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To mitigate congestion caused by drivers cruising in search of parking, performance-based pricing schemes have received a significant amount of attention. However, several recent studies suggest location, time-of-day, and awareness of policies are the primary factors that drive parking decisions. Harnessing data provided by the Seattle Department of Transportation and considering the aforementioned decision-making factors, we analyze the spatial and temporal properties of curbside parking demand and propose methods that can improve traditional policies with straightforward modifications by advancing the understanding of where and when to administer pricing policies. Specifically, we develop a Gaussian mixture model based technique to identify zones with similar parking demand as quantified by spatial autocorrelation. In support of this technique, we introduce a metric based on the repeatability of our Gaussian mixture model to investigate temporal consistency.

A lâmina de água armazenada no solo, em uma área vegetada, apresenta variação espaço-temporal, o que estabelece incertezas em relação à região a ser monitorada para fins de controle de irrigação. Tendo-se como premissa o fato de que o seu padrão espacial de distribuição é persistente no tempo, pode-se identificar a região que sistematicamente apresenta valores médios representativos de toda a área, empregando as análises de estabilidade temporal. Mas esta persistência temporal em uma região sempre está associada a uma variabilidade em torno da média geral, o que levou à proposição de um indicador da consistência temporal (ICT). Para o desenvolvimento do trabalho, cultivou-se Urochloa decumbens em uma área experimental, solo de textura muito argilosa, durante dois ciclos anuais. Em cada ciclo, foram feitas medidas da lâmina de água armazenada no solo (ARM), durante ciclos de secamento do solo, e também do índice de área foliar (IAF) da cultura. O emprego de técnicas geoestatísticas, associadas à análise de estabilidade temporal foram realizadas. Os resultados evidenciam que é possível caracterizar o ICT como um indicador adequado e conveniente como suporte à tomada de decisão em questões referentes ao monitoramento de água no solo em sistemas agrícolas irrigados.

Host-country terrorism poses extreme uncertainty to MNEs’ foreign operations. However, little is known about how MNEs respond to host-country terrorist attacks and how they make the related decisions. Drawing on the research on MNEs’ responses to exogenous shocks and the inter-firm imitation literature, we argue that MNEs obtain relevant information from peers’ actions under terrorist attacks to inform their own responses. Thus, MNEs are more likely to divest under host-country terrorist attacks when peers also divest. Furthermore, the impact of peers’ divestments on focal MNEs’ divestments is magnified by the temporal consistency in peers’ behavioral pattern under terrorist attacks, as temporal consistency reduces inferential difficulties. Empirical evidence based on 93 Fortune US companies and their 8698 foreign subsidiaries over 2005–2015 support our hypotheses. Our study extends the literature on MNEs’ responses to host-country exogenous shocks and on inter-firm imitation by demonstrating the social amplification of terrorism threat and the role of temporal consistency in inter-firm imitation under extreme uncertainty. Practically, we stress that MNE executives should always consider their firms’ idiosyncratic situations when modeling peer MNEs’ actions, and be mindful that they are especially likely to imitate peers’ temporally consistent behaviors under extreme uncertainty.

Current approaches of human body skeleton extraction mainly suffer from following problems: insufficient temporal and spatial continuity, unrobust to background, ambient noise, etc. This paper proposes a three-dimensional human body skeleton extraction method from consecutive meshes. We extract the consistent skeletons from consecutive surfaces based on shape segmentation and skeleton sequences; then, we present a spatiotemporal skeleton optimization model to adjust the skeleton sequences. Experiments on multiview images captured from a light field device demonstrate that our method captures more complete and accurate skeletons compared to state-of-the-art methods.

1. Resting metabolic rate (RMR) is a fundamental feature of animal biology that reflects the baseline level of energy expenditure. There are two main strategies that can address energy demands; animals can reallocate energy from maintenance by reducing RMR to meet energy demands (compensation model) or they can increase intake rate by increasing metabolic activities (performance model). 2. Orb‐web spiders are sit‐and‐wait foragers that typically reside at the centre of their web waiting to intercept prey. Given their sedentary resource acquisition strategy, it is predicted that lower RMR is favoured to reduce self‐maintenance energetic costs and to allow greater allocation to oogenesis (i.e., egg sac development). 3. In this study, we tested temporal variation in RMR of female Argiope radon (Araneae: Araneidae) spiders in response to mating status. Then we tested the degree to which between‐individual variation in the parental RMR relates to reproductive output and spiderling early life‐history traits. 4. Despite the notable between‐individual variation, we found a temporal consistency of RMR in the female spiders at early adulthood. Mated females significantly reduced their RMR by around 35% compared to their unmated stage which supports compensation model. However, there was a significant correlation between female RMR and mass of the egg sac in these spiders which is an evidence for performance model. 5. Our findings suggest that energy management in this species is a complex phenomenon, both strategies are in effect simultaneously at within‐ and between‐individual level shaping the individuals' phenotype.

In this paper, we propose a new 4D segmentation formulation, aiming to improve the temporal consistency of adults' brain tissue segmentation. In our method, tissue segmentation result is represented by the membership functions of the tissues, which are derived as a result of minimizing energy in a fuzzy C-means (FCM) framework. We first introduce a variational formulation with a temporal consistency constraint on the membership functions, then convert the constraint to a vector-valued function from which the membership functions are directly computed according to an analytically defined mapping. These vector-valued functions capture the bias field and intensity means of each tissue addressing the temporal variation in intensity inhomogeneities and the intensity means of the images. The effectiveness is demonstrated on the Baltimore Longitudinal Study of Aging (BLSA) benchmark dataset. Our method takes few parameters which are easy to tune and achieves 96.4% of TC factor for gray matter, 98.1% of TC factor for white matter, significantly superior to the compared methods on this research line.

Video stitching enables creation of videos with a wide field of view from videos captured from ordinary, probably moderately displaced, cameras. In this paper, a novel video stitching algorithm based on multi-view feature detection and registration using region-based statistics has been proposed. While stitching the videos captured using moving cameras, the motion in those cameras will affect the alignment of video frames. Movements in the cameras and in the objects will affect the temporal consistency while stitching the videos. Temporally reliable feature points are considered to maintain both spatial and temporal consistency using multi-view spatiotemporal feature points in this paper. Experiments have been carried out on different data sets, and the results showed the better and effective performance of the proposed approach.

Annual global land cover maps (GLC) are being provided by several operational monitoring efforts. However, map validation is lagging, in the sense that the annual land cover maps are often not validated. Concurrently, users such as the climate and land management community require information on the temporal consistency of multi-date GLC maps and stability in their accuracy. In this study, we propose a framework for operational validation of annual global land cover maps using efficient means for updating validation datasets that allow timely map validation according to recommendations in the CEOS Stage-4 validation guidelines. The framework includes a regular update of a validation dataset and continuous map validation. For the regular update of a validation dataset, a partial revision of the validation dataset based on random and targeted rechecking (areas with a high probability of change) is proposed followed by additional validation data collection. For continuous map validation, an accuracy assessment of each map release is proposed including an assessment of stability in map accuracy addressing the user needs on the temporal consistency information of GLC map and map quality. This validation approach was applied to the validation of the Copernicus Global Land Service GLC product (CGLS-LC100). The CGLS-LC100 global validation dataset was updated from 2015 to 2019. The update was done through a partial revision of the validation dataset and an additional collection of sample validation sites. From the global validation dataset, a total of 40% (10% for each update year) was revisited, supplemented by a targeted revision focusing on validation sample locations that were identified as possibly changed using the BFAST time series algorithm. Additionally, 6720 sample sites were collected to represent possible land cover change areas within 2015 and 2019. Through this updating mechanism, we increased the sampling intensity of validation sample sites in possible land cover change areas within the period. Next, the dataset was used to validate the annual GLC maps of the CGLS-LC100 product for 2015–2019. The results showed that the CGLS-LC100 annual GLC maps have about 80% overall accuracy showing high temporal consistency in general. In terms of stability in class accuracy, herbaceous wetland class showed to be the least stable over the period. As more operational land cover monitoring efforts are upcoming, we emphasize the importance of updated map validation and recommend improving the current validation practices and guidelines towards operational map validation so that long-term land cover maps and their uncertainty information are well understood and properly used.

Understanding the spatiotemporal dynamics of global urbanization over a long time series is increasingly important for sustainable development goals. The harmonized time-series nighttime light (NTL) composites by fusing multi-source NTL observations provide a long and consistent record of the nightscape for characterizing and understanding the global urban dynamics. In this study, we generated a global dataset of annual urban extents (1992–2020) using consistent NTL observations and analyzed the spatiotemporal patterns of global urban dynamics over nearly 30 years. The urbanized areas associated with locally high-intensity human activities were mapped from the time-series global NTL imagery using a new stepwise-partitioning framework. This framework includes three components: (1) clustering of NTL signals to generate potential urban clusters; (2) identification of optimal thresholds to delineate annual urban extents; and (3) check of temporal consistency to correct pixel-level urban dynamics. We found that the global urban land area percentage to the Earth’s land surface raised from 0.22 % to 0.69 % in 1992 and 2020, respectively. Urban dynamics over the past three decades at the continent, country, and city levels exhibit various spatiotemporal patterns. Our resulting global urban extents (1992–2020) were evaluated using other urban remote sensing products and socioeconomic data. The evaluations indicate that this dataset is reliable for characterizing spatial extents associated with intensive human settlement and high-intensity socioeconomic activities. The dataset of global urban extents from this study can provide unique information to capture the historical and future trajectories of urbanization, and understand and tackle the urbanization impacts on food security, biodiversity, climate change, and public well-being and health. This dataset can be downloaded from https://doi.org/10.6084/m9.figshare.16602224.v1 (Zhao et al., 2021).

International audience; Imitation learning allows learning complex behaviors given demonstrations. Early approaches belonging to either Behavior Cloning or Inverse Reinforcement Learning were however of limited scalability to complex environments. A more promising approach termed as Generative Adversarial Imitation Learning tackles the imitation learning problem by drawing a connection with Generative Adversarial Networks. In this work, we advocate the use of this class of methods and investigate possible extensions by endowing them with global temporal consistency, in particular through a contrastive learning based approach.

Remote patient monitoring has only become more relevant in the current pandemic context. Contactless sensing extracts vital signs (ex. heart rate) and other physiological information from signals obtained from non-contact hardware such as video cameras. Such a technology could have a significant impact on the healthcare system by enabling a physician to remotely acquire quantitative measurements of a patient. This could help prevent sudden deterioration of patient condition, improve the efficiency and support for medical decision making and diagnostics, and reduce costs and hospitalizations. In this work, a practical and fully automatic video-based contactless monitoring framework for the evaluation of patient heart rate is presented. The framework allows the calculation of heart rate in quasi realtime and with a high accuracy, using a consumer grade laptop equipped with a usb-connected webcam. The proposed heart rate assessment algorithm combines state-of-the art methods in video- and signal-processing for contactless sensing; to attain high accuracy predictions, an additional probabilistic formulation that improves the temporal consistency is proposed and used along with live error rejection. To validate the framework, twentytwo subjects were recorded under varying physiological states (total of 111 two-minute long videos). Heart rate predictions were compared to medical-grade sensors and showed an accuracy of -0.17±1.81 bpm with a MAE of 0.91 bpm., 2021 IEEE 9th International Conference on HealthCare Informatics (ICHI), August 9-12, 2021, Victoria, BC, Canada

Intermittently powered energy-harvesting devices enable new applications in inaccessible environments. Program executions must be robust to unpredictable power failures, introducing new challenges in programmability and correctness. One hard problem is that input operations have implicit constraints, embedded in the behavior of continuously powered executions, on when input values can be collected and used. This paper aims to develop a formal framework for enforcing these constraints. We identify two key properties -- freshness (i.e., uses of inputs must satisfy the same time constraints as in continuous executions) and temporal consistency (i.e., the collection of a set of inputs must satisfy the same time constraints as in continuous executions). We formalize these properties and show that they can be enforced using atomic regions. We develop Ocelot, an LLVM-based analysis and transformation tool targeting Rust, to enforce these properties automatically. Ocelot provides the programmer with annotations to express these constraints and infers atomic region placement in a program to satisfy them. We then formalize Ocelot's design and show that Ocelot generates correct programs with little performance cost or code changes., Updates for camera-ready, add DOI

The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for urban impervious surface mapping at both fine-spatial and fine-temporal resolutions. The STSRM involves two main steps: unmixing the coarse-spatial-fine-temporal remote sensing data to class fraction images, and downscaling the fraction images to sub-pixel land cover maps. Yet, challenges exist in each step when applying STSRM in mapping impervious surfaces. First, the impervious surfaces have high spectral variability (i.e., high intra-class and low inter-class variability), which impacts the accurate extraction of sub-pixel scale impervious surface fractions. Second, downscaling the fraction images to sub-pixel land cover maps is an ill-posed problem and would bring great uncertainty and error in the predictions. This paper proposed a new Spatiotemporal Continuous Impervious Surface Mapping (STCISM) method to deal with these challenges in fusing Landsat and Google Earth imagery. The STCISM used the Multiple Endmember Spectral Mixture Analysis and the Fisher Discriminant Analysis to minimize the within-class variability and maximize the between-class variability to reduce the spectral unmixing uncertainty. In addition, the STCISM adopted a new temporal consistency check model to incorporate temporal contextual information to reduce the uncertainty in the time-series impervious surface prediction maps. Unlike the traditional temporal consistency check model that assumed the impervious-to-pervious conversion is unlikely to happen, the new model allowed the bidirectional conversions between pervious and impervious surfaces. The temporal consistency check was used as a post-procession method to correct the errors in the prediction maps. The proposed STCISM method was used to predict time-series impervious surface maps at 5 m resolution of Google Earth image at the Landsat frequency. The results showed that the proposed STCISM outperformed the STSRM model without using the temporal consistency check and the STSRM model using the temporal consistency check based on the unidirectional pervious-to-impervious surface conversion rule.

Deep neural networks (DNNs) for highly automated driving are often trained on a large and diverse dataset, and evaluation metrics are reported usually on a per-frame basis. However, when evaluated on video sequences, the predictions are often unstable between consecutive frames. As such unstable predictions over time can lead to severe safety consequences, there is a growing need to understand, evaluate, and improve the temporal consistency of DNNs. In this paper, we explore such a temporal characteristic and propose a novel unsupervised temporal consistency (TC) loss that penalizes unstable semantic segmentation predictions. This loss function is used in a two-stage training scheme to jointly optimize for both, accuracy of semantic segmentation predictions, and its temporal consistency based on video sequences. We demonstrate that our training strategy helps in improving the temporal consistency of two state-of-the-art semantic segmentation networks on two different road-scenes datasets. We report an absolute 4.25% improvement in the mean temporal consistency (mTC) of the HRNetV2 network and an absolute 2.78% improvement on the DeepLabv3+ network, both evaluated on the Cityscapes dataset, with only a slight decrease in accuracy. When evaluating on the same video sequences using a synthetic dataset Sim KI-A, we show absolute improvements in both, accuracy (2.19% mIoU) and temporal consistency (0.21% mTC) for the DeepLabv3+ network. We confirm similar improvements for the HRNetV2 network.

Multiple object tracking (MOT) is a critical module for enabling autonomous vehicles to achieve safe planing and navigation in cluttered environments. In tracking-by-detection systems, there are inevitably many false positives and misses among learning-based input detections. The challenge for MOT is to combine these detections into tracks, and filter them based on their uncertainties, states, and temporal consistency to achieve accurate and persistent tracks. In this paper, we propose to solve the 3D MOT problem for autonomous driving applications using a random finite set-based (RFS) Multiple Measurement Models filter (RFS-M3). In partiuclar, we propose multiple measurement models for a Poisson multi-Bernoulli mixture (PMBM) filter in support of different application scenarios. Our RFS-M3 filter can naturally model these uncertainties accurately and elegantly. We combine the learning-based detections with our RFS-M3 tracker through incorporating the detection confidence score into the PMBM prediction and update step. The superior experimental results of our RFS-M3 tracker on Waymo, Argoverse and nuSceness datasets illustrate that our RFS-M3 tracker outperforms state-of-the-art deep learning-based and traditional filter-based approaches. To the best of our knowledge, this represents a first successful attempt for employing an RFS-based approach in conjunction with 3D learning-based amodal detections for 3D MOT applications with comprehensive validation using challenging datasets made available by industry leaders.

International audience; Video-based motion analysis recently appeared to be a promising approach in neonatal intensive care units for monitoring the state of preterm newborns since it is contact-less and noninvasive. However it is important to remove periods when the newborn is absent or an adult is present from the analysis. In this paper, we propose a method for automatic detection of preterm newborn presence in incubator and open bed. We learn a specific model for each bed type as the camera placement differs a lot and the encountered situations are different between both. We break the problem down into two binary classifications based on deep transfer learning that are fused afterwards: newborn presence detection on the one hand and adult presence detection on the other hand. Moreover, we adopt a strategy of decision intervals fusion in order to take advantage of temporal consistency. We test three deep neural network that were pre-trained on ImageNet: VGG16, MobileNetV2 and InceptionV3. Two classifiers are compared: support vector machine and a small neural network. Our experiments are conducted on a database of 120 newborns. The whole method is evaluated on a subset of 25 newborns including 66 days of video recordings. In incubator, we reach a balanced accuracy of 86%. In open bed, the performance is lower because of a much wider variety of situations whereas less data are available.

The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) tree cover (MOD44B) is an annual product that depicts the fractional coverage of trees on the earth’s terrestrial surface at 250m resolution. It has to be noted that the training data of this product is limited to trees above 5 m in height. However, this product has been widely applied in savanna ecosystems where many woody plant species seldom reach 5 m tall. Though the short woody plants in savanna ecosystems could have been reflected in this product due to similar spectral implication as that of tall trees, its applicability has never been rigorously tested. Given so, this study aims to fill this void. It was tested in Texas savanna, which is featured by a wide precipitation gradient and an accompanying woody plant cover gradation. Firstly, we assessed the ability of the MODIS tree cover product in reflecting woody plant cover at pixel level. A per-pixel comparison was conducted between the tree cover values and reference measurement of woody plant cover based on 1-m resolution digital orthophotos. Secondly, we assessed the ability of this tree cover product in reflecting savanna landscape pattern, particularly the potential woody cover pattern over the precipitation gradient in Texas savanna. The MODIS tree cover product based potential woody cover pattern was assessed against the reference pattern of the study area. Thirdly, we assessed the year-to-year consistency of this annual product, under the assumption that tree cover changes very little within one-year period. Reduced major axis regression was conducted between the corresponding tree cover pixels of all successive years from 2000 to 2016. Research results suggest that the MODIS tree cover product has great potential in reflecting woody plant cover at pixel level by empirical calibration. Moreover, this tree cover product demonstrates the ability of revealing the increasing trend of potential woody cover with mean annual precipitation (MAP) across Texas savanna. Furthermore, the lack of temporal consistency is revealed for this annual product. It is worth noting that the potential woody cover pattern endowed by the precipitation gradient in Texas savanna is in a three-segment shape.

Highlights • A harmonization procedure of GEOV2 and MODIS fAPAR data is introduced. • Systematic overestimations of GEOV2 anomalies compared to MODIS are removed. • Temporal consistent standardized anomalies can be used in drought studies., The temporal consistency of the fAPAR GEOV2 full time series (constituted by data derived from SPOT-VGT1/2 and PROBA-V) is analyzed against the single-sensor MODIS dataset, with a particular focus on the most recent fAPAR anomalies (z-scores) produced from PROBA-V in the period 2014–2017. The intercomparison highlights a systematic overestimation of GEOV2 fAPAR z-scores when compared to MODIS fAPAR, likely related to the observed positive bias (over 90% of the domain) in the PROBA-V vs. SPOT-VGT1/2 relationship. A simple two-step harmonization procedure has been proposed to remove this discrepancy, based on two separate linear corrections of SPOT-VGT1/2 (2001–2013) and PROBA-V (2014–2017) data with respect to MODIS, followed by a time lag correction. The harmonized GEOV2 time series preserves the overall dynamic of fAPAR, while removing the sensor bias and improving the consistency with MODIS data. The fAPAR anomalies from the harmonized GEOV2 time series provide unbiased estimates of z-scores that are overall well correlated (R = 0.55 ± 0.25) with the MODIS fAPAR anomalies.

The nighttime light (NTL) observations from Defense Meteorological Satellite Program/Operational Linescane System (DMSP/OLS) offer great potentials to study urban dynamics from regional to global scales, for more than two decades. In this paper, we presented a new approach to develop spatially and temporally consistent global urban maps from 1992 to 2013, using the DMSP/OLS NTL observations. First, potential urban clusters were delineated using the NTL data and a segmentation method. Then, a quantile-based approach was used to remove rural and suburban areas sequentially in the potential urban clusters. Finally, the derived series of urban extents in the entire study period (1992–2013) were improved for temporal consistency. We found the percentage of global urban areas relative to the world's land surface area increased from 0.23% in 1992 to 0.53% in 2013. Asia is the continent with the most significant urban growth, worldwide. The time series of global urban maps were evaluated for the spatial agreement and temporal consistency using a variety of widely used independent land-cover products. This evaluation indicates that the proposed approach is robust and performs well in deriving global urban dynamics across different spatial scales, i.e., cluster, province (or state), country, and region. Moreover, this quantile-based approach is advantageous, compared with other methods used in previous studies, because it does not require additional data for enhancement or calibration. The new time series of urban maps from this study offer a new dataset for studying global urbanization during the past decades and unique information to explore potential future trajectories of urban development, which appears to be distinct for different nations/regions, globally. Such information is pre-requisite for achieving the sustainable development goals, and associated targets, during ensuing decades.

Majority of the current literature on mental construal has focused on effects of varying construal levels on preference shifts whereas this research investigates the influence of mental construal on the change of preference consistency over time. Building on construal level theory, we propose that high-level construal, which creates abstract, and decontextualized mental representations, leads individuals to more consistent preferences than low-level construal, which creates concrete, and contextualized mental representations. Furthermore we examine the effect of having a matching versus non-matching construal level at two different evaluation instances, on achieving greater extents of consistency. To test this prediction a mixed experimental design is employed, in which participants evaluated electronic products at two different sessions. It is demonstrated that when participants have the same construal level at two points in time, their evaluations become similar since they mentally construe the objects in the same way whereas when the construal level differs at these two points, participants focus on different aspects of the products, form different evaluations and have less consistent preferences.

Recent studies show that wars were more prevalent during colder periods in human history. Nevertheless, the temporal consistency of the climate-war correlation in Europe over extended period has ra...

Temporal consistency information plays an important role in video segmentation. Some previous automatic annotation methods inevitably caused the temporal consistency information loss of sequential frames when processing sparse labeled frames. Thus, as a supplement, interpolation method was proposed to compensate motion flows between two frames via interpolating high-precision frames and corresponding labels, but its quality is remained to be improved. We propose a new interpolation module to smooth the large motion between frames and preserve temporal consistency. First, we use an optical flow estimator to directly estimate the optical flow, and then refine the interpolation frame by combining the features of the original frames. We evaluate our method on the MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset. Experimental results demonstrate our method can well preserve the temporal consistency information and outperform the state-of-the-art method in segmentation accuracy.