Simplified 1D Empirical Model for Volumetric Behavior of High-Carbonate Clay

The Guadalquivir blue marl is a high plasticity overconsolidated carbonate clay. This soil presents an elevated fragility and high susceptibility to moisture changes. These characteristics have caused many geotechnical accidents, such as the Aznalcollar dam failure, in Seville (Spain). A comprehensive test campaign has been conducted to determine the physical and chemical properties of the blue marl. Analysis by scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) allowed characterising its internal structure, revealing clear differences between the macro and the microstructure. A novel model for predicting the volumetric deformation (under oedometric conditions) of the Guadalquivir blue marl with suction and vertical pressure changes has been proposed. The model, based on data from shrink-swell tests, provides an acceptable estimation of the volumetric behaviour of the soil with a relatively simple set of parameters. The results were experimentally verified by suction-controlled oedometer tests and showed an acceptable agreement with the data measured. It has been specified when swelling. shrinkage or collapse occur

Simplified PBEE to Estimate Economic Seismic Risk for Buildings

A seismic risk assessment is often performed on behalf of a buyer of large commercial buildings in seismically active regions. One outcome of the assessment is that a probable maximum loss (PML) is computed. PML is of limited use to real-estate investors as it has no place in a standard financial analysis and reflects too long a planning period for what-if scenarios. We introduce an alternative to PML called probable frequent loss (PFL), defined as the mean loss resulting from an economic-basis earthquake such as shaking with 10% exceedance probability in 5 years. PFL is approximately related to expected annualized loss (EAL) through a site economic hazard coefficient (H) introduced here. PFL and EAL offer three advantages over PML: (1) meaningful planning period; (2) applicability in financial analysis (making seismic risk a potential market force); and (3) can be estimated by a rigorous but simplified PBEE method that relies on a single linear structural analysis. We illustrate using 15 example buildings, including a 7-story nonductile reinforced-concrete moment-frame building in Van Nuys, CA and 14 buildings from the CUREE-Caltech Woodframe Project

Simplified heat engine

In Sterling-cycle heat engine, pneumatic system is used to drive displacer/regenerator, eliminating mechanical linkages and valves

Gauge Mediation Simplified

Gauge mediation of supersymmetry breaking is drastically simplified using generic superpotentials without U(1)_R symmetry by allowing metastable vacua.Comment: 4 page

Pairwise Comparisons Simplified

This study examines the notion of generators of a pairwise comparisons matrix. Such approach decreases the number of pairwise comparisons from $n\cdot (n-1)$ to $n-1$. An algorithm of reconstructing of the PC matrix from its set of generators is presented.Comment: 15 pages, two figure

Simplified SIMPs and the LHC

The existence of Dark Matter (DM) in the form of Strongly Interacting Massive Particles (SIMPs) may be motivated by astrophysical observations that challenge the classical Cold DM scenario. Other observations greatly constrain, but do not completely exclude, the SIMP alternative. The signature of SIMPs at the LHC may consist of neutral, hadron-like, trackless jets produced in pairs. We show that the absence of charged content can provide a very efficient tool to suppress dijet backgrounds at the LHC, thus enhancing the sensitivity to a potential SIMP signal. We illustrate this using a simplified SIMP model and present a detailed feasibility study based on simulations, including a dedicated detector response parametrization. We evaluate the expected sensitivity to various signal scenarios and tentatively consider the exclusion limits on the SIMP elastic cross section with nucleons.Comment: 18 pages, 7 figure

Simplified Neural Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) is the task of modifying a statistical model trained on labeled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain. Existing state-of-the-art UDA approaches use neural networks to learn representations that can predict the values of subset of important features called "pivot features." In this work, we show that it is possible to improve on these methods by jointly training the representation learner with the task learner, and examine the importance of existing pivot selection methods.Comment: To be presented at NAACL 201