The World of Yesterday. Memories of a European eveals itself as a nostalgic
confession about a lost world, yet it can also be read today as a lucid reflection on the
fragility of European civilization. Viewed through this nostalgic lens, Stefan Zweig’s
volume delicately captures the gradual disappearance of individual freedoms, the abando-
nment of fundamental laws and principles, and the transformation of the state into an
instrument of repression. Austria, his homeland, undergoes a process of legal disintegration
that culminates in the Nazi annexation and the establishment of dictatorship. This collapse
of the rule of law in Austria is not sudden, but gradual—much like in other European
countries of the time. Zweig observes how society, too naïve and confident in the stability
of old structures, fails to react in time to halt this decay. A collective passivity enables the
rise of authoritarian regimes that exploit the weaknesses of democratic systems to seize
power. In this sense, the book becomes a fundamental lesson for the present: without
respect for the rule of law and its institutions, without a vigilant society to safeguard them,
any democracy risks sliding into authoritarianism. Through his testamentary work, Zweig
offers not merely a historical warning, but also a call to awareness of the dangers that
threaten freedom even within the most advanced democracies. What is most disturbing, as
M. Gîndu observes, is that—disregarding the historical context and the faces of evil that
animate it—everything that unfolds in the book feels terrifyingly current. At the end of the
memoir, Zweig notes that although Europe and its ideals of liberty and fundamental rights
have been destroyed, there still remains a glimmer of hope—that these principles will one
day flourish again, even if in a different time and form. Although his recollections are
deeply marked by melancholy over the disappearance of the civilized world, the entire text
becomes, in this reading, a search for meaning in turbulent and shifting times—a
reconciliation with one’s own condition and a reminder that, amid chaos, there remains
room for fragile yet vital moments of beauty. This is the enduring power of nostalgia: it
urges us not merely to mourn the past, but to remember, to remain conscious, and to act.
Law in general. Comparative and uniform law. Jurisprudence
James Price, Guillermo Valenzuela-Venegas, Oskar Vågerö
et al.
The large-scale deployment of wind power is central to Europe`s energy transition but faces challenges due to its social and environmental impacts on communities. Here we assess how the tolerance of local stakeholders to such impacts translates across spatial scales to shape the cost and design of the continent`s net-zero electricity system using a soft-linked modelling framework. We find that lower impact tolerance can reduce the role of onshore wind in Europe reaching net-zero by up to 84% relative to a future where wind enjoys higher acceptance, with other low carbon sources needing to be scaled up to compensate. This translates into total European electricity system costs increasing by between 2-14% while some countries see costs escalating by 20% or more. Our results show that the local acceptance of onshore wind is a key structural driver of the system and highlight the system value of policies to promote it.
The study investigates the juridico-technological architecture of international public health instruments, focusing on their implementation across India, the European Union, the United States and low- and middle-income countries (LMICs), particularly in Sub-Saharan Africa. It addresses a research lacuna: the insufficient harmonisation between normative health law and algorithmic public health infrastructures in resource-constrained jurisdictions. The principal objective is to assess how artificial intelligence augments implementation of instruments grounded in IHR 2005 and the WHO FCTC while identifying doctrinal and infrastructural bottlenecks. Using comparative doctrinal analysis and legal-normative mapping, the study triangulates legislative instruments, WHO monitoring frameworks, AI systems including BlueDot, Aarogya Setu and EIOS, and compliance metrics. Preliminary results show that AI has improved early detection, surveillance precision and responsiveness in high-capacity jurisdictions, whereas LMICs face infrastructural deficits, data privacy gaps and fragmented legal scaffolding. The findings highlight the relevance of the EU Artificial Intelligence Act and GDPR as regulatory prototypes for health-oriented algorithmic governance and contrast them with embryonic AI integration and limited internet penetration in many LMICs. The study argues for embedding AI within a rights-compliant, supranationally coordinated regulatory framework to secure equitable health outcomes and stronger compliance. It proposes a model for algorithmic treaty-making inspired by FCTC architecture and calls for WHO-led compliance mechanisms modelled on the WTO Dispute Settlement Body to enhance pandemic preparedness, surveillance equity and transnational governance resilience.
L’Europa nella sua storia millenaria, nelle sue alterne, e quasi mai indolori vicende, nel tentativo di risolvere una
complessità dettata dall’autorevolezza delle diverse leadership che si sono succedute e riconfigurate man
mano nel tempo, secondo capacità di conquista del potere o attraverso presunti diritti dinastici, rappresenta un
buon laboratorio tra ciò che è un susseguirsi e un sovrapporsi di storie di imperi e ciò che è l’effetto della
crescita di un impero spesso sotteso al concetto di imperialismo. Nel loro evolversi come modelli di governance
e di pianificazione territoriale, gli imperi si sono presentati nella storia dell’umanità, ed europea, non solo quali
formule concentrate di potere e di potenza ma anche quali veicoli di distribuzione di idee e di valori, creando
contaminazioni decisive nei popoli che li compongono sino a provocarne sia l’ascesa che lo stesso declino,
entrambi dovuti alle condizioni economiche di supporto o alla tenuta interna, in termini di consenso,
dell’autorità imperiale e della sua riconosciuta o meno legittimità. Ottone d’Asburgo-Lorena (o Ottone von
Habsburg-Lothringen), deputato al parlamento europeo nel 1979 per l’Unione cristiano-sociale tedesca (CSU),
e riconfermato sino al 1999, non mise mai da parte il significato “europeo” dell’impero sovranazionale austroungarico.
Parole chiave: Aristide Briand, Ausgleich, Confederazione germanica, Conferenza di Berlino, continentalismo,
Europa dei popoli, europeismo, Francesco Giuseppe, Grande Guerra, Guerra dei Trent’anni, Gustav Stresemann,
Impero ottomano, liberalismo, liberismo, machtpolitik, Orbis Europeus Christianus, ordine continentale, Ottone
von Habsburg, Pace di Augusta, Pace di Carlowitz, Pace di Vestfalia, Paneuropa, Parlamento europeo, Questione
d’Oriente, Richard Nikolaus Graf Coudenhove-Kalergi, Totalitarismi, Vienna, Weimar
L’article analyse le recul progressif du modèle français de protection des espaces naturels fondé sur l’acquisition publique, fragilisé par la baisse des ressources et le recul des acquisitions du Conservatoire du littoral et des départements. Face à cette crise, émergent des outils alternatifs – en particulier les obligations réelles environnementales (ORE), inspirées des conservation easements américaines – qui permettent de protéger des terrains privés sans les acquérir. Leur institutionnalisation est toutefois limitée par la persistance de plusieurs freins dont notamment leur faible attractivité fiscale, l’absence de registre national, et le principe d’indépendance des législations qui empêche leur intégration dans la planification urbaine. L’étude montre ainsi que la France adopte les instruments américains sans en transposer la logique profonde, ce qui conduit à une situation paradoxale : l’État n’a plus les moyens d’acquérir pour protéger, mais n’assume pas pleinement une stratégie de protection sans acquisition.
The recent success of large language models (LLMs) has sparked a growing interest in training large-scale models. As the model size continues to scale, concerns are growing about the depletion of high-quality, well-curated training data. This has led practitioners to explore training approaches like Federated Learning (FL), which can leverage the abundant data on edge devices while maintaining privacy. However, the decentralization of training datasets in FL introduces challenges to scaling large models, a topic that remains under-explored. This paper fills this gap and provides qualitative insights on generalizing the previous model scaling experience to federated learning scenarios. Specifically, we derive a PAC-Bayes (Probably Approximately Correct Bayesian) upper bound for the generalization error of models trained with stochastic algorithms in federated settings and quantify the impact of distributed training data on the optimal model size by finding the analytic solution of model size that minimizes this bound. Our theoretical results demonstrate that the optimal model size has a negative power law relationship with the number of clients if the total training compute is unchanged. Besides, we also find that switching to FL with the same training compute will inevitably reduce the upper bound of generalization performance that the model can achieve through training, and that estimating the optimal model size in federated scenarios should depend on the average training compute across clients. Furthermore, we also empirically validate the correctness of our results with extensive training runs on different models, network settings, and datasets.
This chapter explores the complexities of sports governance, taxation, dispute resolution, and the impact of digital transformation within the sports sector. This study identifies a critical research gap regarding the integration of innovative technologies to enhance governance and talent identification in sports law. The objective is to evaluate how data-driven approaches and AI can optimize recruitment processes; also ensuring compliance with existing regulations. A comprehensive analysis of current governance structures and taxation policies,(ie Income Tax Act and GST Act), reveals preliminary results indicating that reform is necessary to support sustainable growth in the sports economy. Key findings demonstrate that AI enhances player evaluation by minimizing biases and expanding access to diverse talent pools. While the Court of Arbitration for Sport provides an efficient mechanism for dispute resolution. The implications emphasize the need for regulatory reforms that align taxation policies with international best practices, promoting transparency and accountability in sports organizations. This research contributes valuable insights into the evolving dynamics of sports management, aiming to foster innovation and integrity in the industry.
For strategic litigation, the existence of independent judicial institutions is a prerequisite. In this Article, based on the case of Poland, I analyze what happens when some domestic judicial institutions are weakened and how this affects the ability of different stakeholders to engage in strategic litigation. I argue that strategic litigation was an important tool used by civil society and crucial for countering democratic backsliding in Poland in 2015–2023. In addition to traditional actors involved in strategic litigation in Poland, new ones have joined—such as the Human Rights Commissioner (the “Ombudsman”) or increased their involvement—such as corporate actors. Also, the prosecution office, controlled by the populist government, became active in litigation conducted by right-wing NGOs. Paradoxically, the rule of law crisis also resulted in the popularization of strategic litigation before the Court of Justice of the European Union and some domestic courts, which began to apply the Constitution directly.
Law of Europe, Law in general. Comparative and uniform law. Jurisprudence
(Series Information) European Papers - A Journal on Law and Integration, 2024 9(1), 423-442 | Article | (Table of Contents) I. Introduction. – II. The legal principles governing EU coordination in international law-making fora: a weakness or a strength of Member States' external sovereignty? – III. The case of the Maritime Labour Convention: the journey of human rights from the EU to China via international law. – III.1. The Conven-tion and the EU's role in its elaboration. – III.2. The EU's rationale for uploading human rights standards into international law: a rights-oriented approach. – III.3. The impact of the Maritime Labour Conven-tion in China. – IV. Conclusion. | (Abstract) This Article presents two arguments and explores the relationship between them. First, the principles governing coordination between the EU and its Member States in multilateral fora (mainly, sincere co-operation and unity in the EU's representation) serve to increase the Member States' influence in inter-national law-making. Thus, there is a trade-off between the autonomy of Member States to determine their own positions in multilateral fora, and their capacity to influence such fora: the lesser the former, the greater the latter. Second, such an influence can be used by the EU and its Member States to pro-mote human rights laws abroad, “uploading” high standards into multilateral treaties, which are subse-quently “downloaded” by third states through ratification and implementation. Therefore, there is a link between the mentioned EU external relations law principles (which are a “condition” for a success-ful promotion) and the obligation to promote values set in arts 3(5) and 21 TEU (which provides the “direction” of the promotion). Consequently, when Member States complain about excessive EU intru-sion into their autonomy through common positions in multilateral fora, they should bear in mind that they are not only bound by the above-mentioned legal principles, but that their obligation to promote certain values abroad is also at stake. The case of the EU's influence on the Maritime Labour Conven-tion and its impact on Chinese law and policy is used to illustrate the arguments.
Accurately measuring magnetic field strength in the interstellar medium, including giant molecular clouds (GMCs), remains a significant challenge. We present a machine learning approach using Denoising Diffusion Probabilistic Models (DDPMs) to estimate magnetic field strength from synthetic observables such as column density, dust continuum polarization vector orientation angles, and line-of-sight (LOS) nonthermal velocity dispersion. We trained three versions of the DDPM model: the 1-channel DDPM (using only column density), the 2-channel DDPM (incorporating both column density and polarization angles), and the 3-channel DDPM (which combines column density, polarization angles, and LOS nonthermal velocity dispersion). We assessed the models on both synthetic test samples and new simulation data that were outside the training set's distribution. The 3-channel DDPM consistently outperformed both the other DDPM variants and the power-law fitting approach based on column density alone, demonstrating its robustness in handling previously unseen data. Additionally, we compared the performance of the Davis-Chandrasekhar-Fermi (DCF) methods, both classical and modified, to the DDPM predictions. The classical DCF method overestimated the magnetic field strength by approximately an order of magnitude. Although the modified DCF method showed improvement over the classical version, it still fell short of the precision achieved by the 3-channel DDPM.
Dominic Laniewski, Eric Lanfer, Simon Beginn
et al.
Low Earth Orbit Satellite Networks such as Starlink promise to provide world-wide Internet access. While traditionally designed for stationary use, a new dish, released in April 2023 in Europe, provides mobile Internet access including in-motion usage, e.g., while mounted on a car. In this paper, we design and build a mobile measurement setup. Our goal is to fully autonomously conduct continuous Starlink measurements while the car is in motion. We share our practical experiences, including challenges regarding the permanent power supply. We measure the Starlink performance over the span of two months from mid-January to mid-March 2024 when the car is in motion. The measurements consist of all relevant network parameters, such as the download and upload throughput, the RTT, and packet loss, as well as detailed power consumption data. We analyze our dataset to assess Starlink's mobile performance in Central Europe, Germany, and compare it to stationary measurements in proximity. We find that the mobile performance is significantly worse than stationary performance. The power consumption of the new dish is higher, but seems to be more correlated to the heating function of the dish than to the speed of the vehicle.
Coping with prolonged periods of low availability of wind and solar power, also referred to as renewable energy droughts or "Dunkelflaute", emerges as a key challenge for realizing decarbonized energy systems based on renewable energy sources. Here we investigate the role of long-duration electricity storage and geographical balancing in dealing with such events, combining a time series analysis of renewable availability with power sector modeling of 35 historical weather years. We find that extreme droughts define long-duration storage operation and investment. Assuming policy-relevant interconnection in our model, we find 351 TWh long-duration storage capacity or 7% of yearly electricity demand in the least-cost system that can cope with the most extreme event in Europe. While nuclear power can partially reduce storage needs, the storage-mitigating effect of fossil backup plants in combination with carbon removal is limited. Policymakers and system planners should prepare for a rapid expansion of long-duration storage to safeguard the renewable energy transition in Europe.
Caterina Morelli, Simone Boccaletti, Paolo Maranzano
et al.
The assessment of corporate sustainability performance is extremely relevant in facilitating the transition to a green and low-carbon intensity economy. However, companies located in different areas may be subject to different sustainability and environmental risks and policies. Henceforth, the main objective of this paper is to investigate the spatial and temporal pattern of the sustainability evaluations of European firms. We leverage on a large dataset containing information about companies' sustainability performances, measured by MSCI ESG ratings, and geographical coordinates of firms in Western Europe between 2013 and 2023. By means of a modified version of the Chavent et al. (2018) hierarchical algorithm, we conduct a spatial clustering analysis, combining sustainability and spatial information, and a spatiotemporal clustering analysis, which combines the time dynamics of multiple sustainability features and spatial dissimilarities, to detect groups of firms with homogeneous sustainability performance. We are able to build cross-national and cross-industry clusters with remarkable differences in terms of sustainability scores. Among other results, in the spatio-temporal analysis, we observe a high degree of geographical overlap among clusters, indicating that the temporal dynamics in sustainability assessment are relevant within a multidimensional approach. Our findings help to capture the diversity of ESG ratings across Western Europe and may assist practitioners and policymakers in evaluating companies facing different sustainability-linked risks in different areas.
Abstract - Some texts by Alfenus Varo and Titius Aristo speak of an actio negatoria granted in cases where the servitude relationship appears difficult to configure: since the formula ‘ius non esse ’ is also used by Alfenus in place of reivindicatio, it appears extremely likely are not hypotheses of edictal actions but of formulas dating back to the procedure of the agere per
sponsionem.
Rafid Mahmood, James Lucas, Jose M. Alvarez
et al.
Modern deep learning systems require huge data sets to achieve impressive performance, but there is little guidance on how much or what kind of data to collect. Over-collecting data incurs unnecessary present costs, while under-collecting may incur future costs and delay workflows. We propose a new paradigm for modeling the data collection workflow as a formal optimal data collection problem that allows designers to specify performance targets, collection costs, a time horizon, and penalties for failing to meet the targets. Additionally, this formulation generalizes to tasks requiring multiple data sources, such as labeled and unlabeled data used in semi-supervised learning. To solve our problem, we develop Learn-Optimize-Collect (LOC), which minimizes expected future collection costs. Finally, we numerically compare our framework to the conventional baseline of estimating data requirements by extrapolating from neural scaling laws. We significantly reduce the risks of failing to meet desired performance targets on several classification, segmentation, and detection tasks, while maintaining low total collection costs.