ethicalabs/Research-Intent-Collab huggingface dataset:
Fault-tolerant quantum computer architectures using hierarchies of quantum error-correcting codes.

Quantum computers have been shown to efficiently solve a class of problems for which no efficient solution is otherwise known. Physical systems can implement quantum computation, but devising realistic schemes is an extremely challenging problem largely due to the effect of noise. A quantum computer that is capable of correctly solving problems more rapidly than modern digital computers requires some use of so-called fault-tolerant components. Code-based fault-tolerance using quantum error-correcting codes is one of the most promising and versatile of the known routes for fault-tolerant quantum computation. This dissertation presents three main, new results about code-based fault-tolerant quantum computer architectures. The first result is a large new family of quantum codes that go beyond stabilizer codes, the most well-studied family of quantum codes. Our new family of codeword stabilized codes contains all known codes with optimal parameters. Furthermore, we show how to systematically find, construct, and understand such codes as a pair of codes: an additive quantum code and a classical (nonlinear) code. Second, we resolve an open question about universality of so-called transversal gates acting on stabilizer codes. Such gates are universal for classical fault-tolerant computation, but they were conjectured to be insufficient for universal fault-tolerant quantum computation. We show that transversal gates have a restricted form and prove that some important families of them cannot be quantum universal. This is strong evidence that so-called quantum software is necessary to achieve universality, and, therefore, fault-tolerant quantum computer architecture is fundamentally different from classical computer architecture. Finally, we partition the fault-tolerant design problem into levels of a hierarchy of concatenated codes and present methods, compatible with rigorous threshold theorems, for numerically evaluating these codes.
Dark Matter as Dense Color Superconductor

We discuss a novel cold dark matter candidate which is formed from the ordinary quarks during the QCD phase transition when the axion domain wall undergoes an unchecked collapse due to the tension in the wall. If a large number of quarks is trapped inside the bulk of a closed axion domain wall, the collapse stops due to the internal Fermi pressure. In this case the system in the bulk, may reach the critical density when it undergoes a phase transition to a color superconducting phase with the ground state being the quark condensate, similar to BCS theory. If this happens, the new state of matter representing the diquark condensate with a large baryon number B > 10^{20} becomes a stable soliton-like configuration. Consequently, it may serve as a novel cold dark matter candidate.
AI-Augmented Creativity: Evaluating the Role of Generative Models in Music Composition

Artificial Intelligence (AI) has become an increasingly influential force in the domain of creative expression, with generative models now playing a significant role in music composition. These models, particularly those built on deep learning architectures, are capable of producing melodies, harmonies, rhythms, and full musical pieces in a variety of styles. While AI's contributions to music composition raise questions about authorship, originality, and artistic value, they also open new pathways for human-machine collaboration and creative augmentation. This paper explores the foundational technologies behind AI-generated music, including recurrent neural networks, transformers, and variation auto encoders. It investigates the use of generative models in both autonomous composition and co- creative systems. Use cases in film scoring, commercial music production, and educational tools are analyzed. Real- world applications and collaborations between musicians and AI systems are reviewed to understand the strengths and limitations of current technologies. Ethical and philosophical considerations surrounding creativity, intellectual property, and cultural representation are discussed. Finally, the paper considers the challenges of musical coherence, expressivity, and human-AI interaction, and outlines future directions in multimodal creativity, personalized composition, and real-time improvisation. AI-augmented music composition stands as a dynamic intersection of technology and art, expanding the boundaries of what it means to create music.
Toward Cosmological Simulations of Dark Matter on Quantum Computers

Abstract                State-of-the-art cosmological simulations on classical computers are limited by time, energy, and memory usage. Quantum computers can perform some calculations exponentially faster than classical computers, using exponentially less energy and memory, and may enable extremely large simulations that accurately capture the whole dynamic range of structure in the universe within statistically representative cosmic volumes. However, not all computational tasks exhibit a “quantum advantage.” Quantum circuits act linearly on quantum states, so nonlinearities (e.g., self-gravity in cosmological simulations) pose a significant challenge. Here we outline one potential approach to overcome this challenge and solve the (nonlinear) Schrödinger–Poisson equations for the evolution of self-gravitating dark matter, based on a hybrid quantum–classical variational algorithm framework (Lubasch et al.). We demonstrate the method with a proof-of-concept mock quantum simulation, envisioning a future where quantum computers will one day lead simulations of dark matter.
Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis

Histopathological evaluation of tissue sections remains the gold standard for the diagnosis, classification, and grading of breast cancer (BC). The widespread adoption of whole-slide imaging (WSI) has enabled the digitization of histological slides and facilitated the development of artificial intelligence (AI) approaches for computational pathology. In recent years, machine learning and deep learning (DL) algorithms have been increasingly investigated for the analysis of hematoxylin and eosin (H&amp;E)-stained images, with potential applications in tumor detection, histological classification, prognostic stratification, and prediction of treatment response. This narrative review summarizes recent developments in AI-driven models applied to BC histopathology and discusses their potential role in supporting diagnostic and prognostic assessment. Several studies have demonstrated the promising performance of DL algorithms in tasks such as the detection of lymph node metastases, assessment of residual tumor after neoadjuvant therapy, and prediction of clinical outcomes from histopathological images. Emerging research has also explored the possibility of inferring molecular and biomarker information from histology images, although these approaches currently identify statistical associations rather than direct molecular measurements. Despite the rapid expansion of this research field, significant barriers remain before routine clinical implementation can be achieved. Key challenges include dataset bias, variability in staining and image acquisition, limited external validation across institutions, and the need for transparent and reproducible model development. In addition, the translation of AI-based systems into clinical practice requires compliance with regulatory frameworks governing software used for medical purposes, such as those established by the U.S. Food and Drug Administration. Overall, AI represents a promising research direction in computational pathology and may contribute to decision-support tools capable of assisting pathologists in the analysis of digital slides. Continued efforts toward methodological rigor, large multicenter datasets, and prospective validation studies will be essential to determine the future role of AI in BC histopathology.
sj-docx-1-mpp-10.1177_23814683231202993 – Supplemental material for Cost-Effectiveness Analysis of CT-Based Finite Element Modeling for Osteoporosis Screening in Secondary Fracture Prevention: An Early Health Technology Assessment in the Netherlands

Supplemental material, sj-docx-1-mpp-10.1177_23814683231202993 for Cost-Effectiveness Analysis of CT-Based Finite Element Modeling for Osteoporosis Screening in Secondary Fracture Prevention: An Early Health Technology Assessment in the Netherlands by Jieyi Li, Marco Viceconti, Xinshan Li, Pinaki Bhattacharya, David M. J. Naimark and Anwar Osseyran in MDM Policy &amp; Practice
Weather Forecasting Using Photovoltaic System and Neural Network

This paper presents the applicability of Artificial Neural Network (ANN) for weather forecasting using a Photovoltaic system. The main objective is to predict daily weather conditions based on various measured parameters gained from the PV system. In this work, Multiple Multilayer Perceptron (MMLP) network with majority voting technique was used and trained using Levenberg Marquardt (LM) algorithm. Voting technique is widely used in many applications to solve real world problem. Different techniques of voting are used such as majority rules, decision making, consensus democracy, consensus government and supermajority. The way of the voting technique is different depending on the problem involved. Majority voting technique was applied in the study so that the performance of MMLP can be approved as compared to single MLP network. The proposed work has been used to classify four weather conditions; rain, cloudy, dry day and storm. The system can be used to represent a warning system for likely adverse conditions. Experimental results demonstrate that the applied technique gives better performance than the conventional ANN concept of choosing an MLP with least number of hidden neurons.
Statistical mechanics perspective on the phase transition in vertex covering of finite-connectivity random graphs

The vertex-cover problem is studied for random graphs $G_{N,cN}$ having $N$ vertices and $cN$ edges. Exact numerical results are obtained by a branch-and-bound algorithm. It is found that a transition in the coverability at a $c$-dependent threshold $x=x_c(c)$ appears, where $xN$ is the cardinality of the vertex cover. This transition coincides with a sharp peak of the typical numerical effort, which is needed to decide whether there exists a cover with $xN$ vertices or not. For small edge concentrations $c\ll 0.5$, a cluster expansion is performed, giving very accurate results in this regime. These results are extended using methods developed in statistical physics. The so called annealed approximation reproduces a rigorous bound on $x_c(c)$ which was known previously. The main part of the paper contains an application of the replica method. Within the replica symmetric ansatz the threshold $x_c(c)$ and the critical backbone size $b_c(c)$ can be calculated. For $c
The Connection and Distinction Between “Responsibility to Protect” and “Humanitarian Intervention” under International Law

This paper explores the connection and distinction between the “Responsibility to Protect” (R2P) and “Humanitarian Intervention” within the framework of international law. R2P emphasizes limited international intervention in cases of severe human rights violations, while humanitarian intervention focuses on using coercive means to safeguard human well-being during crises and conflicts. Through case studies, we observe diverse applications and outcomes of R2P and humanitarian intervention in past international affairs, reflecting global community stances on human rights protection and maintaining global security. The paper also reveals challenges and controversies in the practical implementation of these concepts. Key findings of are follow. R2P emphasizes preventive measures, highlighting the importance of taking action before a human rights crisis escalates into a broader humanitarian crisis, with humanitarian intervention serving as a means to implement R2P. R2P provides a robust legal and ethical foundation for humanitarian intervention, integrating widely recognized legal and ethical principles into international consensus, ensuring the legitimacy of humanitarian intervention in specific contexts. R2P emphasizes multilateral decision-making and consultation in the decision-making process, minimizing controversies associated with unilateral actions and garnering broad international support for intervention efforts. In practice, R2P and humanitarian intervention complement each other, constituting essential tools for the international community to uphold human values and protect victims from widespread atrocities. These research findings lay the groundwork for a more comprehensive understanding and improvement of the principles of R2P and humanitarian intervention.
Computerized Clinical Decision Support: A Technology to Implement and Validate Evidence Based Guidelines

Faced with a documented crisis of patients not receiving appropriate care, there is a need to implement and refine evidence-based guidelines (EBGs) to ensure that patients receive the best care available. Although valuable in content, among their deficiencies, EBGs do not provide explicit methods to bring proven therapies to the bedside. Computerized information technology, now an integral part of the US healthcare system at all levels, presents clinicians with information from laboratory, imaging, physiologic monitoring systems, and many other sources. It is imperative that we clinicians use this information technology to improve medical care and efficacy of its delivery. If we do not do this, nonclinicians will use this technology to tell us how to practice medicine. Computerized clinical decision support (CCDS) offers a powerful method to use this information and implement a broad range of EBGs. CCDS is a technology that can be used to develop, implement, and refine computerized protocols for specific processes of care derived from EBGs, including complex care provided in intensive care units. We describe this technology as a desirable option for the trauma community to use information technology and maintain the trauma surgeon/intensivist's essential role in specifying and implementing best care for patients. We describe a process of logical protocol development based on standardized clinical decision making to enable EBGs. The resulting logical process is readily computerized, and, when properly implemented, provides a stable platform for systematic review and study of the process and interventions.: CCDS to implement and refine EBG derived computerized protocols offers a method to decrease variability, test interventions, and validate improved quality of care.
The United Nations Security Council and the Enduring Challenge of the Use of Force in Inter-state Relations

The United Nations Security Council has, after the end of the Cold War, become a markedly more active player on the international stage. The number of resolutions adopted – in particular resolutions adopted under Chapter VII of the Charter of the United Nations – the number of peacekeeping missions, and the number of mandatory sanctions regimes have all increased considerably. The Council has also broadened its view of security, and adopted several resolutions that deal with thematic issues rather than with specific conflicts. In this paper, we raise the question of whether this increased activity has been accompanied by a corresponding increase in the effectiveness of the Council. We do this by analysing how the Council has developed its reaction to the use of force in inter-state relations. Throughout the existence of the United Nations, the prohibition of the use of force, other than in self-defence, has been one of the most fundamental rules that the Council is assigned the responsibility to uphold. We conclude that despite the increased level of activity on the part of the Council since the end of the Cold War, its ability to react consistently and authoritatively to violations of the prohibition of the use of force has not been strengthened.
Beyond rhetoric? Evaluating the Responsibility to Protect as a norm of humanitarian intervention

The purpose of this paper is to examine whether, after 16 years or more of its development, R2P has emerged as an international norm of intervention. As an idea, it is clear that R2P has a considerable degree of prominence within international discourse. As we will come to see, there has been much work undertaken in the development of its language and conceptual framework over the years. But, whilst this architecture is important to the process of its development as an international norm, it will be argued that more is needed to support the claim that R2P has fully emerged as an international norm, i.e R2P’s capacity to influence and shape the decision-making of states.
Calculation of biodiversity level between different land-uses to improve conservation outcomes of biodiversity offsetting

Abstract   The goal of the mitigation hierarchy is to counteract the losses of biodiversity due to development projects by avoiding, reducing and as a last resort, by offsetting these losses. However, the implementation of offset measures is currently very unsatisfactory from a biodiversity perspective, and many development projects do not incorporate them. Here, we proposed a method for comparing biodiversity level between different land-uses to propose a set of alternative offset measures offering the same biodiversity outcomes. Biodiversity level (including both bird and bat communities) was calculated from the variation in biodiversity from each land-uses (fallow, grass strip, bush, grass strip with bushes, hedgerow) compared to crops (control). In order to account for local specificities of environmental assessment studies, we modulated the calculation of the biodiversity level following three examples of scenarios that differed according to the weights assigned to different species. For each scenario, hedgerow was always the land-use that generated the greatest biodiversity level, and was found to be the best alternative in terms of effect size. The advantage of this method and its weighting system is that the opinions of local stakeholders and specificities of species, such as their sensibility level to the development project or their conservation status, can be taken into account, resulting in the results being better appropriated. Thus, offset measures could be implemented more efficiently thanks to multi-taxonomic calculations and weighing possibilities, improving biodiversity outcomes. This method can also be used to compare the residual loss of biodiversity and biodiversity gains from implemented offset measures, using the same biodiversity metric.
Leveraging deep learning models for optimized cargo tracking and transportation efficiency in Logistics

Amid the digital transformation of the logistics industry, smart logistics algorithms have emerged as a crucial technology to enhance efficiency and reduce costs. This paper reviews the evolution of traditional logistics technologies and highlights the pivotal roles played by advancements such as the Internet of Things, big data analytics, artificial intelligence, and automation in driving logistics innovation. It delves into the application of intelligent logistics algorithms across areas like path optimization, intelligent scheduling, data mining and prediction, and smart warehousing. To address the challenge of inconsistencies between training and testing objectives, the paper introduces DRL4Route, a deep reinforcement learning-based framework for path optimization, along with the DRL4Route-GAE model. Extensive offline experiments and online deployments validate that the model significantly outperforms existing optimal benchmark methods on real datasets, improving metrics like location deviation squared and top-three location prediction accuracy. These research findings provide essential support for advancing the intelligent development of the logistics industry.
Application of probabilistic safety assessment (PSA) to the power reactor innovative small module (PRISM)

Several countries show interest in the Generation-IV power reactor innovative small module (PRISM), including: Canada, Japan, Korea, Saudi Arabia and the United Kingdom. Generation IV International Forum (GIF) has recommended the utilizing of probabilistic safety assessment (PSA) in evaluating the safety of Generation-IV reactors. This paper reviews the PSA performed for PRISM using SAPHIRE 7.27 code. This work shows that the core damage frequency (CDF) of PRISM for a single module is estimated by 8.5E-8/year which is lower than the Generation-IV target that is 1E-6 core damage per year. The social risk of PRISM (likelihood of latent cancer fatality) with evacuation is estimated by 9.0E-12/year which is much lower than the basic safety objective (BSO) that is 1E-7/year. The social risk without evacuation is estimated by 1.2E- 11/year which is also much lower than the BSO. For the individual risk (likelihood of prompt fatality), it is concluded that it can be considered negligible with evacuation (1.0E-13/year). Assuming no evacuation, the individual risk is 2.7E-10/year which is again much lower than the BSO. In comparison with other PSAs performed for similar sodium fast reactors (SFRs), it shows that PRISM concept has the lowest CDF.
Understanding the Physics of Particle Accelerators

This open access book introduces readers to the physics of particle accelerators, by means of beam dynamics simulations and exercises using the computer code ZGOUBI. The respective chapters are organized chronologically and trace the historical development of accelerators from electrostatic columns to storage rings, to the numerous variations on resonant acceleration and focusing techniques, while also addressing side aspects such as synchrotron radiation and spin dynamics. The book offers computer simulations in which readers can manipulate, guide, and accelerate charged particles and particle beams in most types of particle accelerator. By performing these simulation exercises, they will acquire a deeper understanding of charged particle beam optics, accelerator physics and technology, as well as the why and how of when to use one technology or the other. These exercises guide readers through a virtual world of accelerator and beam simulations, and involve e.g. manipulating beams for cancer therapy, producing synchrotron radiation for condensed matter research, accelerating polarized ion beams for nuclear physics research, etc. In addition to acquiring an enhanced grasp of physics, readers will discover the basic theoretical and practical aspects of particle accelerators’ main components: guiding and focusing magnets, radio-wave accelerating cavities, wigglers, etc.