Adapting Decision DAGs for Multipartite Ranking

European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010Multipartite ranking is a special kind of ranking for problems in which classes exhibit an order. Many applications require its use, for instance, granting loans in a bank, reviewing papers in a conference or just grading exercises in an education environment. Several methods have been proposed for this purpose. The simplest ones resort to regression schemes with a pre- and post-process of the classes, what makes them barely useful. Other alternatives make use of class order information or they perform a pairwise classi cation together with an aggregation function. In this paper we present and discuss two methods based on building a Decision Directed Acyclic Graph (DDAG). Their performance is evaluated over a set of ordinal benchmark data sets according to the C-Index measure. Both yield competitive results with regard to stateof- the-art methods, specially the one based on a probabilistic approach, called PR-DDA

Opening a Pandora’s Flask on a Prototype Catalytic Direct Arylation Reaction of Pentafluorobenzene : The Ag2CO3/Pd(OAc)2/PPh3 System

Direct C-H functionalization reactions have opened new avenues in catalysis, removing the need for prefunctionalization of at least one of the substrates. Although C-H functionalization catalyzed by palladium complexes in the presence of a base is generally considered to proceed by the CMD/AMLA-6 mechanism, recent research has shown that silver(I) salts, frequently used as bases, can function as C-H bond activators instead of (or in addition to) palladium(II). In this study, we examine the coupling of pentafluorobenzene 1 to 4-iodotoluene 2a (and its analogues) to form 4-(pentafluorophenyl)toluene 3a catalyzed by palladium(II) acetate with the commonplace PPh3 ligand, silver carbonate as base, and DMF as solvent. By studying the reaction of 1 with Ag2CO3/PPh3 and with isolated silver (triphenylphosphine) carbonate complexes, we show the formation of C-H activation products containing the Ag(C6F5)(PPh3)n unit. However, analysis is complicated by the lability of the Ag-PPh3 bond and the presence of multiple species in the solution. The speciation of palladium(II) is investigated by high-resolution-MAS NMR (chosen for its suitability for suspensions) with a substoichiometric catalyst, demonstrating the formation of an equilibrium mixture of Pd(Ar)(κ1-OAc)(PPh3)2 and [Pd(Ar)(μ-OAc)(PPh3)]2 as resting states (Ar = Ph, 4-tolyl). These two complexes react stoichiometrically with 1 to form coupling products. The catalytic reaction kinetics is investigated by in situ IR spectroscopy revealing a two-term rate law and dependence on [Pdtot/nPPh3]0.5 consistent with the dissociation of an off-cycle palladium dimer. The first term is independent of [1], whereas the second term is first order in [1]. The observed rates are very similar with Pd(PPh3)4, Pd(Ph)(κ1-OAc)(PPh3)2, and [Pd(Ph)(μ-OAc)(PPh3)]2 catalysts. The kinetic isotope effect varied significantly according to conditions. The multiple speciation of both AgI and PdII acts as a warning against specifying the catalytic cycles in detail. Moreover, the rapid dynamic interconversion of AgI species creates a level of complexity that has not been appreciated previously

Geographic population structure analysis of worldwide human populations infers their biogeographical origins

The search for a method that utilizes biological information to predict humans’ place of origin has occupied scientists for millennia. Over the past four decades, scientists have employed genetic data in an effort to achieve this goal but with limited success. While biogeographical algorithms using next-generation sequencing data have achieved an accuracy of 700 km in Europe, they were inaccurate elsewhere. Here we describe the Geographic Population Structure (GPS) algorithm and demonstrate its accuracy with three data sets using 40,000–130,000 SNPs. GPS placed 83% of worldwide individuals in their country of origin. Applied to over 200 Sardinians villagers, GPS placed a quarter of them in their villages and most of the rest within 50 km of their villages. GPS’s accuracy and power to infer the biogeography of worldwide individuals down to their country or, in some cases, village, of origin, underscores the promise of admixture-based methods for biogeography and has ramifications for genetic ancestry testing

Transverse momentum spectra of charged particles in proton-proton collisions at s=900\sqrt{s} = 900 GeV with ALICE at the LHC

The inclusive charged particle transverse momentum distribution is measured in proton-proton collisions at $\sqrt{s} = 900$ GeV at the LHC using the ALICE detector. The measurement is performed in the central pseudorapidity region $(|\eta|<0.8)$ over the transverse momentum range $0.15<p_{\rm T}<10$ GeV/$c$. The correlation between transverse momentum and particle multiplicity is also studied. Results are presented for inelastic (INEL) and non-single-diffractive (NSD) events. The average transverse momentum for $|\eta|<0.8$ is $\left<p_{\rm T}\right>_{\rm INEL}=0.483\pm0.001$ (stat.) $\pm0.007$ (syst.) GeV/$c$ and \left_{\rm NSD}=0.489\pm0.001 (stat.) $\pm0.007$ (syst.) GeV/$c$, respectively. The data exhibit a slightly larger $\left<p_{\rm T}\right>$ than measurements in wider pseudorapidity intervals. The results are compared to simulations with the Monte Carlo event generators PYTHIA and PHOJET.Comment: 20 pages, 8 figures, 2 tables, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/390

A population-scale temporal case–control evaluation of COVID-19 disease phenotype and related outcome rates in patients with cancer in England (UKCCP)

Patients with cancer are at increased risk of hospitalisation and mortality following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, the SARS-CoV-2 phenotype evolution in patients with cancer since 2020 has not previously been described. We therefore evaluated SARS-CoV-2 on a UK populationscale from 01/11/2020-31/08/2022, assessing case-outcome rates of hospital assessment(s), intensive care admission and mortality. We observed that the SARS-CoV-2 disease phenotype has become less severe in patients with cancer and the non-cancer population. Case-hospitalisation rates for patients with cancer dropped from 30.58% in early 2021 to 7.45% in 2022 while case-mortality rates decreased from 20.53% to 3.25%. However, the risk of hospitalisation and mortality remains 2.10x and 2.54x higher in patients with cancer, respectively. Overall, the SARS-CoV-2 disease phenotype is less severe in 2022 compared to 2020 but patients with cancer remain at higher risk than the non-cancer population. Patients with cancer must therefore be empowered to live more normal lives, to see loved ones and families, while also being safeguarded with expanded measures to reduce the risk of transmission

Estimating the Support of a High-Dimensional Distribution

Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a “simple” subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data