Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data

Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on predicting taxi demand in the near future by learning patterns from historical data. However, they fail in case of abnormality because dispersal events with abnormally high demand are non-repetitive and violate common assumptions such as smoothness in demand change over time. Instead, in this paper we argue that dispersal events follow a complex pattern of trips and other related features in the past, which can be used to predict such events. Therefore, we formulate the dispersal event prediction problem as a survival analysis problem. We propose a two-stage framework (DILSA), where a deep learning model combined with survival analysis is developed to predict the probability of a dispersal event and its demand volume. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 2014-2016. Results show that DILSA can predict events in the next 5 hours with F1-score of 0.7 and with average time error of 18 minutes. It is orders of magnitude better than the state-ofthe-art deep learning approaches for taxi demand prediction.Comment: To appear in AAAI-19 proceedings. The reason for the replacement was the misspelled author name in the meta-data field. Author name was corrected from "Ynahua Li" to "Yanhua Li". The author list in the paper was correct and remained unchange

Understanding change – developing a typology of therapy outcomes from the experience of adolescents with depression

Background: Outcome measures mostly focusing on symptom reduction to measure change cannot indicate whether any personally meaningful change has occurred. There is a need to broaden the current understanding of outcomes for adolescent depression and identify whether holistic, interlinked patterns of change may be more clinically meaningful. Objective: To create a typology of therapy outcomes based on the experiences of adolescents with depression. Method: Interview data from 83 participants from a clinical trial of the psychological treatment of adolescent depression was analysed using ideal type analysis. Results: Six ideal types were constructed, reflecting different evaluations of the holistic impact of therapy: “I’ve worked on my relationships”, “With the insight from therapy, and feeling validated, I can cope with life challenges better”, “My mood still goes up and down”, “If I want things to change, I need to help myself”, “Therapy might help, but it hasn’t been enough”, and “I don’t feel therapy has helped me”. Conclusion: Assessing change using outcome measures may not reflect the interconnected experience for adolescents or the contextual meaning of symptom change. The typology developed offers a way of considering the impact of therapy, taking into account how symptom change is experienced within a broader perspective

PHLPP1 deletion restores pancreatic β-cell survival and normoglycemia in the db/db mouse model of obesity-associated diabetes

The Pleckstrin homology domain leucine-rich repeat protein phosphatases (PHLPPs) are novel therapeutic targets for the restoration of β-cell survival and function in diabetes. Their upregulation and activation in β-cells under conditions of both type 1 and type 2 diabetes directly correlates with β-cell failure; β-cell death and loss of insulin secretory function through disturbance of cell survival control mechanisms. PHLPPs directly dephosphorylate and regulate activities of β-cell survival-dependent kinases AKT and MST1 constituting a regulatory triangle loop to control β-cell apoptosis. PHLPP1 deletion in severely diabetic leptin receptor-deficient db/db mice restored normoglycemia and β-cell area through increased β-cell proliferation and reduced β-cell apoptosis. The beneficial effects of PHLPP1 deficiency in a severe mouse model of obesity and diabetes make PHLPP a new target for β-cell-directed diabetes therapy

Improvement of the Track-based Alignment Procedure of the CMS Muon System

The Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) is used to explore subatomic interactions through proton-proton collisions. The resulting out- burst of particles from these high energy collisions is then tracked and analyzed through a sophisticated cylindrical layering of subdetectors. Proper alignment of the outermost sub- detector on the endcaps of the cylinder, the Cathode Strip Chambers (CSC), is essential for an accurate reconstruction of momenta of various particles, especially for physics pro- cesses with muon signatures. The Reference-Target Algorithm developed and used by CMS for muon chamber alignment has been demonstrated to achieve a precision of better than 300-400 microns. However, the upcoming increase in beam energy of the LHC may allow production of new heavy particles that decay to TeV-scale muons, predicted, for example, in models that explain the weakness of gravity by new space dimensions. Optimization of the experiment's physics potential for higher energy calls for improved precision of muon alignment, which is currently limited by systematic e ects. This study focuses on identi- fying the potential systematic e ects, evaluating their impact, and proposing solutions or improvements to mitigate these e ects

A Factor-Analytic Approach To Peasant Differentiation And Household Food Security In Zimbabwe

This paper classifies households for the purposes of rural food security analysis using the multivariate statistical technique of factor analysis. This technique, applied to the social sciences, assumes that socio-economic behaviour can be related to stable underlying structures. These could be stable social strata dictating particular tendencies. With appropriate variables, factor analysis can reveal underlying social structures, and in the process identify important variables accounting for variation. The technique is preferable to univariate analysis, because it relies less on prior subjective assessment of the importance of explanatory variables. However, the major weakness of factor analysis is that it assumes the existence of well developed stable structures with unique distinguishable characteristics. Complications arise if the variables under analysis are subject to structural change. In this case, some subjectivity is inevitable since unambiguous factor classifications become implausible. Despite these problems the technique produces interesting and useful results for the peasantry in Zimbabwe. For several years during the first decade of independence, Zimbabwe boasted aggregate food surpluses-- to which the peasantry contributed significantly. This 'success story', however, masks a different reality, one in which many rural inhabitants remained poor and food insecure, and in which an even larger number of families was highly vulnerable to poverty. Based on information collected for 67 households in two communal areas of Mashonaland West, we demonstrate that household food security is a serious problem in Zimbabwe. Our analysis shows that only a small group of households are responsible for the production successes. Households classified as 'poor' and 'vulnerable', together constitute more than three quarters of the sample population. This is more clearly apparent when production, marketing and income data are examined over a two year period (1986/87 - 1987/88), including one drought year. Some members of the 'vulnerable middle' stratum also do well in terms of production, output sales and food security, but only in years of adequate rainfall. In the drought year, the economic condition of the vulnerable households is less robust, support from relatives, and from the state, is needed to avert disaster. An interesting finding, but not uncommon to Southern Africa, is that household survival across all the strata is highly dependent on access to non-farm incomes. These are especially important for improving the food security of 'poor' households. An implication of the findings of factor analysis is that food security policies in Zimbabwe need to pay more attention to addressing a complex set of inter- and intra-household distributional problems. Attempting to enhance rural household food security by primarily raising aggregate food output, be that for national, regional, or local markets, is likely to prove inadequate. A number of inequalities need to be recognised within the peasantry. For example, in the distribution of productive resources, in the distribution of well-paid rural and urban jobs, in the gender distribution of labour effort, and in the distribution of incomes within families. As shown in the paper, the peasantry in Zimbabwe is divided along many lines, and failure to take that into account, in terms of food and agricultural policy, can often lead to misplaced emphasis and action. Factor analysis can help to distinguish economic strata in the countryside and identify some of the basic causes of household food insecurity within different strata. A first step towards improving food security is to know the different characteristics of insecurity and poverty. The paper contains a preliminary check-list of indicators to help policy makers and development practitioners identify poor and vulnerable households in regions 11 and ifi of Zimbabwe. The authors are aware that several other dimensions affecting food insecurity in communal areas are not dealt with in this paper. The examination of other important influences on individual and household food security, will be the subject of subsequent papers, e.g., the effect of different marketing structures, of gender differences within and across households, and of different cropping patterns

LHC analysis-specific datasets with Generative Adversarial Networks

Using generative adversarial networks (GANs), we investigate the possibility of creating large amounts of analysis-specific simulated LHC events at limited computing cost. This kind of generative model is analysis specific in the sense that it directly generates the high-level features used in the last stage of a given physics analyses, learning the N-dimensional distribution of relevant features in the context of a specific analysis selection. We apply this idea to the generation of muon four-momenta in $Z \to \mu\mu$ events at the LHC. We highlight how use-case specific issues emerge when the distributions of the considered quantities exhibit particular features. We show how substantial performance improvements and convergence speed-up can be obtained by including regression terms in the loss function of the generator. We develop an objective criterion to assess the geenrator performance in a quantitative way. With further development, a generalization of this approach could substantially reduce the needed amount of centrally produced fully simulated events in large particle physics experiments.Comment: 14 pages, 11 figure

Pheochromocytoma and pregnancy with abruptio placenta

A 41 year old previously healthy woman (Gravida 4, para 3) was presented to our hospital at 29 weeks gestation, with bleeding Per Vagina (PV) and severe hypertension (190/100). She underwent a routine obstetric examination at 12 weeks gestation and since then she has not undergone any antenatal follow-up. She developed episodes of severe headache, dizziness, sweating, and nausea. She visited a private hospital and was noted to be severely hypertensive (190/120) with headache and palpitations. An ultrasound abdomen was done which showed left suprarenal mass, and   a diagnosis of pheochromocytoma was made. She was treated there with antihypertensive medications. When Blood pressure got controlled, she was discharged against medical advice. At 29 weeks, she suddenly developed severe headache and bleeding PV. She visited our centre and was diagnosed to have abruptio- placenta with foetal distress. An emergency caesarean section was done, and following which the patient was treated in the ICU with antihypertensive under invasive monitoring. An MRI demonstrated a left pheochromocytoma. A laparoscopic adrenelectomy was planned later and she got discharged on antihypertensive following an uneventful period of recovery. She got operated later in her country. A laparoscopic left adrenelectomy was done. She is off all medications now and is currently asymptomatic