Analysis of Galaxy Formation with Hydrodynamics

We present a hydrodynamical code based on the Smooth Particle Hydrodynamics technique implemented in an AP3M code aimed at solving the hydrodynamical and gravitational equations in a cosmological frame. We analyze the ability of the code to reproduce standard tests and perform numerical simulations to study the formation of galaxies in a typical region of a CDM model. These numerical simulations include gas and dark matter particles and take into account physical processes such as shock waves, radiative cooling, and a simplified model of star formation. Several observed properties of normal galaxies such as $M_{gas}/M_{total}$ ratios, the luminosity function and the Tully-Fisher relation are analyzed within the limits imposed by numerical resolution.Comment: 21 pages, 2 postscript tables. Submitted MNRAS 04.03.9

Sharp error terms for return time statistics under mixing conditions

We describe the statistics of repetition times of a string of symbols in a stochastic process. Denote by T(A) the time elapsed until the process spells the finite string A and by S(A) the number of consecutive repetitions of A. We prove that, if the length of the string grows unbondedly, (1) the distribution of T(A), when the process starts with A, is well aproximated by a certain mixture of the point measure at the origin and an exponential law, and (2) S(A) is approximately geometrically distributed. We provide sharp error terms for each of these approximations. The errors we obtain are point-wise and allow to get also approximations for all the moments of T(A) and S(A). To obtain (1) we assume that the process is phi-mixing while to obtain (2) we assume the convergence of certain contidional probabilities

Pilot Metal Workload in Flight Operation: a Case Study of Indonesian Civilian Pilot

This type of activity or work with high stress level and requires more concentration and attention, in this case is the aircraft operation. Thereby mental workload is the most dominant than the physical workload. And this is what should have been a concern, because if mental workload endured by pilot is excessive, it will lower down the quality of work and lead to work safety; in this case the aircraft operation. Subjective Workload Assessment Technique (SWAT) method is used to measure mental workload value, this method consists of three dimensions with their levels, there are: time, mental effort, and psychological stress load. The aim of this study was to know the mental workload of the pilot of an aircraft in flight dimensions: phases of time, phase of flight, terrain condition, and weather, and identifies what factors the most dominant for build of mental workload. The results of studies showed that pilot mental workload will increase when a pilot faced with flight conditions do at early morning (00.00-05:59 am), during weekend and enters the peak season period, and the aircraft will be landing procedures, and also in case of change of wind conditions in flight, and will increasingly when pilot exposed to aircraft operating with route condition which has a land surface is mountainious. This study also showed that the time dimension factor (T) significantly affects the mental workload of pilots, indicating that they put more emphasis on this factor when they are considering workloads

Interactive exploration of population scale pharmacoepidemiology datasets

Population-scale drug prescription data linked with adverse drug reaction (ADR) data supports the fitting of models large enough to detect drug use and ADR patterns that are not detectable using traditional methods on smaller datasets. However, detecting ADR patterns in large datasets requires tools for scalable data processing, machine learning for data analysis, and interactive visualization. To our knowledge no existing pharmacoepidemiology tool supports all three requirements. We have therefore created a tool for interactive exploration of patterns in prescription datasets with millions of samples. We use Spark to preprocess the data for machine learning and for analyses using SQL queries. We have implemented models in Keras and the scikit-learn framework. The model results are visualized and interpreted using live Python coding in Jupyter. We apply our tool to explore a 384 million prescription data set from the Norwegian Prescription Database combined with a 62 million prescriptions for elders that were hospitalized. We preprocess the data in two minutes, train models in seconds, and plot the results in milliseconds. Our results show the power of combining computational power, short computation times, and ease of use for analysis of population scale pharmacoepidemiology datasets. The code is open source and available at: https://github.com/uit-hdl/norpd_prescription_analyse

Formal Verification of Security Protocol Implementations: A Survey

Automated formal verification of security protocols has been mostly focused on analyzing high-level abstract models which, however, are significantly different from real protocol implementations written in programming languages. Recently, some researchers have started investigating techniques that bring automated formal proofs closer to real implementations. This paper surveys these attempts, focusing on approaches that target the application code that implements protocol logic, rather than the libraries that implement cryptography. According to these approaches, libraries are assumed to correctly implement some models. The aim is to derive formal proofs that, under this assumption, give assurance about the application code that implements the protocol logic. The two main approaches of model extraction and code generation are presented, along with the main techniques adopted for each approac

Can Conformally Coupled Modified Gravity Solve The Hubble Tension?

The discrepancy between early-Universe inferences and direct measurements of the Hubble constant, known as the Hubble tension, recently became a pressing subject in high precision cosmology. As a result, a large variety of theoretical models have been proposed to relieve this tension. In this work we analyze a conformally-coupled modified gravity (CCMG) model of an evolving gravitational constant due to the coupling of a scalar field to the Ricci scalar, which becomes active around matter-radiation equality, as required for solutions to the Hubble tension based on increasing the sound horizon at recombination. The model is theoretically advantageous as it has only one free parameter in addition to the baseline $\Lambda$CDM ones. Inspired by similar recent analyses of so-called early-dark-energy models, we constrain the CCMG model using a combination of early and late-Universe cosmological datasets. In addition to the Planck 2018 cosmic microwave background (CMB) anisotropies and weak lensing measurements, baryon acoustic oscillations and the Supernova H0 for the Equation of State datasets, we also use large-scale structure (LSS) datasets such as the Dark Energy Survey year 1 and the full-shape power spectrum likelihood from the Baryon Oscillation Spectroscopic Survey, including its recent analysis using effective field theory, to check the effect of the CCMG model on the (milder) S8 tension between the CMB and LSS. We find that the CCMG model can slightly relax the Hubble tension, with $H_0 = 69.6 \pm 1.6$ km/s/Mpc at 95% CL, while barely affecting the S8 tension. However, current data does not exhibit strong preference for CCMG over the standard cosmological model. Lastly, we show that the planned CMB-S4 experiment will have the sensitivity required to distinguish between the CCMG model and the more general class of models involving an evolving gravitational constant.Comment: 14 pages, 4 figures, 9 table

Learning and Transferring IDs Representation in E-commerce

Many machine intelligence techniques are developed in E-commerce and one of the most essential components is the representation of IDs, including user ID, item ID, product ID, store ID, brand ID, category ID etc. The classical encoding based methods (like one-hot encoding) are inefficient in that it suffers sparsity problems due to its high dimension, and it cannot reflect the relationships among IDs, either homogeneous or heterogeneous ones. In this paper, we propose an embedding based framework to learn and transfer the representation of IDs. As the implicit feedbacks of users, a tremendous amount of item ID sequences can be easily collected from the interactive sessions. By jointly using these informative sequences and the structural connections among IDs, all types of IDs can be embedded into one low-dimensional semantic space. Subsequently, the learned representations are utilized and transferred in four scenarios: (i) measuring the similarity between items, (ii) transferring from seen items to unseen items, (iii) transferring across different domains, (iv) transferring across different tasks. We deploy and evaluate the proposed approach in Hema App and the results validate its effectiveness.Comment: KDD'18, 9 page

A reduced semantics for deciding trace equivalence using constraint systems

Many privacy-type properties of security protocols can be modelled using trace equivalence properties in suitable process algebras. It has been shown that such properties can be decided for interesting classes of finite processes (i.e., without replication) by means of symbolic execution and constraint solving. However, this does not suffice to obtain practical tools. Current prototypes suffer from a classical combinatorial explosion problem caused by the exploration of many interleavings in the behaviour of processes. M\"odersheim et al. have tackled this problem for reachability properties using partial order reduction techniques. We revisit their work, generalize it and adapt it for equivalence checking. We obtain an optimization in the form of a reduced symbolic semantics that eliminates redundant interleavings on the fly.Comment: Accepted for publication at POST'1

A Dataset of Social-Psychological and Emotional Reactions During the COVID-19 Pandemic Across Four European Countries

In April 2020, only a few weeks after the COVID-19 pandemic had erupted, we conducted an online survey and collected data from 2031 individuals in four European countries (Germany, the Netherlands, Spain and the United Kingdom) using a cross-sectional design. Participants recruited on Cint completed new and pre-existing measures of socio-political and populist attitudes perceived threats, appraisals (anger at the government, anger at transgressors of hygiene measures, anxiety about coronavirus via the appraisals of health-related threats), conspiracy mentality, moral reasoning, threat estimation (coronavirus, climate, symbolic material/safety), news consumption, support for and compliance with governmental hygiene measures, subjective social status and demographics. The dataset is stored on figshare repository. It can be used to study social-psychological, emotional, socio-political and socio-economic factors of the COVID-19 pandemic