Influence Maximization with Bandits

We consider the problem of \emph{influence maximization}, the problem of maximizing the number of people that become aware of a product by finding the `best' set of `seed' users to expose the product to. Most prior work on this topic assumes that we know the probability of each user influencing each other user, or we have data that lets us estimate these influences. However, this information is typically not initially available or is difficult to obtain. To avoid this assumption, we adopt a combinatorial multi-armed bandit paradigm that estimates the influence probabilities as we sequentially try different seed sets. We establish bounds on the performance of this procedure under the existing edge-level feedback as well as a novel and more realistic node-level feedback. Beyond our theoretical results, we describe a practical implementation and experimentally demonstrate its efficiency and effectiveness on four real datasets.Comment: 12 page

Data from the Indian drug regulator and from Clinical Trials Registry-India does not always match

IntroductionIn India, regulatory trials, which require the drug regulator’s permission, must be registered with the Clinical Trials Registry-India (CTRI) as of 19 March 2019. In this study, for about 300 trials, we aimed to identify the CTRI record that matched the trial for which the regulator had given permission. After identifying ‘true pairs’, our goal was to determine whether the sites and Principal Investigators mentioned in the permission letter were the same as those mentioned in the CTRI record.MethodsWe developed a methodology to compare the regulator’s permission letters with CTRI records. We manually validated 151 true pairs by comparing the titles, the drug interventions, and the indications. We then examined discrepancies in their trial sites and Principal Investigators.ResultsOur findings revealed substantial variations in the number and identity of sites and Principal Investigators between the permission letters and the CTRI records.DiscussionThese discrepancies raise concerns about the accuracy and transparency of regulatory trials in India. We recommend easier data extraction from regulatory documents, cross-referencing regulatory documents and CTRI records, making public the changes to approval letters, and enforcing oversight by Institutional Ethics Committees for site additions or deletions. These steps will increase transparency around regulatory trials running in India

Open-Retrieval Conversational Question Answering

Conversational search is one of the ultimate goals of information retrieval. Recent research approaches conversational search by simplified settings of response ranking and conversational question answering, where an answer is either selected from a given candidate set or extracted from a given passage. These simplifications neglect the fundamental role of retrieval in conversational search. To address this limitation, we introduce an open-retrieval conversational question answering (ORConvQA) setting, where we learn to retrieve evidence from a large collection before extracting answers, as a further step towards building functional conversational search systems. We create a dataset, OR-QuAC, to facilitate research on ORConvQA. We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers. Our extensive experiments on OR-QuAC demonstrate that a learnable retriever is crucial for ORConvQA. We further show that our system can make a substantial improvement when we enable history modeling in all system components. Moreover, we show that the reranker component contributes to the model performance by providing a regularization effect. Finally, further in-depth analyses are performed to provide new insights into ORConvQA.Comment: Accepted to SIGIR'2

Tracking Target Signal Strengths on a Grid using Sparsity

Multi-target tracking is mainly challenged by the nonlinearity present in the measurement equation, and the difficulty in fast and accurate data association. To overcome these challenges, the present paper introduces a grid-based model in which the state captures target signal strengths on a known spatial grid (TSSG). This model leads to \emph{linear} state and measurement equations, which bypass data association and can afford state estimation via sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of the novel model, two types of sparsity-cognizant TSSG-KF trackers are developed: one effects sparsity through $\ell_1$-norm regularization, and the other invokes sparsity as an extra measurement. Iterative extended KF and Gauss-Newton algorithms are developed for reduced-complexity tracking, along with accurate error covariance updates for assessing performance of the resultant sparsity-aware state estimators. Based on TSSG state estimates, more informative target position and track estimates can be obtained in a follow-up step, ensuring that track association and position estimation errors do not propagate back into TSSG state estimates. The novel TSSG trackers do not require knowing the number of targets or their signal strengths, and exhibit considerably lower complexity than the benchmark hidden Markov model filter, especially for a large number of targets. Numerical simulations demonstrate that sparsity-cognizant trackers enjoy improved root mean-square error performance at reduced complexity when compared to their sparsity-agnostic counterparts.Comment: Submitted to IEEE Trans. on Signal Processin

Working with Young People Who Offend : An Examination of the Literature Regarding Violence, Substance Misuse and Harmful Sexual Behaviour

This paper presents a review of the recent literature relating to effective practice with young people displaying harmful sexual behaviour (HSB), violence or risky substance misuse. The intention is to build upon and update the 2007 literature review Research and practice in risk assessment and risk management of children and young people engaging in offending behaviour, funded by the Risk Management Authority (RMA) and carried out by the Scottish Centre for Crime and Justice Research (SCCJR)