A Growth Oriented Dual Income Tax

This paper proposes a growth-oriented dual-income tax by combining an allowance for corporate equity with a broadly defined flat tax on personal capital income. Revenue losses are compensated by an increase in the value added tax. The paper demonstrates the neutrality properties of the reform with respect to investment, firm financial decisions and organizational choice. Tax rates are chosen to prevent income shifting from labor to capital income. The reform decisively strengthens investment of domestically owned firms as well as home and foreign based multinationals and boosts savings. Simulations with a calibrated growth model for Switzerland indicate that the reform could add between 2 to 3 percent of GDP in the long run, depending on the specific scenario. Given the slow nature of capital accumulation, it also imposes considerable costs in the short run. We also consider a tax smoothing scenario to offset the intergenerationally redistributive effects.tax reform, investment, financial structure, growth

k-semisimple elements and pseudotori

Dietz C. k-semisimple elements and pseudotori. Bielefeld: Universität Bielefeld; 2013

The climate beta

How does climate-change mitigation affect the aggregate consumption risk borne by future generations? In other words, what is the ‘climate beta’? In this paper we argue using a combination of theory and integrated assessment modelling that the climate beta is positive and close to unity for maturities of up to about one hundred years. This is because the positive effect on the climate beta of uncertainty about exogenous, emissions-neutral technological progress overwhelms the negative effect on the climate beta of uncertainty about the carbon-climate-response, particularly the climate sensitivity, and the damage intensity of warming. Mitigating climate change therefore has no insurance value to hedge the aggregate consumption risk borne by future generations. On the contrary, it increases that risk, which justifies a relatively high discount rate on the expected benefits of emissions reductions. However, the stream of undiscounted expected benefits is also increasing in the climate beta, and this dominates the discounting effect so that overall the net present value of carbon emissions abatement is increasing in the climate beta

The climate beta

Mitigation reduces the expected future damages from climate change,flbut how does it affect the aggregate risk borne by future generations?flThis raises the question of the ‘climate beta’, i.e., the elasticity of climatefldamages with respect to a change in aggregate consumption. Inflthis paper we show that the climate beta is positive if the main sourceflof uncertainty is exogenous, emissions-neutral technological progress,flimplying that mitigation has no hedging value. But these results areflreversed if the main source of uncertainty is related to the carbonclimate-flresponse and the damage intensity of warming. We then showflthat in the DICE integrated assessment model the climate beta is positivefland close to unity. In estimating the social cost of carbon, thisflwould justify using a relatively high rate to discount expected climatefldamages. However, the stream of undiscounted expected climate damagesflis also increasing in the climate beta. We show that this dominatesflthe discounting effect, so that the social cost of carbon is in fact largerflthan when discounting expected damages at the risk-free rate

The climate beta

Mitigation reduces the expected future damages from climate change,flbut how does it affect the aggregate risk borne by future generations?flThis raises the question of the ‘climate beta’, i.e., the elasticity of climatefldamages with respect to a change in aggregate consumption. Inflthis paper we show that the climate beta is positive if the main sourceflof uncertainty is exogenous, emissions-neutral technological progress,flimplying that mitigation has no hedging value. But these results areflreversed if the main source of uncertainty is related to the carbonclimate-flresponse and the damage intensity of warming. We then showflthat in the DICE integrated assessment model the climate beta is positivefland close to unity. In estimating the social cost of carbon, thisflwould justify using a relatively high rate to discount expected climatefldamages. However, the stream of undiscounted expected climate damagesflis also increasing in the climate beta. We show that this dominatesflthe discounting effect, so that the social cost of carbon is in fact largerflthan when discounting expected damages at the risk-free rate

Best Practice in Surgical Treatment of Malignant Head and Neck Tumors

Purpose of review: Defining the best practice of surgical care for patients affected by malignant head and neck tumors is of great importance. In this review we aim to describe the evolution of “best practice” guidelines in the context of quality-of-care measures and discuss current evidence on “best practice” for the surgical treatment of cancers of the sino-nasal tract, skull base, aero-digestive tract, and the neck. Recent findings: Current evidence based on certain structure and outcome indicators, but mostly based on process indicators already helps defining the framework of “Best practice” for head and neck cancer surgery. However, many aspects of surgical treatment still require in-depth research. Summary: While a framework of “Best practice” strategies already exists for the conduction of the surgical treatment of head and neck cancers, many questions still require additional research in particular in case of rare histologies in the head and neck region

Towards Adversarial Resilience in Proactive Detection of Botnet Domain Names by using MTD

Artificial Intelligence is often part of state-of-the-art Intrusion Detection Systems. However, attackers use Artificial Intelligence to improve their attacks and circumvent IDS systems. Botnets use artificial intelligence to improve their Domain Name Generation Algorithms. Botnets pose a serious threat to networks that are connected to the Internet and are an enabler for many cyber-criminal activities (e.g., DDoS attacks, banking fraud and cyber-espionage) and cause substantial economic damage. To circumvent detection and prevent takedown actions, bot-masters use DGAs to create, maintain and hide C&C infrastructures. Furthermore, botmasters often release its source code to prevent detection, leading to numerous similar botnets that are created and maintained by different botmasters. As these botnets are based on nearly the same source code basis, they often share similar observable behavior. Current work on detection of DGAs is often based on applying machine learning techniques, as they are capable to generalize and to also detect yet unknown derivatives of a known botnets. However, these machine learning based classifiers can be circumvented by applying adversarial learning techniques. As a consequence, there is a need for resilience against adversarial learning in current Intrusion Detection Systems. In our work, we focus on adversarial learning in DNS based IDSs from the perspective of a network operator. Further, we present our concept to make existing and future machine learning based IDSs more resilient against adversarial learning attacks by applying multi-level Moving Target Defense strategies

Proactive Botnet Detection and Defense at Internet scale

Botnets provide the basis for various cyber-threats. However, setting up a complex botnet infrastructure often involves registration of domain names in the domain name system (DNS). Active as well as passive monitoring approaches can be used in the detection of domains that are registered for botnets and other malicious activities. We present a novel architecture for proactive botent detection and defense based on large-scale DNS measurement and smart pattern recognition using machine learning