ethicalabs/Research-Intent-Collab huggingface dataset:
The rights of small indigenous peoples of the North to land and traditional use of natural resources: legal regulation and judicial practice

The article examines pressing issues in the legal regulation and judicial protection of the rights of small indigenous peoples of the North to land and traditional natural resource use, which are of fundamental importance to them. At the same time, the study aims to demonstrate the degree of correlation between changes in this sphere in the context of the ethnic amendments to the Constitution of the Russian Federation in 2020 and the rulings of the Constitutional Court of the Russian Federation, which for the first time interpreted the legislation on these peoples in an expanded manner from the standpoint of preserving traditional culture. Within this framework, the article analyzes the state of regulation of these rights under federal laws in relation to international legal and constitutional requirements, as well as recent legal innovations aimed at strengthening them. In conjunction with the regulatory system, judicial practice is examined as a means of clarifying and filling gaps in legislation concerning the rights of northern peoples, particularly regarding the registration of individuals belonging to these groups and their exercise of rights to land use, traditional hunting, and fishing. The article notes some progress in these areas, but vital issues for these ethnic communities remain without adequate legal resolution, in particular, the gratuitous use of land and other natural resources, priority access to such lands and resources, the participation of representatives of northern peoples in decision-making affecting their interests, and the optimization of interaction with subsoil users. The authors suggest that this situation is partly due to the continued adherence of ordinary courts to a formalistic approach in adjudicating cases, as well as their disregard for the special constitutional and legal status of Russia’s small indigenous peoples. Higher judicial instances have also failed to demonstrate proactive engagement with these issues, as evidenced by the absence of judicial practice reviews and, at times, a clear reluctance to identify the constitutional and legal meaning of specific guarantees enshrined for northern peoples. T.G.Morshchakova, to whose anniversary this article is dedicated, has drawn attention to these problems of judicial formalism. This article serves, in a sense, as a confirmation of the formalism in judicial practice, illustrated through the interpretation of the rights of northern peoples to land and traditional natural resource use.
Context in object detection: a systematic literature review

Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and effectiveness of object detectors. For example, where recognizing an isolated object might be challenging, context information can improve comprehension of the scene. This study explores the impact of various context-based approaches to object detection. Initially, we investigate the role of context in object detection and survey it from several perspectives. We then review and discuss the most recent context-based object detection approaches and compare them. Finally, we conclude by addressing research questions and identifying gaps for further studies. More than 265 publications are included in this survey, covering different aspects of context in different categories of object detection, including general object detection, video object detection, small object detection, camouflaged object detection, zero-shot, one-shot, and few-shot object detection. This literature review presents a comprehensive overview of the latest advancements in context-based object detection, providing valuable contributions such as a thorough understanding of contextual information and effective methods for integrating various context types into object detection, thus benefiting researchers.
Gradient Equilibrium in Online Learning: Theory and Applications

We present a new perspective on online learning that we refer to as gradient equilibrium: a sequence of iterates achieves gradient equilibrium if the average of gradients of losses along the sequence converges to zero. In general, this condition is not implied by, nor implies, sublinear regret. It turns out that gradient equilibrium is achievable by standard online learning methods such as gradient descent and mirror descent with constant step sizes (rather than decaying step sizes, as is usually required for no regret). Further, as we show through examples, gradient equilibrium translates into an interpretable and meaningful property in online prediction problems spanning regression, classification, quantile estimation, and others. Notably, we show that the gradient equilibrium framework can be used to develop a debiasing scheme for black-box predictions under arbitrary distribution shift, based on simple post hoc online descent updates. We also show that post hoc gradient updates can be used to calibrate predicted quantiles under distribution shift, and that the framework leads to unbiased Elo scores for pairwise preference prediction.
Critical Care Resource Nurse Team

The Critical Care Resource Nurse Team (CCRNT) evolved from our traditional Rapid Response Team (RRT). The CCRNT is a unique paradigm created using the conceptual framework of the American Association of Critical Care Nursing Synergy Model for Patient Care. The goal of the CCRNT is to help align nurse competencies with patient needs. This team was seen as a much-needed strong clinical support by nursing leadership as large numbers of inexperienced new graduate registered nurses were hired and senior experienced nurses were leaving the workforce. The CCRNT supports 24/7 RRT, Code Blue, Code Stroke, Emergency Airway Response Team, Early Warning System, Medical Emergency Team, simulation team training, and bedside nurse support and mentoring. Coverage expanded from inpatient medical/surgical areas to all areas of the medical center and included hospital-wide initiatives. The focus for the CCRNT changed to a system-wide nursing and patient safety model to improve practice and intervene in near-miss events. This model improved communication between disciplines, provided critical surveillance of at-risk patients, supported patient safety with a significant decrease in patient mortality, and offered a 24/7 expert critical care nurse resource team for staff.
DILIE: Deep Internal Learning for Image Enhancement

We consider the generic deep image enhancement problem where an input image is transformed into a perceptually better-looking image. Recent methods for image enhancement consider the problem by performing style transfer and image restoration. The methods mostly fall into two categories: training data-based and training data-independent (deep internal learning methods). We perform image enhancement in the deep internal learning framework. Our Deep Internal Learning for Image Enhancement framework enhances content features and style features and uses contextual content loss for preserving image context in the enhanced image. We show results on both hazy and noisy image enhancement. To validate the results, we use structure similarity and perceptual error, which is efficient in measuring the unrealistic deformation present in the images. We show that the proposed framework outperforms the relevant state-of-the-art works for image enhancement.
A Hybrid Recommendation-Based Brokerage System for Compound Freight Logistics: Integrating Collaborative Filtering and Knowledge-Based Ranking

The rapid digitization of freight logistics has intensified the need for intelligent brokerage platforms that can efficiently match shippers with carriers in compound (multi-carrier, multi-modal) supply chain environments. Traditional brokerage systems often rely on one-to-one assignment or simple rule-based filters, which limit shipper choice, fail to learn from historical preferences, and overlook the inherent trade-offs between cost, transit time, reliability, and sustainability. This paper addresses these limitations by proposing a hybrid recommendation-based brokerage system for compound freight logistics. Drawing on advances in recommender systems, the framework integrates two complementary approaches: (i) collaborative filtering, which learns latent shipper preferences from historical booking and rating patterns across similar logistics users, and (ii) knowledge-based recommendation, which encodes domain-specific constraints such as cargo type, weight limits, delivery time windows, and regulatory compliance. A ranking module then generates a personalized, ordered list of qualified carriers or multi-leg transport chains for each shipment request. Unlike conventional assignment models, the system preserves shipper agency while reducing search and negotiation overhead. The proposed architecture is evaluated using a simulated freight dataset derived from real-world logistics operations, with performance measured against baseline methods (e.g., pure collaborative filtering, rule-based assignment) in terms of precision@k, user satisfaction (implicitly modeled via carrier selection rates), and computational efficiency. Preliminary results indicate that the hybrid approach significantly outperforms single-strategy baselines, particularly in cold-start scenarios where new shippers or infrequent lanes appear. This work contributes to the emerging intersection of recommender systems and freight logistics, offering a scalable, preference-aware alternative to traditional brokerage models and opening new directions for personalized, multi-objective logistics decision support.
The Lateral Trigger Probability function for the ultra-high energy cosmic ray showers detected by the pierre auger observatory

info:eu-repo/semantics/published
The Accretion Disk Limit Cycle Mechanism in the Black Hole X‐Ray Binaries: Toward an Understanding of the Systematic Effects

43 pages, 14 figures
Using a deep neural network for the design of an optical photonic crystal half-subtractor

In this paper, a new structure, to our knowledge, for an optical half-subtractor using periodic photonic crystal structures is presented. The difference between this structure and previous ones is that a deep neural network (DNN) is used to optimize the proposed structure. A network composed of silicon rods in air is utilized to achieve the desired structure. The waveguides of this half-subtractor are designed using linear defects and a very small number of point defects. The variable parameters in this simulation are the defect rods, which are optimized by altering them. This structure, due to its simplicity in design and small size, is suitable for use in optical integrated circuits. Another advantage of this structure is the equal power output for the high states. In this study, the plane wave expansion (PWE) method was used to calculate the band structure, and the finite difference time domain (FDTD) method was used to calculate the light emission and output of light power.
Protein Complex Structure Prediction With <scp>AlphaFold</scp>‐Enhanced <scp>HDOCK</scp> in <scp>CAPRI</scp> Rounds 47–55

ABSTRACTProtein–protein interactions play a critical role in numerous biological processes, and understanding these interactions is essential for deciphering cellular mechanisms and designing therapeutic interventions. Predicting protein–protein complex structures by computational methods is an important approach to studying protein–protein interactions. The CAPRI (Critical Assessment of PRediction of Interactions) experiment has served as a benchmark for evaluating computational methods for predicting protein complex structures. We participated in CAPRI Rounds 47–55 and continuously refined our complex structure prediction strategies throughout this period. Initially, our approach was based on a hybrid docking strategy that combined template‐based and ab initio docking methods. However, starting from Round 53, we integrated AlphaFold into our prediction pipeline. Inspired by the experiences of other participants in Round 54, we further refined our use of AlphaFold by enhancing the sampling strategy, which significantly improved our prediction accuracy in Round 55.
The Implementation of Quality Management Systems in Laboratory, Nursing, Radiology and Their Impact on Patient Care and Safety

Quality management systems (QMS) have been increasingly implemented in healthcare settings to improve patient care and safety. This study aims to explore the implementation of QMS in laboratory, nursing, and radiology departments and their impact on patient care and safety. The implementation of QMS in the laboratory involves the establishment of quality control measures, standard operating procedures, and continuous monitoring of processes to ensure accuracy and reliability of test results. Nursing is at the forefront of patient care, and the implementation of QMS in nursing departments focuses on standardizing care processes, improving communication and collaboration among healthcare teams, and ensuring patient safety through the use of evidence-based practices. Radiology departments also benefit from the implementation of QMS, as it helps to standardize imaging protocols, ensure equipment maintenance and calibration, and improve the accuracy and timeliness of diagnostic reports. The impact of QMS implementation on patient care and safety is significant. Studies have shown that healthcare organizations with robust QMS in place have lower rates of medical errors, reduced patient complications, and improved patient outcomes. Furthermore, QMS implementation can also lead to cost savings by reducing waste, improving efficiency, and minimizing the need for rework.
Examples of inconsistency in optimization by expected improvement

We consider the 1D Expected Improvement optimization based on Gaussian processes having spectral densities converging to zero faster than exponentially. We give examples of problems where the optimization trajectory is not dense in the design space. In particular, we prove that for Gaussian kernels there exist smooth objective functions for which the optimization does not converge on the optimum.
IMPLICATION OF CHANGE THE VIEW CONSTITUTIONAL COURT IN JUDICIAL REVIEW ABOUT OPEN LEGAL POLICY THROUGH THE CONSTITUTIONAL COURT ‘S DECISION NUMBER: 116/PUU-XXI/2023

The view of the Constitutional Court towards the judicial review, which is an open legal policy, has changed with the emergence of new arguments, about the understanding of one of the constitutional principles, namely the principle of proportionality. The Decision of the Indonesian Constitutional Court Number 116/PUU-XXI/2023 has stated that Article 416 paragraph (1) of Law Number 7 of 2017 concerning Conditional Unconstitutional General Elections. This paper examines the implications of this Constitutional Court decision on the implementation of Law Number 7 of 2017, especially with regard to Article 416 paragraph (1) and the possibility of changing the Constitutional Court's attitude towards the provisions of the Presidential Threshold (Article 222). This study is a doctrinal study, using primary and secondary legal materials, in the form of laws and regulations, Constitutional Court decisions, research results relevant to the study. The results of this study stated that the establishment of the Constitutional Court finally made changes, with new arguments being put forward when interpreting whether or not it was in accordance with the constitution. This change will open the opportunity for the repetition of test cases on the Presidential Threshold, which have been rejected by the Constitutional Court.
Study of mechanisms of thermal cracking by means of molecular dynamic simulation of organic geopolymers using reaction force field - ReaxFF

Kerogen, the insoluble fraction of sedimentary organic matter, is a complex mixing of organic macromolecules, the structure of which evolves during geological times as a function of temperature mainly. The thermal evolution of kerogen is at the origin of hydrocarbon deposits in sedimentary basins. Understanding and quantifying the physicochemical processes associated to this transformation is therefore important to improve the evaluation of petroleum systems. During this study, two geopolymers were selected in order to represent the thermal decomposition of typical structures in natural kerogen. Firstly, an experimental protocol was set up (1) to define molecular structures of the geopolymers, and (2) to describe primary mechanisms of decomposition of both geopolymers. The two samples were analysed using off-line pyrolysis experiments, at five reference temperatures comprised between 200 to 300°C. A kinetic scheme accounting for early decomposition processes was derived from these experimental observations for each geopolymer. Secondly, molecular models were submitted to molecular dynamic simulations using a reactive force field (ReaxFF). ReaxFF simulations provided theoretical supports to the key-processes derived from laboratory experiments. On the overall, results of this study suggested that kinetic models in one step (= parallels reactions implemented in standard (commercial) basin simulators do not adequately reproduce the physicochemistry of organic matter decomposition processes in natural source rocks
A new model to study galactic cosmic ray modulation in an asymmetrically bounded heliosphere

We developed a two‐dimensional steady state numerical model that can describe the modulation of galactic cosmic rays in an arbitrarily shaped heliospheric outer modulation boundary. The model is based on the well‐known Parker transport equation for cosmic rays and incorporates: (1) gradient and curvature drift effects including drift along a wavy neutral sheet, (2) adiabatic energy changes, (3) diffusion parallel and anisotropic diffusion perpendicular to the heliospheric magnetic field, and (4) outward convection due to a latitude‐dependent expanding solar wind plasma. The model is described together with results illustrating the effects of an asymmetric heliospheric boundary on the spatial and energy variations of galactic cosmic ray protons throughout the heliosphere. The cosmic ray distribution in the inner heliosphere was only marginally altered, but significant changes were found beyond ∼30 AU, especially in the upwind direction. Concerning the energy dependence of these effects the maximum cosmic ray redistribution occurred at ∼50 MeV. At energies of &gt; 5 GeV the effects became insignificant. Assuming a highly asymmetric heliosphere, we calculated a longitudinal gradient of 0.4 % deg−1 for 1 GV protons at 50 AU, which may be considered an upper limit.
The Definition of the Soul in Defining Humanity: From Ancient Greek Philosophy to Ontological Discussions in Artificial Intelligence Ethics

The foundational secret of Western philosophy lies in the concept of the soul. The intricacy of this notion concerns the intrinsic question of human existence, guiding individuals toward truth and goodness. It occupies the intersection of human nature and divinity, anthropology and theology. Plato’s description of the cosmic origin of the soul is, to some extent, imbued with the mythological character inherent in Western philosophy. Plato posits that the soul and body are separate; the soul is an eternal, transcendent, and independent spiritual entity, and the existence of the theory of Forms must be mediated through the soul. Aristotle, on the other hand, offers a theory regarding human cognitive abilities; he rejects the separation of the soul and body, while asserting that the soul holds a superior and fundamental existence in relation to the body. Plato’s doctrine (centered on justice) and Aristotle’s theory (centered on virtue) present significant divergences, yet both emphasize the essential value of the human soul, asserting that human reason is the essence of the soul. This reason, as the essential attribute of humanity, ultimately aims to establish a rational order among human beings.
Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion

Knowledge Graph Completion (KGC) attempts to predict missing facts in a Knowledge Graph (KG). Recently, there's been an increased focus on designing KGC methods that can excel in the inductive setting, where a portion or all of the entities and relations seen in inference are unobserved during training. Numerous benchmark datasets have been proposed for inductive KGC, all of which are subsets of existing KGs used for transductive KGC. However, we find that the current procedure for constructing inductive KGC datasets inadvertently creates a shortcut that can be exploited even while disregarding the relational information. Specifically, we observe that the Personalized PageRank (PPR) score can achieve strong or near SOTA performance on most datasets. In this paper, we study the root cause of this problem. Using these insights, we propose an alternative strategy for constructing inductive KGC datasets that helps mitigate the PPR shortcut. We then benchmark multiple popular methods using the newly constructed datasets and analyze their performance. The new benchmark datasets help promote a better understanding of the capabilities and challenges of inductive KGC by removing any shortcuts that obfuscate performance. The code and dataset and can be found at https://github.com/HarryShomer/Better-Inductive-KGC.
Computational Design and Additive Manufacturing of 3D-Printed Prosthetics for Enhanced Mobility and Performance

This paper discusses the potential application of computational design and additive manufacturing in veterinary prosthetics, demonstrated with the example of a feline limb. A finite element analysis (FEA)-based design optimization framework was used to develop eight prosthetic leg geometries in CAD and analyze them in ANSYS (2024R) under a static loading condition, to evaluate their performance under the loading condition. Structural variations were in the form of grooves, holes and shell reinforcements, which were investigated to see how they affect stiffness, stress distribution and viability of the 3D-printed prosthetics. Design variations were introduced through the inclusion or exclusion of structural attributes such as grooves, holes, and shell reinforcements. The simulations evaluated total deformation, von Mises stress, and equivalent elastic strain to determine the mechanical efficiency of each configuration. Results showed that designs incorporating shells and holes provided the best overall performance, as groove-free designs minimized stress concentrations and achieved the highest stiffness. Notably, the configuration featuring a hole and shell without grooves achieved the lowest mean deformation (14.09 µm) and stress values, making it the most structurally viable and suitable for real-life application. This study highlights the potential of 3D printing to produce cost-effective, patient-specific prosthetics and underscores the importance of structural optimization for improving biomechanical compatibility and mobility. The next stage of this work will involve fabricating the optimized design using 3D printing, followed by mechanical testing to validate the simulation results and assess real-world performance. Future work will also incorporate topology optimization to further reduce weight while maintaining structural integrity.