En este ensayo, quisiera proponer una reflexión inicial sobre la significativa convergencia entre el iusfeminismo y el pacifismo jurídico en la crítica a la guerra. En primer lugar, mencionaré brevemente algunas consideraciones sobre el iusfeminismo como matriz constitutiva de las teorías críticas del derecho; posteriormente, intentaré analizar la naturaleza del pacifismo jurídico como una teoría crítica del Derecho. A continuación, tras precisar por qué el iusfeminismo y el pacifismo jurídico deben entenderse como “saberes inéditos”, me detendré, de forma esquemática, en el análisis de su convergencia metodológica, teórica y temática.
Artificial intelligence is reshaping science, society, and power. Yet many debates over its likely impact remain fixated on extremes: utopian visions of universal benefit and dystopian fears of existential doom, or an arms race between the U.S. and China, or the Global North and Global South. What's missing is a serious conversation about distribution - who gains, who loses, and who decides. The global AI landscape is increasingly defined not just by geopolitical divides, but by the deepening imbalance between public governance and private control. As governments struggle to keep up, power is consolidating in the hands of a few tech firms whose influence now rivals that of states. If the twentieth century saw the rise of international institutions, the twenty-first may be witnessing their eclipse - replaced not by a new world order, but by a digital oligarchy. This essay explores what that shift means for international law, global equity, and the future of democratic oversight in an age of silicon sovereignty.
Heather J. Alexander, Jonathan A. Simon, Frédéric Pinard
The law draws a sharp distinction between objects and persons, and between two kinds of persons, the ''fictional'' kind (i.e. corporations), and the ''non-fictional'' kind (individual or ''natural'' persons). This paper will assess whether we maximize overall long-term legal coherence by (A) maintaining an object classification for all future AI systems, (B) creating fictional legal persons associated with suitably advanced, individuated AI systems (giving these fictional legal persons derogable rights and duties associated with certified groups of existing persons, potentially including free speech, contract rights, and standing to sue ''on behalf of'' the AI system), or (C) recognizing non-fictional legal personhood through legal identity for suitably advanced, individuated AI systems (recognizing them as entities meriting legal standing with non-derogable rights which for the human case include life, due process, habeas corpus, freedom from slavery, and freedom of conscience). We will clarify the meaning and implications of each option along the way, considering liability, copyright, family law, fundamental rights, civil rights, citizenship, and AI safety regulation. We will tentatively find that the non-fictional personhood approach may be best from a coherence perspective, for at least some advanced AI systems. An object approach may prove untenable for sufficiently humanoid advanced systems, though we suggest that it is adequate for currently existing systems as of 2025. While fictional personhood would resolve some coherence issues for future systems, it would create others and provide solutions that are neither durable nor fit for purpose. Finally, our review will suggest that ''hybrid'' approaches are likely to fail and lead to further incoherence: the choice between object, fictional person and non-fictional person is unavoidable.
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.
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.
This paper provides a comprehensive doctrinal analysis of the evolving landscape of environmental law, encompassing both international and national perspectives. The analysis delves into key doctrinal themes, including the precautionary principle; the polluter pays principle, and sustainable development, examining their application and interpretation in the context of contemporary environmental challenges. Drawing on a wide range of case-law, this study explores landmark environmental cases that have significantly shaped the trajectory of environmental jurisprudence. Through a comparative examination of various national approaches to environmental regulation, the paper highlights the diverse strategies adopted to address environmental issues and the challenges encountered in their implementation. Furthermore, the paper outlines the critical role played by international legal instruments in shaping national environmental laws, emphasizing the need for cohesive global efforts to combat pressing environmental concerns. The conclusion offers insights into the contemporary challenges faced by environmental law, proposing potential legal reforms and recommendations for a sustainable and effective environmental regulatory framework. This doctrinal analysis aims to contribute to the ongoing discourse on environmental law, fostering a deeper understanding of the legal mechanisms essential for preserving our planet's ecological balance and ensuring a sustainable future for generations to come. Thus, in the final development recognized so far, that is understanding environmental law based on ecocentric perspective where we recognize the value of every natural component, does not actually evade the idea that human beings shall pursue their interests to safeguard their present and future generations. The thesis therefore ought to analyse the different instruments which were born out of different environmental ethical considerations at different points of time. The methodology of understanding the environmental law rationales in the light of ethics is termed as —deep level enquiry. In the present context, the environmental law involves one or more than one ethical perspective. These perspectives may be anthropocentric or non- anthropocentric. When we term, environmental law to be anthropocentric, we generally link it up with human rights morals. Human rights which are understood to be inalienable to every human being has got its recognition after several years of struggle.
Enforcement and management scholars alike expect that countries participating in an international agreement will more likely achieve predetermined targets than nonparticipating countries will. The management school ascribes this expected association to a constraining effect of the treaty; the enforcement school ascribes it to a screening effect. If the latter conjecture is correct, the association between participation and target achievement should significantly weaken (or even vanish) when controlling for targets' ambition level and other confounding factors. We test this hypothesis on a new dataset comprising three protocols under the Convention on Long-Range Transboundary Air Pollution (CLRTAP). Our results suggest that the positive association between participation and target achievement is robust to controlling for confounding factors; hence, our data suggests that these CLRTAP protocols have indeed constrained participating states.
The paper focuses on some labour law questions which arise from work in the metaverse. The first question is whether meta-work could be the next new type of work as standard employment relationship, which is going through a transformation in general. Indeed, the idea of personal work – as a main pillar of the employment relationship – was challenged by platform work in the recent years, but metaverse seems to further question the old paradigms. The article shortly examines the question of wages, for instance, as the metaverse generally relies on cryptocurrency (CC) to pay for transactions and purchases. Subsequently, the paper mainly concentrates on the analysis of health and safety at work and of the discrimination ban in metaverse.
Law in general. Comparative and uniform law. Jurisprudence, Labor. Work. Working class
Resumo O presente artigo aborda o modo pelo qual a relação entre as obras de Augusto Teixeira de Freitas e Friedrich Carl von Savigny é retratada pela literatura de viés jurídico-historiográfico no Brasil. Parte-se da hipótese de que os mencionados juristas participam da dimensão histórica do paradigma contemporâneo da civilística brasileira, configurando, dessa maneira, parte da anatomia da tradição jurídica nacional. O desenvolvimento da investigação demonstra que a relação entre Freitas e Savigny é propagada como um lugar-comum literário que, além de exercer funções retóricas, sugere uma relativa independência entre o conhecimento científico-historiográfico e a articulação da memória no campo jurídico. Tal lugar-comum serviria como suporte para a demonstração da aproximação entre o direito civil brasileiro e o alemão - significando, com isso, o afastamento em relação ao direito português e ao modelo francês de codificação civil -, além de medida da originalidade e qualidade da obra de Teixeira de Freitas, bem como da própria civilística nacional.
Law in general. Comparative and uniform law. Jurisprudence
Álvaro dos Santos Maciel , Benizete Ramos de Medeiros
Crises econômicas e a precarização do Direito do Trabalho tem gerado flexibilizações nefastas para o trabalhador. A terceirização e o desmonte preconizado pela Reforma Trabalhista têm sido apontados como instrumentos para recuperar a economia. Todavia, o crescimento do trabalho análogo à escravidão é uma realidade crescente e a terceirização das relações empregatícias tem aberto espaço para tal violação. O presente artigo analisa tais institutos com um viés crítico fulcrado nas teorias marxistas preconizadas por Jean-Paul de Gaudemar e a mobilização da força do trabalho para o capital, já que são retiradas do trabalhador todas as possibilidades materiais de existência social digna, exceto a da venda de sua força de trabalho por vezes o expondo a situações vulneráveis e desumanas.Faz-se uma abordagem de teorias Sociológicas e Jurídicas, no contexto legal com apresentação de casos empíricos na tentativa de que medidas efetivas sejam tomadas para combater a precarização do trabalho e a exploração dos trabalhadores, com a devida punição dos infratores, reanálise do instituto da terceirização pós reforma trabalhista, bem como a criação de políticas que viabilizem a transparência das informações que contribuampara um desenvolvimento sustentável e uma nova educação.
Law in general. Comparative and uniform law. Jurisprudence, Political science (General)
Our project aims at helping and supporting stakeholders in refugee status adjudications, such as lawyers, judges, governing bodies, and claimants, in order to make better decisions through data-driven intelligence and increase the understanding and transparency of the refugee application process for all involved parties. This PhD project has two primary objectives: (1) to retrieve past cases, and (2) to analyze legal decision-making processes on a dataset of Canadian cases. In this paper, we present the current state of our work, which includes a completed experiment on part (1) and ongoing efforts related to part (2). We believe that NLP-based solutions are well-suited to address these challenges, and we investigate the feasibility of automating all steps involved. In addition, we introduce a novel benchmark for future NLP research in refugee law. Our methodology aims to be inclusive to all end-users and stakeholders, with expected benefits including reduced time-to-decision, fairer and more transparent outcomes, and improved decision quality.
In this paper, we establish an explicit higher reciprocity law for the polynomial ring over a nonprincipal ultraproduct of finite fields. Such an ultraproduct can be taken over the same finite field, which allows to recover the classical higher reciprocity law for the polynomial ring $\mathbb{F}_q[t]$ over a finite field $\mathbb{F}_q$ that is due to Dedekind, Kühne, Artin, and Schmidt. On the other hand, when the ultraproduct is taken over finite fields of unbounded cardinalities, we obtain an explicit higher reciprocity law for the polynomial ring over an infinite field in both characteristics $0$ and $p >0$ for some prime $p$. We then use the higher reciprocity law to prove an analogue of the Grunwald--Wang theorem for such a polynomial ring in both characteristics $0$ and $p > 0$ for some prime $p$.
The article substantiates the Ukrainian system of guarantees of the rights and freedoms of internally displaced persons to determine ways to increase their effectiveness in martial law. Research on the legal status of IDPs in Ukraine has been conducted since 2014, but a new wave of forced displacement has provoked numerous changes and transformations in this special status. In addition, Russia's full-scale invasion has caused countless new challenges. Therefore, further research and justification of the legal status of IDPs are essential.
 General guarantees include political, economic, social, ideological and cultural guarantees. We propose to include normative-legal and organizational-legal to special (legal) guarantees. The effectiveness of the guarantee depends on the interconnectedness of all types of guarantees and their coordinated functioning. Development of normative-legal and institutional guarantees, clearly conditioned by the existence of a number of general legal guarantees (political, economic, social, cultural and ideological).
 The condition for restoring the violated rights and freedoms of Ukrainians, obtaining fair satisfaction is the investigation of numerous crimes and violations of international law, as well as the prosecution of criminals.
 Under martial law, laws are passed quickly and on an extraordinary scale, which is justified. But such rates are not commensurate with society's usual expectations of legal regulation. Therefore, it is essential to be widely informed at the national and municipal levels about changes in legislation, especially in the interests of internally displaced persons. One of the conditions for evacuating civilians from the war zone and blocked territories is confidence in support and protection from the state and society. It is vital to minimise bureaucratic procedures and simplify administrative and social services access.
This work extends the proof of a local version of the linearized second law involving an entropy current with non-negative divergence by including the arbitrary non-minimal coupling of scalar and $U(1)$ gauge fields with gravity. In recent works, the construction of entropy current to prove the linearized second law rested on an important assumption about the possible matter couplings to gravity: the corresponding matter stress tensor was assumed to satisfy the null energy conditions. However, the null energy condition can be violated, even classically, when the non-minimal coupling of matter fields to gravity is considered. Considering small dynamical perturbations around stationary black holes in diffeomorphism invariant theories of gravity with non-minimal coupling to scalar or gauge fields, we prove that an entropy current with non-negative divergence can still be constructed. The additional non-minimal couplings that we have incorporated contribute to the entropy current, which may even survive in the equilibrium limit. We also obtain a spatial current on the horizon apart from the entropy density in out-of-equilibrium situations. We achieve this by using a boost symmetry of the near horizon geometry, which constraints the off-shell structure of a specific component of the equations of motion with newer terms due to the non-minimal couplings. The final expression for the entropy current is $U(1)$ gauge-invariant for gauge fields coupled to gravity. We explicitly check that the entropy current obtained from our abstract arguments is consistent with the expressions already available in the literature for specific model theories involving non-minimal coupling of matter with higher derivative theories of gravity. Finally, we also argue that the physical process version of the first law holds for these theories with arbitrary non-minimal matter couplings.
Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance? This question is of critical importance in applications such as autonomous driving or medical imaging where collecting data is expensive and time-consuming. Overestimating or underestimating data requirements incurs substantial costs that could be avoided with an adequate budget. Prior work on neural scaling laws suggest that the power-law function can fit the validation performance curve and extrapolate it to larger data set sizes. We find that this does not immediately translate to the more difficult downstream task of estimating the required data set size to meet a target performance. In this work, we consider a broad class of computer vision tasks and systematically investigate a family of functions that generalize the power-law function to allow for better estimation of data requirements. Finally, we show that incorporating a tuned correction factor and collecting over multiple rounds significantly improves the performance of the data estimators. Using our guidelines, practitioners can accurately estimate data requirements of machine learning systems to gain savings in both development time and data acquisition costs.
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.
There are many previous studies on the Hopf algebra $K(n)_*(K(n))$, the stable cooperations of $n$th Morava $K$-theory at an odd prime. Whereas the main part of $K(n)_*(K(n))$ corepresents the group-valued functor consisting of strict automorphisms of the Honda formal group law of height $n$, relations between the whole structure of $K(n)_*(K(n))$ including the exterior part and formal group laws have not been investigated well. Firstly, we constitute a functor $C(-)$ which is given by the fiber product of two natural homomorphism between subgroups of automorphisms of formal group laws, and the Hopf algebra $C_*$ corepresenting $C(-)$. Next, we construct a Hopf algebra homomorphism $κ^*:C_*\to K(n)_*(K(n))$ naturally. To relate $C_*$ to $K(n)_*(K(n))$, we use stable comodule algebras which are introduced by Boardman. From the algebra structure of $K(n)_*(K(n))$ which is given by Würgler and Yagita, we see that $κ^*$ is an isomorphism. Since we formulate $C_*$ by using formal group laws, the isomorphism $κ^*$ clarifies relationship between the Hopf algebra structure of $K(n)_*(K(n))$ including the exterior algebra part and formal group laws.
A partir de la conceptualización de la cesantía en edad avanzada y de la vejez, así como de los requisitos que se exigen en la actual Ley del Seguro Social para acceder a las prestaciones que se confieren en ambos seguros, en este trabajo se analizan las modificaciones que introduce la iniciativa con proyecto de Decreto por el que se reforman, adicionan y derogan diversas disposiciones de la Ley del Seguro Social y de la Ley de los Sistemas de Ahorro para el Retiro, aprobada y publicada en el Diario Oficial de la Federación del 16 de diciembre de 2020, y su inconvencionalidad al amparo del Convenio (número 102) sobre la seguridad social (norma mínima).
Peter Henderson, Ben Chugg, Brandon Anderson
et al.
We explore the promises and challenges of employing sequential decision-making algorithms -- such as bandits, reinforcement learning, and active learning -- in law and public policy. While such algorithms have well-characterized performance in the private sector (e.g., online advertising), the tendency to naively apply algorithms motivated by one domain, often online advertisements, can be called the "advertisement fallacy." Our main thesis is that law and public policy pose distinct methodological challenges that the machine learning community has not yet addressed. Machine learning will need to address these methodological problems to move "beyond ads." Public law, for instance, can pose multiple objectives, necessitate batched and delayed feedback, and require systems to learn rational, causal decision-making policies, each of which presents novel questions at the research frontier. We discuss a wide range of potential applications of sequential decision-making algorithms in regulation and governance, including public health, environmental protection, tax administration, occupational safety, and benefits adjudication. We use these examples to highlight research needed to render sequential decision making policy-compliant, adaptable, and effective in the public sector. We also note the potential risks of such deployments and describe how sequential decision systems can also facilitate the discovery of harms. We hope our work inspires more investigation of sequential decision making in law and public policy, which provide unique challenges for machine learning researchers with potential for significant social benefit.