Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems

Online personalized recommendation services are generally hosted in the cloud where users query the cloud-based model to receive recommended input such as merchandise of interest or news feed. State-of-the-art recommendation models rely on sparse and dense features to represent users' profile information and the items they interact with. Although sparse features account for 99% of the total model size, there was not enough attention paid to the potential information leakage through sparse features. These sparse features are employed to track users' behavior, e.g., their click history, object interactions, etc., potentially carrying each user's private information. Sparse features are represented as learned embedding vectors that are stored in large tables, and personalized recommendation is performed by using a specific user's sparse feature to index through the tables. Even with recently-proposed methods that hides the computation happening in the cloud, an attacker in the cloud may be able to still track the access patterns to the embedding tables. This paper explores the private information that may be learned by tracking a recommendation model's sparse feature access patterns. We first characterize the types of attacks that can be carried out on sparse features in recommendation models in an untrusted cloud, followed by a demonstration of how each of these attacks leads to extracting users' private information or tracking users by their behavior over time

Protocol for an economic evaluation alongside a cluster randomised controlled trial: cost-effectiveness of Learning Clubs, a multicomponent intervention to improve women’s health and infant’s health and development in Vietnam

Introduction: Economic evaluations of complex interventions in early child development are required to guide policy and programme development, but a few are yet available. Methods and analysis: Although significant gains have been made in maternal and child health in resource- constrained environments, this has mainly been concentrated on improving physical health. The Learning Clubs programme addresses both physical and mental child and maternal health. This study is an economic evaluation of a cluster randomised controlled trial of the impact of the Learning Clubs programme in Vietnam. It will be conducted from a societal perspective and aims to identify the cost-effectiveness and the economic and social returns of the intervention. A total of 1008 pregnant women recruited from 84 communes in a rural province in Vietnam will be included in the evaluation. Health and cost data will be gathered at three stages of the trial and used to calculate incremental cost-effectiveness ratios per percentage point improvement of infant’s development, infant’s health and maternal common mental disorders expressed in quality-adjusted life years gained. The return on investment will be calculated based on improvements in productivity, the results being expressed as benefit–cost ratios. Ethics and dissemination: The trial was approved by Monash University Human Research Ethics Committee (Certificate Number 2016–0683), Australia, and approval was extended to include the economic evaluation (Amendment Review Number 2018-0683-23806); and the Institutional Review Board of the Hanoi School of Public Health (Certificate Number 017-377IDD- YTCC), Vietnam. Results will be disseminated through academic journals and conference presentations

Current trends in ATRA delivery for cancer therapy

All-Trans Retinoic Acid (ATRA) is the most active metabolite of vitamin A. It is critically involved in the regulation of multiple processes, such as cell differentiation and apoptosis, by activating specific genomic pathways or by influencing key signaling proteins. Furthermore, mounting evidence highlights the anti-tumor activity of this compound. Notably, oral administration of ATRA is the first choice treatment in Acute Promyelocytic Leukemia (APL) in adults and NeuroBlastoma (NB) in children. Regrettably, the promising results obtained for these diseases have not been translated yet into the clinics for solid tumors. This is mainly due to ATRA-resistance developed by cancer cells and to ineffective delivery and targeting. This up-to-date review deals with recent studies on different ATRA-loaded Drug Delivery Systems (DDSs) development and application on several tumor models. Moreover, patents, pre-clinical, and clinical studies are also reviewed. To sum up, the main aim of this in-depth review is to provide a detailed overview of the several attempts which have been made in the recent years to ameliorate ATRA delivery and targeting in cancer

Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo

In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model

Detecting synchrony in EEG: A comparative study of functional connectivity measures

© 2018 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 12 month embargo from date of publication (December 2018) in accordance with the publisher’s archiving policyIn neuroscience, there is considerable current interest in investigating the connections between different parts of the brain. EEG is one modality for examining brain function, with advantages such as high temporal resolution and low cost. Many measures of connectivity have been proposed, but which is the best measure to use? In this paper, we address part of this question: which measure is best able to detect connections that do exist, in the challenging situation of non-stationary and noisy data from nonlinear systems, like EEG. This requires knowledge of the true relationship between signals, hence we compare 26 measures of functional connectivity on simulated data (unidirectionally coupled Hénon maps, and simulated EEG). To determine whether synchrony is detected, surrogate data were generated and analysed, and a threshold determined from the surrogate ensemble. No measure performed best in all tested situations. The correlation and coherence measures performed best on stationary data with many samples. S-estimator, correntropy, mean-phase coherence (Hilbert), mutual information (kernel), nonlinear interdependence (S) and nonlinear interdependence (N) performed most reliably on non-stationary data with small to medium window sizes. Of these, correlation and S-estimator have execution times that scale slower with the number of channels and the number of samples

Corrigendum: Chronic Exposure to Malaria Is Associated with Inhibitory and Activation Markers on Atypical Memory B Cells and Marginal Zone-Like B Cells

[This corrects the article DOI: 10.3389/fimmu.2017.00966.]

Using genetics to understand the causal influence of higher BMI on depression

Background: Depression is more common in obese than non-obese individuals, especially in women, but the causal relationship between obesity and depression is complex and uncertain. Previous studies have used genetic variants associated with BMI to provide evidence that higher body mass index (BMI) causes depression, but have not tested whether this relationship is driven by the metabolic consequences of BMI nor for differences between men and women. Methods: We performed a Mendelian randomization study using 48 791 individuals with depression and 291 995 controls in the UK Biobank, to test for causal effects of higher BMI on depression (defined using self-report and Hospital Episode data). We used two genetic instruments, both representing higher BMI, but one with and one without its adverse metabolic consequences, in an attempt to 'uncouple' the psychological component of obesity from the metabolic consequences. We further tested causal relationships in men and women separately, and using subsets of BMI variants from known physiological pathways. Results: Higher BMI was strongly associated with higher odds of depression, especially in women. Mendelian randomization provided evidence that higher BMI partly causes depression. Using a 73-variant BMI genetic risk score, a genetically determined one standard deviation (1 SD) higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals [odds ratio (OR): 1.18, 95% confidence interval (CI): 1.09, 1.28, P = 0.00007) and women only (OR: 1.24, 95% CI: 1.11, 1.39, P = 0.0001). Meta-analysis with 45 591 depression cases and 97 647 controls from the Psychiatric Genomics Consortium (PGC) strengthened the statistical confidence of the findings in all individuals. Similar effect size estimates were obtained using different Mendelian randomization methods, although not all reached P < 0.05. Using a metabolically favourable adiposity genetic risk score, and meta-analysing data from the UK biobank and PGC, a genetically determined 1 SD higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals (OR: 1.26, 95% CI: 1.06, 1.50], P = 0.010), but with weaker statistical confidence. Conclusions: Higher BMI, with and without its adverse metabolic consequences, is likely to have a causal role in determining the likelihood of an individual developing depression.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.DH_/Department of Health/United Kingdom MC_PC_17228/MRC_/Medical Research Council/United Kingdom MC_QA137853/MRC_/Medical Research Council/United Kingdom 104150/Z/14/Z/WT_/Wellcome Trust/United Kingdom MR/M005070/1/MRC_/Medical Research Council/United Kingdom WT097835MF/WT_/Wellcome Trust/United Kingdompublished version, accepted version (12 month embargo), submitted versio

Corrigendum to: “Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis” [J Hepatol (2020) 241-251]

It has come to our attention that there are errors in the Table 2 of the original manuscript “Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis”. The Variance Explained for SNP rs738409 has been incorrectly reported as 0.9. The correct value is 0.29. The amino acid changes for SNPs rs111723834, rs58542926 and rs738409 have been incorrectly reported as A561G, I148M and E167K, respectively. The correct amino acid changes are R561Q, E167K and I148M, respectively. SNP rs4820268 variant type (synonymous) and amino acid change (D521D) have also been corrected. Please see the corrected Table 2 below. These errors have occured during manual editing of the table and do not affect the results and conclusions of this article. The authors would like to apologise for any inconvenience caused

Antimicrobial essential oil formulation: chitosan coated nanoemulsions for nose to brain delivery

Brain infections as meningitis and encephalitis are attracting a great interest. Challenges in the treatment of these diseases are mainly represented by the blood brain barrier (BBB) that impairs the efficient delivery of even very potent drugs to reach the brain. The nose to the brain administration route, is a non-invasive alternative for a quick onset of action, and enables the transport of numerous medicinal agents straight to the brain thus workarounding the BBB through the highly vascularized olfactory region. In this report, Thymus vulgaris and Syzygium aromaticum essential oils (EOs) were selected to be included in chitosan coated nanoemulsions (NEs). The EOs were firstly analyzed to determine their chemical composition, then used to prepare NEs, that were deeply characterized in order to evaluate their use in intranasal administration. An in vitro evaluation against a collection of clinical isolated bacterial strains was carried out for both free and nanoemulsioned EOs. Chitosan coated NEs showed to be a potential and effective intranasal formulation against multi-drug resistant Gram-negative bacteria such as methicillin-susceptible Staphylococcus aureus and multi-drug resistant Gram-negative microorganisms including carbapenem-resistant Acinetobacter baumannii and Klebsiella pneumoniae