Unitarity Constraints on Higgs Portals

Dark matter that was once in thermal equilibrium with the Standard Model is generally prohibited from obtaining all of its mass from the electroweak phase transition. This implies a new scale of physics and mediator particles to facilitate dark matter annihilation. In this work, we focus on dark matter that annihilates through a generic Higgs portal. We show how partial wave unitarity places an upper bound on the mass of the mediator (or dark) Higgs when its mass is increased to be the largest scale in the effective theory. For models where the dark matter annihilates via fermion exchange, an upper bound is generated when unitarity breaks down around 8.5 TeV. Models where the dark matter annihilates via fermion and higgs boson exchange push the bound to 45.5 TeV. We also show that if dark matter obtains all of its mass from a new symmetry breaking scale that scale is also constrained. We improve these constraints by requiring perturbativity in the Higgs sector up to each unitarity bound. In this limit, the bounds on the dark symmetry breaking vev and the dark Higgs mass are now 2.4 and 3 TeV, respectively, when the dark matter annihilates via fermion exchange. When dark matter annihilates via fermion and higgs boson exchange, the bounds are now 12 and 14.2 TeV, respectively. The available parameter space for Higgs portal dark matter annihilation is outlined. We also show how the bounds are improved if Higgs portal dark matter is only a fraction of the observed relic abundance. Finally, we discuss how to apply these arguments to other dark matter scenarios and discuss prospects for direct detection and future collider searches. If the Higgs portal is responsible for dark matter annihilation, planned direct detection experiments will cover almost all the parameter space. The ILC and/or VLHC, however, is needed to establish the Higgs portal mechanism

On the Equivalence Between Deep NADE and Generative Stochastic Networks

Neural Autoregressive Distribution Estimators (NADEs) have recently been shown as successful alternatives for modeling high dimensional multimodal distributions. One issue associated with NADEs is that they rely on a particular order of factorization for $P(\mathbf{x})$. This issue has been recently addressed by a variant of NADE called Orderless NADEs and its deeper version, Deep Orderless NADE. Orderless NADEs are trained based on a criterion that stochastically maximizes $P(\mathbf{x})$ with all possible orders of factorizations. Unfortunately, ancestral sampling from deep NADE is very expensive, corresponding to running through a neural net separately predicting each of the visible variables given some others. This work makes a connection between this criterion and the training criterion for Generative Stochastic Networks (GSNs). It shows that training NADEs in this way also trains a GSN, which defines a Markov chain associated with the NADE model. Based on this connection, we show an alternative way to sample from a trained Orderless NADE that allows to trade-off computing time and quality of the samples: a 3 to 10-fold speedup (taking into account the waste due to correlations between consecutive samples of the chain) can be obtained without noticeably reducing the quality of the samples. This is achieved using a novel sampling procedure for GSNs called annealed GSN sampling, similar to tempering methods that combines fast mixing (obtained thanks to steps at high noise levels) with accurate samples (obtained thanks to steps at low noise levels).Comment: ECML/PKDD 201

Modeling migration dynamics of immigrants: the case of the Netherlands

In this paper we analyze the demographic factors that influence the migration dynamics of recent immigrants to The Netherlands. We show how we can allow for both permanent and temporary migrants. Based on data from Statistics Netherlands we analyze both the departure and the return from abroad for recent non-Dutch immigrants to The Netherlands. Results disclose differences among migrants by migration motive and by country of origin and lend support to our analytical framework. Combining both models, for departure and returning, provides the probability that a specific migrant ends-up in The Netherlands. It also yields a framework for predicting the migration dynamics over the life-cycle. We can conclude that for a complete view of the migration dynamics it is important to allow for both permanent (stayers) migrants and temporary (movers) migrants and that return from abroad should not be neglected.migration dynamics;mover-stayer model;return migration

Instrumental variable estimation for duration data

In this article we focus on time-to-event studies with arandomised treatment assignment that may be compromised byselective compliance. Contrary to most of the extensive literatureon evaluation studies we do not consider the effect of thetreatment on some average outcome but on the hazard rate. Intime-to-event studies the treatment may vary over time. Anothercomplication of duration data is that they are usually heavycensored. Censoring limits the observation period, but is not afeature of the treatment program. Therefore, a natural choice isto relate the treatment to the hazard rate. We show that even ifthe compliance is selective, we can still use the randomisation toestimate the impact of the program corrected for selectivecompliance on the hazard. The only requirement is thatparticipation in the program is affected by a variable that is notcorrelated with the baseline duration.We develop an Instrumental Variable estimation procedure for theGeneralized Accelerated Failure Time (GAFT) model. The GAFT modelis a duration data model that encompasses two competing approachesto such data; the (Mixed) Proportional Hazard (MPH) model and theAccelerated Failure Time (AFT) model. We discuss the large sampleproperties of this Instrumental Linear Rank Estimation and showhow we can improve its efficiency. The estimator is used tore-analyze the data from the Illinois unemployment bonusexperiment.Duration model;Endogenous treatment;Instrumental variable;Semi-parametric

Optimal acid rain abatement policy in Europe

Acid rain causes greater environmental damage than would occur if countries act cooperatively. Based on new estimates of sulphur abatement cost functions, the potential gains from cooperation are calculated for Europe. Various cooperative abatement rates are compared with the rates implied by recent international agreements. The distinction is made between primary and secondary abatement, and their respective roles are discussed.Environmental Management; abatement; acid rain; cooperation

Modelling the time on unemployment insurance benefits

A duration model based on the time on Unemployment Insurance (UI) benefits instead of a model based on the time till re-employment is more relevant from a cost-benefit perspective. The contribution of this paper is to extend the standard (mixed) Proportional Hazard model to account for an upper bound on the duration. We use a modified mover-stayer model to this end and discuss the interpretation of the parameters. In an empirical application we compare the method with the standard analysis of unemployment duration. We also derive the expected UI-benefit costs implied by the model for some typical unemployed individuals.mover-stayer model;UI-benefits;maximum duration;mixed proportional hazard

q-series and L-functions related to half-derivatives of the Andrews--Gordon identity

Studied is a generalization of Zagier's q-series identity. We introduce a generating function of L-functions at non-positive integers, which is regarded as a half-differential of the Andrews--Gordon q-series. When q is a root of unity, the generating function coincides with the quantum invariant for the torus knot.Comment: 21 pages, related papers can be found from http://gogh.phys.s.u-tokyo.ac.jp/~hikami

Political attention to environmental issues: Analyzing policy punctuations in the Netherlands

One of the most dramatized features in Al Gore's movie The Inconvenient Truth is the effects of a rising sea-level in the Netherlands. The film is an example of how the mobilization of bias in the Netherlands resulted in sudden high levels of attention for climate change problems. We analyze agenda setting on Dutch environmental policy, using various policy issue datasets about parliamentary activities, media, and expert organizations and focusing on the interrelations between these policy venues. All datasets are coded by the same topic codebook. The findings show that interest in environmental issues is largely determined by the state of the economy, unexpected incidents, and the competition for attention with other issues in the political arena. We show that political interest in environmental issues has initially been flagging, since the environment was mostly seen as a European topic, and Europe has not been popular since the referendum on a European Constitution. However, once the climate change problem was translated to a national problem, popular attention increased enormously. We conclude that climate change framed as a European problem does not increase attention, nationalization of the problem does

Struvite (MgNH4PO4.6H2O) solubility and its application to a piggery effluent problem

Struvite (MgNH4P04• 6H20) solution chemistry was studied in order to understand a struvite scaling problem which exists in the pipe network of a series of 4 effluent lagoons at a piggery. Struvite deposits frequently occur in piping from the final effluent pond intended for irrigation purposes, causing severe scaling of the pipe, making irrigation or other end uses of the effluent impossible. The conditional solubility constant (P. = SMg .SNH3-N•SPO.-P, equilibrium S values (M) was determined over the range of pH values (6.8-8.5) and solution concentrations of Mg, NHa-N and P04-P close to field conditions, and at a temperature of 30°C. A comparison of field and laboratory data indicates struvite precipitates from the second lagoon onwards. The solubility data obtained also indicate potential for precipitation as a method of nutrient removal from wastewaters

Contractive De-noising Auto-encoder

Auto-encoder is a special kind of neural network based on reconstruction. De-noising auto-encoder (DAE) is an improved auto-encoder which is robust to the input by corrupting the original data first and then reconstructing the original input by minimizing the reconstruction error function. And contractive auto-encoder (CAE) is another kind of improved auto-encoder to learn robust feature by introducing the Frobenius norm of the Jacobean matrix of the learned feature with respect to the original input. In this paper, we combine de-noising auto-encoder and contractive auto- encoder, and propose another improved auto-encoder, contractive de-noising auto- encoder (CDAE), which is robust to both the original input and the learned feature. We stack CDAE to extract more abstract features and apply SVM for classification. The experiment result on benchmark dataset MNIST shows that our proposed CDAE performed better than both DAE and CAE, proving the effective of our method.Comment: Figures edite