Unbiased Learning to Rank with Unbiased Propensity Estimation

Learning to rank with biased click data is a well-known challenge. A variety of methods has been explored to debias click data for learning to rank such as click models, result interleaving and, more recently, the unbiased learning-to-rank framework based on inverse propensity weighting. Despite their differences, most existing studies separate the estimation of click bias (namely the \textit{propensity model}) from the learning of ranking algorithms. To estimate click propensities, they either conduct online result randomization, which can negatively affect the user experience, or offline parameter estimation, which has special requirements for click data and is optimized for objectives (e.g. click likelihood) that are not directly related to the ranking performance of the system. In this work, we address those problems by unifying the learning of propensity models and ranking models. We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank. Based on this observation, we propose a Dual Learning Algorithm (DLA) that jointly learns an unbiased ranker and an \textit{unbiased propensity model}. DLA is an automatic unbiased learning-to-rank framework as it directly learns unbiased ranking models from biased click data without any preprocessing. It can adapt to the change of bias distributions and is applicable to online learning. Our empirical experiments with synthetic and real-world data show that the models trained with DLA significantly outperformed the unbiased learning-to-rank algorithms based on result randomization and the models trained with relevance signals extracted by click models

Domino-type progressive collapse analysis of a multi-span simply-supported bridge: A case study

Hongqi Viaduct, a multi-span simply-supported bridge in Zhuzhou city, Hunan Province, China, collapsed progressively during the mechanical demolishing of the bridge on May 17, 2009. Totally nine spans collapsed in the accident and it is a typical domino-type progressive collapse. The accident resulted in the loss of 9 lives and 16 injuries. Investigations were conducted after the accident to determine the cause of the unexpected progressive collapse. This paper is aimed at presenting a summary of the bridge before and after the incident, the demolishing plans and field investigations after the accident. To better understand the cause and mechanism of the progressive collapse, a numerical simulation is carried out. A detail 3D finite element (FE) model is developed by using the explicit FE code LS-DYNA. The bridge components including the bridge slabs, wall-type piers, longitudinal and transverse reinforcement bars are included in the model. The non-linear material behaviour including the strain rate effects of the concrete and steel rebar are considered. The model is used to simulate the bridge collapse induced by demolishing, and the domino-type progressive collapse of the bridge is clearly captured. Based on the numerical results, the reason for the failure is discussed and better understood. Finally, the possible mitigation methods of such progressive collapses of multi-span viaducts are suggested

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

Carbon Nanotubes by a CVD Method. Part II: Formation of Nanotubes from (Mg, Fe)O Catalysts

The aim of this paper is to study the formation of carbon nanotubes (CNTs) from different Fe/MgO oxide powders that were prepared by combustion synthesis and characterized in detail in a companion paper. Depending on the synthesis conditions, several iron species are present in the starting oxides including Fe2+ ions, octahedral Fe3+ ions, Fe3+ clusters, and MgFe2O4-like nanoparticles. Upon reduction during heating at 5 °C/min up to 1000 °C in H2/CH4 of the oxide powders, the octahedral Fe3+ ions tend to form Fe2+ ions, which are not likely to be reduced to metallic iron whereas the MgFe2O4-like particles are directly reduced to metallic iron. The reduced phases are R-Fe, Fe3C, and ç-Fe-C. Fe3C appears as the postreaction phase involved in the formation of carbon filaments (CNTs and thick carbon nanofibers). Thick carbon nanofibers are formed from catalyst particles originating from poorly dispersed species (Fe3+ clusters and MgFe2O4-like particles). The nanofiber outer diameter is determined by the particle size. The reduction of the iron ions and clusters that are well dispersed in the MgO lattice leads to small catalytic particles (<5 nm), which tend to form SWNTS and DWNTs with an inner diameter close to 2 nm. Well-dispersed MgFe2O4-like particles can also be reduced to small metal particles with a narrow size distribution, producing SWNTs and DWNTs. The present results will help in tailoring oxide precursors for the controlled formation of CNTs

The See-Saw Mechanism, Neutrino Yukawa Couplings, LFV Decays l_i to l_j + gamma and Leptogenesis

The LFV charged lepton decays mu to e + gamma, tau to e + gamma and tau to mu + gamma and thermal leptogenesis are analysed in the MSSM with see-saw mechanism of neutrino mass generation and soft SUSY breaking with universal boundary conditions. The case of hierarchical heavy Majorana neutrino mass spectrum, M_1 10^9 GeV. Considering the natural range of values of the heaviest right-handed Majorana neutrino mass, M_3 > 5*10^{13} GeV, and assuming that the soft SUSY breaking universal gaugino and/or scalar masses have values in the range of few 100 GeV, we derive the combined constraints, which the existing stringent upper limit on the mu to e + gamma decay rate and the requirement of successful thermal leptogenesis impose on the neutrino Yukawa couplings, heavy Majorana neutrino masses and SUSY parameters. Results for the three possible types of light neutrino mass spectrum -- normal and inverted hierarchical and quasi-degenerate -- are obtained.Comment: 25 pages, 9 figures; typos corrected, few clarifying comments and one figure added; version submitted for publicatio

Interplay of LFV and slepton mass splittings at the LHC as a probe of the SUSY seesaw

We study the impact of a type-I SUSY seesaw concerning lepton flavour violation (LFV) both at low-energies and at the LHC. The study of the di-lepton invariant mass distribution at the LHC allows to reconstruct some of the masses of the different sparticles involved in a decay chain. In particular, the combination with other observables renders feasible the reconstruction of the masses of the intermediate sleptons involved in $\chi_2^0\to \tilde \ell \,\ell \to \ell \,\ell\,\chi_1^0$ decays. Slepton mass splittings can be either interpreted as a signal of non-universality in the SUSY soft breaking-terms (signalling a deviation from constrained scenarios as the cMSSM) or as being due to the violation of lepton flavour. In the latter case, in addition to these high-energy processes, one expects further low-energy manifestations of LFV such as radiative and three-body lepton decays. Under the assumption of a type-I seesaw as the source of neutrino masses and mixings, all these LFV observables are related. Working in the framework of the cMSSM extended by three right-handed neutrino superfields, we conduct a systematic analysis addressing the simultaneous implications of the SUSY seesaw for both high- and low-energy lepton flavour violation. We discuss how the confrontation of slepton mass splittings as observed at the LHC and low-energy LFV observables may provide important information about the underlying mechanism of LFV.Comment: 50 pages, 42 eps Figures, typos correcte

Autoimmunity conferred by chs3-2D relies on CSA1, its adjacent TIR-NB-LRR encoding neighbour

Plant innate immunity depends on the function of a large number of intracellular immune receptor proteins, the majority of which are structurally similar to mammalian nucleotidebinding oligomerization domain (NOD)-like receptor (NLR) proteins. CHILLING SENSITIVE 3 (CHS3) encodes an atypical Toll/Interleukin 1 Receptor (TIR)-type NLR protein with an additional Lin-11, Isl-1 and Mec-3 (LIM) domain at its C-terminus. The gain-of-function mutant allele chs3-2D exhibits severe dwarfism and constitutively activated defense responses, including enhanced resistance to virulent pathogens, high defence marker gene expression, and salicylic acid accumulation. To search for novel regulators involved in CHS3-mediated immune signaling, we conducted suppressor screens in the chs3-2D and chs3-2D pad4-1 genetic backgrounds. Alleles of sag101 and eds1-90 were isolated as complete suppressors of chs3-2D, and alleles of sgt1b were isolated as partial suppressors of chs3-2D pad4-1. These mutants suggest that SAG101, EDS1-90, and SGT1b are all positive regulators of CHS3-mediated defense signaling. Additionally, the TIR-type NLR-encoding CSA1 locus located genomically adjacent to CHS3 was found to be fully required for chs3-2D-mediated autoimmunity. CSA1 is located 3.9kb upstream of CHS3 and is transcribed in the opposite direction. Altogether, these data illustrate the distinct genetic requirements for CHS3-mediated defense signaling

Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017

Based on published literature and typical profiles from the Nankai University source library, a total of 3326 chemical profiles of the main primary sources of ambient particulate matter (PM) across China from 1987 to 2017 are investigated and reviewed to trace the evolution of their main components and identify the main influencing factors concerning their evolution. In general, the source chemical profiles are varied with respect to their sources and are influenced by different sampling methods. The most complicated profiles are likely attributed to coal combustion (CC) and industrial emissions (IE). The profiles of vehicle emissions (VE) are dominated by organic carbon (OC) and elemental carbon (EC), and vary due to the changing standards of sulfur and additives in gasoline and diesel as well as the sampling methods used. In addition to the sampling methods used, the profiles of biomass burning (BB) and cooking emissions (CE) are also impacted by the different biofuel categories and cooking types, respectively. The variations of the chemical profiles of different sources, and the homogeneity of the subtype source profiles within the same source category are examined using uncertainty analysis and cluster analysis. As a result, a relatively large variation is found in the source profiles of CC, VE, IE, and BB, indicating that these sources urgently require the establishment of local profiles due to their high uncertainties. The results presented highlight the need for further investigation of more specific markers (e.g., isotopes, organic compounds, and gaseous precursors), in addition to routinely measured components, in order to properly discriminate sources. Although the chemical profiles of the main sources have been previously reported in the literature, it should be noted that some of these chemical profiles are currently out of date and need to be updated immediately. Additionally, in the future, specific focus should be placed on the source profile subtypes, especially with respect to local IE in China.</p