RankMerging: A supervised learning-to-rank framework to predict links in large social network

Uncovering unknown or missing links in social networks is a difficult task because of their sparsity and because links may represent different types of relationships, characterized by different structural patterns. In this paper, we define a simple yet efficient supervised learning-to-rank framework, called RankMerging, which aims at combining information provided by various unsupervised rankings. We illustrate our method on three different kinds of social networks and show that it substantially improves the performances of unsupervised metrics of ranking. We also compare it to other combination strategies based on standard methods. Finally, we explore various aspects of RankMerging, such as feature selection and parameter estimation and discuss its area of relevance: the prediction of an adjustable number of links on large networks.Comment: 43 pages, published in Machine Learning Journa

Spin Hall effect due to intersubband-induced spin-orbit interaction in symmetric quantum wells

We investigate the intrinsic spin Hall effect in two-dimensional electron gases in quantum wells with two subbands, where a new intersubband-induced spin-orbit coupling is operative. The bulk spin Hall conductivity $\sigma^z_{xy}$ is calculated in the ballistic limit within the standard Kubo formalism in the presence of a magnetic field $B$ and is found to remain finite in the B=0 limit, as long as only the lowest subband is occupied. Our calculated $\sigma^z_{xy}$ exhibits a nonmonotonic behavior and can change its sign as the Fermi energy (the carrier areal density $n_{2D}$) is varied between the subband edges. We determine the magnitude of $\sigma^z_{xy}$ for realistic InSb quantum wells by performing a self-consistent calculation of the intersubband-induced spin-orbit coupling.Comment: 7 pages, 3 figure

Spin-orbit interaction in symmetric wells with two subbands

We investigate the spin-orbit (s-o) interaction in two-dimensional electron gases (2DEGs) in quantum wells with two subbands. From the $8\times 8$ Kane model, we derive a new inter-subband-induced s-o term which resembles the functional form of the Rashba s-o -- but is non-zero even in \emph{symmetric} structures. This follows from the distinct parity of the confined states (even/odd) which obliterates the need for asymmetric potentials. We self-consistently calculate the new s-o coupling strength for realistic wells and find it comparable to the usual Rashba constant. Our new s-o term gives rise to a non-zero ballistic spin-Hall conductivity, which changes sign as a function of the Fermi energy ($\epsilon_F$), and can induce an unusual \emph{zitterbewegung} with cycloidal trajectories \textit{without} magnetic fields.Comment: v2: 4 two-column pages, 3 figures (added spin Hall conductivity and self-consistent calculation

RankMerging:a supervised learning-to-rank framework to predict links in large social networks

Uncovering unknown or missing links in social networks is a difficult task because of their sparsity and because links may represent different types of relationships, characterized by different structural patterns. In this paper, we define a simple yet efficient supervised learning-to-rank framework, called RankMerging, which aims at combining information provided by various unsupervised rankings. We illustrate our method on three different kinds of social networks and show that it substantially improves the performances of unsupervised methods of ranking as well as standard supervised combination strategies. We also describe various properties of RankMerging, such as its computational complexity, its robustness to feature selection and parameter estimation and discuss its area of relevance: the prediction of an adjustable number of links on large networks

Adaptive Optimization of Chemical Reactions with Minimal Experimental Information

Optimizing reaction conditions depends on expert chemistry knowledge and laborious exploration of reaction parameters. To automate this task and augment chemical intuition, we here report a computational tool to navigate search spaces. Our approach (LabMate.ML) integrates random sampling of 0.03%–0.04% of all search space as input data with an interpretable, adaptive machine-learning algorithm. LabMate.ML can optimize many real-valued and categorical reaction parameters simultaneously, with minimal computational resources and time. In nine prospective proof-of-concept studies pursuing distinctive objectives, we demonstrate how LabMate.ML can identify optimal goal-oriented conditions for several different chemistries and substrates. Double-blind competitions and the conducted expert surveys reveal that its performance is competitive with that of human experts. LabMate.ML does not require specialized hardware, affords quantitative and interpretable reactivity insights, and autonomously formalizes chemical intuition, thereby providing an innovative framework for informed, automated experiment selection toward the democratization of synthetic chemistry.D.R. is a Swiss National Science Foundation Fellow (grant nos. P2EZP3_168827 and P300P2_177833). E.A.H. is supported by the Herchel Smith Fellowship awarded by Williams College. G.J.L.B. is a Royal Society URF (URF\R\180019). T.R. is an Investigador Auxiliar supported by FCT Portugal (CEECIND/00887/2017). T.R. acknowledges the H2020 (TWINN-2017 ACORN, grant no. 807281), FCT/FEDER (02/SAICT/2017, grant no. 28333). D.R. acknowledges the MIT-IBM Watson AI Lab and the MIT SenseTime coalition for funding. The authors are extremely grateful to several colleagues for suggesting Ugi reaction conditions, and to Prof. R. Langer and Prof. G. Traverso, who provided invaluable comments on the research and manuscript. The authors are indebted to Prof. R. Moreira for access to the CEM microwave reactor; Dr. F. Corzana for technical assistance with HRMS; and the 13 graduate students, 17 postdoctoral researchers, and eight principal investigators across Austria, Denmark, Portugal, Spain, the United Kingdom, and the United States who took part in the survey. We thank R. Rodrigues for help in producing Figure 1. The survey was approved by the iMM and MIT (COUHES protocol 1809514426). The authors also thank the four anonymous reviewers for their most insightful comments.info:eu-repo/semantics/publishedVersio

Regime de tributação do alojamento local

O alojamento local, apesar de não ser novidade, tem sido um fenómeno do alojamento turístico nos últimos anos como alternativa à indústria hoteleira tradicional, sendo um fator explicativo do crescimento do turismo em Portugal, nomeadamente nos grandes centros urbanos (Porto e Lisboa). O rápido crescimento da atividade de alojamento local levou a que houvesse a necessidade de criar um diploma próprio. A divulgação do alojamento local tem evoluído através da utilização de plataformas online e a promoção de experiências com a vida quotidiana dos residentes também proporcionou a uma maior adesão ao alojamento local. O alojamento local pode ser tributado em sede de IRS ou em sede de IRC. Neste âmbito, devido à atualidade do tema e aos diferentes regimes de tributação, há interesse em elaborar um trabalho que analise os seus regimes e verifique qual o mais favorável. A análise vai incidir num estudo de casos múltiplos, onde os rendimentos de pessoas coletivas, relativos a alojamento local, vão ser calculados em sede de IRS e posteriormente comparado o imposto pago em sede de IRS e IRC. Depois de concluído o enquadramento da atividade de alojamento local em sede de IRS, é possivel afirmar que dependendo do caso em que está inserido, o regime de tributação mais vantajoso pode variar.Local accommodation, although not new, has been a phenomenon of tourist accommodation in recent years, as an alternative to the traditional hotel industry, being an explanatory factor of the growth of tourism in Portugal, particularly in large urban centers (Porto and Lisbon). The rapid growth of local accommodation activity led to the need to create a diploma of its own. The dissemination of local accommodation has evolved through the use of online platforms and the promotion of experiences with the daily lives of residents has also provided greater support to local accommodation. Local accommodation may be taxed at IRS or IRC. In this context, due to the timeliness of the theme and the different taxation regimes, there is an interest in preparing a paper that analyzes their procedures and verifies which one is most favourable. The analysis will focus on a multiple case study, where the income of legal persons, related to local accommodation, will be calculated in IRS and then compared the tax paid in IRS and IRC. Once the local accommodation activity is being framed in the IRS, it is possible to state that depending on the case in which it is inserted, the most advantageous taxation regime may vary

Computational advances in combating colloidal aggregation in drug discovery.

Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.D.R. is a Swiss National Science Foundation Fellow (Grants P2EZP3_168827 and P300P2_177833). G.J.L.B. is a Royal Society URF (UF110046 and URF/R/180019), an iFCT Investigator (IF/00624/2015), and the recipient of an ERC StG (TagIt, Grant Agreement 676832). T.R. and G.J.L.B. acknowledge Marie Sklodowska-Curie ITN Protein Conjugates (Grant Agreement 675007) for funding. T.R. is a Marie Curie Fellow (Grant Agreement 743640). T.R. acknowledges the H2020 (TWINN-2017 ACORN, Grant Agreement 807281) and POR Lisboa 2020/FEDER (02/SAICT/2017, Grant Agreement Lisboa-01-0145-FEDER-028333) for funding. D.R. acknowledges the MIT-IBM Watson AI Lab and the MIT SenseTime coalition for funding