The Scope of an Autonomous Attack

‘Attack’ is an important term of art in international humanitarian law that serves as the basic unit of reference for many targeting obligations. It is often also asserted that human commanders of autonomous weapon systems (AWS) must make legal determinations ‘per individual attack’. Divergent interpretations on what constitutes an attack nevertheless lead to drastically different conclusions with regard to the technology’s lawfulness: interpreted narrowly (‘each shot’), it precludes AWS technology entirely, while interpreted broadly (‘each activation’), it sanctions extensive autonomous activity. This paper theorises that imprecision on the scope of attack is an underappreciated aspect of the AWS controversy that hampers theoretical and diplomatic advancements. The legal boundaries of autonomous attacks are analysed through the lens of targeting law, and a scaling methodology is proposed that allows commanders to determine the maximum extent to which autonomous activity may still lawfully be grouped into one single attack. The paper argues that both overly narrow and broad interpretations are inconsistent with targeting principles and practice, instead favouring a middle-ground approach based on temporal and spatial proximity that properly respects international humanitarian law’s (IHL) balancing philosophy between humanitarian and military interests. Through consideration of practical scenarios, the paper subsequently demonstrates how this impacts the application of targeting rules, such as at what intervals the commander’s duty to verify or cancel is triggered and under what circumstances successive autonomous engagements may be grouped together for proportionality assessments

A call to rethink African scholars beyond “local experts”:mobility, race, and gender in Europe

Development discourses have been widely criticized for creating hierarchical dichotomies, such as “developed” (the global North) and “developing” (the global majority), with the former being the ideal standard to which the rest must catch up. The development paradigm has infiltrated academic spaces globally, including international research collaborations, creating various categories such as (non)scientific (local) expertise. We see such hierarchies as mechanisms of legitimation to maintain the ongoing subjugation of African scholars based on the historical and contemporary asymmetries in global knowledge production. Informed by the experiences of five female African doctoral researchers in the Netherlands, this paper problematizes and disrupts the concepts of “Expert” and “local expert”. We question the relevance of these concepts in a context where global knowledge production continues to feed from coloniality and also question the old power relations that continue to enable knowledge inequalities between the global North and global South.</p

The experimental philosophy of logic and formal epistemology:Conditionals

Classical logic was long believed to provide the norms of reasoning. But more recently researchers interested in the norms of reasoning have shifted their attention toward probability theory and various concepts and rules that can be defined in probabilistic terms. In philosophy, this shift gave rise to formal epistemology, while in psychology, it led to the New Paradigm psychology of reasoning. Whereas there has traditionally been a clear division of labor between philosophers and psychologists working on reasoning, the past decade has seen an increasing collaboration between philosophers and psychologists, from which an experimental philosophy of logic and formal epistemology emerged. An area in which the fruits of this collaboration have been par-ticularly in evidence is the research concerned with conditionals and conditional rea-soning. This chapter showcases contributions to this area to underline the value of the said branch of experimental philosophy more generally.</p

Markovnikov-Selective Cobalt-Catalyzed Wacker-Type Oxidation of Styrenes into Ketones under Ambient Conditions Enabled by Hydrogen Bonding

The replacement of palladium catalysts for Wacker-type oxidation of olefins into ketones by first-row transition metals is a relevant approach for searching more sustainable protocols. Besides highly sophisticated iron catalysts, all the other first-row transition metal complexes have only led to poor activities and selectivities. Herein, we show that the cobalt-tetraphenylporphyrin complex is a competent catalyst for the aerobic oxidation of styrenes into ketones with silanes as the hydrogen sources. Remarkably, under room temperature and air atmosphere, the reactions were exceedingly fast (up to 10 minutes) with a low catalyst loading (1 mol %) while keeping an excellent chemo- and Markovnikov-selectivity (up to 99 % of ketone). Unprecedently high TOF (864 h−1) and TON (5,800) were reached for the oxidation of aromatic olefins under these benign conditions. Mechanistic studies suggest a reaction mechanism similar to the Mukaiyama-type hydration of olefins with a change in the last fundamental step, which controls the chemoselectivity, thanks to a unique hydrogen bonding network between the ethanol solvent and the cobalt peroxo intermediate.</p

Effective divisors on projectivized Hodge bundles and modular Forms

We construct vector‐valued modular forms on moduli spaces of curves and abelian varieties using effective divisors in projectivized Hodge bundles over moduli of curves. Cycle relations tell us the weight of these modular forms. In particular, we construct basic modular forms for genus 2 and 3. We also discuss modular forms on the moduli of hyperelliptic curves. In that case, the relative canonical bundle is a pull back of a line bundle on a ℙ1‐bundle over the moduli of hyperelliptic curves and we extend that line bundle to a compactification so that its push down is (close to) the Hodge bundle and use this to construct modular forms. In the Appendix, we use our method to calculate divisor classes in the dual projectivized k‐Hodge bundle determined by Gheorghita-Tarasca and by Korotkin-Sauvaget-Zograf

Leaf microbiome dysbiosis triggered by T2SS-dependent enzyme secretion from opportunistic Xanthomonas pathogens

In healthy plants, the innate immune system contributes to maintenance of microbiota homoeostasis, while disease can be associated with microbiome perturbation or dysbiosis, and enrichment of opportunistic plant pathogens like Xanthomonas. It is currently unclear whether the microbiota change occurs independently of the opportunistic pathogens or is caused by the latter. Here we tested if protein export through the type-2 secretion system (T2SS) by Xanthomonas causes microbiome dysbiosis in Arabidopsis thaliana in immunocompromised plants. We found that Xanthomonas strains secrete a cocktail of plant cell wall-degrading enzymes that promote Xanthomonas growth during infection. Disease severity and leaf tissue degradation were increased in A. thaliana mutants lacking the NADPH oxidase RBOHD. Experiments with gnotobiotic plants, synthetic bacterial communities and wild-type or T2SS-mutant Xanthomonas revealed that virulence and leaf microbiome composition are controlled by the T2SS. Overall, a compromised immune system in plants can enrich opportunistic pathogens, which damage leaf tissues and ultimately cause microbiome dysbiosis by facilitating growth of specific commensal bacteria

Political narratives in representation:Maiden speeches of ethnic minority members of parliament

The maiden speech – the first speech given by a newly elected member of parliament (MP) – is a tradition in many parliaments, a personalized rite of passage to political power. As ethnic minority MPs remain relative newcomers, the maiden speech is, for them, even more politically charged. How do ethnic minority MPs represent their identities in this transformative moment? Our data set includes 93 ethnic minority MPs who have held a seat in the Dutch parliament, covering 88 maiden speeches, spanning 11 cycles (1986–2023). The diachronic and intersectional analysis shows that the relation between descriptive, substantive and symbolic representation for historically marginalized groups fluctuates and is influenced by the political environment. The ‘firsts’ of a particular gender/ethnicity intersectional group are less likely to narrate a minority identity than non-firsts. Progressive party ideology influences the extent to which ethnic minority MPs emphasize an (intersectional) minoritized identity. Personal stories and family histories are often used to counter stereotypes, unmute silenced cultures and share values. The focus on the maiden speech as a political narrative sheds light on the blurry lines between substantive, symbolic and descriptive representation. The political narrative is a strategic tool for MPs from historically disadvantaged groups to represent collective identities

Multiphasic Continuous-Flow Reactors for Handling Gaseous Reagents in Organic Synthesis:Enhancing Efficiency and Safety in Chemical Processes

The use of reactive gaseous reagents for the production of active pharmaceutical ingredients (APIs) remains a scientific challenge due to safety and efficiency limitations. The implementation of continuous-flow reactors has resulted in rapid development of gas-handling technology because of several advantages such as increased interfacial area, improved mass- and heat transfer, and seamless scale-up. This technology enables shorter and more atom-economic synthesis routes for the production of pharmaceutical compounds. Herein, we provide an overview of literature from 2016 onwards in the development of gas-handling continuous-flow technology as well as the use of gases in functionalization of APIs

Domain Generalization in Time Series Forecasting

Domain generalization aims to design models that can effectively generalize to unseen target domains by learning from observed source domains. Domain generalization poses a significant challenge for time series data, due to varying data distributions and temporal dependencies. Existing approaches to domain generalization are not designed for time series data, which often results in suboptimal or unstable performance when confronted with diverse temporal patterns and complex data characteristics. We propose a novel approach to tackle the problem of domain generalization in time series forecasting. We focus on a scenario where time series domains share certain common attributes and exhibit no abrupt distribution shifts. Our method revolves around the incorporation of a key regularization term into an existing time series forecasting model: domain discrepancy regularization. In this way, we aim to enforce consistent performance across different domains that exhibit distinct patterns. We calibrate the regularization term by investigating the performance within individual domains and propose the domain discrepancy regularization with domain difficulty awareness. We demonstrate the effectiveness of our method on multiple datasets, including synthetic and real-world time series datasets from diverse domains such as retail, transportation, and finance. Our method is compared against traditional methods, deep learning models, and domain generalization approaches to provide comprehensive insights into its performance. In these experiments, our method showcases superior performance, surpassing both the base model and competing domain generalization models across all datasets. Furthermore, our method is highly general and can be applied to various time series models