and consequently the amount of subsidence in the near future, i.e., 2017, are predicted. The small average relative error (~ 3.5%) between the predicted land subsidence and the InSAR measurements demonstrates the high performance of the proposed inverse modeling approach.

Supplemental Material for Investigating the Relationships Between Temporal and Spatial Ratio Estimation and Magnitude Discrimination Using Structural Equation Modeling: Evidence for a Common Ratio Processing System

Supplemental Material for Investigating the Relationships Between Temporal and Spatial Ratio Estimation and Magnitude Discrimination Using Structural Equation Modeling: Evidence for a Common Ratio Processing System

Moderate physical activity alters the estimation of time, but not space.

Moderate physical activity can influence cognitive functions and visual cortical activity. However, little is known about the effects of exercise on fundamental perceptual domains, such as spatial and temporal representation. Here we tackled this issue by testing the impact of physical activity on a temporal estimation task in a group of adult volunteers in three different conditions: (1) in a resting condition (baseline), (2) during moderate physical activity (cycling in place – PA), and (3) approximately 15 to 20 min following the physical activity phase, in which participants were seated and returned to a regular heart rate (POST). We show that physical activity specifically impacts time perception, inducing a consistent overestimation for durations in the range of milliseconds. Notably, the effect persisted in the POST session, ruling out the main contribution of either heart rate or cycling rhythmicity. In a control experiment, we found that spatial perception (distance estimation) was unaffected by physical activity, ruling out a major contribution of arousal and fatigue to the observed temporal distortion. We speculate that physical exercise might alter temporal estimation either by up-regulating the dopaminergic system or modulating GABAergic inhibition.

National-scale temporal estimation of South Korean Forest carbon stocks using a machine learning-based meta model

The objective of the present study is to develop an integrated meta model, i.e. a surrogate model for process-based model for estimating forest carbon stocks in South Korea. Two meta models which cover for the process-based model were firstly developed—meta forest growth and meta forest biomass and dead organic matter carbon (FBDC) by adopting a multi-layer feedforward neural network (ML-FFNN) with a scaled conjugated gradient for training. Due to dependence of the forest carbon stocks on growth and climate variability, it was possible to interrelate the two meta models, and define a single integrated meta model. For the meta forest growth model, scientific uncertainties surrounding the driving mechanisms of tree growth in the process-based model increased model complexity, resulting in a relatively fair model performance (R2 = 0.776), compared to a near-perfect meta FBDC model performance (R2 = 0.997). The integrated meta model maintained an intermediate performance (R2 = 0.822). The integrated meta model did well to capture the spatial patterning of carbon stocks when compared to those of previous process-based models, although the former had a limited capacity to capture extreme highs associated with Quercus acutissima Carruth. As the integrated meta model was developed by using data including the impact of climate change, it is applicable to use for the forest management, for example, the implementation of Nationally Determined Contributions in the future.

Variasi spasial dan temporal nilai-b pada gempa bumi di wilayah Sulawesi Tengah, Gorontalo, dan sekitarnya menggunakan metode robust fitting

This study discusses variation in seismic and tectonic modeled by a Gutenberg-Richter relationship for earthquakes in the Central Sulawesi, Gorontalo, and surrounding areas using the Robust Fitting Method (RFM) with the weight function of Tukey’s bisquare. The declustering process on earthquake data is carried out using the Reasenberg equation. The values for both parameters are analyzed spatially and temporally. In the spatial analysis, the research area is divided into 43 grids. In the temporal analysis, the research area is divided into zone A and zone B. The data grouping is done using a sliding time window method, i.e., grouping 50 earthquake catalogs with 5 overlapping events. The results according to spatial analysis show that the b-values range from 0.38 – 1.19. Areas with low b-values (0.38 – 0.7) occur around the Palu-Koro Fault, i.e., Palu city, Malacca strait, and to Toli-Toli, and also in the northern region of Gorontalo, i.e., the subduction plate of the Sulawesi Sea. Meanwhile, high b-values (0.71 – 1.19) are in the Tomini Bay area which is an area with frequent occurrence of earthquakes but has the small potential to generate large-scale earthquakes. The results of the temporal b-value estimation in zones A and B range between values of 0.38 - 1.25. The b-values appear to decrease before the occurrence of major earthquakes in 1996 and 2018 in zone A. The b-values decreased before the occurrence of major earthquakes in 1990, 1991, 2000, and 2008 in zone B. However, the b-values cannot be used as a precursor before the big earthquake in 1997.
 Keywords: Tukey’s bisquare, Reasenberg equation, Gutenberg-Richter relationship, sliding time window, Robust Fitting Method. MSC2020: 86A15

Spatiotemporal estimation of model error to improve soil moisture analysis in ensemble Kalman filter data assimilation

As a key variable in the surface hydrological cycle and energy balance, soil moisture is often analyzed using data assimilation to improve precise and high spatiotemporal resolution from multisource data. However, accurate estimation of model error in soil moisture data assimilation is not sufficient in spatial and temporal dimensions. This paper proposes a spatiotemporal error estimation method that integrates the spatial and temporal dimensions of the study area into model error estimation. The in-situ data was regarded as the “true value” for the temporal dimensions model error estimation. The triple collocation (TC) technique was used to estimate the spatial error of the model. Based on the aforementioned estimation, a linear method to construct the relationship between spatial and temporal information was proposed to fuse the spatiotemporal model error. Result from the proposed method was compared against the overall model error estimation and spatial estimation methods in ensemble Kalman filter (EnKF) data assimilation. The ERA-Interim reanalysis data and in-situ soil moisture data from Washington State in 2016 were used to evaluate the performance of EnKF data assimilation. Using statistical metrics, including the root mean square error (RMSE), the mean bias error (MBE), and the Pearson correlation coefficient (i.e., R-value), the results show that our proposed approach, which uses spatiotemporal approach to model error estimation in EnKF data assimilation, yielded more accurate soil moisture estimates. When ERA-Interim reanalysis soil moisture data was used as the reference data, compared with the overall model error estimation EnKF Data Assimilation Experiment, RMSE and MBE of spatiotemporal model error estimation EnKF Data Assimilation Experiment were reduced by 0.0062 and 0.0061 m3 / m3, respectively, and R remained basically unchanged. When using in-situ measurements as reference data, RMSE and MBE decreased by 0.0034 and 0.0038 m3 / m3, respectively, and R increased by 0.0365. Compared with the other estimation approaches in the experiments, the spatiotemporal error estimation approach greatly improved the estimation accuracy of soil moisture analysis, yielding the closest values and variation trends with the in-situ measurements. Fully considering the temporal and spatial variation of model error and improving the estimation accuracy of model error can provide more accurate model information for data assimilation. The proposed method and framework in this paper can be used in model error estimation and data assimilation, particularly in the fields of numerical weather forecasting, natural environment monitoring, and geographical environment research.

Yeşilırmak Havzasında İklim Değişim Senaryoları Altında Gelecekteki Sediment Verimi ve Sediment Tutma Kapasitesinin InVEST Model ile Değerlendirilmesi

Climate change is expected to cause more severe environmental problems, including soil erosion. This study aims to examine the effects of climate change on the sediment yield and sediment retention capacity through soil erosion in the Yeşilırmak Basin with the InVEST sediment delivery ratio model. RCP4.5 and RCP8.5 scenarios of the GFDL-CM3 (Geophysical Fluid Dynamics Laboratory Climate Model Version III) general circulation model were used to examine the effects of climate change on soil erosion. Decreases in the sediment yield and sediment retention rates of Yeşilırmak Basin were projected up to 9.48% and 12.47% in 2070, respectively. The main reason for the decrease in soil erosion rates is the decrease in rainfall erosivity resulting from the decrease in precipitation amounts, and the effect of this decrease is predicted to have a positive impact on wetland systems and agricultural lands. In addition, this research points to the advantages of the InVEST model approach in spatial and temporal estimation of the possible effects of future climate change on soil erosion.

Elastic temporal alignment for few‐shot action recognition

Few-shot action recognition aims to learn a classification model with good generalisation ability when trained with only a few labelled videos. However, it is difficult to learn discriminative feature representations for videos in such a setting. The Elastic Temporal Alignment (ETA) for few-shot action recognition is proposed. First, a convolutional neural network is employed to extract feature representations of video frames sparsely sampled from videos. In order to obtain the similarity of two videos, a temporal alignment estimation function is utilised to estimate the matching score between each pair of frames from the two videos through an elastic alignment mechanism. The analysis shows that when we judge whether two frames from respective videos are matched, multiple adjacent frames in the videos should be considered, so as to embody the temporal information. Thus, before feeding per-frame feature vectors of videos into the temporal alignment estimation function, a temporal message passing function is leveraged to propagate the information of per-frame features in the temporal domain. The method has been evaluated on four action recognition datasets, including Kinetics, Something-Something V2, HMDB51, and UCF101. The experimental results verify the effectiveness of ETA and show its superiority over state-of-the-art methods.

A novel motion-based online temporal calibration method for multi-rate sensors fusion

Accurate and robust calibration is an essential prerequisite for multi-rate sensors fusion. However, most existing calibration methods ignore the temporal calibration and assumed the timestamps of the multi-rate sensors are precisely aligned; more importantly, many approaches are designed for offline calibration. For these reasons, this paper develops a novel online temporal calibration method for multi-rate sensors fusion based on the motion constrains of the sensors. In this new calibration framework, the high update rate inertial measurement unit (IMU) is utilized as the unified calibrating references, while other moderate or low-frequency target sensors can be estimated based on the reference IMU. As a result, the targetless, online, and high-precision temporal self-calibration can be achieved. During the calibration, an improved multi-state constraint Kalman filter (I-MSCKF) algorithm is proposed for both position and temporal states estimation of the multi-rate sensors to establish a multi-constraint filter and correct the temporal offset error in a real-time manner. Furthermore, the motion constraints models in the two-dimensional (2D) planar and three-dimensional (3D) space are developed from per-sensor ego-motion to enhance the robust and reliable abilities of the proposed temporal self-calibration method. Experimental results demonstrate that the proposed method can accurately and online estimate the temporal offset error and transformation parameters, which significantly improves the performance of moving trajectory estimation for robots equipped with the multi-rate sensors.

Dynamic Connectivity Analysis Using Adaptive Window Size

In this paper, we propose a new method to study and evaluate the time-varying brain network dynamics. The proposed RICI-imCPCC method (relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient) is based on an adaptive window size and the imaginary part of the complex Pearson correlation coefficient. It reduces the weaknesses of the existing method of constant sliding window analysis with narrow and wide windows. These are the low temporal precision and low reliability for short connectivity periods for wide windows, and high susceptibility to noise for narrow windows, all resulting in low estimation accuracy. The proposed method overcomes these shortcomings by dynamically adjusting the window width using the RICI rule, which is based on the statistical properties of the area around the observed sample. In this paper, we compare the RICI-imCPCC with the existing constant sliding window analysis method and describe its advantages. First, the mathematical principles are established. Then, the comparison between the existing and the proposed method using synthetic and real electroencephalography (EEG) data is presented. The results show that the proposed RICI-imCPCC method has improved temporal resolution and estimation accuracy compared to the existing method and is less affected by the noise. The estimation error energy calculated for the RICI-imCPCC method on synthetic signals was lower by a factor of compared to the error of the constant sliding window analysis using narrow window size imCPCC, by a factor of compared to using wide window size imCPCC, by a factor of compared to using narrow window size wPLI, and by a factor of compared to using wide window size wPLI. Analysis of the real signals shows the ability of the proposed method to detect a P300 response and to detect a decrease in dynamic connectivity due to desynchronization and blockage of mu-rhythms.

Phytochemical analyses and toxicity of Nerium oleander (Apocynaceae) leaf extracts against Chaitophorus leucomelas Koch, 1854 (Homoptera: Aphididae)

In recent decades, phytochemicals in plant extracts have been recognized as having insecticidal activity against insect pests. They have also been described as an alternative method to synthetic chemicals. The present study is a contribution on the insecticidal activity, under semi-natural conditions, of extracts of Nerium oleander L. on viviparous females of Chaitophorus leucomelas . Two extraction methods were performed with polar solvent to obtain two extracts used to carry out the insecticide tests. To identify the highest number of metabolites present in the plant three approaches were performed using supercritical fluid, ultrasound and enzymatic methods. Thirty-eight bioactive molecules were identified by GC/MS, some of which could be responsible for the insecticidal activity on viviparous females of C. leucomelas . The enzymatic extraction showed a very high monoterpene content of 34.62%, while the supercritical fluid extraction gave a content of 71.91% of oxygenated terpenic compounds. The results relating to the temporal estimation of insecticidal activity of the three studied concentrations of pure methanol and methanol-water extracts were found to be effective in terms of toxicity to viviparous females of C. leucomelas . A 100% mortality rate was observed 4 days after treatment at a concentration of 5.15 g/m 2.

Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic.

Monitoring the fine spatiotemporal distribution of urban GDP is a critical research topic for assessing the impact of the COVID-19 outbreak on economic and social growth. Based on nighttime light (NTL) images and urban land use data, this study constructs a GDP machine learning and linear estimation model. Based on the linear model with better effect, the monthly GDP of 34 cities in China is estimated and the GDP spatialization is realized, and finally the GDP spatiotemporal correction is processed. This study analyzes the fine spatiotemporal distribution of GDP, reveals the spatiotemporal change trend of GDP in China’s major cities during the current COVID-19 pandemic, and explores the differences in the economic impact of the COVID-19 pandemic on China’s major cities. The result shows: (1) There is a significant linear association between the total value of NTL and the GDP of subindustries, with R2 models generated by the total value of NTL and the GDP of secondary and tertiary industries being 0.83 and 0.93. (2) The impact of the COVID-19 pandemic on the GDP of cities with varied degrees of development and industrial structures obviously varies across time and space. The GDP of economically developed cities such as Beijing and Shanghai are more affected by COVID-19, while the GDP of less developed cities such as Xining and Lanzhou are less affected by COVID-19. The GDP of China’s major cities fell significantly in February. As the COVID-19 outbreak was gradually brought under control in March, different cities achieved different levels of GDP recovery. This study establishes a fine spatial and temporal distribution estimation model of urban GDP by industry; it accurately monitors and assesses the spatial and temporal distribution characteristics of urban GDP during the COVID-19 pandemic, reveals the impact mechanism of the COVID-19 pandemic on the economic development of major Chinese cities. Moreover, economically developed cities should pay more attention to the spread of the COVID-19 pandemic. It should do well in pandemic prevention and control in airports and stations with large traffic flow. At the same time, after the COVID-19 pandemic is brought under control, they should speed up the resumption of work and production to achieve economic recovery. This study provides scientific references for COVID-19 pandemic prevention and control measures, as well as for the formulation of urban economic development policies.

Artificial Intelligence-Based Prognostic Model for Urologic Cancers: A SEER-Based Study.

Simple SummaryWe describe a risk profile reconstruction model for cancer-specific survival estimation for continuous time points after urologic cancer diagnosis. We used artificial intelligence (AI)-based algorithms, a national cancer registry data, and accessible clinical parameters for the risk-profile reconstruction. We derived a risk stratification model and estimated the minimum follow-up duration and the likelihood for risk stability in prostate, kidney, and testicular cancers. The estimated follow-up duration was in alignment with recognized clinical guidelines for these cancers. Moreover, the estimated follow-up duration was differed by the cancer origin and the disease dissemination status. Overall, the reconstruction of the population’s risk profile for the cancer-specific prognostic score estimation is feasible using AI and has potential application in clinical settings to improve risk stratification and surveillance management.Background: Prognostication is essential to determine the risk profile of patients with urologic cancers. Methods: We utilized the SEER national cancer registry database with approximately 2 million patients diagnosed with urologic cancers (penile, testicular, prostate, bladder, ureter, and kidney). The cohort was randomly divided into the development set (90%) and the out-held test set (10%). Modeling algorithms and clinically relevant parameters were utilized for cancer-specific mortality prognosis. The model fitness for the survival estimation was assessed using the differences between the predicted and observed Kaplan–Meier estimates on the out-held test set. The overall concordance index (c-index) score estimated the discriminative accuracy of the survival model on the test set. A simulation study assessed the estimated minimum follow-up duration and time points with the risk stability. Results: We achieved a well-calibrated prognostic model with an overall c-index score of 0.800 (95% CI: 0.795–0.805) on the representative out-held test set. The simulation study revealed that the suggestions for the follow-up duration covered the minimum duration and differed by the tumor dissemination stages and affected organs. Time points with a high likelihood for risk stability were identifiable. Conclusions: A personalized temporal survival estimation is feasible using artificial intelligence and has potential application in clinical settings, including surveillance management.

Modulations of one's sense of agency during human-machine interactions: A behavioural study using a full humanoid robot.

Although previous investigations reported a reduced sense of agency when individuals act with traditional machines, little is known about the mechanisms underpinning interactions with human-like automata. The aim of this study was twofold: (1) to investigate the effect of the machine's physical appearance on the individuals' sense of agency and (2) to explore the cognitive mechanisms underlying the individuals' sense of agency when they are engaged in a joint task. Twenty-eight participants performed a joint Simon task together with another human or an automated artificial system as a co-agent. The physical appearance of the automated artificial system was manipulated so that participants could cooperate either with a servomotor or a full humanoid robot during the joint task. Both participants' response times and temporal estimations of action-output delays (i.e., an implicit measure of agency) were collected. Results showed that participants' sense of agency for self- and other-generated actions sharply declined during interactions with the servomotor compared with the human-human interactions. Interestingly, participants' sense of agency for self- and other-generated actions was reinforced when participants interacted with the humanoid robot compared with the servomotor. These results are discussed further.

3D Imaging With Moving Fringe Structured Illumination Microscopy

Structured Light Illumination Microscopy (SIM) has proved itself as a very effective method to improve the resolution of a widefield (WF) fluorescent microscope. In this paper, we demonstrate a new approach to three-dimensional (3D) imaging with the SIM, using a moving fringe (MF) illumination pattern. Instead of the standard three-beam standing wave illumination pattern, our method requires a two-beam one, varying along the optical axis. Each axial layer of the MF illumination pattern contains single-spatial-frequency interference fringes, traversing the space with its own speed (temporal frequency), proportional to the axial offset of such layer from the excitation plane. The different axial layers of a fluorescent object, excited with the MF illumination, will emit a continuous amplitude modulated fluorescent signal with the frequency of modulation proportional to the temporal frequency of the moving fringe pattern. The fine 3D image reconstruction is achieved via extracting the spatial location of the fluorescent object from the temporal frequency of amplitude modulated signal emitted by it. Since in our approach the problem of 3D image reconstruction is reduced to the problem of accurate temporal frequency estimation, any of the well-known spectrum estimation techniques can be applied to the problem, allowing the axial resolution improvement far beyond the limits of the classical 3D SIM. In this research, we suggest using the Minimum-norm method for the proposed MF SIM system, which gives a superior resolving power in spectrum estimation. Simulation results show that such a simple and rapid hardware implementation in combination with a straightforward signal processing method can, however, deliver an improvement in axial resolution far beyond the classical 3D SIM approach.