U-Duality Invariance of the Four-dimensional Born-Infeld Theory

We calculate the Hamiltonian of a compactified D4-brane, with general fluxes and moduli, and find the BPS-mass. The results are invariant under the complete U-duality SO(5,5,Z).Comment: 9 pages, Late

U-Duality of Born-Infeld on the Noncommutative Two-Torus

We discuss Born-Infeld on the noncommutative two-torus as a description of compactified string theory. We show that the resulting theory, including the fluctuations, is manifestly invariant under the T-duality group SO(2,2;Z). The BPS mass even has a full SL(3,Z)xSL(2,Z) U-duality symmetry. The direct identification of the noncommutative parameter \theta with the B-field modulus however seems to be problematic at finite volume

Gauge Bundles and Born-Infeld on the Noncommutative Torus

In this paper, we describe non-abelian gauge bundles with magnetic and electric fluxes on higher dimensional noncommutative tori. We give an explicit construction of a large class of bundles with nonzero magnetic 't Hooft fluxes. We discuss Morita equivalence between these bundles. The action of the duality is worked out in detail for the four-torus. As an application, we discuss Born-Infeld on this torus, as a description of compactified string theory. We show that the resulting theory, including the fluctuations, is manifestly invariant under the T-duality group SO(4,4;Z). The U-duality invariant BPS mass-formula is discussed shortly. We comment on a discrepancy of this result with that of a recent calculation.Comment: 22 pages, LaTeX2e. Small errors correcte

Trust in a multi-tenant, logistics, data sharing infrastructure:Opportunities for blockchain technology

In support of the trend towards ever more complex supply chain collaboration for the physical Internet, a trusted, multi-tenant (and interoperable) data sharing infrastructure has to be enabled. Trust is a condition sine qua non organizations may not be prepared to share potentially competitive sensitive information. As such, trust has to be an essential design aspect for any multi-tenant data sharing infrastructure for the data sharing stakeholders To overcome the challenges for trusted data sharing, various reference architectures for a trusted, multi-tenant, data sharing infrastructure are being developed. As such, the Industrial Data Space (IDS) initiative is currently gaining attention. It’s based on the architectural principles of keeping the data owner in control over his data and keeping data, data processing and data distribution at the source. Its reference architecture is strongly grounded on a role / stakeholder model for the intermediary trusted roles to enable peer-to-peer data sharing over a controlled and trusted connector infrastructure. The intermediary trusted roles may contain and process meta-data on the data sources, the data transactions and/or on the identities of the parties involved in the data sharing. This paper focuses on the role of blockchain technology for improving trust levels for such intermediary trusted roles

Near-flat space limit and Einstein manifolds

We study the near-flat space limit for strings on AdS(5)xM(5), where the internal manifold M(5) is equipped with a generic metric with U(1)xU(1)xU(1) isometry. In the bosonic sector, the limiting sigma model is similar to the one found for AdS(5)xS(5), as the global symmetries are reduced in the most general case. When M(5) is a Sasaki-Einstein space like T(1,1), Y(p,q) and L(p,q,r), whose dual CFT's have N=1 supersymmetry, the near-flat space limit gives the same bosonic sector of the sigma model found for AdS(5)xS(5). This indicates the generic presence of integrable subsectors in AdS/CFT.Comment: 30 pages, 1 figur

Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking.</p

Lung cancer biomarker testing : perspective from Europe

A questionnaire on biomarker testing previously used in central European countries was extended and distributed in Western and Central European countries to the pathologists participating at the Pulmonary Pathology Society meeting 26-28 June 2019 in Dubrovnik, Croatia. Each country was represented by one responder. For recent biomarkers the availability and reimbursement of diagnoses of molecular alterations in non-small cell lung carcinoma varies widely between different, also western European, countries. Reimbursement of such assessments varies widely between unavailability and payments by the health care system or even pharmaceutical companies. The support for testing from alternative sources, such as the pharmaceutical industry, is no doubt partly compensating for the lack of public health system support, but it is not a viable or long-term solution. Ideally, a structured access to testing and reimbursement should be the aim in order to provide patients with appropriate therapeutic options. As biomarker enabled therapies deliver a 50% better probability of outcome success, improved and unbiased reimbursement remains a major challenge for the future.Peer reviewe

REFORMS: Reporting Standards for Machine Learning Based Science

Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear reporting standards for ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist ($\textbf{Re}$porting Standards $\textbf{For}$ $\textbf{M}$achine Learning Based $\textbf{S}$cience). It consists of 32 questions and a paired set of guidelines. REFORMS was developed based on a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility