Tennis manipulation: can we help serena williams win another tournament?

This article focuses on the question of whether a certain candidate’s (player’s) chance to advance further in a tennis tournament can be increased when the ordering of the tournament can be controlled (manipulated by the organizers) according to his own preferences. Is it possible to increase the number of ranking points a player will receive? And most importantly, can it be done in reasonable computational time? The answers to these questions differ for different settings. e.g., the information available on the outcome of each game, the significance of the number of points gained or of the number of games won. We analyzed five different variations of these tournament questions. First the complexity hardness of trying to control the tournaments is shown. Then, the tools of parametrized complexity are used to investigate the source of the problems’ hardness. Specifically, we check whether this hardness holds when the size of the problem is bounded. The findings of this analysis show that it is possible under certain circumstances to control the tournament in favor of a specific candidate in order to help him advance further in the tournament.     

Inferring contextual preferences using deep encoder-decoder learners

ABSTRACT

Context-aware systems enable the sensing and analysis of user context in order to provide personalised services. Our study is part of growing research efforts examining how high-dimensional data collected from mobile devices can be utilised to infer users’ dynamic preferences that are learned over time. We suggest novel methods for inferring the category of the item liked in a specific contextual situation, by applying encoder-decoder learners (long short-term memory networks and auto encoders) on mobile sensor data. In these approaches, the encoder-decoder learners reduce the dimensionality of the contextual features to a latent representation which is learned over time. Given new contextual sensor data from a user, the latent patterns discovered from each deep learner is used to predict the liked item’s category in the given context. This can greatly enhance a variety of services, such as mobile online advertising and context-aware recommender systems. We demonstrate our contribution with a point of interest (POI) recommender system in which we label contextual situations with the items’ categories. Empirical results utilising a real world data set of contextual situations derived from mobile phones sensors log show a significant improvement (up to 73% improvement) in prediction accuracy compared with state of the art classification methods.     

Why Neighbor-Joining Works

We show that the neighbor-joining algorithm is a robust quartet method for constructing trees from distances. This leads to a new performance guarantee that contains Atteson’s optimal radius bound as a special case and explains many cases where neighbor-joining is successful even when Atteson’s criterion is not satisfied. We also provide a proof for Atteson’s conjecture on the optimal edge radius of the neighbor-joining algorithm. The strong performance guarantees we provide also hold for the quadratic time fast neighbor-joining algorithm, thus providing a theoretical basis for inferring very large phylogenies with neighbor-joining.     

Exploring the perceived business value of the flexibility enabled by information technology infrastructure

We developed a multidimensional definition of IT infrastructure (ITI) and applied it in exploring the perceived strategic payoffs of ITI-enabled flexibility. We began by developing a typology of theoretical approaches that can be used to organize the literature and then developed a multidimensional model by conceptualizing how flexibility can be enabled through technical, human, and process elements of ITI and how these are interrelated. We used a resource-based view of the firm and a dynamic capabilities perspective to account for competitive impacts of the flexibility. Finally, we hypothesized on the moderating effects of organizational size and reporting level of the top IT executive. Data collected from 293 IT managers showed that the range of managerial ITI capabilities, which were positively affected by all areas of IT personnel knowledge and skills, was responsible for the competitive impacts of the ITI-enabled flexibility. Multigroup analyses showed that large organizational size or reporting to the CEO reduced the positive effects of the range of managerial ITI capabilities on competitive impacts.     

Genetic algorithm-based feature set partitioning for classification problems

Feature set partitioning generalizes the task of feature selection by partitioning the feature set into subsets of features that are collectively useful, rather than by finding a single useful subset of features. This paper presents a novel feature set partitioning approach that is based on a genetic algorithm. As part of this new approach a new encoding schema is also proposed and its properties are discussed. We examine the effectiveness of using a Vapnik–Chervonenkis dimension bound for evaluating the fitness function of multiple, oblivious tree classifiers. The new algorithm was tested on various datasets and the results indicate the superiority of the proposed algorithm to other methods.     

WND-CHARM: Multi-purpose image classification using compound image transforms

We describe a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provide classification accuracy comparable to state-of-the-art task-specific image classifiers. The proposed image classifier first extracts a large set of 1025 image features including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are computed on the raw image, transforms of the image, and transforms of transforms of the image. The feature values are then used to classify test images into a set of pre-defined image classes. This classifier was tested on several different problems including biological image classification and face recognition. Although we cannot make a claim of universality, our experimental results show that this classifier performs as well or better than classifiers developed specifically for these image classification tasks. Our classifier's high performance on a variety of classification problems is attributed to (i) a large set of features extracted from images; and (ii) an effective feature selection and weighting algorithm sensitive to specific image classification problems. The algorithms are available for free download from openmicroscopy.org.     

A multi-layer model for the early detection of COVID-19

Current COVID-19 screening efforts mainly rely on reported symptoms and the potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that uses four layers of information: (i) sociodemographic characteristics of the individual, (ii) spatio-temporal patterns of the disease, (iii) medical condition and general health consumption of the individual and (iv) information reported by the individual during the testing episode. We evaluated our model on 140 682 members of Maccabi Health Services who were tested for COVID-19 at least once between February and October 2020. These individuals underwent, in total, 264 516 COVID-19 PCR tests, out of which 16 512 were positive. Our multi-layer model obtained an area under the curve (AUC) of 81.6% when evaluated over all the individuals in the dataset, and an AUC of 72.8% when only individuals who did not report any symptom were included. Furthermore, considering only information collected before the testing episode—i.e. before the individual had the chance to report on any symptom—our model could reach a considerably high AUC of 79.5%. Our ability to predict early on the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be used for a more efficient testing policy.     

Guanine‐Based Biogenic Photonic‐Crystal Arrays in Fish and Spiders

Biological photonic systems composed of anhydrous guanine crystals evolved separately in several taxonomic groups. Here, two such systems found in fish and spiders, both of which make use of anhydrous guanine crystal plates to produce structural colors, are examined. Measurements of the photonic‐crystal structures using cryo‐SEM show that the crystal plates in both fish skin and spider integument are ～20‐nm thick. The reflective unit in the fish comprises stacks of single plates alternating with ～230‐nm‐thick cytoplasm layers. In the spiders the plates are formed as doublet crystals, cemented by 30‐nm layers of amorphous guanine, and are stacked with ～200 nm of cytoplasm between crystal doublets. They achieve light reflective properties through the control of crystal morphology and stack dimensions, reaching similar efficiencies of light reflectivity in both fish skin and spider integument. The structure of guanine plates in spiders are compared with the more common situation in which guanine occurs in the form of relatively unorganized prismatic crystals, yielding a matt white coloration.