Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences

We propose a neural sequence-to-sequence model for direction following, a task that is essential to realizing effective autonomous agents. Our alignment-based encoder-decoder model with long short-term memory recurrent neural networks (LSTM-RNN) translates natural language instructions to action sequences based upon a representation of the observable world state. We introduce a multi-level aligner that empowers our model to focus on sentence "regions" salient to the current world state by using multiple abstractions of the input sentence. In contrast to existing methods, our model uses no specialized linguistic resources (e.g., parsers) or task-specific annotations (e.g., seed lexicons). It is therefore generalizable, yet still achieves the best results reported to-date on a benchmark single-sentence dataset and competitive results for the limited-training multi-sentence setting. We analyze our model through a series of ablations that elucidate the contributions of the primary components of our model.Comment: To appear at AAAI 2016 (and an extended version of a NIPS 2015 Multimodal Machine Learning workshop paper

The mutational landscape in chronic myelomonocytic leukemia and its impact on allogeneic hematopoietic cell transplantation outcomes: A Center for Blood and Marrow Transplantation Research (CIBMTR) analysis

Somatic mutations are recognized as an important prognostic factor in chronic myelomonocytic leukemia (CMML). However, limited data are available regarding their impact on outcomes after allogeneic hematopoietic cell transplantation (HCT). In this registry analysis conducted in collaboration with the Center for International Blood and Marrow Transplantation Registry database/sample repository, we identified 313 adult patients with CMML (median age: 64 years, range, 28- 77) who underwent allogeneic HCT during 2001-2017 and had an available biospecimen in the form of a peripheral blood sample obtained prior to the start of conditioning. In multivariate analysis, a CMML-specific prognostic scoring system (CPSS) score of intermediate-2 (HR=1.46, P=0.049) or high (HR=3.22, P=0.0004) correlated significantly with overall survival. When the molecularly informed CPSS-Mol prognostic model was applied, a high CPSS-Mol score (HR=2 P=0.0079) correlated significantly with overall survival. The most common somatic mutations were in ASXL1 (62%), TET2 (35%), KRAS/NRAS (33% combined), and SRSF2 (31%). DNMT3A and TP53 mutations were associated with decreased overall survival (HR=1.70 [95% CI: 1.11-2.60], P=0.0147 and HR=2.72 [95% CI: 1.37-5.39], P=0.0042, respectively) while DNMT3A, JAK2, and TP53 mutations were associated with decreased disease-free survival (HR=1.66 [95% CI: 1.11-2.49], P=0.0138, HR=1.79 [95% CI: 1.06-3.03], P=0.0293, and HR=2.94 [95% CI: 1.50-5.79], P=0.0018, respectively). The only mutation associated with increased relapse was TP53 (HR=2.94, P=0.0201). Nonetheless, the impact of TP53 mutations specifically should be interpreted cautiously given their rarity in CMML. We calculated the goodness of fit measured by Harrell\u27s C-index for both the CPSS and CPSS-Mol, which were very similar. In summary, via registry data we have determined the mutational landscape in patients with CMML who underwent allogeneic HCT, and demonstrated an association between CPSS-Mol and transplant outcomes although without major improvement in the risk prediction beyond that provided by the CPSS

A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

Various saliency models have been developed over the years. The performance of saliency models is typically evaluated based on databases of experimentally recorded adult eye fixations. Although studies on infant gaze patterns have attracted much attention recently, saliency based models have not been widely applied for prediction of infant gaze patterns. In this study, we conduct a comprehensive comparison study of eight state-ofthe- art saliency models on predictions of experimentally captured fixations from infants and adults. Seven evaluation metrics are used to evaluate and compare the performance of saliency models. The results demonstrate a consistent performance of saliency models predicting adult fixations over infant fixations in terms of overlap, center fitting, intersection, information loss of approximation, and spatial distance between the distributions of saliency map and fixation map. In saliency and baselines models performance ranking, the results show that GBVS and Itti models are among the top three contenders, infants and adults have bias toward the centers of images, and all models and the center baseline model outperformed the chance baseline model

Understanding the Unique Assembly History of Central Group Galaxies

Central Galaxies (CGs) in massive halos live in unique environments with formation histories closely linked to that of the host halo. In local clusters they have larger sizes ($R_e$) and lower velocity dispersions (sigma) at fixed stellar mass M_star, and much larger R_e at a fixed $\sigma$ than field and satellite galaxies (non-CGs). Using spectroscopic observations of group galaxies selected from the COSMOS survey, we compare the dynamical scaling relations of early-type CGs and non-CGs at z~0.6, to distinguish possible mechanisms that produce the required evolution. CGs are systematically offset towards larger R_e at fixed $\sigma$ compared to non-CGs with similar M_star. The CG R_e-M_star relation also shows differences, primarily driven by a sub-population (~15%) of galaxies with large $R_e$, while the M_star-sigma relations are indistinguishable. These results are accentuated when double Sersic profiles, which better fit light in the outer regions of galaxies, are adopted. They suggest that even group-scale CGs can develop extended components by these redshifts that can increase total $R_e$ and M_star estimates by factors of ~2. To probe the evolutionary link between our sample and cluster CGs, we also analyze two cluster samples at z~0.6 and z~0. We find similar results for the more massive halos at comparable z, but much more distinct CG scaling relations at low-z. Thus, the rapid, late-time accretion of outer components, perhaps via the stripping and accretion of satellites, would appear to be a key feature that distinguishes the evolutionary history of CGs.Comment: 18 pages, 14 Figures, ApJ in pres

Incentivised smoking cessation intervention with pregnant women: findings from a pilot program in Northamptonshire, UK

Smoking is understood as the primary cause of preventable morbidity and premature death in the UK. In Northamptonshire, UK, the rate of smoking among adults was 20.9% (approximately 144,607 people) in 2011/12. Among pregnant women, compared to the national average (13.2%), the rate of smoking at time of delivery was higher in Northamptonshire (16%) in 2011/12. In terms of smoking cessation programs during pregnancy, incentivised smoking cessation schemes have been more frequently utilised when attempting to reduce rates of smoking among pregnant women. While smoking cessation interventions broadly accounted for a 6% increase in late-pregnancy abstinence rates compared to control interventions, only those that contained an incentivised component showed a significantly larger effect (RR 0.76, 95% CI 0.71 to 0.81). This paper presents preliminary findings of an incentivised smoking cessation pilot intervention in Northamptonshire which aimed to recruit 50 pregnant women who smoke and evaluate the feasibility of the incentive programme in terms of its: uptake of stop smoking services; numbers of those setting a quit date; effectiveness to reduce smoking following referral to stop smoking services (i.e. 4 weeks after quit date); effectiveness to reduce smoking status at delivery and the psychosocial outcomes of incentivised smoking cessation programs for pre- and post-natal women. This research applied a mixed quantitative and qualitative approach to assess the aggregated effectiveness of the program (through cross-sectional analysis) and understand individual-level positive and negative experiences of the program (through storytelling and in-depth interviews). We will report initial results (data collection currently underway) that will include baseline profile data and uptake of the incentive programme. It is important to note that gendered roles and experiences may make it more difficult for some women to access treatment and support for smoking cessation, given the heightened stigma surrounding smoking during pregnancy and mothers who smoke. This presentation will, therefore, also emphasize findings that report gendered influences on smoking such as partner influence, socioeconomic impact of lone-motherhood and individual, societal and structural stigma surrounding mothers that smoke

High density NV sensing surface created via He^(+) ion implantation of (12)^C diamond

We present a promising method for creating high-density ensembles of nitrogen-vacancy centers with narrow spin-resonances for high-sensitivity magnetic imaging. Practically, narrow spin-resonance linewidths substantially reduce the optical and RF power requirements for ensemble-based sensing. The method combines isotope purified diamond growth, in situ nitrogen doping, and helium ion implantation to realize a 100 nm-thick sensing surface. The obtained 10^(17) cm^(-3) nitrogen-vacancy density is only a factor of 10 less than the highest densities reported to date, with an observed spin resonance linewidth over 10 times more narrow. The 200 kHz linewidth is most likely limited by dipolar broadening indicating even further reduction of the linewidth is desirable and possible.Comment: 5 pages including references. 3 figure

Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection

Classic approaches to content moderation typically apply a rule-based heuristic approach to flag content. While rules are easily customizable and intuitive for humans to interpret, they are inherently fragile and lack the flexibility or robustness needed to moderate the vast amount of undesirable content found online today. Recent advances in deep learning have demonstrated the promise of using highly effective deep neural models to overcome these challenges. However, despite the improved performance, these data-driven models lack transparency and explainability, often leading to mistrust from everyday users and a lack of adoption by many platforms. In this paper, we present Rule By Example (RBE): a novel exemplar-based contrastive learning approach for learning from logical rules for the task of textual content moderation. RBE is capable of providing rule-grounded predictions, allowing for more explainable and customizable predictions compared to typical deep learning-based approaches. We demonstrate that our approach is capable of learning rich rule embedding representations using only a few data examples. Experimental results on 3 popular hate speech classification datasets show that RBE is able to outperform state-of-the-art deep learning classifiers as well as the use of rules in both supervised and unsupervised settings while providing explainable model predictions via rule-grounding.Comment: ACL 2023 Main Conferenc