Dijet Resonance Search with Weak Supervision Using root S=13 TeV pp Collisions in the ATLAS Detector

This Letter describes a search for narrowly resonant new physics using a machine-learning anomaly detection procedure that does not rely on signal simulations for developing the analysis selection. Weakly supervised learning is used to train classifiers directly on data to enhance potential signals. The targeted topology is dijet events and the features used for machine learning are the masses of the two jets. The resulting analysis is essentially a three-dimensional search A → BC, for mA ∼ OðTeVÞ, mB; mC ∼ Oð100 GeVÞ and B, C are reconstructed as large-radius jets, without paying a penalty associated with a large trials factor in the scan of the masses of the two jets. The full run 2 ffiffi s p ¼ 13 TeV pp collision dataset of 139 fb−1 recorded by the ATLAS detector at the Large Hadron Collider is used for the search. There is no significant evidence of a localized excess in the dijet invariant mass spectrum between 1.8 and 8.2 TeV. Cross-section limits for narrow-width A, B, and C particles vary with mA, mB, and mC. For example, when mA ¼ 3 TeV and mB ≳ 200 GeV, a production cross section between 1 and 5 fb is excluded at 95% confidence level, depending on mC. For certain masses, these limits are up to 10 times more sensitive than those obtained by the inclusive dijet search. These results are complementary to the dedicated searches for the case that B and C are standard model boson

A mixed-data evaluation in group TOPSIS with differentiated decision power

[[abstract]]This main objective of this paper is to provide decision support for mixed data in group Technique for Order Preference by Similarity to Idea Solution (TOPSIS) with differentiated decision power. We use a signum function to compare the ordinal performance of alternatives on any qualitative criterion, or the partial information provided by decision makers. The proposed process for ordinal information is uniformly coherent with the traditional TOPSIS steps, preserving the characteristic of distance-based utilities. Ordinal weights are also considered herein, and the decision power of the group members is formulated by their weights under an agreement in the group. Two examples demonstrate that the proposed approach has some benefits and achieves robustness with two types of sensitivity analyses. Some discussions and their limitations to the approach are also provided.[[notice]]補正完

Prevention of acute kidney injury and protection of renal function in the intensive care unit

Acute renal failure on the intensive care unit is associated with significant mortality and morbidity. To determine recommendations for the prevention of acute kidney injury (AKI), focusing on the role of potential preventative maneuvers including volume expansion, diuretics, use of inotropes, vasopressors/vasodilators, hormonal interventions, nutrition, and extracorporeal techniques. A systematic search of the literature was performed for studies using these potential protective agents in adult patients at risk for acute renal failure/kidney injury between 1966 and 2009. The following clinical conditions were considered: major surgery, critical illness, sepsis, shock, and use of potentially nephrotoxic drugs and radiocontrast media. Where possible the following endpoints were extracted: creatinine clearance, glomerular filtration rate, increase in serum creatinine, urine output, and markers of tubular injury. Clinical endpoints included the need for renal replacement therapy, length of stay, and mortality. Studies are graded according to the international Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) group system Several measures are recommended, though none carries grade 1A. We recommend prompt resuscitation of the circulation with special attention to providing adequate hydration whilst avoiding high-molecular-weight hydroxy-ethyl starch (HES) preparations, maintaining adequate blood pressure using vasopressors in vasodilatory shock. We suggest using vasopressors in vasodilatory hypotension, specific vasodilators under strict hemodynamic control, sodium bicarbonate for emergency procedures administering contrast media, and periprocedural hemofiltration in severe chronic renal insufficiency undergoing coronary intervention

Using surrogate weights for handling preference strength in multi-criteria decisions

Various proposals for how to eliminate some of the obstacles in multi-criteria decision making exist and methods for introducing so called surrogate weights have proliferated for some time in the form of ordinal ranking methods for the critria weights. Considering the decision quality, one main problem is that the input information to ordinal methods is often too restricted. At the same time, decision-makers often possess more background information, for example regarding the relative strengths of the criteria, and might want to use that. Thus, some form of strength relation often exists that can be utilised when transforming orderings into weights. In this article, using a quite extensive simulation approach, we suggest a thorough testing methodology and analyse the relevance of a set of ordering methods including to what extent these improve the efficacy of rank order weights and provide a reasonable base for decision making

Characterizations of Nonsmooth Robustly Quasiconvex Functions

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Two criteria for the robust quasiconvexity of lower semicontinuous functions are established in terms of Fréchet subdifferentials in Asplund spaces. The first criterion extends to such spaces a result established by Barron et al. (Discrete Contin Dyn Syst Ser B 17:1693–1706, 2012). The second criterion is totally new even if it is applied to lower semicontinuous functions on finite-dimensional spaces

Automatic Criteria Weight Generation for Multi-criteria Decision Making Under Uncertainty

Real-life decision situations almost invariably involve large uncertainties. In particular, there are several difficulties connected with the elicitation of probabilities, utilities, and criteria weights. In this article, we explore and test a robust multi-criteria weight generating method covering a broad set of decision situations, but which still is reasonably simple to use. We cover an important class of methods for criteria weight elicitation and propose the use of a reinterpretation of an efficient family (rank exponent) of methods for modelling and evaluating multi-criteria decision problems under uncertainty. We find that the rank exponent (RX) family generates the most efficient and robust weighs and works very well under different assumptions. Furthermore, it is stable under varying assumptions regarding the decision-makers’ mindset and internal modelling. We also provide an example to show how the algorithm can be used in a decision-making context. It is exemplified with a problem of selecting strategies for combatting COVID-19