Search Behaviour On Photo Sharing Platforms

The behaviour, goals, and intentions of users while searching for images in large scale online collections are not well understood, with image search log analysis providing limited insights, in part because they tend only to have access to user search and result click information. In this paper we study user search behaviour in a large photo-sharing platform, analyzing all user actions during search sessions (i.e. including post result-click pageviews). Search accounts for a significant part of user interactions with such platforms, and we show differences between the queries issued on such platforms and those on general image search. We show that search behaviour is influenced by the query type, and also depends on the user. Finally, we analyse how users behave when they reformulate their queries, and develop URL class prediction models for image search, showing that query-specific models significantly outperform query-agnostic models. The insights provided in this paper are intended as a launching point for the design of better interfaces and ranking models for image search. © 2013 IEEE.published_or_final_versio

An Algorithm for Strong Stability in the Student-Project Allocation Problem With Ties

We study a variant of the Student-Project Allocation problem with lecturer preferences over Students where ties are allowed in the preference lists of students and lecturers (spa-st). We investigate the concept of strong stability in this context. Informally, a matching is strongly stable if there is no student and lecturer l such that if they decide to form a private arrangement outside of the matching via one of l’s proposed projects, then neither party would be worse off and at least one of them would strictly improve. We describe the first polynomial-time algorithm to find a strongly stable matching or report that no such matching exists, given an instance of spa-st. Our algorithm runs in O(m2) time, where m is the total length of the students’ preference lists

Analysis of algorithm components and parameters : some case studies

Modern high-performing algorithms are usually highly parameterised, and can be configured either manually or by an automatic algorithm configurator. The algorithm performance dataset obtained after the configuration step can be used to gain insights into how different algorithm parameters influence algorithm performance. This can be done by a number of analysis methods that exploit the idea of learning prediction models from an algorithm performance dataset and then using them for the data analysis on the importance of variables. In this paper, we demonstrate the complementary usage of three methods along this line, namely forward selection, fANOVA and ablation analysis with surrogates on three case studies, each of which represents some special situations that the analyses can fall into. By these examples, we illustrate how to interpret analysis results and discuss the advantage of combining different analysis methods.Postprin

Second asymptomatic carotid surgery trial (ACST-2): a randomised comparison of carotid artery stenting versus carotid endarterectomy

Background: Among asymptomatic patients with severe carotid artery stenosis but no recent stroke or transient cerebral ischaemia, either carotid artery stenting (CAS) or carotid endarterectomy (CEA) can restore patency and reduce long-term stroke risks. However, from recent national registry data, each option causes about 1% procedural risk of disabling stroke or death. Comparison of their long-term protective effects requires large-scale randomised evidence. Methods: ACST-2 is an international multicentre randomised trial of CAS versus CEA among asymptomatic patients with severe stenosis thought to require intervention, interpreted with all other relevant trials. Patients were eligible if they had severe unilateral or bilateral carotid artery stenosis and both doctor and patient agreed that a carotid procedure should be undertaken, but they were substantially uncertain which one to choose. Patients were randomly allocated to CAS or CEA and followed up at 1 month and then annually, for a mean 5 years. Procedural events were those within 30 days of the intervention. Intention-to-treat analyses are provided. Analyses including procedural hazards use tabular methods. Analyses and meta-analyses of non-procedural strokes use Kaplan-Meier and log-rank methods. The trial is registered with the ISRCTN registry, ISRCTN21144362. Findings: Between Jan 15, 2008, and Dec 31, 2020, 3625 patients in 130 centres were randomly allocated, 1811 to CAS and 1814 to CEA, with good compliance, good medical therapy and a mean 5 years of follow-up. Overall, 1% had disabling stroke or death procedurally (15 allocated to CAS and 18 to CEA) and 2% had non-disabling procedural stroke (48 allocated to CAS and 29 to CEA). Kaplan-Meier estimates of 5-year non-procedural stroke were 2·5% in each group for fatal or disabling stroke, and 5·3% with CAS versus 4·5% with CEA for any stroke (rate ratio [RR] 1·16, 95% CI 0·86–1·57; p=0·33). Combining RRs for any non-procedural stroke in all CAS versus CEA trials, the RR was similar in symptomatic and asymptomatic patients (overall RR 1·11, 95% CI 0·91–1·32; p=0·21). Interpretation: Serious complications are similarly uncommon after competent CAS and CEA, and the long-term effects of these two carotid artery procedures on fatal or disabling stroke are comparable. Funding: UK Medical Research Council and Health Technology Assessment Programme

A combined approach for analysing heuristic algorithms

When developing optimisation algorithms, the focus often lies on obtaining an algorithm that is able to outperform other existing algorithms for some performance measure. It is not common practice to question the reasons for possible performance differences observed. These types of questions relate to evaluating the impact of the various heuristic parameters and often remain unanswered. In this paper, the focus is on gaining insight in the behaviour of a heuristic algorithm by investigating how the various elements operating within the algorithm correlate with performance, obtaining indications of which combinations work well and which do not, and how all these effects are influenced by the specific problem instance the algorithm is solving. We consider two approaches for analysing algorithm parameters and components—functional analysis of variance and multilevel regression analysis—and study the benefits of using both approaches jointly. We present the results of a combined methodology that is able to provide more insights than when the two approaches are used separately. The illustrative case studies in this paper analyse a large neighbourhood search algorithm applied to the vehicle routing problem with time windows and an iterated local search algorithm for the unrelated parallel machine scheduling problem with sequence-dependent setup times.PostprintPeer reviewe