Selfish Dark Matter

We present a mechanism where a particle asymmetry in one sector is used to generate an asymmetry in another sector. The two sectors are not coupled through particle number violating interactions and are not required to be in thermal contact with each other. When this mechanism is applied to baryogenesis in asymmetric dark matter models, we find that the dark matter particles can be extremely light, e.g. much lighter than an eV, and that in some cases there is no need to annihilate away the symmetric component of dark matter. We discuss a concrete realization of the mechanism with signals in direct detection, at the LHC, at $B$-factories or future beam dump experiments.Comment: 18+5 pages, 2 figures; Journal version: Added references, small changes to the free-streaming length estimate

Геомеханика разрушения и регламент тампонажного упрочнения пород вокруг наклонных стволов вязкопластическими растворами

Наведено підсумки шахтних досліджень руйнування порід навколо стволів вугільних шахт та обґрунтовано параметри їх зміцнення вязкопластичними розчинами.Research results are mine destruction of rocks around the shafts of coal mines and reasonable options to strengthen viscoplastic solutions

Binary Models for Marginal Independence

Log-linear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical log-linear models provides a general framework for modelling conditional independences. However, with the exception of special structures, marginal independence hypotheses cannot be accommodated by these traditional models. Focusing on binary variables, we present a model class that provides a framework for modelling marginal independences in contingency tables. The approach taken is graphical and draws on analogies to multivariate Gaussian models for marginal independence. For the graphical model representation we use bi-directed graphs, which are in the tradition of path diagrams. We show how the models can be parameterized in a simple fashion, and how maximum likelihood estimation can be performed using a version of the Iterated Conditional Fitting algorithm. Finally we consider combining these models with symmetry restrictions

Measurement of the CMS Magnetic Field

The measurement of the magnetic field in the tracking volume inside the superconducting coil of the Compact Muon Solenoid (CMS) detector under construction at CERN is done with a fieldmapper designed and produced at Fermilab. The fieldmapper uses 10 3-D B-sensors (Hall probes) developed at NIKHEF and calibrated at CERN to precision 0.05% for a nominal 4 T field. The precise fieldmapper measurements are done in 33840 points inside a cylinder of 1.724 m radius and 7 m long at central fields of 2, 3, 3.5, 3.8, and 4 T. Three components of the magnetic flux density at the CMS coil maximum excitation and the remanent fields on the steel-air interface after discharge of the coil are measured in check-points with 95 3-D B-sensors located near the magnetic flux return yoke elements. Voltages induced in 22 flux-loops made of 405-turn installed on selected segments of the yoke are sampled online during the entire fast discharge (190 s time-constant) of the CMS coil and integrated offline to provide a measurement of the initial magnetic flux density in steel at the maximum field to an accuracy of a few percent. The results of the measurements made at 4 T are reported and compared with a three-dimensional model of the CMS magnet system calculated with TOSCA.Comment: 4 pages, 5 figures, 15 reference

Demographic Inference and Representative Population Estimates from Multilingual Social Media Data

Social media provide access to behavioural data at an unprecedented scale and granularity. However, using these data to understand phenomena in a broader population is difficult due to their non-representativeness and the bias of statistical inference tools towards dominant languages and groups. While demographic attribute inference could be used to mitigate such bias, current techniques are almost entirely monolingual and fail to work in a global environment. We address these challenges by combining multilingual demographic inference with post-stratification to create a more representative population sample. To learn demographic attributes, we create a new multimodal deep neural architecture for joint classification of age, gender, and organization-status of social media users that operates in 32 languages. This method substantially outperforms current state of the art while also reducing algorithmic bias. To correct for sampling biases, we propose fully interpretable multilevel regression methods that estimate inclusion probabilities from inferred joint population counts and ground-truth population counts. In a large experiment over multilingual heterogeneous European regions, we show that our demographic inference and bias correction together allow for more accurate estimates of populations and make a significant step towards representative social sensing in downstream applications with multilingual social media.Comment: 12 pages, 10 figures, Proceedings of the 2019 World Wide Web Conference (WWW '19

Constraints on a Massive Dirac Neutrino Model

We examine constraints on a simple neutrino model in which there are three massless and three massive Dirac neutrinos and in which the left handed neutrinos are linear combinations of doublet and singlet neutrinos. We examine constraints from direct decays into heavy neutrinos, indirect effects on electroweak parameters, and flavor changing processes. We combine these constraints to examine the allowed mass range for the heavy neutrinos of each of the three generations.Comment: latex, 29 pages, 7 figures (not included), MIT-CTP-221

Functional connectivity of the Precuneus reflects effectiveness of visual restitution training in chronic hemianopia

Contains fulltext : 222045.pdf (publisher's version ) (Open Access

Elaborating Transition Interface Sampling Methods

We review two recently developed efficient methods for calculating rate constants of processes dominated by rare events in high-dimensional complex systems. The first is transition interface sampling (TIS), based on the measurement of effective fluxes through hypersurfaces in phase space. TIS improves efficiency with respect to standard transition path sampling (TPS) rate constant techniques, because it allows a variable path length and is less sensitive to recrossings. The second method is the partial path version of TIS. Developed for diffusive processes, it exploits the loss of long time correlation. We discuss the relation between the new techniques and the standard reactive flux methods in detail. Path sampling algorithms can suffer from ergodicity problems, and we introduce several new techniques to alleviate these problems, notably path swapping, stochastic configurational bias Monte Carlo shooting moves and order-parameter free path sampling. In addition, we give algorithms to calculate other interesting properties from path ensembles besides rate constants, such as activation energies and reaction mechanisms.Comment: 36 pages, 5 figure