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Optical flow based detection and tracking of moving objects for autonomous vehicles
Get PDFAccurate velocity estimation of surrounding moving objects and their trajectories are critical elements of perception systems in Automated/Autonomous Vehicles (AVs) with a direct impact on their safety. These are non-trivial problems due to the diverse types and sizes of such objects and their dynamic and random behaviour. Recent point cloud based solutions often use Iterative Closest Point (ICP) techniques, which are known to have certain limitations. For example, their computational costs are high due to their iterative nature, and their estimation error often deteriorates as the relative velocities of the target objects increase (>2 m/sec). Motivated by such shortcomings, this paper first proposes a novel Detection and Tracking of Moving Objects (DATMO) for AVs based on an optical flow technique, which is proven to be computationally efficient and highly accurate for such problems. This is achieved by representing the driving scenario as a vector field and applying vector calculus theories to ensure spatiotemporal continuity. We also report the results of a comprehensive performance evaluation of the proposed DATMO technique, carried out in this study using synthetic and realworld data. The results of this study demonstrate the superiority of the proposed technique, compared to the DATMO techniques in the literature, in terms of estimation accuracy and processing time in a wide range of relative velocities of moving objects. Finally, we evaluate and discuss the sensitivity of the estimation error of the proposed DATMO technique to various system and environmental parameters, as well as the relative velocities of the moving objects
The beguiling behaviour of narcissistic CEOs : evidence from repurchase announcements
Get PDFWe study whether Chief Executive Officer (CEO) narcissism affects a firm's share repurchase announcements and their implementations. Using signature characteristics as a measure of narcissism, we find that US firms with narcissist CEOs are more likely to make repurchase announcements and announce higher repurchase dollar amounts. However, these firms are less likely to follow through. Actual repurchases by these firms are less frequent, and they use a smaller amount of cash for share buyback because they have a higher cashflow sensitivity of cash. Narcissist CEOs’ repurchase announcements are less driven by market timing and have a lower announcement effect compared to those by other CEOs. The higher rate and amount of repurchase announcements are more pronounced in poorly governed firms with narcissistic CEOs. These results are robust to various specifications including a difference-in-difference set-up using CEOs’ exogenous turnover, controlling for other CEO traits and using an alternative measure of narcissism based on pronoun usage in CEO communications. Collectively, the results presented in this study demonstrate that narcissist CEOs play a critical role in the intensity of share repurchase announcements and their executions, particularly for firms with weaker governance structures
Technology emergence as a structuring process : a complexity theory perspective on blockchain
Get PDFEfficient local linearity regularization to overcome catastrophic overfitting
No full textCatastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to
%). For models trained with multi-step AT, it has been observed that the loss function behaves locally linearly with respect to the input, this is however lost in single-step AT. To address CO in single-step AT, several methods have been proposed to enforce local linearity of the loss via regularization. However, these regularization terms considerably slow down training due to Double Backpropagation. Instead, in this work, we introduce a regularization term, called ELLE, to mitigate CO effectively and efficiently in classical AT evaluations, as well as some more difficult regimes, e.g., large adversarial perturbations and long training schedules. Our regularization term can be theoretically linked to curvature of the loss function and is computationally cheaper than previous methods by avoiding Double Backpropagation. Our thorough experimental validation demonstrates that our work does not suffer from CO, even in challenging settings where previous works suffer from it. We also notice that adapting our regularization parameter during training (ELLE-A) greatly improves the performance, specially in large
setups. Our implementation is available in https://github.com/LIONS-EPFL/ELLE
Clinical effectiveness of an online supervised group physical and mental health rehabilitation programme for adults with post-covid-19 condition (REGAIN study) : multicentre randomised controlled trial
Get PDFObjective To evaluate whether a structured online supervised group physical and mental health rehabilitation programme can improve health related quality of life compared with usual care in adults with post-covid-19 condition (long covid).
Design Pragmatic, multicentre, parallel group, superiority randomised controlled trial.
Setting England and Wales, with home based interventions delivered remotely online from a single trial hub.
Participants 585 adults (26-86 years) discharged from NHS hospitals at least three months previously after covid-19 and with ongoing physical and/or mental health sequelae (post-covid-19 condition), randomised (1:1.03) to receive the Rehabilitation Exercise and psycholoGical support After covid-19 InfectioN (REGAIN) intervention (n=298) or usual care (n=287).
Interventions Best practice usual care was a single online session of advice and support with a trained practitioner. The REGAIN intervention was delivered online over eight weeks and consisted of weekly home based, live, supervised, group exercise and psychological support sessions.
Main outcome measures The primary outcome was health related quality of life using the patient reported outcomes measurement information system (PROMIS) preference (PROPr) score at three months. Secondary outcomes, measured at three, six, and 12 months, included PROMIS subscores (depression, fatigue, sleep disturbance, pain interference, physical function, social roles/activities, and cognitive function), severity of post-traumatic stress disorder, general health, and adverse events.
Results Between January 2021 and July 2022, 39 697 people were invited to take part in the study and 725 were contacted and eligible. 585 participants were randomised. Mean age was 56 (standard deviation (SD) 12) years, 52% were female participants, mean health related quality of life PROMIS-PROPr score was 0.20 (SD 0.17), and mean time from hospital discharge was 323 (SD 144) days. Compared with usual care, the REGAIN intervention led to improvements in health related quality of life (adjusted mean difference in PROPr score 0.03 (95% confidence interval 0.01 to 0.05), P=0.02) at three months, driven predominantly by greater improvements in the PROMIS subscores for depression (1.39 (0.06 to 2.71), P=0.04), fatigue (2.50 (1.19 to 3.81), P<0.001), and pain interference (1.80 (0.50 to 3.11), P=0.01). Effects were sustained at 12 months (0.03 (0.01 to 0.06), P=0.02). Of 21 serious adverse events, only one was possibly related to the REGAIN intervention. In the intervention group, 141 (47%) participants fully adhered to the programme, 117 (39%) partially adhered, and 40 (13%) did not receive the intervention.
Conclusions In adults with post-covid-19 condition, an online, home based, supervised, group physical and mental health rehabilitation programme was clinically effective at improving health related quality of life at three and 12 months compared with usual care.
Trial registration ISRCTN registry ISRCTN11466448
Data, disclosure and duties : balancing privacy and safeguarding in the context of UK university student sexual misconduct complaints
Get PDFGlobal burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021 : a systematic analysis for the Global Burden of Disease Study 2021
Get PDFBackground
Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations.
Methods
The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds.
Findings
The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles.
Interpretation
Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere
The role of networks to overcome large-scale challenges in tomography : the non-clinical tomography users research network
Get PDFOur ability to visualize and quantify the internal structures of objects via computed tomography (CT) has fundamentally transformed science. As tomographic tools have become more broadly accessible, researchers across diverse disciplines have embraced the ability to investigate the 3D structure-function relationships of an enormous array of items. Whether studying organismal biology, animal models for human health, iterative manufacturing techniques, experimental medical devices, engineering structures, geological and planetary samples, prehistoric artifacts, or fossilized organisms, computed tomography has led to extensive methodological and basic sciences advances and is now a core element in science, technology, engineering, and mathematics (STEM) research and outreach toolkits. Tomorrow's scientific progress is built upon today's innovations. In our data-rich world, this requires access not only to publications but also to supporting data. Reliance on proprietary technologies, combined with the varied objectives of diverse research groups, has resulted in a fragmented tomography-imaging landscape, one that is functional at the individual lab level yet lacks the standardization needed to support efficient and equitable exchange and reuse of data. Developing standards and pipelines for the creation of new and future data, which can also be applied to existing datasets is a challenge that becomes increasingly difficult as the amount and diversity of legacy data grows. Global networks of CT users have proved an effective approach to addressing this kind of multifaceted challenge across a range of fields. Here we describe ongoing efforts to address barriers to recently proposed FAIR (Findability, Accessibility, Interoperability, Reuse) and open science principles by assembling interested parties from research and education communities, industry, publishers, and data repositories to approach these issues jointly in a focused, efficient, and practical way. By outlining the benefits of networks, generally, and drawing on examples from efforts by the Non-Clinical Tomography Users Research Network (NoCTURN), specifically, we illustrate how standardization of data and metadata for reuse can foster interdisciplinary collaborations and create new opportunities for future-looking, large-scale data initiatives
Self-compassion, camouflaging, and mental health in autistic adults
Get PDFBackground: Previous research shows that symptoms of anxiety and depression are positively correlated with camouflaging and negatively correlated with self-compassion in autistic adults. However, no study to date has considered the inter-relationships between autistic traits, camouflaging, self-compassion, and mental health in autistic adults.
Methods: In this study, autistic adults (n = 294) completed demographics (sex, age, and ethnicity), the Autism Spectrum Quotient, the Camouflaging Autistic Traits-Questionnaire, the Self-Compassion Scale, the General- ized Anxiety Disorder-7 Scale, the Patient Health Questionnaire-9 and the Liebowitz Social Anxiety Scale. Results: We found a negative correlation between social camouflaging and self-compassion (rpartial = -0.483, p < 0.001). Serial mediation analyses revealed that camouflaging and self-compassion may indirectly influence the association between autistic traits and mental health outcomes both independently and through each other. Conclusions: The findings of this research provide greater insight into the mental health experiences of autistic adults and can inform the development of tailored interventions that target camouflaging and self-compassion
