Set-theoretic Types for Erlang

Erlang is a functional programming language with dynamic typing. The language offers great flexibility for destructing values through pattern matching and dynamic type tests. Erlang also comes with a type language supporting parametric polymorphism, equi-recursive types, as well as union and a limited form of intersection types. However, type signatures only serve as documentation, there is no check that a function body conforms to its signature. Set-theoretic types and semantic subtyping fit Erlang's feature set very well. They allow expressing nearly all constructs of its type language and provide means for statically checking type signatures. This article brings set-theoretic types to Erlang and demonstrates how existing Erlang code can be statically typechecked without or with only minor modifications to the code. Further, the article formalizes the main ingredients of the type system in a small core calculus, reports on an implementation of the system, and compares it with other static typecheckers for Erlang.Comment: 14 pages, 9 figures, IFL 2022; latexmk -pdf to buil

Role of a BRICS Bank in a multipolar world : is China establishing a new international organization to change the international order or to legitimize Chinese FDI?

published_or_final_versionInternational and Public AffairsMasterMaster of International and Public Affair

A Difference Version of Nori's Theorem

We consider (Frobenius) difference equations over (F_q(s,t), phi) where phi fixes t and acts on F_q(s) as the Frobenius endomorphism. We prove that every semisimple, simply-connected linear algebraic group G defined over F_q can be realized as a difference Galois group over F_{q^i}(s,t) for some i in N. The proof uses upper and lower bounds on the Galois group scheme of a Frobenius difference equation that are developed in this paper. The result can be seen as a difference analogue of Nori's Theorem which states that G(F_q) occurs as (finite) Galois group over F_q(s).Comment: 29 page

Effects of mitochondrial dysfunction on the immunological properties of microglia

<p>Abstract</p> <p>Background</p> <p>Neurodegenerative diseases are characterized by both mitochondrial dysfunction and activation of microglia, the macrophages of the brain. Here, we investigate the effects of mitochondrial dysfunction on the activation profile of microglial cells.</p> <p>Methods</p> <p>We incubated primary mouse microglia with the mitochondrial toxins 3-nitropropionic acid (3-NP) or rotenone. These mitochondrial toxins are known to induce neurodegeneration in humans and in experimental animals. We characterized lipopolysaccharide- (LPS-) induced microglial activation and the alternative, interleukin-4- (IL-4-) induced microglial activation in these mitochondrial toxin-treated microglial cells.</p> <p>Results</p> <p>We found that, while mitochondrial toxins did not affect LPS-induced activation, as measured by release of tumor necrosis factor α (TNF-α), interleukin-6 (IL-6) and interleukin-1β (IL-1β), they did inhibit part of the IL-4-induced alternative activation, as measured by arginase activity and expression, induction of insulin-like growth factor 1 (IGF-1) and the counteraction of the LPS induced cytokine release.</p> <p>Conclusions</p> <p>Mitochondrial dysfunction in microglial cells inhibits part of the IL-4-induced alternative response. Because this alternative activation is considered to be associated with wound healing and an attenuation of inflammation, mitochondrial dysfunction in microglial cells might contribute to the detrimental effects of neuroinflammation seen in neurodegenerative diseases.</p

Markers of Myocardial Ischemia in Patients with Obstructive Sleep Apnea and Coronary Artery Disease

Obstructive sleep apnea (OSA) is characterized by intermittent hypoxia during sleep. We tested the hypothesis that nocturnal myocardial ischemia is detectable by ST segment depression and elevation of high sensitive troponin T (hsTrop T) and B-type natriuretic peptide (NT-proBNP) in patients with OSA and coexisting coronary artery disease (CAD). Twenty-one patients with OSA and CAD and 20 patients with OSA alone underwent in-hospital polysomnography. Blood samples for hsTrop T and NTproBNP measurements were drawn before and after sleep. ST segment depression was measured at the time of maximum oxygen desaturation during sleep. The apnea-hypopnea-index (AHI), oxygen saturation nadir, and time in bed with oxygen saturation of ≤80% were similar in both groups. Levels of hsTrop T and NT-proBNP did not differ significantly before and after sleep but NT-proBNP levels were significantly higher in patients suffering from OSA and CAD compared to patients with OSA alone. No significant ST depression was found at the time of oxygen saturation nadir in either group. Despite the fact that patients with untreated OSA and coexisting CAD experienced severe nocturnal hypoxemia, we were unable to detect myocardial ischemia or myocyte necrosis based on significant ST segment depression or elevation of hsTrop T and NT-proBNP, respectively

Stratiform and convective rain classification using machine learning models and micro rain radar

Rain type classification into convective and stratiform is an essential step required to improve quantitative precipitation estimations by remote sensing instruments. Previous studies with Micro Rain Radar (MRR) measurements and subjective rules have been performed to classify rain events. However, automating this process by using machine learning (ML) models provides the advantages of fast and reliable classification with the possibility to classify rain minute by minute. A total of 20,979 min of rain data measured by an MRR at Das in northeast Spain were used to build seven types of ML models for stratiform and convective rain type classification. The proposed classification models use a set of 22 parameters that summarize the reflectivity, the Doppler velocity, and the spectral width (SW) above and below the so-called separation level (SL). This level is defined as the level with the highest increase in Doppler velocity and corresponds with the bright band in stratiform rain. A pre-classification of the rain type for each minute based on the rain microstructure provided by the collocated disdrometer was performed. Our results indicate that complex ML models, particularly tree-based ensembles such as xgboost and random forest which capture the interactions of different features, perform better than simpler models. Applying methods from the field of interpretable ML, we identified reflectivity at the lowest layer and the average spectral width in the layers below SL as the most important features. High reflectivity and low SW values indicate a higher probability of convective rainPostprint (published version