CSD: a multi-user similarity metric for community recommendation in online social networks

Communities are basic components in networks. As a promising social application, community recommendation selects a few items (e.g., movies and books) to recommend to a group of users. It usually achieves higher recommendation precision if the users share more interests; whereas, in plenty of communities (e.g., families, work groups), the users often share few. With billions of communities in online social networks, quickly selecting the communities where the members are similar in interests is a prerequisite for community recommendation. To this end, we propose an easy-to-compute metric, Community Similarity Degree (CSD), to estimate the degree of interest similarity among multiple users in a community. Based on 3460 emulated Facebook communities, we conduct extensive empirical studies to reveal the characteristics of CSD and validate the effectiveness of CSD. In particular, we demonstrate that selecting communities with larger CSD can achieve higher recommendation precision. In addition, we verify the computation efficiency of CSD: it costs less than 1 hour to calculate CSD for over 1 million of communities. Finally, we draw insights about feasible extensions to the definition of CSD, and point out the practical uses of CSD in a variety of applications other than community recommendation.This work has been funded by China Scholarship Council. It has also been partially funded by the Ministerio de Economia y Competitividad of SPAIN through the project BigDatAAM (FIS2013-47532-C3-3-P), H2020-DS-2014-1 through the TYPES Project under Grant Agreement number 653449, State Key Laboratory of Geo-Information Engineering (No. SKLGIE2014-M-2-2). and the Program of National Natural Science Foundation of China (No. 41404025)

Domain Adaptation for Time Series Under Feature and Label Shifts

Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents challenges due to the dynamic temporal structure variations across domains. This leads to feature shifts in the time and frequency representations. Additionally, the label distributions of tasks in the source and target domains can differ significantly, posing difficulties in addressing label shifts and recognizing labels unique to the target domain. Effectively transferring complex time series models remains a formidable problem. We present Raincoat, the first model for both closed-set and universal domain adaptation on complex time series. Raincoat addresses feature and label shifts by considering both temporal and frequency features, aligning them across domains, and correcting for misalignments to facilitate the detection of private labels. Additionally, Raincoat improves transferability by identifying label shifts in target domains. Our experiments with 5 datasets and 13 state-of-the-art UDA methods demonstrate that Raincoat can improve transfer learning performance by up to 16.33% and can handle both closed-set and universal domain adaptation.Comment: Accepted by ICML 2023; 29 pages (14 pages main paper + 15 pages supplementary materials). Code: see https://github.com/mims-harvard/Raincoa

Role of Iodine Recycling on Sea-Salt Aerosols in the Global Marine Boundary Layer

Heterogeneous uptake of hypoiodous acid (HOI), the dominant inorganic iodine species in the marine boundary layer (MBL), on sea-salt aerosol (SSA) to form iodine monobromide and iodine monochloride has been adopted in models with assumed efficiency. Recently, field measurements have reported a much faster rate of this recycling process than previously assumed in models. Here, we conduct global model simulations to quantify the range of effects of iodine recycling within the MBL, using Conventional, Updated, and Upper-limit coefficients. When considering the Updated coefficient, iodine recycling significantly enhances gaseous inorganic iodine abundance (similar to 40%), increases halogen atom production rates (similar to 40% in I, >100% in Br, and similar to 60% in Cl), and reduces oxidant levels (-7% in O-3, -2% in OH, and -4% in HO2) compared to the simulation without the process. We appeal for further direct measurements of iodine species, laboratory experiments on the controlling factors, and multiscale simulations of iodine heterogeneous recycling.Peer reviewe

Automatic Detection of Malignant Masses in Digital Mammograms Based on a MCET-HHO Approach

Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy.Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy

Multiple functional regression with both discrete and continuous covariates

International audienceIn this paper we present a nonparametric method for extending functional regression methodology to the situation where more than one functional covariate is used to predict a functional response. Borrowing the idea from Kadri et al. (2010a), the method, which support mixed discrete and continuous explanatory variables, is based on estimating a function-valued function in reproducing kernel Hilbert spaces by virtue of positive operator-valued kernels

CLT in Functional Linear Regression Models

International audienceWe propose in this work to derive a CLT in the functional linear regression model to get confidence sets for prediction based on functional linear regression. The main difficulty is due to the fact that estimation of the functional parameter leads to a kind of ill-posed inverse problem. We consider estimators that belong to a large class of regularizing methods and we first show that, contrary to the multivariate case, it is not possible to state a CLT in the topology of the considered functional space. However, we show that we can get a CLT for the weak topology under mild hypotheses and in particular without assuming any strong assumptions on the decay of the eigenvalues of the covariance operator. Rates of convergence depend on the smoothness of the functional coefficient and on the point in which the prediction is made

Syntenic relationships between cucumber (Cucumis sativus L.) and melon (C. melo L.) chromosomes as revealed by comparative genetic mapping

<p>Abstract</p> <p>Background</p> <p>Cucumber, <it>Cucumis sativus </it>L. (2n = 2 × = 14) and melon, <it>C. melo </it>L. (2n = 2 × = 24) are two important vegetable species in the genus <it>Cucumis </it>(family Cucurbitaceae). Both species have an Asian origin that diverged approximately nine million years ago. Cucumber is believed to have evolved from melon through chromosome fusion, but the details of this process are largely unknown. In this study, comparative genetic mapping between cucumber and melon was conducted to examine syntenic relationships of their chromosomes.</p> <p>Results</p> <p>Using two melon mapping populations, 154 and 127 cucumber SSR markers were added onto previously reported F₂- and RIL-based genetic maps, respectively. A consensus melon linkage map was developed through map integration, which contained 401 co-dominant markers in 12 linkage groups including 199 markers derived from the cucumber genome. Syntenic relationships between melon and cucumber chromosomes were inferred based on associations between markers on the consensus melon map and cucumber draft genome scaffolds. It was determined that cucumber Chromosome 7 was syntenic to melon Chromosome I. Cucumber Chromosomes 2 and 6 each contained genomic regions that were syntenic with melon chromosomes III+V+XI and III+VIII+XI, respectively. Likewise, cucumber Chromosomes 1, 3, 4, and 5 each was syntenic with genomic regions of two melon chromosomes previously designated as II+XII, IV+VI, VII+VIII, and IX+X, respectively. However, the marker orders in several syntenic blocks on these consensus linkage maps were not co-linear suggesting that more complicated structural changes beyond simple chromosome fusion events have occurred during the evolution of cucumber.</p> <p>Conclusions</p> <p>Comparative mapping conducted herein supported the hypothesis that cucumber chromosomes may be the result of chromosome fusion from a 24-chromosome progenitor species. Except for a possible inversion, cucumber Chromosome 7 has largely remained intact in the past nine million years since its divergence from melon. Meanwhile, many structural changes may have occurred during the evolution of the remaining six cucumber chromosomes. Further characterization of the genomic nature of <it>Cucumis </it>species closely related to cucumber and melon might provide a better understanding of the evolutionary history leading to modern cucumber.</p

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

SummaryBackground The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation

Evidence for a hopping mechanism in metal|single molecule|metal junctions involving conjugated metal–terpyridyl complexes; potential-dependent conductances of complexes [M(pyterpy)₂] ²⁺ (M = Co and Fe; pyterpy = 4′-(pyridin-4-yl)-2,2′:6′,2′′-terpyridine) in ionic liquid

Extensive studies of various families of conjugated molecules in metal|molecule|metal junctions suggest that the mechanism of conductance is usually tunnelling for molecular lengths < ca. 4 nm, and that for longer molecules, coherence is lost as a hopping element becomes more significant. In this work we present evidence that, for a family of conjugated, redox-active metal complexes, hopping may be a significant factor for even the shortest molecule studied (ca. 1 nm between contact atoms). The length dependence of conductance for two series of such complexes which differ essentially in the number of conjugated 1,4-C₆H₄- rings in the structures has been studied, and it is found that the junction conductances vary linearly with molecular length, consistent with a hopping mechanism, whereas there is significant deviation from linearity in plots of log(conductance) vs. length that would be characteristic of tunnelling, and the slopes of the log(conductance)–length plots are much smaller than expected for an oligophenyl system. Moreover, the conductances of molecular junctions involving the redox–active molecules, [M(pyterpy)₂] ²⁺/³⁺ (M = Co, Fe) have been studied as a function of electrochemical potential in ionic liquid electrolyte, and the conductance–overpotential relationship is found to fit well with the Kuznetsov–Ulstrup relationship, which is essentially a hopping description