On Colorful Bin Packing Games

We consider colorful bin packing games in which selfish players control a set of items which are to be packed into a minimum number of unit capacity bins. Each item has one of $m\geq 2$ colors and cannot be packed next to an item of the same color. All bins have the same unitary cost which is shared among the items it contains, so that players are interested in selecting a bin of minimum shared cost. We adopt two standard cost sharing functions: the egalitarian cost function which equally shares the cost of a bin among the items it contains, and the proportional cost function which shares the cost of a bin among the items it contains proportionally to their sizes. Although, under both cost functions, colorful bin packing games do not converge in general to a (pure) Nash equilibrium, we show that Nash equilibria are guaranteed to exist and we design an algorithm for computing a Nash equilibrium whose running time is polynomial under the egalitarian cost function and pseudo-polynomial for a constant number of colors under the proportional one. We also provide a complete characterization of the efficiency of Nash equilibria under both cost functions for general games, by showing that the prices of anarchy and stability are unbounded when $m\geq 3$ while they are equal to 3 for black and white games, where $m=2$. We finally focus on games with uniform sizes (i.e., all items have the same size) for which the two cost functions coincide. We show again a tight characterization of the efficiency of Nash equilibria and design an algorithm which returns Nash equilibria with best achievable performance

Citizen participation in news

The process of producing news has changed significantly due to the advent of the Web, which has enabled the increasing involvement of citizens in news production. This trend has been given many names, including participatory journalism, produsage, and crowd-sourced journalism, but these terms are ambiguous and have been applied inconsistently, making comparison of news systems difficult. In particular, it is problematic to distinguish the levels of citizen involvement, and therefore the extent to which news production has genuinely been opened up. In this paper we perform an analysis of 32 online news systems, comparing them in terms of how much power they give to citizens at each stage of the news production process. Our analysis reveals a diverse landscape of news systems and shows that they defy simplistic categorisation, but it also provides the means to compare different approaches in a systematic and meaningful way. We combine this with four case studies of individual stories to explore the ways that news stories can move and evolve across this landscape. Our conclusions are that online news systems are complex and interdependent, and that most do not involve citizens to the extent that the terms used to describe them imply

SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects

Recent advances in image generation gave rise to powerful tools for semantic image editing. However, existing approaches can either operate on a single image or require an abundance of additional information. They are not capable of handling the complete set of editing operations, that is addition, manipulation or removal of semantic concepts. To address these limitations, we propose SESAME, a novel generator-discriminator pair for Semantic Editing of Scenes by Adding, Manipulating or Erasing objects. In our setup, the user provides the semantic labels of the areas to be edited and the generator synthesizes the corresponding pixels. In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input, the SESAME discriminator is composed of two input streams that independently process the image and its semantics, using the latter to manipulate the results of the former. We evaluate our model on a diverse set of datasets and report state-of-the-art performance on two tasks: (a) image manipulation and (b) image generation conditioned on semantic labels

Tailored carrier/bacteria technology for rehabilitation of areas with pesticide-containing pollution – AQUAREHAB WP2

Postprint (published version

Case Report Protein-Loosing Entropathy Induced by Unique Combination of CMV and HP in an Immunocompetent Patient

Protein-losing gastroenteropathies are characterized by an excessive loss of serum proteins into the gastrointestinal tract, resulting in hypoproteinemia (detected as hypoalbuminemia), edema, and, in some cases, pleural and pericardial effusions. Protein-losing gastroenteropathies can be caused by a diverse group of disorders and should be suspected in a patient with hypoproteinemia in whom other causes, such as malnutrition, proteinuria, and impaired liver protein synthesis, have been excluded. In this paper, we present a case of protein-losing enteropathy in a 22-year-old immunocompetent male with a coinfection of CMV and Hp

Repair of gaps opposite lesions by homologous recombination in mammalian cells

Damages in the DNA template inhibit the progression of replication, which may cause single-stranded gaps. Such situations can be tolerated by translesion DNA synthesis (TLS), or by homology-dependent repair (HDR), which is based on transfer or copying of the missing information from the replicated sister chromatid. Whereas it is well established that TLS plays an important role in DNA damage tolerance in mammalian cells, it is unknown whether HDR operates in this process. Using a newly developed plasmid-based assay that distinguishes between the three mechanisms of DNA damage tolerance, we found that mammalian cells can efficiently utilize HDR to repair DNA gaps opposite an abasic site or benzo[a]pyrene adduct. The majority of these events occurred by a physical strand transfer (homologous recombination repair; HRR), rather than a template switch mechanism. Furthermore, cells deficient in either the human RAD51 recombination protein or NBS1, but not Rad18, exhibited decreased gap repair through HDR, indicating a role for these proteins in DNA damage tolerance. To our knowledge, this is the first direct evidence of gap-lesion repair via HDR in mammalian cells, providing further molecular insight into the potential activity of HDR in overcoming replication obstacles and maintaining genome stability

Inheritance patterns in citation networks reveal scientific memes

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and we validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.Comment: 8 two-column pages, 5 figures; accepted for publication in Physical Review

GAN-based multiple adjacent brain MRI slice reconstruction for unsupervised alzheimer’s disease diagnosis

Unsupervised learning can discover various unseen diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with disease stages. Therefore, we propose a two-step method using Generative Adversarial Network-based multiple adjacent brain MRI slice reconstruction to detect AD at various stages: (Reconstruction) Wasserstein loss with Gradient Penalty + L1 loss---trained on 3 healthy slices to reconstruct the next 3 ones---reconstructs unseen healthy/AD cases; (Diagnosis) Average/Maximum loss (e.g., L2 loss) per scan discriminates them, comparing the reconstructed/ground truth images. The results show that we can reliably detect AD at a very early stage with Area Under the Curve (AUC) 0.780 while also detecting AD at a late stage much more accurately with AUC 0.917; since our method is fully unsupervised, it should also discover and alert any anomalies including rare disease.Comment: 10 pages, 4 figures, Accepted to Lecture Notes in Bioinformatics (LNBI) as a volume in the Springer serie