A translation mechanism for recommendations

An important class of distributed Trust-based solutions is based on the information sharing. A basic requirement of such systems is the ability of participating agents to effectively communicate, receiving and sending messages that can be interpreted correctly. Unfortunately, in open systems it is not possible to postulate a common agreement about the representation of a rating, its semantic meaning and cognitive and computational mechanisms behind a trust-rating formation. Social scientists agree to consider unqualified trust values not transferable, but a more pragmatic approach would conclude that qualified trust judgments are worth being transferred as far as decisions taken considering others’ opinion are better than the ones taken in isolation. In this paper we investigate the problem of trust transferability in open distributed environments, proposing a translation mechanism able to make information exchanged from one agent to another more accurate and useful. Our strategy implies that the parties involved disclose some elements of their trust models in order to understand how compatible the two systems are. This degree of compatibility is used to weight exchanged trust judgements. If agents are not compatible enough, transmitted values can be discarded. We define a complete simulation environment where agents are modelled with characteristics that may differ. We show how agents’ differences deteriorate the value of recommendations so that agents obtain better predictions on their own. We then show how different translation mechanisms based on the degree of compatibility improve drastically the quality of recommendations

The “Child Health Evidence Week” and GRADE grid may aid transparency in the deliberative process of guideline development

AbstractObjectiveTo explore the evidence translation process during a 1-week national guideline development workshop (“Child Health Evidence Week”) in Kenya.Study Design and SettingNonparticipant observational study of the discussions of a multidisciplinary guideline development panel in Kenya. Discussions were aided by GRADE (Grading of Recommendations Assessment, Development, and Evaluation) grid.ResultsThree key thematic categories emerged: 1) “referral to other evidence to support or refute the proposed recommendations;” 2) “assessment of the presented research evidence;” and 3) “assessment of the local applicability of evidence.” The types of evidence cited included research evidence and anecdotal evidence based on clinician experiences. Assessment of the research evidence revealed important challenges in the translation of evidence into recommendations, including absence of evidence, low quality or inconclusive evidence, inadequate reporting of key features of the management under consideration, and differences in panelists’ interpretation of the research literature. A broad range of factors with potential to affect local applicability of evidence were discussed.ConclusionThe process of the “Child Health Evidence Week” combined with the GRADE grid may aid transparency in the deliberative process of guideline development, and provide a mechanism for comprehensive assessment, documentation, and reporting of multiple factors that influence the quality and applicability of guideline recommendations

Proposal for preparing the Lao legal framework Plan 2000

This is a proposal for Lao PDR's Plan 2000 submitted by professors Ann and Bob Seidman, which was aimed at helping Lao PDR to strengthen its legal framework and legislative drafting

The Steep Road to Happily Ever After: An Analysis of Current Visual Storytelling Models

Visual storytelling is an intriguing and complex task that only recently entered the research arena. In this work, we survey relevant work to date, and conduct a thorough error analysis of three very recent approaches to visual storytelling. We categorize and provide examples of common types of errors, and identify key shortcomings in current work. Finally, we make recommendations for addressing these limitations in the future.Comment: Accepted to the NAACL 2019 Workshop on Shortcomings in Vision and Language (SiVL

Translating Phrases in Neural Machine Translation

Phrases play an important role in natural language understanding and machine translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is difficult to integrate them into current neural machine translation (NMT) which reads and generates sentences word by word. In this work, we propose a method to translate phrases in NMT by integrating a phrase memory storing target phrases from a phrase-based statistical machine translation (SMT) system into the encoder-decoder architecture of NMT. At each decoding step, the phrase memory is first re-written by the SMT model, which dynamically generates relevant target phrases with contextual information provided by the NMT model. Then the proposed model reads the phrase memory to make probability estimations for all phrases in the phrase memory. If phrase generation is carried on, the NMT decoder selects an appropriate phrase from the memory to perform phrase translation and updates its decoding state by consuming the words in the selected phrase. Otherwise, the NMT decoder generates a word from the vocabulary as the general NMT decoder does. Experiment results on the Chinese to English translation show that the proposed model achieves significant improvements over the baseline on various test sets.Comment: Accepted by EMNLP 201

Reproducibility of cognitive endpoints in clinical trials: Lessons from neurofibromatosis type 1

OBJECTIVE: Rapid developments in understanding the molecular mechanisms underlying cognitive deficits in neurodevelopmental disorders have increased expectations for targeted, mechanism-based treatments. However, translation from preclinical models to human clinical trials has proven challenging. Poor reproducibility of cognitive endpoints may provide one explanation for this finding. We examined the suitability of cognitive outcomes for clinical trials in children with neurofibromatosis type 1 (NF1) by examining test-retest reliability of the measures and the application of data reduction techniques to improve reproducibility. METHODS: Data were analyzed from the STARS clinical trial (n = 146), a multi-center double-blind placebo-controlled phase II trial of lovastatin, conducted by the NF Clinical Trials Consortium. Intra-class correlation coefficients were generated between pre- and post-performances (16-week interval) on neuropsychological endpoints in the placebo group to determine test-retest reliabilities. Confirmatory factor analysis was used to reduce data into cognitive domains and account for measurement error. RESULTS: Test-retest reliabilities were highly variable, with most endpoints demonstrating unacceptably low reproducibility. Data reduction confirmed four distinct neuropsychological domains: executive functioning/attention, visuospatial ability, memory, and behavior. Test-retest reliabilities of latent factors improved to acceptable levels for clinical trials. Applicability and utility of our model was demonstrated by homogeneous effect sizes in the reanalyzed efficacy data. INTERPRETATION: These data demonstrate that single observed endpoints are not appropriate to determine efficacy, partly accounting for the poor test-retest reliability of cognitive outcomes in clinical trials in neurodevelopmental disorders. Recommendations to improve reproducibility are outlined to guide future trial design