DeepWalk: Online Learning of Social Representations

We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide $F_1$ scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.Comment: 10 pages, 5 figures, 4 table

Temperature-dependent High-Frequency Performance of Deep Submicron Ion-Implanted AlGaN/GaN HEMTs

A study of the low temperature DC and RF performance of deep submicron AlGaN/GaN high electron mobility transistors (HEMTs) is reported. From 300 K to 100 K both extrinsic transconductance and drain current increase by 30%, mainly due to the lowering of the optical phonon scattering that allows higher electron mobility. Source and drain resistances improve too, which contributes to the 15-20% increase of ft and fmax. The low temperature small signal model has also been extracted accurately at every 50 K. Inductances and capacitances remain constant in the range of temperatures measured. The intrinsic transconductance can be also considered temperature independent, but the output conductance decreases from 300 K to 100 K indicating a better confinement of the 2DEG. The HEMT performance obtained at 100 K can be reached at room temperature by reducing the parasitic resistances and improving the GaN buffer isolation

BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees

The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts use a small sample of their entire data during their initial stage of analysis to make quick decisions (e.g., what features or hyperparameters to use) and use the entire dataset only in later stages (i.e., when they have converged to a specific model). This sampling, however, is performed in an ad-hoc fashion. Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase. Moreover, without systematic support for sampling operators, many optimizations and reuse opportunities are lost. In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML training. BlinkML allows users to make error-computation tradeoffs: instead of training a model on their full data (i.e., full model), BlinkML can quickly train an approximate model with quality guarantees using a sample. The quality guarantees ensure that, with high probability, the approximate model makes the same predictions as the full model. BlinkML currently supports any ML model that relies on maximum likelihood estimation (MLE), which includes Generalized Linear Models (e.g., linear regression, logistic regression, max entropy classifier, Poisson regression) as well as PPCA (Probabilistic Principal Component Analysis). Our experiments show that BlinkML can speed up the training of large-scale ML tasks by 6.26x-629x while guaranteeing the same predictions, with 95% probability, as the full model.Comment: 22 pages, SIGMOD 201

Estimation in high dimensions: a geometric perspective

This tutorial provides an exposition of a flexible geometric framework for high dimensional estimation problems with constraints. The tutorial develops geometric intuition about high dimensional sets, justifies it with some results of asymptotic convex geometry, and demonstrates connections between geometric results and estimation problems. The theory is illustrated with applications to sparse recovery, matrix completion, quantization, linear and logistic regression and generalized linear models.Comment: 56 pages, 9 figures. Multiple minor change

Alternative transmission routes in the malaria elimination era: an overview of transfusion-transmitted malaria in the Americas

Submitted by Janaína Nascimento ([email protected]) on 2019-02-21T11:59:00Z No. of bitstreams: 1 ve_Alho_Regina_etal_INI_2017.pdf: 1190158 bytes, checksum: 8937322faefa31c89eb1bbd2f7d134a3 (MD5)Approved for entry into archive by Janaína Nascimento ([email protected]) on 2019-02-25T11:34:33Z (GMT) No. of bitstreams: 1 ve_Alho_Regina_etal_INI_2017.pdf: 1190158 bytes, checksum: 8937322faefa31c89eb1bbd2f7d134a3 (MD5)Made available in DSpace on 2019-02-25T11:34:33Z (GMT). No. of bitstreams: 1 ve_Alho_Regina_etal_INI_2017.pdf: 1190158 bytes, checksum: 8937322faefa31c89eb1bbd2f7d134a3 (MD5) Previous issue date: 2017Universidade do Estado do Amazonas. Manaus, AM, Brasil / Fundação de Hematologia e Hemoterapia do Amazonas. Manaus, AM, Brasil.Fundação de Medicina Tropical Dr. Heitor Vieira Dourado. Manaus, AM, Brasil.Universidade do Estado do Amazonas. Manaus, AM, Brasil / Fundação de Medicina Tropical Dr. Heitor Vieira Dourado. Manaus, AM, Brasil.Fundação de Hematologia e Hemoterapia do Amazonas. Manaus, AM, Brasil.Universidade do Estado do Amazonas. Manaus, AM, Brasil.Universidade do Estado do Amazonas. Manaus, AM, Brasil / Fundação de Medicina Tropical Dr. Heitor Vieira Dourado. Manaus, AM, Brasil.Sem afiliação.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto de Pesquisas Leônidas e Maria Deane. Manaus, AM, Brasil.Universidade do Estado do Amazonas. Manaus, AM, Brasil / Fundação de Medicina Tropical Dr. Heitor Vieira Dourado. Manaus, AM, Brasil.Universidade do Estado do Amazonas. Manaus, AM, Brasil / Fundação de Medicina Tropical Dr. Heitor Vieira Dourado. Manaus, AM, Brasil / Fundação Oswaldo Cruz. Instituto de Pesquisas Leônidas e Maria Deane. Manaus, AM, Brasil.Background: Transfusion-transmitted (TT) malaria is an alternative infection route that has gained little attention from authorities, despite representing a life-threatening condition. There has been no systematic review of this health problem in American countries. The aim of this study was to describe the clinical and epidemiological characteristics of TT malaria in the Americas and identify factors associated with lethality based on the studies published in the literature. Methods: Potentially relevant papers in all languages were retrieved from MEDLINE and LILACS. Additional articles were obtained from reviews and original papers. Publications on screening of candidate blood donors and on surveillance of TT malaria cases were included. Odds ratios with respective 95% confidence intervals (95% CI) were calculated. Epidemiological characteristics of blood donors of TT malaria cases, including a pooled positivity of different tests for malaria diagnosis, were retrieved. Results: A total of 63 publications regarding TT malaria from seven countries were included, from 1971 to 2016. A total of 422 cases of TT malaria were recorded. Most TT malaria cases were in females (62.0%) and 39.5% were in the ≥61 years-old age group. About half of all cases were from Mexico (50.7%), 40.3% from the United States of America (USA) and 6.6% from Brazil. Gyneco-obstetrical conditions (67.3%), surgical procedures (20.6%) and complications from neoplasias (6.1%) were the most common indications of transfusion. Packed red blood cells (RBCs) (50.7%) and whole blood (43.3%) were the blood products mostly associated with TT malaria. Cases were mostly caused by Plasmodium malariae (58.4%), followed by Plasmodium vivax (20.7%) and Plasmodium falciparum (17.9%). A total of 66.6% of cases were diagnosed by microscopy. Incubation period of 2–3 weeks was the most commonly observed (28.6%). Lethality was seen in 5.3% of cases and was associated with living in non-endemic countries, P. falciparum infection and concomitant neoplastic diseases. Conclusion: There is an important research and knowledge gap regarding the TT malaria burden in Latin American countries where malaria remains endemic. No screening method that is practical, affordable and suitably sensitive is available at blood banks in Latin American countries, where infections with low parasitaemia contribute greatly to transmission. Lethality from TT malaria was not negligible. TT malaria needs to be acknowledged and addressed in areas moving toward elimination

Optimal sampling of MRI slices for the assessment of knee cartilage volume for cross-sectional and longitudinal studies

BACKGROUND: MRI slices of 1.5 mm thickness have been used in both cross sectional and longitudinal studies of osteoarthritis, but is difficult to apply to large studies as most techniques used in measuring knee cartilage volumes require substantial post-image processing. The aim of this study was to determine the optimal sampling of 1.5 mm thick slices of MRI scans to estimate knee cartilage volume in males and females for cross-sectional and longitudinal studies. METHODS: A total of 150 subjects had a sagittal T1-weighted fat-suppressed MRI scan of the right knee at a partition thickness of 1.5 mm to determine their cartilage volume. Fifty subjects had both baseline and 2-year follow up MRI scans. Lateral, medial tibial and patellar cartilage volumes were calculated with different samples from 1.5 mm thick slices by extracting one in two, one in three, and one in four to compare to cartilage volume and its rate of change. Agreement was assessed by means of intraclass correlation coefficient (ICC) and Bland & Altman plots. RESULTS: Compared to the whole sample of 1.5 mm thick slices, measuring every second to fourth slice led to very little under or over estimation in cartilage volume and its annual change. At all sites and subgroups, measuring every second slice had less than 1% mean difference in cartilage volume and its annual rate of change with all ICCs ≥ 0.98. CONCLUSION: Sampling alternate 1.5 mm thick MRI slices is sufficient for knee cartilage volume measurement in cross-sectional and longitudinal epidemiological studies with little increase in measurement error. This approach will lead to a substantial decrease in post-scan processing time