From Group Recommendations to Group Formation

There has been significant recent interest in the area of group recommendations, where, given groups of users of a recommender system, one wants to recommend top-k items to a group that maximize the satisfaction of the group members, according to a chosen semantics of group satisfaction. Examples semantics of satisfaction of a recommended itemset to a group include the so-called least misery (LM) and aggregate voting (AV). We consider the complementary problem of how to form groups such that the users in the formed groups are most satisfied with the suggested top-k recommendations. We assume that the recommendations will be generated according to one of the two group recommendation semantics - LM or AV. Rather than assuming groups are given, or rely on ad hoc group formation dynamics, our framework allows a strategic approach for forming groups of users in order to maximize satisfaction. We show that the problem is NP-hard to solve optimally under both semantics. Furthermore, we develop two efficient algorithms for group formation under LM and show that they achieve bounded absolute error. We develop efficient heuristic algorithms for group formation under AV. We validate our results and demonstrate the scalability and effectiveness of our group formation algorithms on two large real data sets.Comment: 14 pages, 22 figure

A question of trust: can we build an evidence base to gain trust in systematic review automation technologies?

Background Although many aspects of systematic reviews use computational tools, systematic reviewers have been reluctant to adopt machine learning tools. Discussion We discuss that the potential reason for the slow adoption of machine learning tools into systematic reviews is multifactorial. We focus on the current absence of trust in automation and set-up challenges as major barriers to adoption. It is important that reviews produced using automation tools are considered non-inferior or superior to current practice. However, this standard will likely not be sufficient to lead to widespread adoption. As with many technologies, it is important that reviewers see “others” in the review community using automation tools. Adoption will also be slow if the automation tools are not compatible with workflows and tasks currently used to produce reviews. Many automation tools being developed for systematic reviews mimic classification problems. Therefore, the evidence that these automation tools are non-inferior or superior can be presented using methods similar to diagnostic test evaluations, i.e., precision and recall compared to a human reviewer. However, the assessment of automation tools does present unique challenges for investigators and systematic reviewers, including the need to clarify which metrics are of interest to the systematic review community and the unique documentation challenges for reproducible software experiments. Conclusion We discuss adoption barriers with the goal of providing tool developers with guidance as to how to design and report such evaluations and for end users to assess their validity. Further, we discuss approaches to formatting and announcing publicly available datasets suitable for assessment of automation technologies and tools. Making these resources available will increase trust that tools are non-inferior or superior to current practice. Finally, we identify that, even with evidence that automation tools are non-inferior or superior to current practice, substantial set-up challenges remain for main stream integration of automation into the systematic review process

Transfer Learning for Multi-language Twitter Election Classification

Both politicians and citizens are increasingly embracing social media as a means to disseminate information and comment on various topics, particularly during significant political events, such as elections. Such commentary during elections is also of interest to social scientists and pollsters. To facilitate the study of social media during elections, there is a need to automatically identify posts that are topically related to those elections. However, current studies have focused on elections within English-speaking regions, and hence the resultant election content classifiers are only applicable for elections in countries where the predominant language is English. On the other hand, as social media is becoming more prevalent worldwide, there is an increasing need for election classifiers that can be generalised across different languages, without building a training dataset for each election. In this paper, based upon transfer learning, we study the development of effective and reusable election classifiers for use on social media across multiple languages. We combine transfer learning with different classifiers such as Support Vector Machines (SVM) and state-of-the-art Convolutional Neural Networks (CNN), which make use of word embedding representations for each social media post. We generalise the learned classifier models for cross-language classification by using a linear translation approach to map the word embedding vectors from one language into another. Experiments conducted over two election datasets in different languages show that without using any training data from the target language, linear translations outperform a classical transfer learning approach, namely Transfer Component Analysis (TCA), by 80% in recall and 25% in F1 measure

Fur-mites of the family Atopomelidae (Acari: Astigmata) parasitic on Philippine mammals: systematics, phylogeny, and host-parasite relationships

http://deepblue.lib.umich.edu/bitstream/2027.42/56439/1/MP196.pd

Micro-Ramp Flow Control for Oblique Shock Interactions: Comparisons of Computational and Experimental Data

Computational fluid dynamics was used to study the effectiveness of micro-ramp vortex generators to control oblique shock boundary layer interactions. Simulations were based on experiments previously conducted in the 15 x 15 cm supersonic wind tunnel at NASA Glenn Research Center. Four micro-ramp geometries were tested at Mach 2.0 varying the height, chord length, and spanwise spacing between micro-ramps. The overall flow field was examined. Additionally, key parameters such as boundary-layer displacement thickness, momentum thickness and incompressible shape factor were also examined. The computational results predicted the effects of the micro-ramps well, including the trends for the impact that the devices had on the shock boundary layer interaction. However, computing the shock boundary layer interaction itself proved to be problematic since the calculations predicted more pronounced adverse effects on the boundary layer due to the shock than were seen in the experiment

Modular Invariance of Finite Size Corrections and a Vortex Critical Phase

We analyze a continuous spin Gaussian model on a toroidal triangular lattice with periods $L_0$ and $L_1$ where the spins carry a representation of the fundamental group of the torus labeled by phases $u_0$ and $u_1$. We find the {\it exact finite size and lattice corrections}, to the partition function $Z$, for arbitrary mass $m$ and phases $u_i$. Summing $Z^{-1/2}$ over phases gives the corresponding result for the Ising model. The limits $m\rightarrow0$ and $u_i\rightarrow0$ do not commute. With $m=0$ the model exhibits a {\it vortex critical phase} when at least one of the $u_i$ is non-zero. In the continuum or scaling limit, for arbitrary $m$, the finite size corrections to $-\ln Z$ are {\it modular invariant} and for the critical phase are given by elliptic theta functions. In the cylinder limit $L_1\rightarrow\infty$ the ``cylinder charge'' $c(u_0,m^2L_0^2)$ is a non-monotonic function of $m$ that ranges from $2(1+6u_0(u_0-1))$ for $m=0$ to zero for $m\rightarrow\infty$.Comment: 12 pages of Plain TeX with two postscript figure insertions called torusfg1.ps and torusfg2.ps which can be obtained upon request from [email protected]

Psychosocial treatments of behavior symptoms in dementia: a systematic review of reports meeting quality standards.

OBJECTIVE: To provide a systematic review of selected experimental studies of psychosocial treatments of behavioral disturbances in dementia. Psychosocial treatments are defined here as strategies derived from one of three psychologically oriented paradigms (learning theory, unmet needs and altered stress thresholds). METHOD: English language reports published or in press by December 2006 were identified by means of database searches, checks of previous reviews and contact with recognized experts. Papers were appraised with respect to study design, participants' characteristics and reporting details. Because people with dementia often respond positively to personal contact, studies were included only if control conditions entailed similar levels of social attention or if one treatment was compared with another. RESULTS: Only 25 of 118 relevant studies met every specification. Treatment proved more effective than an attention control condition in reducing behavioral symptoms in only 11 of the 25 studies. Effect sizes were mostly small or moderate. Treatments with moderate or large effect sizes included aromatherapy, ability-focused carer education, bed baths, preferred music and muscle relaxation training. CONCLUSIONS: Some psychosocial interventions appear to have specific therapeutic properties, over and above those due to the benefits of participating in a clinical trial. Their effects were mostly small to moderate with a short duration of action. This limited action means that treatments will work best in specific, time-limited situations. In the few studies that addressed within-group differences, there were marked variations in response. Some participants benefited greatly from a treatment, while others did not. Interventions proved more effective when tailored to individuals' preferences

The flower mites of Trinidad III: The genus Rhinoseius (Acari: Ascidae)

http://deepblue.lib.umich.edu/bitstream/2027.42/56428/1/MP184.pd