G-torsors and universal torsors over nonsplit del Pezzo surfaces

Let S be a smooth del Pezzo surface that is defined over a field K and splits over a Galois extension L. Let G be either the split reductive group given by the root system of $S_L$ in Pic $S_L$, or a form of it containing the Neron-Severi torus. Let $mathcal{G}$ be the G-torsor over $S_L$ obtained by extension of structure group from a universal torsor $mathcal{T}$ over $S_L$. We prove that $mathcal{G}$ does not descend to S unless $mathcal{T}$ does. This is in contrast to a result of Friedman and Morgan that such $mathcal{G}$ always descend to singular del Pezzo surfaces over $mathbb{C}$ from their desingularizations.

Education and Food Consumption Patterns: Quasi-Experimental Evidence from Indonesia

How does food consumption improve educational outcomes is an important policy issue for developing countries. Applying the Indonesian Family Life Survey (IFLS) 2014, we estimate the returns of food consumption to education and investigate if more educated individuals tend to consume healthier bundles than less-educated individuals do. We implement the Expected Outcome Methodology, which is similar to Average Treatment on The Treated (ATT) conceptualized by Angrist and Pischke (2009). We find that education tends to tilt consumption towards healthier foods. Specifically, individuals with upper secondary or higher levels of education, on average, consume 31.5% more healthy foods than those with lower secondary education or lower levels of education. With respect to unhealthy food consumption, more highly-educated individuals, on average, consume 22.8% less unhealthy food than less-educated individuals. This suggests that education can increase the inequality in the consumption of healthy food bundles. Our study suggests that it is important to design policies to expand education for all for at least up to higher secondary level in the context of Indonesia. Our finding also speaks to the link between food-health gradient and human capital formation for a developing country such as Indonesia.

MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks

We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal-organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.

The Effects of Human Aspects on the Requirements Engineering Process: A Systematic Literature Review

Requirements Engineering (RE) requires the collaboration of various roles in SE, such as requirements engineers, stakeholders and other developers, and it is thus a highly human dependent process in software engineering (SE). Identifying how human aspects such as personality, motivation, emotions, communication, gender, culture and geographic distribution might impact RE would assist us in better supporting successful RE. The main objective of this paper is to systematically review primary studies that have investigated the effects of various human aspects on RE. A systematic literature review (SLR) was conducted and identified 474 initial primary research studies. These were eventually filtered down to 74 relevant, high-quality primary studies. Among the studies, the effects of communication have been considered in many RE studies. Other human aspects such as personality, motivation and gender have mainly been investigated to date related to SE studies including RE as one phase. Findings show that studying more than one human aspect together is beneficial, as this reveals relationships between various human aspects and how they together impact the RE process. However, the majority of these studied combinations of human aspects are unique. From 56.8% of studies that identified the effects of human aspects on RE, 40.5% identified the positive impact, 30.9% negative, 26.2% identified both impacts whereas 2.3% mentioned that there was no impact. This implies that a variety of human aspects positively or negatively affects the RE process and a well-defined theoretical analysis on the effects of different human aspects on RE remains to be defined and practically evaluated. Findings of this SLR help researchers who are investigating the impact of various human aspects on RE by identifying well-studied research areas, and highlight new areas that should be focused on in future research.

Van der Waals Anomaly

In inhomogeneous dielectric media the divergence of the electromagnetic stress is related to the gradients of varepsilon and mu, which is a consequence of Maxwells equations. Investigating spherically symmetric media we show that this seemingly universal relationship is violated for electromagnetic vacuum forces such as the generalized van der Waals and Casimir forces. The stress needs to acquire an additional anomalous pressure. The anomaly is a result of renormalization, the need to subtract infinities in the stress for getting a finite, physical force. The anomalous pressure appears in the stress in media like dark energy appears in the energy-momentum tensor in general relativity. We propose and analyse an experiment to probe the van der Waals anomaly with ultracold atoms. The experiment may not only test an unusual phenomenon of quantum forces, but also an analogue of dark energy, shedding light where nothing is known empirically.

Gravitational deflection of light by rotating black holes in Horndeski gravity

With the assumptions of a quartic scalar field, finite energy of the scalar field in a volume, and vanishing radial component of 4-current at infinity, an exact static and spherically symmetric hairy black hole solution exists in the framework of Horndeski theory with parameter $Q$, which encompasses the Schwarzschild black hole ($Q=0$). We obtain the axially symmetric counterpart of this hairy solution, namely the rotating Horndeski black hole, which contains as a special case the Kerr black hole ($Q=0$). Interestingly, for a set of parameters ($M, a$, and $Q$), there exists an extremal value of the parameter $Q=Q_{e}$, which corresponds to an extremal black hole with degenerate horizons, while for $Q<Q_{e}$, it describes a nonextremal black hole with Cauchy and event horizons, and no black hole for $Q>Q_{e}$. We investigate the effect of the $Q$ on the rotating black hole spacetime geometry and analytically deduce corrections to the light deflection angle from the Kerr and nonrotating Horndeski gravity black hole values. For the S2 source star, we calculate the deflection angle for the Sgr A* model of rotating Horndeski gravity black hole for both prograde and retrograde photons and show that it is larger than the Kerr black hole value.

Addressable quantum gates

We extend the circuit model of quantum comuptation so that the wiring between gates is soft-coded within registers inside the gates. The addresses in these registers can be manipulated and put into superpositions. This aims at capturing indefinite causal orders, as well as making their geometrical layout explicit. We show how to implement the quantum switch and the polarizing beam splitter within our model. One difficulty is that the names used as addresses should not matter beyond the wiring they describe, i.e. the evolution should commute with renamings. Yet, the evolution may act nontrivially on these names. Our main technical contribution is a full characterization of such nameblind matrices.

The Influence of Human Aspects on Requirements Engineering: Software Practitioners Perspective

Requirements Engineering (RE) is a process that requires high collaboration between various roles in software engineering (SE), such as requirements engineers, stakeholders, developers, etc. Their demographics, views, understanding of technologies, working styles, communication and collaboration capabilities make RE highly human dependent. Identifying how human aspects such as motivation, domain knowledge, communication skills, personality, emotions, culture, etc might impact RE would help us to improve the RE activities and SE in general. The aim of this study is to understand current industry perspectives on the influence of human aspects on RE. We surveyed 111 software practitioners involved in RE activities, and our findings show that 86.4% of participants agree, that the success of RE greatly depends on the people involved in it. Software practitioners consider motivation, domain knowledge, attitude, communication skills and personality as highly important human aspects when involved in RE. A set of factors, we categorize as human/social and technical were identified as software practitioners motivation factors when involved in RE activities, where the majority of are motivated due to human/social factors. Furthermore, our findings suggest that software practitioners personality characteristics should also be paid more attention to as they are important when conducting RE effectively.

Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition

Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-making problems. However, in high-risk environments such as healthcare, manufacturing, automotive or aerospace, it is often challenging to bridge the gap between an apparently optimal policy learnt by an agent and its real-world deployment, due to the uncertainties and risk associated with it. Broadly speaking RL agents face two kinds of uncertainty, 1. aleatoric uncertainty, which reflects randomness or noise in the dynamics of the world, and 2. epistemic uncertainty, which reflects the bounded knowledge of the agent due to model limitations and finite amount of information/data the agent has acquired about the world. These two types of uncertainty carry fundamentally different implications for the evaluation of performance and the level of risk or trust. Yet these aleatoric and epistemic uncertainties are generally confounded as standard and even distributional RL is agnostic to this difference. Here we propose how a distributional approach (UA-DQN) can be recast to render uncertainties by decomposing the net effects of each uncertainty. We demonstrate the operation of this method in grid world examples to build intuition and then show a proof of concept application for an RL agent operating as a clinical decision support system in critical care

Formulation of branched transport as geometry optimization

The branched transport problem, a popular recent variant of optimal transport, is a non-convex and non-smooth variational problem on Radon measures. The so-called urban planning problem, on the contrary, is a shape optimization problem that seeks the optimal geometry of a street or pipe network. We show that the branched transport problem with concave cost function is equivalent to a generalized version of the urban planning problem. Apart from unifying these two different models used in the literature, another advantage of the urban planning formulation for branched transport is that it provides a more transparent interpretation of the overall cost by separation into a transport (Wasserstein-1-distance) and a network maintenance term, and it splits the problem into the actual transportation task and a geometry optimization.