Miracle-dz/UniFAR-FTUs huggingface dataset:
Empfehlungen der S3-Leitlinie (AWMF) „Einsatz der extrakorporalen Zirkulation (ECLS/ECMO) bei Herz- und Kreislaufversagen“

Zusammenfassung In den vergangenen Jahren hat der Einsatz mechanischer Unterstützungssysteme für Patienten mit Herz- und Kreislaufversagen kontinuierlich zugenommen, sodass in Deutschland mittlerweile jährlich etwa 3000 ECLS-/ECMO-Systeme implantiert werden. Vor dem Hintergrund bislang fehlender umfassender Leitlinien bestand ein dringlicher Bedarf an der Formulierung evidenzbasierter Empfehlungen zu den zentralen Aspekten der ECLS-/ECMO-Therapie. Im Juli 2015 wurde daher die Erstellung einer S3-Leitlinie durch die Deutsche Gesellschaft für Thorax-, Herz- und Gefäßchirurgie (DGTHG) bei der zuständigen Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften e. V. (AWMF) angemeldet. In einem strukturierten Konsensusprozess mit Einbindung von Experten aus Deutschland, Österreich und der Schweiz, delegiert aus 11 AWMF-Fachgesellschaften, 5 weiteren Fachgesellschaften sowie der Patientenvertretung, entstand unter Federführung der DGTHG die Leitlinie „Einsatz der extrakorporalen Zirkulation (ECLS/ECMO) bei Herz- und Kreislaufversagen“, die im Februar 2021 publiziert wurde. Die Leitlinie fokussiert auf klinische Aspekte der Initiierung, Fortführung, Entwöhnung und Nachsorge und adressiert hierbei auch strukturelle und ökonomische Fragestellungen. Dieser Artikel präsentiert eine Übersicht zu der Methodik und den konsentierten Empfehlungen.
Analyzing a Curriculum Proposal for Brazilian Sign Language

With the aim to analyze a curriculum designed originally to teach Brazilian Sign Language (Libras) as a school subject for deaf students, this paper presents an analysis of the curriculum proposal described by Costa et al. (2013). The analysis was conducted in terms of needs analysis theory. After an introductory part, the paper is structured as follows: an overview on the teaching beliefs and the learning process; the relevance of needs analysis; a curriculum proposal designed to teach Libras as a first language; and the analysis of the curriculum presented. Reflecting on curricula queries is of utmost importance; it becomes an emerging challenge when it is about sign languages. In addition to other important issues discussed and presented in this paper, it is concluded that curriculum proposals for Libras may be a starting point to help the Ministry of Education in Brazil establish curricula guidelines for the area at stake.
Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke

Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research. Determining the volume and location of lesions caused by acute ischemic strokes - in which blood flow is restricted to part of the brain - is crucial to guide treatment and patient prognosis. However, this process is time-consuming and labor-intensive for clinicians. Here, using brain imaging datasets from patients with ischemic strokes, we create an artificial intelligence-based tool to quickly and accurately determine the volume and location of stroke lesions. Our tool outperforms some similar existing approaches, it is fast, publicly available, accessible to non-experts, and it runs on normal computers with minimal computational requirements. As such, it may be useful both for clinicians treating patients and researchers studying ischemic stroke. Liu et al. develop a deep learning-based tool to detect and segment diffusion abnormalities seen on magnetic resonance imaging (MRI) in acute ischemic stroke. The tool is tested in two clinical MRI datasets and outperforms existing algorithms in the detection of small lesions, potentially allowing clinicians and clinical researchers to more quickly and accurately diagnose and assess ischemic strokes.
Principles of the transition from 4G LTE to 5G

Information has become one of the key concepts of the modern world. Hundreds of methods are being developed every day to store, process, and use it. But, one way or another, timely and high-quality data transmission always comes to the fore. If 10 years ago a stationary computer connected to the Internet was able to meet the needs of a person, now we often need to transmit some information in the situation here and now. And then mobile technologies come to the rescue.
Graphdiyne/CdSe quantum dot heterostructure for efficient photoelectrochemical water oxidation

The design and synthesis of efficient catalysts for water oxidation is of significant importance. Here a type of graphdiyne (GDY) based heterostructure of CdSe/GDY was synthesized through a facile in-situ growth strategy including the first growth of GDY nanosheet arrays followed by the nucleation and growth of CdSe quantum dots (QDs) featuring well-dispersed CdSe QDs with uniform size around 3 nm. Experimental results revealed that GDY could effectively induce the growth and well-dispersion of CdSe QDs on GDY, forming new heterostructures and leading to the maximized number of active sites, the reduced recombination of photo-induced electron and hole pairs, and facilitated charge transfer behavior. Benefiting from these superior advantages, CdSe/GDY achieves greatly enhanced catalytic activity toward water oxidation under light illumination, with a small overpotential of 155 mV at the current density of 10 mA cm−2, which is better than those of pristine CdSe QDs and GDY.
Study of methodologies for the formation of a layer that inhibits the formation of bacterial strains in cardiac instruments

Implantable cardiovascular electronic devices are now an essential part of clinical practice in cardiology. The presence in a large number of adult population of problems in the electrical conduction system and several indications with multiple comorbidities has led to an increase in use of implantable cardiovascular electronic devices. However, the application benefit of these devices has been affected by the higher incidence of infections, which represent a serious complication that can lead to significant morbidity, mortality and cost for health centers due to antibiotic treatments, device replacement and longer hospitalization times. For this reason, the present analysis is oriented to possibility of reducing the dissemination of Staphylococcus Aureus by evaluating different techniques for adsorption of a vancomycin antibiotic film in implantable cardiovascular electronic devices. Through this analysis it is possible to establish a new alternative prophylactic method to avoid risks to the health of patients who have problems in the electrical conduction system, which are prone to becoming infected by Staphylococcus Aureus strains during the implantation of the device; in this way, infection can be controlled locally without requiring removal of the initial device and thus avoid an intracardiac compromise that leads to major complications such as endocarditis.
The phytostimulating activity of metal-resistant Bacillus strains isolated from Spolic Technosol of Lake Atamanskoe

This study was devoted to the isolation and identification of metal-resistant strains of microorganisms from soils contaminated with heavy metals, as well as their assessment as potential remediators of contaminated agricultural land. The soils with the highest concentration of zinc were selected as the source of the strains. In the course of the research, 10 strains with the highest zinc resistance were selected. For all strains, the phytostimulating and phytotoxic properties were assessed with the help of morphobiometric indicators. In the course of the study, three strains were selected and the phytostimulating potential was assessed in a model experiment. In the variant with the application of the selected strains, all the assessed characteristics significantly increased, in comparison to the plants grown in the contaminated soil.
The effect of the tank mixtures of humic substances and herbicides on the abundance of microbial communities in chernozem during chickpea cultivation

The effect of treatment of chickpea crops with chemical and biological plant protection agents on several groups of soil microorganisms was studied. Pesticides were used separately and in tank mixtures with humic substances. The treatment of plants with pesticides had a multidirectional effect on the numbers of microorganisms in the soil. It was found that the addition of humic substances to chemical pesticides increases the number of microorganisms in the soil, which is associated with its adaptogenic effect on plants. The use of tank mixtures of humic substances with pesticides on chickpea crops turned out to be most effective with second assortment of pesticides, but it was also effective with standard pesticides. For biological preparations, the opposite effect was observed: biological products were more effective without adding humic substances to them.
Deep learning based simulation of jack-up rig

The article illustrates the methodology of jack-up install simulation with the different compound of soils, including spudcan penetration. The core of this method is the combination of the finite element and data-driven approaches: the first - slow, but precise, and the second, based on machine learning proposed the fast solution with results, closing to the first approach.
Analysis of thermal and structural properties of clinoptilolite zeolite and its possible application as a microwave radiation susceptor

The need for heating systems, increasingly efficient and specific, makes us always think of systems aided by microwave radiation, and in the search for materials that interact with this radiation, although zeolites have stood out as materials of construction, as catalysts, as filtration systems, but they can also be used to heat by microwave action, the following work evaluates the action as a microwave susceptor of a synthetic zeolite, and analyses its thermal behavior, as the basis of a system of specific heating based on said zeolite.
INSURANCE IN ORDER TO MINIMIZING THE EXPECTED LOSS IN WEALTH VIA OPTIMAL CONTROL

In this paper we are interested in Pricing insurance in order to minimizing the expected loss in wealth via optimal control. The objective is to find the policy which maximizes the total wealth in company insurances. For this purpose, First, a dynamic model is introduced to describe the process of receiving premium and paying claims. Then, we introduce the premium variable as the problem control variable. Next, we define an appropriate objective function for the control variable and state variables in order to reduce expected losses and increase the wealth. In the end, one of the main variables is estimated by statistical methods and we solve the optimal control problem by PMP method and finally, numerical example are presented.
Exploring the Heterogeneous Effects of State Price Transparency Laws on Charge Prices, Negotiated Prices, and Operating Costs

To limit the dramatic growth of U.S. health care expenditures, some states have mandated that medical providers publicly report their charge prices. Our study evaluates the heterogeneous effects of this price transparency policy. We use a comprehensive database that covers more than 2,000 hospitals nationwide from 1996 to 2017. We employ a flexible generalized synthetic control method that allows for heterogeneous treatment effects. We find that the price transparency policy not only reduced charge prices by 3.9% (which corresponds to savings of $1,164 per hospital stay) but also diminished negotiated prices by 15.9% and hospital costs by 4.7%. Our estimation results show that the effects on charge prices do not last as long as the impacts on negotiated prices and costs. We also find large heterogeneous responses across hospitals that depend on: (1) hospitals’ past charge prices prior to adopting the price transparency law, that is, high-price hospitals reduce charge and negotiated prices, while low-price hospitals increase charges; (2) hospital characteristics such as ownership, case mix, and payer mix; and (3) hospital size and market competition. We also conduct counterfactuals to predict price changes of non-treated states and find large reductions in negotiated prices.
A Mountain of Debt: Navigating the Legacy of the Pandemic

Abstract The COVID-19 pandemic has triggered a massive increase in global debt levels and exacerbated the trade-offs between the benefits and costs of accumulating government debt. This paper examines these trade-offs by putting the recent debt boom into a historical context. It reports three major findings. First, during the 2020 global recession, both global government and private debt levels rose to record highs, and at their fastest single-year pace, in five decades. Second, the debt-financed, massive fiscal support programs implemented during the pandemic supported activity and illustrated the benefits of accumulating debt. However, as the recovery gains traction, the balance of benefits and costs of debt accumulation could increasingly tilt toward costs. Third, more than two-thirds of emerging market and developing economies are currently in government debt booms. On average, the current booms have already lasted three years longer, and are accompanied by a considerably larger fiscal deterioration, than earlier booms. About half of the earlier debt booms were associated with financial crises in emerging market and developing economies.
The (Heterogenous) Economic Effects of Private Equity Buyouts: A Comment

Davis, Haltiwanger, Handley, Lerner, Lipsius, and Miranda (2021) continue to produce ex- tensions to the Davis, Haltiwanger, Handley, Jarmin, Lerner, and Miranda (2014) collection without addressing the critical research design issues raised in Ayash and Rastad (2017), Ayash and Rastad (2018) and Ayash and Rastad (2019b). Every error in Davis et al. (2014) and Davis, Haltiwanger, Handley, Lerner, Lipsius, and Miranda (2019) is carried over into their manuscript with a new title. First and foremost, the authors misrepresent their sample as specific to lever- aged buyouts; it is not. We clearly illustrates this fact. Policy makers do not have an issue with venture capital and growth equity buyouts giving piles of cash to firms they acquire because of the subsequent investment in R&D, innovation, jobs and physical capital. However, policy makers cannot learn about the subject of interest, controversial leveraged buyouts, when these make up such a small portion of the sample tested in Davis et al. collection (Davis, Haltiwanger, Jarmin, Lerner, and Miranda, 2008 and Davis, Haltiwanger, Jarmin, Lerner, and Miranda, 2011 focus on employment, Davis, Haltiwanger, Jarmin, Lerner, and Miranda, 2009 presented at the World Economic Forum introduces productivity to the analysis, Davis, Haltiwanger, Handley, Jarmin, Lerner, and Miranda, 2013 brings it all together, and the final published version is Davis et al., 2014. Davis et al. (2019) is basically identical to Davis et al. (2021)). Unfortunately, these authors continue to obfuscate our understanding of the (heterogeneous) economic effects of these complex transactions due to the fact that they cannot observe leverage; they treat all buyouts as equivalent to leveraged buyouts. We therefore gladly reiterate our analysis to bring light to these researchers who have been in the dark for more than a decade.
A Unified Framework to Estimate Macroeconomic Stars

We develop a flexible semi-structural time-series model to estimate jointly several macroeconomic "stars" — i.e., unobserved long-run equilibrium levels of output (and growth rate of output), the unemployment rate, the real rate of interest, productivity growth, the price inflation, and wage inflation. The ingredients of the model are in part motivated by economic theory and in part by the empirical features necessitated by the changing economic environment. Following the recent literature on inflation and interest rate modeling, we explicitly model the links between long-run survey expectations and stars to improve the stars' econometric estimation. Our approach permits time variation in the relationships between various components, including time variation in error variances. To tractably estimate the large multivariate model, we use a recently developed precision sampler that relies on Bayesian methods. The by-products of this approach are the time-varying estimates of the wage and price Phillips curves, and the pass-through between prices and wages, both of which provide new insights into these empirical relationships' instability in US data. Generally, the contours of the stars echo those documented elsewhere in the literature — estimated using smaller models — but at times the estimates of stars are different, and these differences can matter for policy. Furthermore, our estimates of the stars are among the most precise. Lastly, we document the competitive real-time forecasting properties of the model and, separately, the usefulness of stars' estimates if they were used as steady-state values in external models.
Personal Bests and Gender

Abstract We connect two large bodies of scientific inquiry. First, important theories in the social sciences establish that human preferences are reference-dependent. Second, a separate field of research documents substantial differences in preferences and attitudes across genders. Specifically, we examine the universe of official classic chess games (more than 250,000 subjects and 22 million games). This allows us to study differences across genders both in cognitive performance (intensive margin) and in competitive participation (extensive margin), using the fact that personal bests act as reference points. We find that males and females behave very differently around their personal bests in both margins.
Seasonality in commodity prices: new approaches for pricing plain vanilla options

We present a new term-structure model for commodity futures prices based on Trolle and Schwartz ( 2009 ), which we extend by incorporating seasonal stochastic volatility represented with two different sinusoidal expressions. We obtain a quasi-analytical representation of the characteristic function of the futures log-prices and closed-form expressions for standard European options’ prices using the fast Fourier transform algorithm. We price plain vanilla options on the Henry Hub natural gas futures contracts, using our model and extant models. We obtain higher accuracy levels with our model than with the extant models.
A Soft-Rigid Hybrid Gripper With Lateral Compliance and Dexterous In-Hand Manipulation

Soft grippers are receiving growing attention due to their compliance-based interactive safety and dexterity. Hybrid gripper with soft actuators enhanced by rigid constraints is receiving growing attention in soft gripper design. With rigid structural components actuated by soft actuators, they could achieve excellent grasping adaptability and payload while also being easy to model and control with conventional kinematics. However, existing works were mostly focused on achieving superior payload and perception with simple planar workspaces, resulting in far less dexterity compared with conventional grippers. In this work, we took inspiration from the human metacarpophalangeal (MCP) joint and proposed a new hybrid gripper design with eight independent muscles. It was shown that adding the MCP complexity was critical in enabling a range of novel features in the hybrid gripper, including in-hand manipulation, lateral passive compliance, as well as new control modes. A prototype gripper was fabricated and tested on our proprietary dual-arm robot platform with vision-guided grasping. With very lightweight pneumatic bellows soft actuators, the gripper could grasp objects over 20 times its weight with lateral compliance. Using the dual-arm platform, highly anthropomorphic dexterous manipulations were demonstrated using two hybrid grippers, from tug of war on a rigid rod to passing a soft towel between two grippers using in-hand manipulation. Matching the novel features and performance specifications of the proposed hybrid gripper, the underlying modeling, actuation, control, and experimental validation details were also presented, offering a promising approach to achieving enhanced dexterity, strength, and compliance in robotic grippers.
On-the-fly Code Activation for Attack Surface Reduction

Modern code reuse attacks are taking full advantage of bloated software. Attackers piece together short sequences of instructions in otherwise benign code to carry out malicious actions. Eliminating these reusable code snippets, known as gadgets, has become one of the prime concerns of attack surface reduction. The aim is to break these chains of gadgets, thereby making such code reuse attacks impossible or substantially less common. Previous work on attack surface reduction has typically tried to eliminate such attacks by subsetting the application, e.g. via user-specified inputs, configurations, or features, or by focusing on third-party libraries to achieve high gadget reductions with minimal interference to the application. In this work we present a general, whole-program attack surface reduction technique called OCA that significantly reduces gadgets and has minor performance degradation. OCA requires no user inputs and leaves all features intact. OCA identifies specific program points and through analysis determines key function sets to enable/disable at runtime. The runtime system, thus, controls the set of enabled functions during execution, thereby significantly reducing the set of active gadgets an attacker can use, and by extension, cutting down the set of active gadget chains dramatically. On SPEC CPU 2017, our framework achieves 73.2% total gadget reduction with only 4% average slowdown. On 10 GNU coreutils applications, it achieves 87.2% reduction. On the nginx server it achieves 80.3% reduction with 2% slowdown. We also provide a gadget chain-breaking study across all applications, and show that our framework breaks the shell-spawning chain in all cases.
Repeated Games, Optimal Channel Capture, and Open Problems for Slotted Multiple Access

This paper revisits a classical problem of slotted multiple access with success, idle, and collision events on each slot. First, results of a 2-user multiple access game are reported. The game was conducted at the University of Southern California over multiple semesters and involved competitions between student-designed algorithms. An algorithm called 4-State was a consistent winner. This algorithm is analyzed and shown to have an optimal expected score when competing against an independent version of itself. The structure of 4-State motivates exploration of the open question of how to minimize the expected time to capture the channel for a n-user situation. It is assumed that the system delivers perfect feedback on the number of users who transmitted at the end of each slot. An efficient algorithm is developed and conjectured to have an optimal expected capture time for all positive integers $n$. Optimality is proven in the special cases $n\in \{1, 2, 3, 4, 6\}$ using a novel analytical technique that introduces virtual users with enhanced capabilities.