Combining Traditional Marketing and Viral Marketing with Amphibious Influence Maximization

In this paper, we propose the amphibious influence maximization (AIM) model that combines traditional marketing via content providers and viral marketing to consumers in social networks in a single framework. In AIM, a set of content providers and consumers form a bipartite network while consumers also form their social network, and influence propagates from the content providers to consumers and among consumers in the social network following the independent cascade model. An advertiser needs to select a subset of seed content providers and a subset of seed consumers, such that the influence from the seed providers passing through the seed consumers could reach a large number of consumers in the social network in expectation. We prove that the AIM problem is NP-hard to approximate to within any constant factor via a reduction from Feige's k-prover proof system for 3-SAT5. We also give evidence that even when the social network graph is trivial (i.e. has no edges), a polynomial time constant factor approximation for AIM is unlikely. However, when we assume that the weighted bi-adjacency matrix that describes the influence of content providers on consumers is of constant rank, a common assumption often used in recommender systems, we provide a polynomial-time algorithm that achieves approximation ratio of $(1-1/e-\epsilon)^3$ for any (polynomially small) $\epsilon > 0$. Our algorithmic results still hold for a more general model where cascades in social network follow a general monotone and submodular function.Comment: An extended abstract appeared in the Proceedings of the 16th ACM Conference on Economics and Computation (EC), 201

Boundary curves of surfaces with the 4-plane property

Let M be an orientable and irreducible 3-manifold whose boundary is an incompressible torus. Suppose that M does not contain any closed nonperipheral embedded incompressible surfaces. We will show in this paper that the immersed surfaces in M with the 4-plane property can realize only finitely many boundary slopes. Moreover, we will show that only finitely many Dehn fillings of M can yield 3-manifolds with nonpositive cubings. This gives the first examples of hyperbolic 3-manifolds that cannot admit any nonpositive cubings.Comment: Published in Geometry and Topology at http://www.maths.warwick.ac.uk/gt/GTVol6/paper21.abs.htm

Theory of the Lattice Boltzmann Equation: Symmetry properties of Discrete Velocity Sets

In the lattice Boltzmann equation, continuous particle velocity space is replaced by a finite dimensional discrete set. The number of linearly independent velocity moments in a lattice Boltzmann model cannot exceed the number of discrete velocities. Thus, finite dimensionality introduces linear dependencies among the moments that do not exist in the exact continuous theory. Given a discrete velocity set, it is important to know to exactly what order moments are free of these dependencies. Elementary group theory is applied to the solution of this problem. It is found that by decomposing the velocity set into subsets that transform among themselves under an appropriate symmetry group, it becomes relatively straightforward to assess the behavior of moments in the theory. The construction of some standard two- and three-dimensional models is reviewed from this viewpoint, and procedures for constructing some new higher dimensional models are suggested

Using Dimmable Lighting for Regulation Capacity and Non-Spinning Reserves in the Ancillary Services Market. A Feasibility Study.

The objective of this Feasibility Study was to identify the potential of dimmable lighting for providing regulation capacity and contingency reserves if massively-deployed throughout the State. We found that one half of the total electric lighting load in the California commercial sector is bottled up in larger buildings that are greater an 50,000 square feet. Retrofitting large California buildings with dimmable lighting to enable fast DR lighting would require an investment of about $1.8 billion and a"fleet" of about 56 million dimming ballasts. By upgrading the existing installed base of lighting and controls (primarily in large commercial facilities) a substantial amount of ancillary services could be provided. Though not widely deployed, today's state-of-the art lighting systems, control systems and communication networks could be used for this application. The same lighting control equipment that is appropriate for fast DR is also appropriate for achieving energy efficiency with lighting on a daily basis. Thus fast DR can leverage the capabilities that are provided by a conventional dimming lighting control system. If dimmable lighting were massively deployed throughout large California buildings (because mandated by law, for example) dimmable lighting could realistically supply 380 MW of non-spinning reserve, 47percent of the total non-spinning reserves needed in 2007

Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations

Crowd-sourcing is a cheap and popular means of creating training and evaluation datasets for machine learning, however it poses the problem of `truth inference', as individual workers cannot be wholly trusted to provide reliable annotations. Research into models of annotation aggregation attempts to infer a latent `true' annotation, which has been shown to improve the utility of crowd-sourced data. However, existing techniques beat simple baselines only in low redundancy settings, where the number of annotations per instance is low ($\le 3$), or in situations where workers are unreliable and produce low quality annotations (e.g., through spamming, random, or adversarial behaviours.) As we show, datasets produced by crowd-sourcing are often not of this type: the data is highly redundantly annotated ($\ge 5$ annotations per instance), and the vast majority of workers produce high quality outputs. In these settings, the majority vote heuristic performs very well, and most truth inference models underperform this simple baseline. We propose a novel technique, based on a Bayesian graphical model with conjugate priors, and simple iterative expectation-maximisation inference. Our technique produces competitive performance to the state-of-the-art benchmark methods, and is the only method that significantly outperforms the majority vote heuristic at one-sided level 0.025, shown by significance tests. Moreover, our technique is simple, is implemented in only 50 lines of code, and trains in seconds.Comment: Accepted at the Web Conference/WWW 2019 (camera ready

Recent Updates in Cancer Immunotherapy: A Comprehensive Review and Perspective of the 2018 China Cancer Immunotherapy Workshop in Beijing

The immune system is the hard-wired host defense mechanism against pathogens as well as cancer. Five years ago, we pondered the question if the era of cancer immunotherapy was upon us (Li et al., Exp Hem Oncol 2013). Exciting progresses have been made at all fronts since then, including (1) sweeping approval of six agents by the US Food and Drug Administration (FDA) to block the PD-1/PD-L1 pathway for treatment of 13 cancer types; (2) a paradigm shifting indication of PD-1 and CTLA4 blockers for the management of a broad class of cancers with DNA mismatch repair defect, the first-ever tissue agnostic approval of cancer drugs; (3) real world practice of adoptive T cell therapy with two CD19-directed chimeric antigen receptor T cell products (CAR-T) for relapsed and/or refractory B cell malignancies including acute lymphoid leukemia and diffuse large B cell lymphoma, signaling the birth of a field now known as synthetic immunology; (4) the award of 2018 Nobel Prize in Physiology and Medicine from the Nobel Committee to Tasuku Honjo and James Allison for their discovery of cancer medicine by inhibition of negative immune regulation ( www.nobelprize.org/prizes/medicine/2018 ); and (5) the emerging new concept of normalizing rather than amplifying anti-tumor immunity for guiding the next wave of revolution in the field of immuno-oncology (IO) (Sanmamed and Chen, Cell 2018).This article will highlight the significant developments of immune-oncology as of October 2018. The US FDA approved indications of all seven immune checkpoint blockers, and two CD19-directed CAR-T products are tabulated for easy references. We organized our discussion into the following sections: introduction, cell therapy, emerging immunotherapeutic strategies, expediting oncology drug development in an era of breakthrough therapies, new concepts in cancer immunology and immunotherapy, and concluding remarks. Many of these topics were covered by the 2018 China Cancer Immunotherapy Workshop in Beijing, the fourth annual conference co-organized by the Chinese American Hematologist and Oncologist Network (CAHON), China FDA (CFDA; now known as China National Medical Product Administration (NMPA)), and the Tsinghua University. We significantly expanded our discussion of important IO developments beyond what were covered in the conference, and proposed a new Three Rs conceptual framework for cancer immunotherapy, which is to reverse tolerance, rejuvenate the immune system, and restore immune homeostasis. We conclude that the future of immuno-oncology as a distinct discipline of cancer medicine has arrived

A constant factor approximation for Nash social welfare with subadditive valuations

We present a constant-factor approximation algorithm for the Nash social welfare maximization problem with subadditive valuations accessible via demand queries. More generally, we propose a template for NSW optimization by solving a configuration-type LP and using a rounding procedure for (utilitarian) social welfare as a blackbox, which could be applicable to other variants of the problem

Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning

In this article we present the supervised iterative projections and rotations (s-ipr) algorithm, a method for learning discriminative incoherent subspaces from data. We derive s-ipr as a supervised extension of our previously proposed iterative projections and rotations (ipr) algorithm for incoherent dictionary learning, and we employ it to learn incoherent sub-spaces that model signals belonging to different classes. We test our method as a feature transform for supervised classification, first by visualising transformed features from a synthetic dataset and from the ‘iris’ dataset, then by using the resulting features in a classification experiment

An algorithm to detect laminar 3-manifolds

We show that there are algorithms to determine if a 3-manifold contains an essential lamination or a Reebless foliation.Comment: Published by Geometry and Topology at http://www.maths.warwick.ac.uk/gt/GTVol7/paper8.abs.htm