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This work tackles the automatic prediction of grasping intention of humans observing their environment. Our target application is the assistance to people with motor disabilities and potential cognitive impairments, using assistive robotics. Our proposal leverages the analysis of human attention captured in the form of gaze fixations recorded by an eye-tracker on the first person video, as the anticipation of prehension actions is a well studied and well known phenomenon. We propose a multi-task system that simultaneously addresses the prediction of human attention in the near future, and the anticipation of grasping actions. Visual attention is modeled as a competitive process between a discrete set of states, each one associated to a well-known gaze movement pattern from visual psychology. We additionally consider an asymmetric multi-task problem, where attention modeling is an auxiliary task that helps to regularize the learning process of the main action prediction task, and propose a constrained multi-task loss that naturally deals with this asymmetry. Our model shows superior performance than other losses for dynamic multi-task learning, current dominant deep architectures for general action forecasting and particularly-tailored models for predicting grasping intention. In particular, it provides state-of-the-art performance in three datasets for egocentric action anticipation, with an average precision of 0.569 and 0.524 in GITW and Sharon datasets, respectively, and an accuracy of 89.2% and a success rate of 51.7% in Invisible dataset.

Freight parking operations occur amid conflicting conditions of public space scarcity, competition with other users, and the inefficient management of loading zones (LZ) at cities’ curbside. The dynamic nature of freight operations, and the static LZ provision and regulation, accentuate these conflicting conditions at specific peak times. This generates supply–demand mismatches of parking infrastructure. These mismatches have motivated the development of Smart LZ that bring together technology, parking infrastructure, and data analytics to allocate space and define dynamic duration limits based on users’ needs. Although the dynamic duration limits unlock the possibility of a responsive LZ management, there is a narrow understanding of factors and analytical tools that support their definition. Therefore, the aim of this paper is twofold. Firstly, to identify factors for enabling dynamic parking durations policies. Secondly, to assess data analytics tools that estimate freight parking durations and LZ occupation levels based on operational and locational features. Semi-structured interviews and focus group analyses showed that public space use assessment, parking demand estimation, enforcement capabilities, and data sharing strategies are the most relevant factors when defining dynamic parking limits. This paper used quantitative models to assess different analytical tools that study LZ occupation and parking durations using tracked freight parking data from the City of Vic (Spain). CatBoost outperformed other machine learning (ML) algorithms and queuing models in estimating LZ occupation and parking durations. This paper contributes to the freight parking field by understanding how data analytics support dynamic parking limits definition, enabling responsive curbside management.

Scarce urban space needs to be wisely managed to avoid cities’ unsustainability. Overlooking freight activities in urban mobility policies leads to conflicting scenarios in public space use with negative consequences in congestion, pollution, crashes, and productivity losses. This paper aims at identifying the unsustainable effects of freight parking practices and the solutions reported in the literature to overcome them. A systematic literature review was conducted to collect quantitative evidence of curbside management impacts on sustainability. Key performance indicators from the 11thSustainable Development Goal (SDG11) were linked to the 55 selected studies. Findings suggested positive impacts from four practices of freight curbside management and the need to include people-centred approaches in the design, implementation, and evaluation of public space policies.

This paper studies demand for public loading zones in urban environments and seeks to develop a machine learning algorithm to predict their demand. Understanding and predicting demand for public loading zones can: (i) support better management of the loading zones and (ii) provide better pre-advice so that transport operators can plan their routes in an optimal way. The methods used are linear regression analysis and neural networks. Six months of parking data from the city of Vic in Spain are used to calibrate and test the models, where the parking data is transformed into a time-series format with forecasting targets. For each loading zone, a different model is calibrated to test which model has the best performance for the loading zone’s particular demand pattern. To evaluate each model’s performance, both root mean square error and mean absolute error are computed. The results show that, for different loading zone demand patterns, different models are better suited. As the prediction horizon increases, predicting further into the future, the neural network approaches start to give better predictions than linear models.

Abstract:The purpose of this study is to investigate which dry port services generate the greatest positive impact on stakeholders and their objectives. Stakeholders identified from the literature are the dry ports, the dry port’s municipality/region, the seaport, shippers, shipping lines, rail operators, and road operators. A method based on multi-actor multi-criteria analysis is applied to allow the evaluation of different scenarios considering the objectives and opinions of multiple stakeholders. The findings show that the basic services of the dry ports studied are crucial to establishing initial operations. Expanding the service portfolio by adding a wider range of customer-oriented value-added services generates extra economic and environmental benefits for stakeholders. The results expand the understanding on stakeholder benefits from the diversified range of dry port services, while the inclusion of multiple stakeholders in the analysis acknowledges the heterogeneity in stakeholder perceptions. Finally, this methodology enables the creation of an extensive set of criteria and indicators for dry port evaluations.

The purpose of this study is to investigate which dry port services generate the greatest positive impact on stakeholders and their objectives. Stakeholders identified from the literature are the dry ports, the dry port’s municipality/region, the seaport, shippers, shipping lines, rail operators, and road operators. A method based on multi-actor multi-criteria analysis is applied to allow the evaluation of different scenarios considering the objectives and opinions of multiple stakeholders. The findings show that the basic services of the dry ports studied are crucial to establishing initial operations. Expanding the service portfolio by adding a wider range of customer-oriented value-added services generates extra economic and environmental benefits for stakeholders. The results expand the understanding on stakeholder benefits from the diversified range of dry port services, while the inclusion of multiple stakeholders in the analysis acknowledges the heterogeneity in stakeholder perceptions. Finally, this methodology enables the creation of an extensive set of criteria and indicators for dry port evaluations.

IMPORTANCE: Intracerebral hemorrhage and arterial ischemic disease share risk factors, to our knowledge, but the association between the 2 conditions remains unknown. OBJECTIVE: To evaluate whether intracerebral hemorrhage was associated with an increased risk of incident ischemic stroke and myocardial infarction. DESIGN, SETTING, AND PARTICIPANTS: An analysis was conducted of pooled longitudinal participant-level data from 4 population-based cohort studies in the United States: the Atherosclerosis Risk in Communities (ARIC) study, the Cardiovascular Health Study (CHS), the Northern Manhattan Study (NOMAS), and the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. Patients were enrolled from 1987 to 2007, and the last available follow-up was December 31, 2018. Data were analyzed from September 1, 2019, to March 31, 2020. EXPOSURE: Intracerebral hemorrhage, as assessed by an adjudication committee based on predefined clinical and radiologic criteria. MAIN OUTCOMES AND MEASURES: The primary outcome was an arterial ischemic event, defined as a composite of ischemic stroke or myocardial infarction, centrally adjudicated within each study. Secondary outcomes were ischemic stroke and myocardial infarction. Participants with prevalent intracerebral hemorrhage, ischemic stroke, or myocardial infarction at their baseline study visit were excluded. Cox proportional hazards regression was used to examine the association between intracerebral hemorrhage and subsequent arterial ischemic events after adjustment for baseline age, sex, race/ethnicity, vascular comorbidities, and antithrombotic medications. RESULTS: Of 55 131 participants, 47 866 (27 639 women [57.7%]; mean [SD] age, 62.2 [10.2] years) were eligible for analysis. During a median follow-up of 12.7 years (interquartile range, 7.7-19.5 years), there were 318 intracerebral hemorrhages and 7648 arterial ischemic events. The incidence of an arterial ischemic event was 3.6 events per 100 person-years (95% CI, 2.7-5.0 events per 100 person-years) after intracerebral hemorrhage vs 1.1 events per 100 person-years (95% CI, 1.1-1.2 events per 100 person-years) among those without intracerebral hemorrhage. In adjusted models, intracerebral hemorrhage was associated with arterial ischemic events (hazard ratio [HR], 2.3; 95% CI, 1.7-3.1), ischemic stroke (HR, 3.1; 95% CI, 2.1-4.5), and myocardial infarction (HR, 1.9; 95% CI, 1.2-2.9). In sensitivity analyses, intracerebral hemorrhage was associated with arterial ischemic events when updating covariates in a time-varying manner (HR, 2.2; 95% CI, 1.6-3.0); when using incidence density matching (odds ratio, 2.3; 95% CI, 1.3-4.2); when including participants with prevalent intracerebral hemorrhage, ischemic stroke, or myocardial infarction (HR, 2.2; 95% CI, 1.6-2.9); and when using death as a competing risk (subdistribution HR, 1.6; 95% CI, 1.1-2.1). CONCLUSIONS AND RELEVANCE: This study found that intracerebral hemorrhage was associated with an increased risk of ischemic stroke and myocardial infarction. These findings suggest that intracerebral hemorrhage may be a novel risk marker for arterial ischemic events.

BackgroundAlthough acute respiratory distress syndrome (ARDS) is associated with high mortality, its direct causal link with death is unclear. Clarifying this link is important to justify costly research on prevention of ARDS.ObjectiveTo estimate the attributable mortality, if any, of ARDS.DesignFirst, we performed a systematic review and meta-analysis of observational studies reporting mortality of critically ill patients with and without ARDS matched for underlying risk factor. Next, we conducted a survival analysis of prospectively collected patient-level data from subjects enrolled in three intensive care unit (ICU) cohorts to estimate the attributable mortality of critically ill septic patients with and without ARDS using a novel causal inference method.ResultsIn the meta-analysis, 44 studies (47 cohorts) involving 56 081 critically ill patients were included. Mortality was higher in patients with versus without ARDS (risk ratio 2.48, 95% CI 1.86 to 3.30; p<0.001) with a numerically stronger association between ARDS and mortality in trauma than sepsis. In the survival analysis of three ICU cohorts enrolling 1203 critically ill patients, 658 septic patients were included. After controlling for confounders, ARDS was found to increase the mortality rate by 15% (95% CI 3% to 26%; p=0.015). Significant increases in mortality were seen for severe (23%, 95% CI 3% to 44%; p=0.028) and moderate (16%, 95% CI 2% to 31%; p=0.031), but not for mild ARDS.ConclusionsARDS has a direct causal link with mortality. Our findings provide information about the extent to which continued funding of ARDS prevention trials has potential to impart survival benefit.PROSPERO Registration NumberCRD42017078313

IMPORTANCE: It is uncertain whether coronavirus disease 2019 (COVID-19) is associated with a higher risk of ischemic stroke than would be expected from a viral respiratory infection. OBJECTIVE: To compare the rate of ischemic stroke between patients with COVID-19 and patients with influenza, a respiratory viral illness previously associated with stroke. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study was conducted at 2 academic hospitals in New York City, New York, and included adult patients with emergency department visits or hospitalizations with COVID-19 from March 4, 2020, through May 2, 2020. The comparison cohort included adults with emergency department visits or hospitalizations with influenza A/B from January 1, 2016, through May 31, 2018 (spanning moderate and severe influenza seasons). EXPOSURES: COVID-19 infection confirmed by evidence of severe acute respiratory syndrome coronavirus 2 in the nasopharynx by polymerase chain reaction and laboratory-confirmed influenza A/B. MAIN OUTCOMES AND MEASURES: A panel of neurologists adjudicated the primary outcome of acute ischemic stroke and its clinical characteristics, mechanisms, and outcomes. We used logistic regression to compare the proportion of patients with COVID-19 with ischemic stroke vs the proportion among patients with influenza. RESULTS: Among 1916 patients with emergency department visits or hospitalizations with COVID-19, 31 (1.6%; 95% CI, 1.1%-2.3%) had an acute ischemic stroke. The median age of patients with stroke was 69 years (interquartile range, 66-78 years); 18 (58%) were men. Stroke was the reason for hospital presentation in 8 cases (26%). In comparison, 3 of 1486 patients with influenza (0.2%; 95% CI, 0.0%-0.6%) had an acute ischemic stroke. After adjustment for age, sex, and race, the likelihood of stroke was higher with COVID-19 infection than with influenza infection (odds ratio, 7.6; 95% CI, 2.3-25.2). The association persisted across sensitivity analyses adjusting for vascular risk factors, viral symptomatology, and intensive care unit admission. CONCLUSIONS AND RELEVANCE: In this retrospective cohort study from 2 New York City academic hospitals, approximately 1.6% of adults with COVID-19 who visited the emergency department or were hospitalized experienced ischemic stroke, a higher rate of stroke compared with a cohort of patients with influenza. Additional studies are needed to confirm these findings and to investigate possible thrombotic mechanisms associated with COVID-19.

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This study sought to describe the patterns of heart failure (HF)-exacerbating medications used among older adults hospitalized for HF and to examine determinants of HF-exacerbating medication use.

There is a long-standing debate in the statistical, epidemiological, and econometric fields as to whether nonparametric estimation that uses machine learning in model fitting confers any meaningful advantage over simpler, parametric approaches in finite sample estimation of causal effects. We address the question: when estimating the effect of a treatment on an outcome, how much does the choice of nonparametric vs parametric estimation matter? Instead of answering this question with simulations that reflect a few chosen data scenarios, we propose a novel approach to compare estimators across a large number of data-generating mechanisms drawn from nonparametric models with semi-informative priors. We apply this proposed approach and compare the performance of two nonparametric estimators (Bayesian adaptive regression tree and a targeted minimum loss-based estimator) to two parametric estimators (a logistic regression-based plug-in estimator and a propensity score estimator) in terms of estimating the average treatment effect across thousands of data-generating mechanisms. We summarize performance in terms of bias, confidence interval coverage, and mean squared error. We find that the two nonparametric estimators can substantially reduce bias as compared to the two parametric estimators in large-sample settings characterized by interactions and nonlinearities while compromising very little in terms of performance even in simple, small-sample settings.

In this paper, we propose a method for solving the permutation problem that is inherent in the separation of convolved mixtures of speech signals in the time-frequency domain. The proposed method obtains the solution through maximization of a contrast function that exploits the similarity of the temporal envelope of the speech spectrum. For this purpose, the contrast calculation uses a global measure of similarity based on the recently developed family of generalized Alpha-Beta divergences, which depend on two tuning parameters, alpha and beta. This parameterization is exploited to best measure the similarity of the speech spectrum and to obtain solutions that are robust against noise and outliers. The ability of this contrast function to solve the permutation problem is supported by a theoretical study that shows that for a simple time-frequency speech model, the contrast value reaches its maximum when the estimated components are properly aligned. Several performance studies demonstrate that the proposed method maintains a high level of permutation correction accuracy in a wide variety of acoustic environments. Moreover, it produces better results than other state-of-the-art methods for solving permutations in highly reverberant environments.

This paper studies the problem of the blind extraction of a subset of bounded component signals from the observations of a linear mixture. In the first part of this paper, we analyze the geometric assumptions of the observations that characterize the problem, and their implications on the mixing matrix and latent sources. In the second part, we solve the problem by adopting the principle of minimizing the risk, which refers to the encoding complexity of the observations in the worst admissible situation. This principle provides an underlying justification of several bounded component analysis (BCA) criteria, including the minimum normalized volume criterion of the estimated sources or the maximum negentropy-likelihood criterion with a uniform reference model for the estimated sources. This unifying framework can explain the differences between the criteria in accordance with their considered hypotheses for the model of the observations. This paper is first presented for the case of the extraction of a complex and multidimensional source, and later is particularized for the case of the extraction of subsets of 1-D complex sources. The results also hold true in the case of real signals, where the obtained criteria for the extraction of a set of 1-D sources usually coincide with the existing BCA criteria.

The great amount of slices in volumetric data sets and limited time prevents human observers from exhaustively pointing their high resolution processing fovea to all regions in the images. Thus, many image-regions are processed with nonfoveal peripheral visual processing. Yet, most studies quantifying human detectability of signals in computer simulated textures and medical image backgrounds, have measured performance without consideration of the location of the signal in the observer's eye relative to the fovea (retinal eccentricity). Here, we measure human observer detectability of signals in CT images as a function of retinal eccentricity. A representative signal was extracted from a liver image and was added to healthy liver backgrounds at random positions. The retinal eccentricities of the signal were manipulated by varying the position of the point at which observers fixated with their eyes. Real-time video-based eye tracking was used to ensure steady fixation. High contrast fiduciary marks indicated the only possible location of the signal which was present in 50% of the images. Single CT slices were presented for 200 ms or 1 second. The observer was instructed to decide whether the image contained a signal (yes/no task). We probed 6 eccentricities with 420 decision trials per eccentricity. We found a large detectability degradation with retinal eccentricity with d' degrading by 50% at an eccentricity of 9 degrees for a 200 ms display time.

The development of model observers for mimicking human detection strategies has followed from symmetric signals in simple noise to increasingly complex backgrounds. In this study we implement different model observers for the complex task of detecting a signal in a 3D image stack. The backgrounds come from real breast tomosynthesis acquisitions and the signals were simulated and reconstructed within the volume. Two different tasks relevant to the early detection of breast cancer were considered: detecting an 8 mm mass and detecting a cluster of microcalcifications. The model observers were calculated using a channelized Hotelling observer (CHO) with dense difference-of-Gaussian channels, and a modified (Partial prewhitening [PPW]) observer which was adapted to realistic signals which are not circularly symmetric. The sustained temporal sensitivity function was used to filter the images before applying the spatial templates. For a frame rate of five frames per second, the only CHO that we calculated performed worse than the humans in a 4-AFC experiment. The other observers were variations of PPW and outperformed human observers in every single case. This initial frame rate was a rather low speed and the temporal filtering did not affect the results compared to a data set with no human temporal effects taken into account. We subsequently investigated two higher speeds at 5, 15 and 30 frames per second. We observed that for large masses, the two types of model observers investigated outperformed the human observers and would be suitable with the appropriate addition of internal noise. However, for microcalcifications both only the PPW observer consistently outperformed the humans. The study demonstrated the possibility of using a model observer which takes into account the temporal effects of scrolling through an image stack while being able to effectively detect a range of mass sizes and distributions.

Introduction:Coronavirus Disease 2019 (COVID-19) is associated with an increased risk of stroke and worse stroke outcomes. A clinical score that can identify high-risk patients could enable closer monitoring and targeted preventative strategies.Methods:We used data from the AHA’s COVID-19 CVD Registry to create a clinical score to predict the risk of stroke among patients hospitalized with COVID-19. We included patients aged >18 years who were hospitalized with COVID-19 at 122 centers from March 2020-March 2021. To build our score, we used demographics, preexisting comorbidities, home medications, and vital sign and lab values at admission. The outcome was a cerebrovascular event, defined as any ischemic or hemorrhagic stroke, TIA, or cerebral vein thrombosis. We used two separate analytical approaches to build the score. First, we used Cox regression with cross validation techniques to identify factors associated with the outcome in both univariable (p<0.10) and multivariable analyses (p<0.05), then assigned points for each variable based on corresponding coefficients. Second, we used regularized Cox regression, XGBoost, and Random Forest machine learning techniques to create an estimator using all available covariates. We used Harrel’s C-statistic to measure discriminatory performance.Results:Among 21,420 patients hospitalized with COVID-19 (mean age 61 years, 54% men), 312 (1.5%) had a cerebrovascular event. Using traditional Cox regression, we created and internally validated a risk stratification score (CANDLE) (Fig) with a C-statistic of 0.66 (95% CI, 0.60-0.72). The machine learning estimator had similar discriminatory performance, with a C-statistic of 0.69 (95% CI, 0.65-0.72). For ischemic stroke or TIA, CANDLE’s C-statistic was 0.67 (95% 0.59-0.76).Conclusion:We developed an easy-to-use clinical score, with similar performance to a machine learning estimator, to help stratify stroke risk among patients hospitalized with COVID-19.

Commercial activity and the underlying freight movements supporting them are essential to urban life. Unfortunately, urban freight has negative effects on sustainability and requires innovative approaches to enhance the positive aspects and minimize the negative ones. There is a common understanding among practitioners, with support from the literature, that the negative impacts of urban freight can be alleviated with better knowledge about the system and by identifying and engaging influential stakeholders. This paper focuses on property owners and their role in encouraging initiatives that lead to more sustainable urban freight delivery practices. Property owners engage in sustainability issues in real estate because they are interested in developing their properties to increase their attractiveness, competitiveness, and future market value. A shopping mall is an example of a property that generates a lot of freight traffic due to intense commercial activity. This paper studies the delivery patterns to a centrally located shopping mall in the city of Gothenburg and identifies how property owners could influence the delivery patterns of their tenants. A range of initiatives is proposed based on the literature and considering the delivery patterns observed. The results show that property owners can include freight deliveries in their sustainability strategy, focusing on fostering collaboration and communication between the tenants and promoting supply chain strategies that consider the type of activities that take place and the scope to reduce the number of truck movements and increase the efficient and sustainable goods deliveries.

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Mineralocorticoid receptor (MR) activation may be deleterious to the cardiovascular system and MR antagonists improve morbidity and mortality of patients with heart failure. However mineralocorticoid signalling in the heart remains largely unknown.