The open maritime traffic analysis dataset

Ships traverse the world’s oceans for a diverse range of reasons, including the bulk transportation of goods and resources, carriage of people, exploration and fishing. The size of the oceans and the fact that they connect a multitude of different countries provide challenges in ensuring the safety of vessels at sea and the prevention of illegal activities. To assist with the tracking of ships at sea, the International Maritime Organisation stipulates the use of the Automatic Identification System (AIS) on board ships. The AIS system periodically broadcasts details of a ship’s position, speed and heading, along with other parameters corresponding to the ship’s type, size and set destination. The availability of AIS data has led to a large effort to develop automated systems which could identify and be used to prevent undesirable incidents at sea. For example, detecting when ships are in danger of colliding, running aground, engaged in illegal activity, traveling at unsafe speeds, or otherwise attempting manoeuvres that exceed their physical capabilities. Despite this interest, there is a lack of a publicly available ‘standard’ dataset that can be used to benchmark different approaches. As such, each new approach to automated maritime activity modelling is tested using a different dataset to previous work, making the comparison of technique efficacy problematic. In this paper a new public dataset of shipping tracks is introduced, containing data for four vessel types: cargo, tanker, fishing and passenger. Each track corresponds to a leg of a vessel’s journey within an area of interest located around the west coast of Australia. The tracks in the dataset have been validated according to a set of rules, consisting of journeys at minimum 10 hours long, with no missing data. The tracks cover a three-year period (2018 to 2020) and are further categorised by month, allowing for the analysis of seasonal variations in shipping. The intention of releasing this dataset is to allow researchers developing methods for maritime behaviour analysis and classification to compare their techniques on a standard set of data. As an example of how this dataset can be used, we use it to build a model of ‘expected’ behaviour trained on data for three vessel categories: cargo, tanker, and passenger vessels, using a convolutional autoencoder architecture. We then demonstrate how this model of ship behaviour can be used to test new data that was not used to build the model to determine whether a track fits the model or is an anomaly. Specifically, we verify that the behaviour of fishing vessels, whose movement patterns are quite different to those of the other three vessel types, is classified as an anomaly when presented to the trained model

Assessment of algorithms for mitosis detection in breast cancer histopathology images

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists

A need for logical and consistent anatomical nomenclature for cutaneous nerves of the limbs

The system of anatomical nomenclature needs to be logical and consistent. However, variations in translation to English of the Latin and Greek terminology used in Nomina Anatomica and Terminologia Anatomica have led to some inconsistency in the nomenclature of cutaneous nerves in the limbs. An historical review of cutaneous nerve nomenclature reveals that there are two general naming conventions: one primarily American and one primarily British. The American convention presents cutaneous nerves of the limbs in the format “medial brachial cutaneous nerve,” while the British convention presents the same nerve as “medial cutaneous nerve of the arm,” thereby translating “brachii” to “of the arm.” If logically and consistently applied throughout the body, the British convention would rename the sural nerve to the “nerve of the calf,” the brachial artery would become the “artery of the arm,” the femoral nerve would be “nerve of the thigh,” and femur would be “bone of the thigh” or “thigh bone.” The British convention leads to many other nomenclatural inconsistencies, which would seem to make learning anatomy more difficult for the beginning student. In this era of contracting anatomy curricula, every effort should be made to keep anatomical nomenclature simple, logical, and consistent. Anat Sci Ed 2:126–134, 2009. © 2009 American Association of Anatomists.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63079/1/90_ftp.pd

Geoeconomic variations in epidemiology, ventilation management, and outcomes in invasively ventilated intensive care unit patients without acute respiratory distress syndrome: a pooled analysis of four observational studies

Background: Geoeconomic variations in epidemiology, the practice of ventilation, and outcome in invasively ventilated intensive care unit (ICU) patients without acute respiratory distress syndrome (ARDS) remain unexplored. In this analysis we aim to address these gaps using individual patient data of four large observational studies. Methods: In this pooled analysis we harmonised individual patient data from the ERICC, LUNG SAFE, PRoVENT, and PRoVENT-iMiC prospective observational studies, which were conducted from June, 2011, to December, 2018, in 534 ICUs in 54 countries. We used the 2016 World Bank classification to define two geoeconomic regions: middle-income countries (MICs) and high-income countries (HICs). ARDS was defined according to the Berlin criteria. Descriptive statistics were used to compare patients in MICs versus HICs. The primary outcome was the use of low tidal volume ventilation (LTVV) for the first 3 days of mechanical ventilation. Secondary outcomes were key ventilation parameters (tidal volume size, positive end-expiratory pressure, fraction of inspired oxygen, peak pressure, plateau pressure, driving pressure, and respiratory rate), patient characteristics, the risk for and actual development of acute respiratory distress syndrome after the first day of ventilation, duration of ventilation, ICU length of stay, and ICU mortality. Findings: Of the 7608 patients included in the original studies, this analysis included 3852 patients without ARDS, of whom 2345 were from MICs and 1507 were from HICs. Patients in MICs were younger, shorter and with a slightly lower body-mass index, more often had diabetes and active cancer, but less often chronic obstructive pulmonary disease and heart failure than patients from HICs. Sequential organ failure assessment scores were similar in MICs and HICs. Use of LTVV in MICs and HICs was comparable (42\ub74% vs 44\ub72%; absolute difference \u20131\ub769 [\u20139\ub758 to 6\ub711] p=0\ub767; data available in 3174 [82%] of 3852 patients). The median applied positive end expiratory pressure was lower in MICs than in HICs (5 [IQR 5\u20138] vs 6 [5\u20138] cm H2O; p=0\ub70011). ICU mortality was higher in MICs than in HICs (30\ub75% vs 19\ub79%; p=0\ub70004; adjusted effect 16\ub741% [95% CI 9\ub752\u201323\ub752]; p<0\ub70001) and was inversely associated with gross domestic product (adjusted odds ratio for a US$10 000 increase per capita 0\ub780 [95% CI 0\ub775\u20130\ub786]; p<0\ub70001). Interpretation: Despite similar disease severity and ventilation management, ICU mortality in patients without ARDS is higher in MICs than in HICs, with a strong association with country-level economic status. Funding: No funding

Maximizing the potential of early childhood education to prevent externalizing behavior problems: A meta-analysis

Early childhood education (ECE) programs offer a promising mechanism for preventing early externalizing behavior problems and later antisocial behavior; yet, questions remain about how to best maximize ECE's potential. Using a meta-analytic database of 31 studies, we examined the overall effect of ECE on externalizing behavior problems and the differential effects of 3 levels of practice, each with increasing specificity and intensity aimed at children's social and emotional development. In short, we found that each successive level of programs did a better job than the prior level at reducing externalizing behavior problems. Level 1 programs, or those without a clear focus on social and emotional development, had no significant effects on externalizing behavior problems relative to control groups (ES=.13 SD, p<.10). On the other hand, level 2 programs, or those with a clear but broad focus on social and emotional development, were significantly associated with modest decreases in externalizing behavior problems relative to control groups (ES=-.10 SD, p<.05). Hence, level 2 programs were significantly better at reducing externalizing behavior problems than level 1 programs (ES=-.23 SD, p<.01). Level 3 programs, or those that more intensively targeted children's social and emotional development, were associated with additional significant reductions in externalizing behavior problems relative to level 2 programs (ES=-.26 SD, p<.05). The most promising effects came from level 3 child social skills training programs, which reduced externalizing behavior problems half of a standard deviation more than level 2 programs (ES=-.50 SD, p<.05)

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

An evolutionary approach to balancing and disrupting real-time strategy games

In most computer games, the level of challenge experienced by a player is dependent on a range of variable factors defined within the game environment. When the end goal of such games is entertainment, the variables are carefully tuned by the designers to achieve a sense of fair, balanced gameplay. For military force design and wargaming applications, where the purpose is to explore elements of a real scenario, the question turns to how the variables can be exploited so as to provide the maximum advantage, and to disrupt the balance in favour of a particular side. In this paper, an automated approach to explore the impact of game variables on game balance is presented and evaluated. Based on an evolutionary algorithm, the approach explores a user-defined set of variables in order to determine optimal combinations of variable values to achieve a defined level of game balance or game disruption. The approach also provides the ability of biasing the search towards a set of user-defined values of the game variables, providing insight into how the most disruption can be achieved with the least amount of deviation from an existing strategy. Two scenarios were developed in a Real-Time Strategy game environment with a focus on verifying the developed approach. Both scenarios were adversarial, with two opposing teams, Red and Blue, with the goal of each team to eliminate the opposition. The level of balance/disruption for a particular set of Blue Team variables was measured as a function of the difference between a target blue win rate and the actual win rate, with a tuneable bias to favour solutions where the solution deviated the least from a ‘fair’ solution (where the Blue Team had the same strength as the Red Team). The first scenario was designed so that both teams were evenly matched in terms of number of units. The scenario was used to explore how the game balance could be disrupted by manipulating variables associated with the Blue Team units. The second scenario was developed so that one team was given significantly more fighting units. This was to test if the approach was able to achieve a particular desired level of balance or disruption despite the starting balance skew. A series of experiments were performed using the developed scenarios to evolve a set of game variables tied to the Blue Team to achieve a range of balance levels while the red team\u27s variables were kept static. The experiments show that it is possible to use the developed approach to balance or disrupt the variables of a game so that the desired level of game balance is achieved. A separate series of experiments also showed that the evolution process could be biased to find game variables that required the smallest amount of change. This is particularly important for balancing video games where designers often prefer only to make slight changes to the variables of a game even when desiring a large difference in a game\u27s balance

Describing Differences between Text Distributions with Natural Language

How do two distributions of texts differ? Humans are slow at answering this, since discovering patterns might require tediously reading through hundreds of samples. We propose to automatically summarize the differences by "learning a natural language hypothesis": given two distributions $D_{0}$ and $D_{1}$, we search for a description that is more often true for $D_{1}$, e.g., "is military-related." To tackle this problem, we fine-tune GPT-3 to propose descriptions with the prompt: "[samples of $D_{0}$] + [samples of $D_{1}$] + the difference between them is_____." We then re-rank the descriptions by checking how often they hold on a larger set of samples with a learned verifier. On a benchmark of 54 real-world binary classification tasks, while GPT-3 Curie (13B) only generates a description similar to human annotation 7% of the time, the performance reaches 61% with fine-tuning and re-ranking, and our best system using GPT-3 Davinci (175B) reaches 76%. We apply our system to describe distribution shifts, debug dataset shortcuts, summarize unknown tasks, and label text clusters, and present analyses based on automatically generated descriptions.Comment: International Conference on Machine Learning, 202