Outward Influence and Cascade Size Estimation in Billion-scale Networks

Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed outward influence (OI), defined as the (expected) number of nodes that a subset of nodes $S$ will activate, excluding the nodes in S. Thus, OI equals, the de facto standard measure, influence spread of S minus |S|. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods. Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence at scale and with rigorous theoretical guarantees. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence, and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the state-of-the art for influence estimation, SIEA is $\Omega(\log^4 n)$ times faster in theory and up to several orders of magnitude faster in practice. For the first time, influence of nodes in the networks of billions of edges can be estimated with high accuracy within a few minutes. Our comprehensive experiments on real-world networks also give evidence against the popular practice of using a fixed number, e.g. 10K or 20K, of samples to compute the "ground truth" for influence spread.Comment: 16 pages, SIGMETRICS 201

Importance Sketching of Influence Dynamics in Billion-scale Networks

The blooming availability of traces for social, biological, and communication networks opens up unprecedented opportunities in analyzing diffusion processes in networks. However, the sheer sizes of the nowadays networks raise serious challenges in computational efficiency and scalability. In this paper, we propose a new hyper-graph sketching framework for inflence dynamics in networks. The central of our sketching framework, called SKIS, is an efficient importance sampling algorithm that returns only non-singular reverse cascades in the network. Comparing to previously developed sketches like RIS and SKIM, our sketch significantly enhances estimation quality while substantially reducing processing time and memory-footprint. Further, we present general strategies of using SKIS to enhance existing algorithms for influence estimation and influence maximization which are motivated by practical applications like viral marketing. Using SKIS, we design high-quality influence oracle for seed sets with average estimation error up to 10x times smaller than those using RIS and 6x times smaller than SKIM. In addition, our influence maximization using SKIS substantially improves the quality of solutions for greedy algorithms. It achieves up to 10x times speed-up and 4x memory reduction for the fastest RIS-based DSSA algorithm, while maintaining the same theoretical guarantees.Comment: 12 pages, to appear in ICDM 2017 as a regular pape

Effects of intravenous diclofenac on postoperative sore throat in patients undergoing laparoscopic surgery at Aga Khan University Hospital, Nairobi: a prospective, randomized, double blind controlled trial

Background: Postoperative sore throat is the commonest complication after endotracheal intubation. The efficacy of intravenous non-steroidal anti-inflammatory drugs in alleviating postoperative sore throat has not been investigated. Objective: To evaluate the effect of intravenous diclofenac sodium on the occurrence and severity of postoperative sore throat. Methods: 42 in-patients scheduled for laparoscopic surgery were randomized into two equal groups to receive either a single dose of 75mg intravenous diclofenac sodium in addition to standard treatment taken at our hospital for the prevention of postoperative sore throat or to receive standard treatment only. All patients were interviewed postoperatively at 2, 6 and 18 hours. Data of the baseline characteristics, the incidence and severity of sore throat were collected. If sore throat was present, a Visual Analogue Score was used to assess its severity. Results: The baseline characteristics of the participants were similar. The majority of the patients undergoing laparoscopic surgery were women. There was no statistically significant difference in the occurrence or severity of postoperative sore throat between the diclofenac and standard treatment groups at 2, 6 and 18 hours postoperatively. Conclusion: Intravenous diclofenac sodium does not reduce the occurrence or severity of postoperative sore throat

Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative

Accurately predicting conversions in advertisements is generally a challenging task, because such conversions do not occur frequently. In this paper, we propose a new framework to support creating high-performing ad creatives, including the accurate prediction of ad creative text conversions before delivering to the consumer. The proposed framework includes three key ideas: multi-task learning, conditional attention, and attention highlighting. Multi-task learning is an idea for improving the prediction accuracy of conversion, which predicts clicks and conversions simultaneously, to solve the difficulty of data imbalance. Furthermore, conditional attention focuses attention of each ad creative with the consideration of its genre and target gender, thus improving conversion prediction accuracy. Attention highlighting visualizes important words and/or phrases based on conditional attention. We evaluated the proposed framework with actual delivery history data (14,000 creatives displayed more than a certain number of times from Gunosy Inc.), and confirmed that these ideas improve the prediction performance of conversions, and visualize noteworthy words according to the creatives' attributes.Comment: 9 pages, 6 figures. Accepted at The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019) as an applied data science pape

Two applications of elementary knot theory to Lie algebras and Vassiliev invariants

Using elementary equalities between various cables of the unknot and the Hopf link, we prove the Wheels and Wheeling conjectures of [Bar-Natan, Garoufalidis, Rozansky and Thurston, arXiv:q-alg/9703025] and [Deligne, letter to Bar-Natan, January 1996, http://www.ma.huji.ac.il/~drorbn/Deligne/], which give, respectively, the exact Kontsevich integral of the unknot and a map intertwining two natural products on a space of diagrams. It turns out that the Wheeling map is given by the Kontsevich integral of a cut Hopf link (a bead on a wire), and its intertwining property is analogous to the computation of 1+1=2 on an abacus. The Wheels conjecture is proved from the fact that the k-fold connected cover of the unknot is the unknot for all k. Along the way, we find a formula for the invariant of the general (k,l) cable of a knot. Our results can also be interpreted as a new proof of the multiplicativity of the Duflo-Kirillov map S(g)-->U(g) for metrized Lie (super-)algebras g.Comment: Published by Geometry and Topology at http://www.maths.warwick.ac.uk/gt/GTVol7/paper1.abs.htm

Technique de lombriculture au Sud Vietnam

Vermicomposting technique in South Vietnam. Earthworms play a major role in organic matter transformation. The vermicomposting allows to combine several advantages: the management of diversified organic wastes, and the production of earthworms and vermicompost. Crop residues and other plant wastes mixed with animal manure from individual farms can be used. In South Vietnam, farmers are rearing some livestock and growing a few number of crop species. From several years, an increasing number of vermicomposting units were set in many farms from the Ho Chi Minh City region. Two kinds of infrastructure materials are used: baked clay blocks or bamboo stems with plastic covers. In South Vietnam, all conditions are pooled to ensure an efficient earthworm production: suitable climate, available organic wastes and materials to build the vermicomposting structures. Both field plot fertility and protein feed for livestock (pigs, poultry, etc.) can be provided by rearing earthworms

Improving Pareto Front Learning via Multi-Sample Hypernetworks

Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem. Due to the inherent trade-off between conflicting objectives, PFL offers a flexible approach in many scenarios in which the decision makers can not specify the preference of one Pareto solution over another, and must switch between them depending on the situation. However, existing PFL methods ignore the relationship between the solutions during the optimization process, which hinders the quality of the obtained front. To overcome this issue, we propose a novel PFL framework namely PHN-HVI, which employs a hypernetwork to generate multiple solutions from a set of diverse trade-off preferences and enhance the quality of the Pareto front by maximizing the Hypervolume indicator defined by these solutions. The experimental results on several MOO machine learning tasks show that the proposed framework significantly outperforms the baselines in producing the trade-off Pareto front.Comment: Accepted to AAAI-2

A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications

Pareto Front Learning (PFL) was recently introduced as an efficient method for approximating the entire Pareto front, the set of all optimal solutions to a Multi-Objective Optimization (MOO) problem. In the previous work, the mapping between a preference vector and a Pareto optimal solution is still ambiguous, rendering its results. This study demonstrates the convergence and completion aspects of solving MOO with pseudoconvex scalarization functions and combines them into Hypernetwork in order to offer a comprehensive framework for PFL, called Controllable Pareto Front Learning. Extensive experiments demonstrate that our approach is highly accurate and significantly less computationally expensive than prior methods in term of inference time.Comment: Under Review at Neural Networks Journa

Carbon Border Adjustment Mechanism Impact Assessment Report for Vietnam

The EU’s Carbon Border Adjustment Mechanism (CBAM) is evolving rapidly, with many uncertainties remaining regarding its long-term scope, embedded emissions calculation, and reactions of EU-trade partners. In its current form, the CBAM can affect Vietnamese enterprises exporting to EU although its direct impacts on Vietnam’s GDP are unlikely significant. If the CBAM is expanded to other trade-intensive sectors of Vietnam or taken up by other key trading partners of Vietnam, the impacts may grow quickly. Therefore, Vietnam should engage proactively with the CBAM and prepare for mitigation of potential impacts. One of the pro-active approaches is to accelerate and deepen the adoption of carbon pricing. This will facilitate energy transition, support achievement of Vietnam’s climate change mitigation target (NDC) under the Paris Agreement and long-term net-zero targets and would allow to harness co-benefits. It is also advisable for Vietnam to engage in constructive dialogues with the EU in order to negotiate a fair implementation of CBAM that takes into account Vietnam’s efforts. A key demand here should be the use of emissions credits instead of having to buy CBAM certificates

A Close Companion Search Around L Dwarfs Using Aperture Masking Interferometry and Palomar Laser Guide Star Adaptive Optics

We present a close companion search around 16 known early L dwarfs using aperture masking interferometry with Palomar laser guide star adaptive optics (LGS AO). The use of aperture masking allows the detection of close binaries, corresponding to projected physical separations of 0.6-10.0 AU for the targets of our survey. This survey achieved median contrast limits of ΔK ~ 2.3 for separations between 1.2λ/D-4λ/D and ΔK ~ 1.4 at 2/3λ/D. We present four candidate binaries detected with moderate-to-high confidence (90%-98%). Two have projected physical separations less than 1.5 AU. This may indicate that tight-separation binaries contribute more significantly to the binary fraction than currently assumed, consistent with spectroscopic and photometric overluminosity studies. Ten targets of this survey have previously been observed with the Hubble Space Telescope as part of companion searches. We use the increased resolution of aperture masking to search for close or dim companions that would be obscured by full aperture imaging, finding two candidate binaries. This survey is the first application of aperture masking with LGS AO at Palomar. Several new techniques for the analysis of aperture masking data in the low signal-to-noise regime are explored