Multiagent Brokerage with CBR

This paper classifies multiagent-based e-commerce into multiagent-based auction, multiagent-based mediation and multiagent-based brokerage and gives a brief survey of related works in each. The paper proposes a framework of CMB, a CBR system for multiagent brokerage, which integrates CBR, intelligent agents and brokerage, in which we also propose a knowledge-based model for CBR. The key insight is that an efficient way for applying CBR in e-commerce is through intelligent agents or multiagent systems, and the work of a human broker should be done by a few intelligent agents in a cooperative way. This approach will facilitate research and development of CBR in multiagent e-commerce

The Cardinal Complexity of Comparison-based Online Algorithms

We consider ordinal online problems, i.e., those tasks that only depend on the pairwise comparisons between elements in the input. E.g., the secretary problem and the game of googol. The natural approach to these tasks is to use ordinal online algorithms that at each step only consider relative ranking among the arrived elements, without looking at the numerical values of the input. We formally study the question of how cardinal algorithms (that can use numerical values of the input) can improve upon ordinal algorithms. We give a universal construction of the input distribution for any ordinal online problem, such that the advantage of the cardinal algorithms over the ordinal algorithms is at most $1+\varepsilon$ for arbitrary small $\varepsilon> 0$. However, the value range of the input elements in this construction is huge: $O\left(\frac{n^3\cdot n!}{\varepsilon}\right)\uparrow\uparrow (n-1)$ for an input sequence of length $n$. Surprisingly, we also identify a natural family of hardcore problems that achieve a matching advantage of $1+ \Omega \left(\frac{1}{\log^{(c)}N}\right),$ where $\log^{(c)}N=\log\log\ldots\log N$ with $c$ iterative logs and $c$ is an arbitrary constant $c\le n-2$. We also consider a simpler variant of the hardcore problem, which we call maximum guessing and is closely related to the game of googol. We provide a much more efficient construction with cardinal complexity $O\left(\frac{1}{\varepsilon}\right)^{n-1}$ for this easier task. Finally, we study the dependency on $n$ of the hardcore problem. We provide an efficient construction of size $O(n)$, if we allow cardinal algorithms to have constant factor advantage against ordinal algorithms

A Transparent Display with Per-Pixel Color and Opacity Control

International audienceWe propose a new display system that composites matted foreground animated graphics and video, with per-pixel controllable emitted color and transparency, over real-world dynamic objects seen through a transparent display. Multiple users can participate simultaneously without any glasses, trackers, or additional devices. The current prototype is deployed as a desktop-monitor-sized transparent display box assembled from commodity hardware components with the addition of a high-frame-rate controllable diffuser

deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data

Gene fusions created by somatic genomic rearrangements are known to play an important role in the onset and development of some cancers, such as lymphomas and sarcomas. RNA-Seq (whole transcriptome shotgun sequencing) is proving to be a useful tool for the discovery of novel gene fusions in cancer transcriptomes. However, algorithmic methods for the discovery of gene fusions using RNA-Seq data remain underdeveloped. We have developed deFuse, a novel computational method for fusion discovery in tumor RNA-Seq data. Unlike existing methods that use only unique best-hit alignments and consider only fusion boundaries at the ends of known exons, deFuse considers all alignments and all possible locations for fusion boundaries. As a result, deFuse is able to identify fusion sequences with demonstrably better sensitivity than previous approaches. To increase the specificity of our approach, we curated a list of 60 true positive and 61 true negative fusion sequences (as confirmed by RT-PCR), and have trained an adaboost classifier on 11 novel features of the sequence data. The resulting classifier has an estimated value of 0.91 for the area under the ROC curve. We have used deFuse to discover gene fusions in 40 ovarian tumor samples, one ovarian cancer cell line, and three sarcoma samples. We report herein the first gene fusions discovered in ovarian cancer. We conclude that gene fusions are not infrequent events in ovarian cancer and that these events have the potential to substantially alter the expression patterns of the genes involved; gene fusions should therefore be considered in efforts to comprehensively characterize the mutational profiles of ovarian cancer transcriptomes

Metal nanoparticle‐hydrogel nanocomposites for biomedical applications – An atmospheric pressure plasma synthesis approach

The development of multifunctional nanocomposite materials is of great interest for various biomedical applications. A popular approach to produce tailored nanocomposites is to incorporate functional nanoparticles into hydrogels. Here, a benign atmospheric pressure microplasma synthesis approach has been deployed for the synthesis of metal and alloy NPs in‐situ in a poly (vinyl alcohol) hydrogel. The formation of gold, silver, and gold‐silver alloy NPs was confirmed via spectroscopic and microscopic characterization techniques. The properties of the hydrogel were not compromised during formation of the composites. Practical applications of the NP/PVA nanocomposites has been demonstrated by anti‐bacterial testing. This establishes AMP processing as a viable one‐step technique for the fabrication of NP/hydrogel composites, with potential multifunctionality for a range of biomedical applications

Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor the evolution of the pandemic, inform the public, and assist governments in decision making. Our goal is to develop a globally applicable method, integrated in a twice daily updated dashboard that provides an estimate of the trend in the evolution of the number of cases and deaths from reported data of more than 200 countries and territories, as well as a seven-day forecast. One of the significant difficulties to manage a quickly propagating epidemic is that the details of the dynamic needed to forecast its evolution are obscured by the delays in the identification of cases and deaths and by irregular reporting. Our forecasting methodology substantially relies on estimating the underlying trend in the observed time series using robust seasonal trend decomposition techniques. This allows us to obtain forecasts with simple, yet effective extrapolation methods in linear or log scale. We present the results of an assessment of our forecasting methodology and discuss its application to the production of global and regional risk maps.Comment: 15 pages including 5 pages of supplementary materia