A Concede-and-Divide Rule for Bankruptcy Problems

The concede-and-divide rule is a basic solution for bankruptcy problems with two claimants.An extension of the concede-and-divide rule to bankruptcy problems with more than two claimants is provided.This extension not only uses the concede-and-divide principle in its procedural definition, but also preserves the main properties of the concede-and-divide rule.Bankruptcy problems;concede-and-divide rule

A Concede-and-Divide Rule for Bankruptcy Problems

The concede-and-divide rule is a basic solution for bankruptcy problems with two claimants.An extension of the concede-and-divide rule to bankruptcy problems with more than two claimants is provided.This extension not only uses the concede-and-divide principle in its procedural definition, but also preserves the main properties of the concede-and-divide rule.

SNE: Signed Network Embedding

Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link. In this paper, we present our signed network embedding model called SNE. Our SNE adopts the log-bilinear model, uses node representations of all nodes along a given path, and further incorporates two signed-type vectors to capture the positive or negative relationship of each edge along the path. We conduct two experiments, node classification and link prediction, on both directed and undirected signed networks and compare with four baselines including a matrix factorization method and three state-of-the-art unsigned network embedding models. The experimental results demonstrate the effectiveness of our signed network embedding.Comment: To appear in PAKDD 201

COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks

For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues. This paper proposes COTA, a system to improve speed and reliability of customer support for end users through automated ticket classification and answers selection for support representatives. Two machine learning and natural language processing techniques are demonstrated: one relying on feature engineering (COTA v1) and the other exploiting raw signals through deep learning architectures (COTA v2). COTA v1 employs a new approach that converts the multi-classification task into a ranking problem, demonstrating significantly better performance in the case of thousands of classes. For COTA v2, we propose an Encoder-Combiner-Decoder, a novel deep learning architecture that allows for heterogeneous input and output feature types and injection of prior knowledge through network architecture choices. This paper compares these models and their variants on the task of ticket classification and answer selection, showing model COTA v2 outperforms COTA v1, and analyzes their inner workings and shortcomings. Finally, an A/B test is conducted in a production setting validating the real-world impact of COTA in reducing issue resolution time by 10 percent without reducing customer satisfaction

ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids

We introduce an unsupervised feature learning approach that embeds 3D shape information into a single-view image representation. The main idea is a self-supervised training objective that, given only a single 2D image, requires all unseen views of the object to be predictable from learned features. We implement this idea as an encoder-decoder convolutional neural network. The network maps an input image of an unknown category and unknown viewpoint to a latent space, from which a deconvolutional decoder can best "lift" the image to its complete viewgrid showing the object from all viewing angles. Our class-agnostic training procedure encourages the representation to capture fundamental shape primitives and semantic regularities in a data-driven manner---without manual semantic labels. Our results on two widely-used shape datasets show 1) our approach successfully learns to perform "mental rotation" even for objects unseen during training, and 2) the learned latent space is a powerful representation for object recognition, outperforming several existing unsupervised feature learning methods.Comment: To appear at ECCV 201

Life after a point-of-care ultrasound course:setting up the right conditions!

Background: Point-of-care Ultrasound (POCUS) is becoming an important diagnostic tool for internal medicine and ultrasound educational programs are being developed. An ultrasound course is often included in such a curriculum. We have performed a prospective observational questionnaire-based cohort study consisting of participants of a POCUS course for internal medicine in the Netherlands in a 2-year period. We investigated the usefulness of an ultrasound course and barriers participants encountered after the course. Results: 55 participants (49%) completed the pre-course questionnaire, 29 (26%) completed the post-course questionnaire, 11 participants (10%) finalized the third questionnaire. The number of participants who performs POCUS was almost doubled after the course (from 34.5 to 65.5%). Almost all participants felt insufficiently skilled before the course which declined to 34.4% after the course. The majority (N = 26 [89.7%]) stated that this 2-day ultrasound course was sufficient enough to perform POCUS in daily practice but also changed daily practice. The most important barriers withholding them from performing ultrasound are lack of experts for supervision, insufficient practice time and absence of an ultrasound machine. Conclusions: This study shows that a 2-day hands-on ultrasound course seems a sufficient first step in an ultrasound curriculum for internal medicine physicians to obtain enough knowledge and skills to perform POCUS in clinical practice but it also changes clinical practice. However, there are barriers in the transfer to clinical practice that should be addressed which may improve curriculum designing

Control of an AUV from thruster actuated hover to control surface actuated flight

An autonomous underwater vehicle (AUV) capable of both low speed hovering and high speed flight-style operation is introduced. To have this capability the AUV is over-actuated with a rear propeller, four control surfaces and four through-body tunnel thrusters. In this work the actuators are modelled and the non-linearities and uncertainties are identified and discussed with specific regard to operation at different speeds. A thruster-actuated depth control algorithm and a flight-style control-surface actuated depth controller are presented. These controllers are then coupled using model reference feedback to enable transition between the two controllers to enable vehicle stability throughout the speed range. Results from 3 degrees-of-freedom simulations of the AUV using the new controller are presented, showing that the controller works well to smoothly transition between controllers. The performance of the depth controller appears asymmetric with better performance whilst diving than ascendin

Hospital-related costs of sepsis around the world:A systematic review exploring the economic burden of sepsis

Aim: The aim of this study was to examine the quality of manuscripts reporting sepsis health care costs and to provide an overview of hospital-related expenditures for sepsis in adult patients around the world. Methods: We systematically searched the PubMed, EMBASE, Cochrane and Google Scholar to identify relevant studies between January 2010 and January 2022. We selected articles that provided costs and cost-effectiveness analyses, defined sepsis and described their cost calculation method. All costs were adjusted to 2020 US dollars. Medians and interquartile ranges (IQRs) for various costs of sepsis were calculated. The quality of economic studies was assessed using the Drummond 10-item checklist. Results: Overall, 26 studies met our eligibility criteria. The mean total hospital costs per patient varied largely, between €1101 and €91,951. The median (IQR) of the total sepsis costs per country were €36,191 (€17,158 - €53,349), which equals €50 (€34 - €84) per capita annually. The relative amount of healthcare budget spent on sepsis was 2.65%, which equals 0.33% of the gross national product (GNP). Conclusion: While general sepsis costs are high, there is considerable variability between countries regarding the costs of sepsis. Further studies examining the impact on sepsis costs, especially on the general ward, can help justify, design and monitor initiatives on prevention, diagnosis, and treatment of this time-critical and potentially preventable disease