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Proportional representation (PR) is often discussed in voting settings as a major desideratum. For the past century or so, it is common both in practice and in the academic literature to look towards the single transferable vote (STV) rule as the solution for achieving PR. Some of the most prominent electoral reform movements around the globe are pushing for the adoption of STV. It has been termed a major open problem to design a voting rule that satisfies the same PR properties as STV and better monotonicity properties. In this paper, we first present a taxonomy of proportional representation axioms for general weak order preferences, some of which generalise and strengthen previously introduced concepts. We then present a rule called the expanding approvals rule (EAR) that satisfies properties stronger than the central PR axiom satisfied by STV, can handle indifferences in a convenient and computationally efficient manner, and also satisfies better candidate monotonicity properties. In view of this, our proposed rule seems to be a compelling solution for achieving proportional representation in voting settings.

We consider the problem of assigning objects probabilistically among a group of agents who may have multi-unit demands. Each agent has linear preferences over the (set of) objects. The most commonly used extension of preferences to compare probabilistic assignments is by means of stochastic dominance, which leads to corresponding notions of envy-freeness, efficiency, and strategy-proofness. We show that equal treatment of equals, efficiency, and strategy-proofness are incompatible. Moreover, anonymity, neutrality, efficiency, and weak strategy-proofness are incompatible. If we strengthen weak strategy-proofness to weak group strategy-proofness, then when agents have single-unit demands, anonymity, neutrality, efficiency, and weak group strategy-proofness become incompatible.

Committee selection with diversity or distributional constraints is a ubiquitous problem. However, many of the formal approaches proposed so far have certain drawbacks including (1) computational intractability in general, and (2) inability to suggest a solution for instances where the hard constraints cannot be met. We propose a cubic-time algorithm for diverse committee selection that satisfies natural axioms and draws on the idea of using soft bounds.

With increasing requirements of higher strength to low weight ratio materials, high-strength low-alloy (HSLA) steel has achieved higher commercial importance. Plasma arc welding has the capability to join metals without edge preparation, weldment in a single pass and minimum angular distortion. Due to these embedded capabilities, plasma arc welding is preferred over conventional joining processes for HSLA steel applications involving part thickness greater than 3 mm. The quality of plasma arc-welded joints is highly dependent on input process parameters. This paper aims to develop empirical models for the prediction of weld bead geometry including front bead height, back bead height, front bead width, and back bead width. A series of tests were conducted to investigate the effect of four input process parameters including current, voltage, welding speed, and plasma gas flow rate on weld bead geometry using a face-centered central composite design. The confirmation experiments and ANOVA results validated the models within 95 % accuracy. Current was found to be the most influential factor affecting the weld bead geometry followed by speed. Furthermore, current and speed and speed and gas flow rates were identified as most influencing interaction factors. The innovation in this research is the empirical modeling of weld bead geometry for HSLA using plasma arc welding.

The rationing of healthcare resources has emerged as an important issue, which has been discussed by medical experts, policy-makers, and the general public. We consider a rationing problem where medical units are to be allocated to patients. Each unit is reserved for one of several categories, and each category has a priority ranking over the patients. We present a class of allocation rules that respect the priorities, comply with the eligibility requirements, allocate the largest feasible number of units, and do not penalize agents for rising in the priority ranking of a category. The rules characterize all possible allocations that satisfy the first three properties and are polynomial-time computable.

We survey existing rules of thumb, propose novel methods, and comprehensively evaluate a number of solutions to the problem of calculating the cost to serve each location in a single-vehicle transport setting. Cost to serve analysis has applications both strategically and operationally in transportation settings. The problem is formally modeled as the traveling salesperson game (TSG), a cooperative transferable utility game in which agents correspond to locations in a traveling salesperson problem (TSP). The total cost to serve all locations in the TSP is the length of an optimal tour. An allocation divides the total cost among individual locations, thus providing the cost to serve each of them. As one of the most important normative division schemes in cooperative games, the Shapley value gives a principled and fair allocation for a broad variety of games including the TSG. We consider a number of direct and sampling-based procedures for calculating the Shapley value, and prove that approximating the Shapley value of the TSG within a constant factor is NP-hard. Treating the Shapley value as an ideal baseline allocation, we survey six proxies for it that are each relatively easy to compute. Some of these proxies are rules of thumb and some are procedures international delivery companies use(d) as cost allocation methods. We perform an experimental evaluation using synthetic Euclidean games as well as games derived from real-world tours calculated for scenarios involving fast-moving goods; where deliveries are made on a road network every day. We explore several computationally tractable allocation techniques that are good proxies for the Shapley value in problem instances of a size and complexity that is commercially relevant.

The Internet revolution has resulted in abundant data from various sources, including social media, traditional media, etcetera. Although the availability of data is no longer an issue, data labelling for exploiting it in supervised machine learning is still an expensive process and involves tedious human efforts. The overall purpose of this study is to propose a strategy to automatically label the unlabeled textual data with the support of active learning in combination with deep learning. More specifically, this study assesses the performance of different active learning strategies in automatic labelling of the textual dataset at sentence and document levels. To achieve this objective, different experiments have been performed on the publicly available dataset. In first set of experiments, we randomly choose a subset of instances from training dataset and train a deep neural network to assess performance on test set. In the second set of experiments, we replace the random selection with different active learning strategies to choose a subset of the training dataset to train the same model and reassess its performance on test set. The experimental results suggest that different active learning strategies yield performance improvement of 7% on document level datasets and 3% on sentence level datasets for auto labelling.

BackgroundWorkflow interruptions are common in the emergency department (ED) of the hospitals for physicians, leading to an increased risk of errors.PurposeThis study aims to understand the baseline activities of the ED doctors and how these are affected by workflow interruptions.MethodsThe study was conducted in two phases to collect the doctor’s perspective (through questionnaire survey) and observer’s perspective (through workflow observation study) about ED doctors’ baseline activities and workflow interruptions. Two different perspectives were obtained to make the insights clearer and more valuable. The point of view of the 223 doctors working in ED of the hospitals was recorded through a questionnaire survey. In the second phase, the observer’s point of view (authors) was obtained through a workflow observation study, and 13 doctors were observed for 160 hours.ResultsDirect communication with patients (37.1%) and ‘documentation and prescription’ (22.7%) were found to be the most frequent activities. The most common interruptions were visual and auditory distractions, rumination (mind-wandering) and intrusion (by co-workers). Also, the time consumed on indirect patient care (6.6%) was higher than direct patient care (4. 2%). Interruptions increase the chances of errors by making it hard for a doctor to resume a primary task after facing interruptions.ConclusionInterruptions increase the chances of errors and make it difficult for the doctors to resume primary tasks (after facing such incidents).

We consider stability concepts for random matchings where agents have preferences over objects and objects have priorities for the agents. When matchings are deterministic, the standard stability concept also captures the fairness property of no (justified) envy. When matchings can be random, there are a number of natural stability and fairness concepts that coincide with stability and no envy whenever matchings are deterministic. We formalize known stability concepts for random matchings for a general setting that allows weak preferences and weak priorities, unacceptability, and an unequal number of agents and objects. We then present a clear taxonomy of the stability concepts and identify logical relations between them. Finally, we present a transformation from the most general setting to the most restricted setting, and show how almost all our stability concepts are preserved by that transformation.