Six-year mortality in a street-recruited cohort of homeless youth in San Francisco, California.

Objectives. The mortality rate of a street-recruited homeless youth cohort in the United States has not yet been reported. We examined the six-year mortality rate for a cohort of street youth recruited from San Francisco street venues in 2004. Methods. Using data collected from a longitudinal, venue-based sample of street youth 15-24 years of age, we calculated age, race, and gender-adjusted mortality rates. Results. Of a sample of 218 participants, 11 died from enrollment in 2004 to December 31, 2010. The majority of deaths were due to suicide and/or substance abuse. The death rate was 9.6 deaths per hundred thousand person-years. The age, race and gender-adjusted standardized mortality ratio was 10.6 (95% CI [5.3-18.9]). Gender specific SMRs were 16.1 (95% CI [3.3-47.1]) for females and 9.4 (95% CI [4.0-18.4]) for males. Conclusions. Street-recruited homeless youth in San Francisco experience a mortality rate in excess of ten times that of the states general youth population. Services and programs, particularly housing, mental health and substance abuse interventions, are urgently needed to prevent premature mortality in this vulnerable population

Failure dynamics of the global risk network

Risks threatening modern societies form an intricately interconnected network that often underlies crisis situations. Yet, little is known about how risk materializations in distinct domains influence each other. Here we present an approach in which expert assessments of risks likelihoods and influence underlie a quantitative model of the global risk network dynamics. The modeled risks range from environmental to economic and technological and include difficult to quantify risks, such as geo-political or social. Using the maximum likelihood estimation, we find the optimal model parameters and demonstrate that the model including network effects significantly outperforms the others, uncovering full value of the expert collected data. We analyze the model dynamics and study its resilience and stability. Our findings include such risk properties as contagion potential, persistence, roles in cascades of failures and the identity of risks most detrimental to system stability. The model provides quantitative means for measuring the adverse effects of risk interdependence and the materialization of risks in the network

Personalizing Dialogue Agents via Meta-Learning

Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency. Collecting such descriptions from existing dialogues is expensive and requires hand-crafted feature designs. In this paper, we propose to extend Model-Agnostic Meta-Learning (MAML)(Finn et al., 2017) to personalized dialogue learning without using any persona descriptions. Our model learns to quickly adapt to new personas by leveraging only a few dialogue samples collected from the same user, which is fundamentally different from conditioning the response on the persona descriptions. Empirical results on Persona-chat dataset (Zhang et al., 2018) indicate that our solution outperforms non-meta-learning baselines using automatic evaluation metrics, and in terms of human-evaluated fluency and consistency.Comment: Accepted in ACL 2019. Zhaojiang Lin* and Andrea Madotto* contributed equally to this wor

Effect of Human Capital on the Entrepreneurship Gender Gap

The presence of a gender gap in entrepreneurship has been well studied in previous literature. There are various contributing factors, including differences in human capital, which has been reviewed less so than social capital. Through a career survey of Wharton MBAs, this research paper 1) examines the presence of an entrepreneurship gender gap; 2) identifies human capital variables that predict entry into entrepreneurship; and 3) determines whether or not there is a human capital gender gap. The results showed both an entrepreneurship and human capital gender gap. Furthermore, experience working at small companies, more years of experience, and experience in finance-related industries were found to be good predictors of entry. Overall, the human capital predictor model explained 6.4% of the variability of entry into entrepreneurship. Though applicability is limited due to the biases of the sample, there are tangible implications for decreasing the entrepreneurship gender gap

NNLL Momentum-Space Resummation for Stop-Pair Production at the LHC

If supersymmetry near the TeV scale is realized in Nature, the pair production of scalar top squarks is expected to be observable at the Large Hadron Collider. Recently, effective field-theory methods were employed to obtain approximate predictions for the cross section for this process, which include soft-gluon emission effects up to next-to-next-to-leading order (NNLO) in perturbation theory. In this work we employ the same techniques to resum soft-gluon emission effects to all orders in perturbation theory and with next-to-next-to-logarithmic (NNLL) accuracy. We analyze the effects of NNLL resummation on the stop-pair production cross section by obtaining NLO+NNLL predictions in pair invariant mass and one-particle inclusive kinematics. We compare the results of these calculations to the approximate NNLO predictions for the cross sections.Comment: 25 pages, 6 figure

Approximate NNLO Predictions for the Stop-Pair Production Cross Section at the LHC

If the minimal supersymmetric standard model at scales of around 1 TeV is realized in nature, the total top-squark pair production cross section should be measurable at the CERN Large Hadron Collider. In this work we present precise predictions for this observable, which are based upon approximate NNLO formulas obtained using soft-collinear effective theory methods.Comment: 36 pages, 7 figures and 11 tables. Version published in JHEP 1307 (2013) 04

Between a Rock and a Cell Phone: Social Media Use during Mass Protests in Iran, Tunisia and Egypt

In this paper we examine the use of social media, and especially Twitter, in Iran, Tunisia and Egypt during the mass political demonstrations and protests in June 2009, December 2010 - January 2011, and February 2011, respectively. We compare this usage with methods and findings from other studies on the use of Twitter in emergency situations, such as natural and man-made disasters. We draw on our own experiences and participant-observations as an eyewitness in Iran (first author), and on Twitter data from Iran, Tunisia and Egypt. In these three cases, Twitter filled a unique technology and communication gap at least partially. We summarize suggested directions for future research with a view of placing this work in the larger context of social media use in conditions of crisis and social convergence

The Impact of Regional Food Cost Differences on the TFP Recommendations

The Thrifty Food Plan (TFP) adapts a national average price and stipulates that all foods should be prepared at home (FAH). Our purpose was to calculate the TFP with regional prices and add Food Away From Home (FAFH) into the TFP model. Measures were calculated and compared across the TFP, the regional models with FAFH, and low-income consumers’ diet pattern. The preliminary results indicated that considering moderate FAFH in the TFP yielded similar nutrient and food group composition as the TFP with FAFH added in it, while greatly increased the practicality and adaptability of the recommendations. However, the regional TFP costs are all larger than the TFP with FAFH cost. These findings may be used by nutrition educators to develop healthful FAFH choices and readjust the TFP allotments for Supplemental Nutrition Assistance Program (SNAP) participants.Regional, Food Cost, The TFP, Consumer/Household Economics, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, Health Economics and Policy,

Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text

Real world multimedia data is often composed of multiple modalities such as an image or a video with associated text (e.g. captions, user comments, etc.) and metadata. Such multimodal data packages are prone to manipulations, where a subset of these modalities can be altered to misrepresent or repurpose data packages, with possible malicious intent. It is, therefore, important to develop methods to assess or verify the integrity of these multimedia packages. Using computer vision and natural language processing methods to directly compare the image (or video) and the associated caption to verify the integrity of a media package is only possible for a limited set of objects and scenes. In this paper, we present a novel deep learning-based approach for assessing the semantic integrity of multimedia packages containing images and captions, using a reference set of multimedia packages. We construct a joint embedding of images and captions with deep multimodal representation learning on the reference dataset in a framework that also provides image-caption consistency scores (ICCSs). The integrity of query media packages is assessed as the inlierness of the query ICCSs with respect to the reference dataset. We present the MultimodAl Information Manipulation dataset (MAIM), a new dataset of media packages from Flickr, which we make available to the research community. We use both the newly created dataset as well as Flickr30K and MS COCO datasets to quantitatively evaluate our proposed approach. The reference dataset does not contain unmanipulated versions of tampered query packages. Our method is able to achieve F1 scores of 0.75, 0.89 and 0.94 on MAIM, Flickr30K and MS COCO, respectively, for detecting semantically incoherent media packages.Comment: *Ayush Jaiswal and Ekraam Sabir contributed equally to the work in this pape