Community Detection on Evolving Graphs

Clustering is a fundamental step in many information-retrieval and data-mining applications. Detecting clusters in graphs is also a key tool for finding the community structure in social and behavioral networks. In many of these applications, the input graph evolves over time in a continual and decentralized manner, and, to maintain a good clustering, the clustering algorithm needs to repeatedly probe the graph. Furthermore, there are often limitations on the frequency of such probes, either imposed explicitly by the online platform (e.g., in the case of crawling proprietary social networks like twitter) or implicitly because of resource limitations (e.g., in the case of crawling the web). In this paper, we study a model of clustering on evolving graphs that captures this aspect of the problem. Our model is based on the classical stochastic block model, which has been used to assess rigorously the quality of various static clustering methods. In our model, the algorithm is supposed to reconstruct the planted clustering, given the ability to query for small pieces of local information about the graph, at a limited rate. We design and analyze clustering algorithms that work in this model, and show asymptotically tight upper and lower bounds on their accuracy. Finally, we perform simulations, which demonstrate that our main asymptotic results hold true also in practice

Contextual Bandits with Cross-learning

In the classical contextual bandits problem, in each round $t$, a learner observes some context $c$, chooses some action $a$ to perform, and receives some reward $r_{a,t}(c)$. We consider the variant of this problem where in addition to receiving the reward $r_{a,t}(c)$, the learner also learns the values of $r_{a,t}(c')$ for all other contexts $c'$; i.e., the rewards that would have been achieved by performing that action under different contexts. This variant arises in several strategic settings, such as learning how to bid in non-truthful repeated auctions (in this setting the context is the decision maker's private valuation for each auction). We call this problem the contextual bandits problem with cross-learning. The best algorithms for the classical contextual bandits problem achieve $\tilde{O}(\sqrt{CKT})$ regret against all stationary policies, where $C$ is the number of contexts, $K$ the number of actions, and $T$ the number of rounds. We demonstrate algorithms for the contextual bandits problem with cross-learning that remove the dependence on $C$ and achieve regret $O(\sqrt{KT})$ (when contexts are stochastic with known distribution), $\tilde{O}(K^{1/3}T^{2/3})$ (when contexts are stochastic with unknown distribution), and $\tilde{O}(\sqrt{KT})$ (when contexts are adversarial but rewards are stochastic).Comment: 48 pages, 5 figure

Regret Minimization with Noisy Observations

In a typical optimization problem, the task is to pick one of a number of options with the lowest cost or the highest value. In practice, these cost/value quantities often come through processes such as measurement or machine learning, which are noisy, with quantifiable noise distributions. To take these noise distributions into account, one approach is to assume a prior for the values, use it to build a posterior, and then apply standard stochastic optimization to pick a solution. However, in many practical applications, such prior distributions may not be available. In this paper, we study such scenarios using a regret minimization model. In our model, the task is to pick the highest one out of $n$ values. The values are unknown and chosen by an adversary, but can be observed through noisy channels, where additive noises are stochastically drawn from known distributions. The goal is to minimize the regret of our selection, defined as the expected difference between the highest and the selected value on the worst-case choices of values. We show that the na\"ive algorithm of picking the highest observed value has regret arbitrarily worse than the optimum, even when $n = 2$ and the noises are unbiased in expectation. On the other hand, we propose an algorithm which gives a constant-approximation to the optimal regret for any $n$. Our algorithm is conceptually simple, computationally efficient, and requires only minimal knowledge of the noise distributions

Online Auctions with Re-usable Goods

This paper concerns the design of mechanisms for online scheduling in which agents bid for access to a re-usable resource such as processor time or wireless network access. Each agent is assumed to arrive and depart dynamically, and in the basic model require the resource for one unit of time. We seek mechanisms that are truthful in the sense that truthful revelation of arrival, departure and value information is a dominant strategy, and that are online in the sense that they make allocation decisions without knowledge of the future. First, we provide two characterizations for the class of truthful online allocation rules. The characterizations extend beyond the typical single-parameter settings, and formalize the role of restricted misreporting in reversing existing price-based characterizations. Second, we present an online auction for unit-length jobs that achieves total value that is 2-competitive with the maximum offline value. We prove that no truthful deterministic online mechanism can achieve a better competitive ratio. Third, we consider revenue competitiveness and prove that no deterministic truthful online auction has revenue that is constant-competitive with that of the offline Vickrey-Clarke-Groves (VCG) mechanism We provide a randomized online auction that achieves a competitive ratio of O(log h), where h is the ratio of maximum value to minimum value among the agents; this mechanism does not require prior knowledge of h. Finally, we generalize our model to settings with multiple re-usable goods and to agents with different job lengths.Engineering and Applied Science

A Systematic Review of Methods Used to Assess Mid-Palatal Suture Maturation

Objectives Use of a reliable method to determine the degree of skeletal development of the mid-palatal suture is important in the choice of treatment between orthopedic maxillary expansion and surgical expansion in adolescents and young adults. The aim of this study was to review the new methods for evaluation of mid-palatal suture maturation.Methods Electronic search was conducted in PubMed and Scopus databases using the following key words: (“mid-palatal suture maturation” OR “mid-palatal suture ossification”) AND (“orthopedic treatment” OR “maxillary expansion” OR “orthodontic*”) to find studies published from 1990 to December 2018 in English that evaluated the mid-palatal suture maturation stage.Results Of 127 papers found, 28 articles met the inclusion criteria and their full texts were reviewed. Finally, 8 met the inclusion criteria. Five studies used cone-beam computed tomography (CBCT) as the imaging modality and examined the quality of the mid-palatal suture maturation and assessed the morphology of the suture. Two studies used bone density and one study used fractal dimensions.Conclusion Since all the innovative methods lack a gold standard and valid histological references, it is not possible to reach a comprehensive conclusion. It is therefore important that the clinicians use several diagnostic criteria for thorough evaluation of the development of mid-palatal suture and decide on the appropriate therapeutic modalit

Photodegradation of reactive blue 19 dye using magnetic nanophotocatalyst α-Fe2O3/WO3: A comparison study of α-Fe2O3/WO3 and WO3/NaOH

The photocatalytic degradation of reactive blue 19 (RB19) dye was investigated in a slurry system using ultraviolet (UV) and light-emitting diode (LED) lamps as light sources and using magnetic tungsten trioxide nanophotocatalysts (α-Fe2O3/WO3 and WO3/NaOH) as photocatalysts. The effects of different parameters including irradiation time, initial concentration of RB19, nanophotocatalyst dosage, and pH were examined. The magnetic nanophotocatalysts were also characterized with different methods including scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM), X-ray diffraction (XRD), photoluminescence (PL), differential reflectance spectroscopy (DRS), Fourier transform infrared spectroscopy (FTIR), and vibrating sample magnetometry (VSM). The XRD and FTIR analyses confirmed the presence of tungsten trioxide on the iron oxide nanoparticles. The VSM analysis confirmed the magnetic ability of the new synthesized nanophotocatalyst α-Fe2O3/WO3 with 39.6 emu/g of saturation magnetization. The reactor performance showed considerable improvement in the α-Fe2O3-modified nanophotocatalyst. The impact of visible light was specifically investigated, and it was compared with UV-C light under the same experimental conditions. The reusability of the magnetic nanophotocatalyst α-Fe2O3/WO3 was tested during six cycles, and the magnetic materials showed an excellent removal efficiency after six cycles, with just a 7% decline