ethicalabs/Research-Intent-Collab huggingface dataset:
Implementation of a Recommender System using Collaborative Filtering

Nowadays, consumers have a lot of choices. Electronic retailers offer a great variety of products. Because of this, there is a need for Recommender Systems. These systems aim to solve the problem of matching consumers with the most appealing products for them. They do this by analyzing either the products information details (Content Based methods) or users social behavior (Collaborative Filtering). This paper describes the Collaborative Filtering technique in more detail. It then presents one of the best methods for CF: the Matrix Factorization technique. Next, it presents two algorithms used for matrix factorization. Then, the paper describes the implementation details of a framework created by us, called Rho, that uses Collaborative Filtering. In the end, we present some results obtained after experimenting with this framework.
Statistical inference for time-changed Lévy processes via composite characteristic function estimation

In this article, the problem of semi-parametric inference on the parameters of a multidimensional Lévy process $L_t$ with independent components based on the low-frequency observations of the corresponding time-changed Lévy process $L_{\mathcal{T}(t)}$, where $\mathcal{T}$ is a nonnegative, nondecreasing real-valued process independent of $L_t$, is studied. We show that this problem is closely related to the problem of composite function estimation that has recently gotten much attention in statistical literature. Under suitable identifiability conditions, we propose a consistent estimate for the Lévy density of $L_t$ and derive the uniform as well as the pointwise convergence rates of the estimate proposed. Moreover, we prove that the rates obtained are optimal in a minimax sense over suitable classes of time-changed Lévy models. Finally, we present a simulation study showing the performance of our estimation algorithm in the case of time-changed Normal Inverse Gaussian (NIG) Lévy processes.
Understanding Groups' Properties as a Means of Improving Collaborative Search Systems

Understanding the similar properties of people involved in group search sessions has the potential to significantly improve collaborative search systems; such systems could be enhanced by information retrieval algorithms and user interface modifications that take advantage of important properties, for example by re-ordering search results using information from group members' combined user profiles. Understanding what makes group members similar can also assist with the identification of groups, which can be valuable for connecting users with others with whom they might undertake a collaborative search. In this workshop paper, we describe our current research efforts towards studying the properties of a variety of group types. We discuss properties of groups that may be relevant to designers of collaborative search systems, and propose ways in which understanding such properties could influence the design of interfaces and algorithms for collaborative Web search.
Size‐adaptive mediastinal multilesion detection in chest CT images via deep learning and a benchmark dataset

AbstractPurposeMany deep learning methods have been developed for pulmonary lesion detection in chest computed tomography (CT) images. However, these methods generally target one particular lesion type, that is, pulmonary nodules. In this work, we intend to develop and evaluate a novel deep learning method for a more challenging task, detecting various benign and malignant mediastinal lesions with wide variations in sizes, shapes, intensities, and locations in chest CT images.MethodsOur method for mediastinal lesion detection contains two main stages: (a) size‐adaptive lesion candidate detection followed by (b) false‐positive (FP) reduction and benign–malignant classification. For candidate detection, an anchor‐free and one‐stage detector, namely 3D‐CenterNet is designed to locate suspicious regions (i.e., candidates with various sizes) within the mediastinum. Then, a 3D‐SEResNet‐based classifier is used to differentiate FPs, benign lesions, and malignant lesions from the candidates.ResultsWe evaluate the proposed method by conducting five‐fold cross‐validation on a relatively large‐scale dataset, which consists of data collected on 1136 patients from a grade A tertiary hospital. The method can achieve sensitivity scores of 84.3% ± 1.9%, 90.2% ± 1.4%, 93.2% ± 0.8%, and 93.9% ± 1.1%, respectively, in finding all benign and malignant lesions at 1/8, 1/4, ½, and 1 FPs per scan, and the accuracy of benign–malignant classification can reach up to 78.7% ± 2.5%.ConclusionsThe proposed method can effectively detect mediastinal lesions with various sizes, shapes, and locations in chest CT images. It can be integrated into most existing pulmonary lesion detection systems to promote their clinical applications. The method can also be readily extended to other similar 3D lesion detection tasks.
Load Model Verification, Validation and Calibration Framework by Statistical Analysis on Field Data

Abstract                   Accurate load models are critical for power system analysis and operation. A large amount of research work has been done on load modeling. Most of the existing research focuses on developing load models, while little has been done on developing formal load model verification and validation (V&amp;V) methodologies or procedures. Most of the existing load model validation is based on qualitative rather than quantitative analysis. In addition, not all aspects of model V&amp;V problem have been addressed by the existing approaches. To complement the existing methods, this paper proposes a novel load model verification and validation framework that can systematically and more comprehensively examine load model’s effectiveness and accuracy. Statistical analysis, instead of visual check, quantifies the load model’s accuracy, and provides a confidence level of the developed load model for model users. The analysis results can also be used to calibrate load models. The proposed framework can be used as a guidance to systematically examine load models for utility engineers and researchers. The proposed method is demonstrated through analysis of field measurements collected from a utility system.
Environmental Conflict and Human Rights: Indigenous Peoples' Struggle Against Land Exploitation in Papua

Environmental conflict in Papua represents a complex intersection of land dispossession, ecological degradation, and the systemic violation of indigenous rights. While substantial research has addressed agrarian conflict in Papua, much of it remains concentrated on historical and structural dimensions particularly the legacy of colonialism in facilitating land exploitation. However, limited attention has been paid to how digital media frames these conflicts and the implications such portrayals have on public perception and the political agency of indigenous communities. This study fills that gap by analyzing how environmental conflicts in Papua are represented in online media and how these representations shape discourses around human rights and indigenous resistance. This research Using a qualitative content analysis, this research draws on secondary data from two major Indonesian news platforms, detik.com and kumparan.com, covering a specific period to ensure contextual relevance. The data comprising news reports, opinion pieces, and analytical articles were analyzed using NVivo 12 Plus to examine sentiment patterns, actor dynamics, and framing mechanisms. Social conflict theory and the principle of Free, Prior and Informed Consent (FPIC) were applied to interpret findings within a rights-based framework. Results reveal a dominant narrative centered on resource exploitation and indigenous opposition, with favorable framing of state and corporate actors and minimal attention given to indigenous perspectives. This framing bias obscures the structural roots of the conflict and undermines public understanding of its multidimensional nature. The study highlights the media’s dual role in shaping discourse and calls for more equitable communication strategies that affirm indigenous participatory rights within sustainable development agendas aligned with human rights and FPIC standards.
Leveraging State Space Models in Long Range Genomics

Long-range dependencies are critical for understanding genomic structure and function, yet most conventional methods struggle with them. Widely adopted transformer-based models, while excelling at short-context tasks, are limited by the attention module's quadratic computational complexity and inability to extrapolate to sequences longer than those seen in training. In this work, we explore State Space Models (SSMs) as a promising alternative by benchmarking two SSM-inspired architectures, Caduceus and Hawk, on long-range genomics modeling tasks under conditions parallel to a 50M parameter transformer baseline. We discover that SSMs match transformer performance and exhibit impressive zero-shot extrapolation across multiple tasks, handling contexts 10 to 100 times longer than those seen during training, indicating more generalizable representations better suited for modeling the long and complex human genome. Moreover, we demonstrate that these models can efficiently process sequences of 1M tokens on a single GPU, allowing for modeling entire genomic regions at once, even in labs with limited compute. Our findings establish SSMs as efficient and scalable for long-context genomic analysis.
Performing Nature’s Valuation: The art of natural capital accounting

Accounting for nature as capital is touted as a promising way of aligning environmental conservation with global capitalism by valuing nature like economic assets. Both its proponents and detractors speculate on what its promises might achieve if they were fully realized, i.e., if nature were actually accounted for, capitalized or commodified. There is, however, an enduring disjunction between vision and execution in this field: the promises simply do not materialize. Economizing nature proves to be extremely complex, raising not only technical hurdles but also intractable conceptual and ontological issues. We suggest taking a critical realist approach to natural capital accounting, acknowledging the inherent resistance of nature to being treated as capital. We consider the arenas dedicated to natural capital accounting as the sites of singular dramaturgical performances, whose effects extend beyond the integration of nature into economic decision making. Drawing on documents, interviews and observations at events dedicated to natural capital accounting, we highlight their theatrical character and reveal the effects they produce. This article aims to contribute to the investigation of environmental governance arenas by emphasizing their significance as venues for symbolic performance and achievement, extending beyond the traditional emphasis on regulatory and hoped-for environmental transformations.
Resisting Modern Agricultural Practices and Urge to Practice Sustainable Agriculture and Food Security in Barbara Kingsolver’s Prodigal Summer

Long-term environmental, social, and economic sustainability is the goal of sustainable agriculture, which aims to satisfy current and future agricultural demands. Having access to enough safe and nourishing food is essential to a healthy and active life. The novel Prodigal Summer revolves around multiple problems, such as environmental conservation, food security, human-nature interdependence, sustainable farming methods, and resistance to modern agricultural practices. The narrative skilfully blends the natural world with the lives of its characters to highlight the tension between conventional farming practices and the expanding movement toward sustainable agriculture. The actors discuss both traditional and contemporary farming practices in the first speech, focusing on how they understand sustainable farming and how they disagree with issues facing the industry today, such as monoculture and genetically modified organisms (GMOs). The second point of contention is how to safeguard food from modern agricultural methods and pesticides. The characters in the novel—Deanna Wolfe, Lusa, and Walker in particular—emphasize the value of biodiversity, organic farming, and the preservation of natural ecosystems in order to maintain a balance between human requirements and environmental care. These characters are acting in unique ways and using their strength and deeds to oppose males and other community members.
Mapping Chinese land system types from the perspectives of land use and management, biodiversity conservation and cultural landscape

Land is an integrated and complex system consisting of multiple natural and anthropogenic elements. The long-term exploitation and utilization of land resources by humans have transformed the terrestrial surface of the Earth and profoundly impacted the spatial patterns of regional land use and land cover. Land use/cover (LULC) data have been extensively used to monitor regional land changes and contribute to land use policy making. However, these products objectively reflect only the current coverage and biophysical attributes of the terrestrial surface but fail to characterize the multiple functions and attributes of the human-environment system (HES). In this study, we present an integrated land system classification for representing the regional HES based on multisource datasets that characterize the attributes of land use and management, biodiversity conservation and cultural landscapes. Then, 40 land system types (LSTs) in China were identified using the self-organizing map (SOM) algorithm. According to the land system map, we found significant east–west differentiation in land use intensity, with high-intensive LSTs occurring mostly in eastern provinces and natural and seminatural LSTs dominating in central and western regions. The multifunctional LSTs that host food production, biodiversity conservation and cultural heritage were estimated to cover 32% of the terrestrial area in China. In summary, our results can provide a basis for regional land assessment and help identify the pressures on and threats to the eco-environment. Mapping regional land systems facilitates an in-depth understanding of human-environment interactions at the landscape level and serves as a useful tool in terms of forging sustainable land use strategies.
The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.
Brocchi, Darwin, and Transmutation: Phylogenetics and Paleontology at the Dawn of Evolutionary Biology

Giambattista Brocchi’s (1814) monograph (see Dominici, Evo Edu Outreach, this issue, 2010) on the Tertiary fossils of the Subappenines in Italy—and their relation to the living molluscan fauna—contains a theoretical, transmutational perspective (“Brocchian transmutation”). Unlike Lamarck (1809), Brocchi saw species as discrete and fundamentally stable entities. Explicitly analogizing the births and deaths of species with those of individual organisms (“Brocchi’s analogy”), Brocchi proposed that species have inherent longevities, eventually dying of old age unless driven to extinction by external forces. As for individuals, births and deaths of species are understood to have natural causes; sequences of births and deaths of species produce genealogical lineages of descent, and faunas become increasingly modernized through time. Brocchi calculated that over 50% of his fossil species are still alive in the modern fauna. Brocchi’s work was reviewed by Horner (1816) in Edinburgh. Brocchi’s influence as a transmutational thinker is clear in Jameson’s (1827) “geological illustrations” in his fifth edition of his translation of Cuvier’s Theory of the Earth (read by his student Charles Darwin) and in the anonymous essays of 1826 and 1827 published in the Edinburgh New Philosophical Journal—which also carried a notice of Brocchi’s death in 1827. The notion that new species replace older, extinct ones—in what today would be called an explicitly phylogenetic context—permeates these essays. Herschel’s (1830) discussion of temporal replacement of species and the modernization of faunas closely mirrors these prior discussions. His book, dedicated to the search for natural causes of natural phenomena, was read by Charles Darwin while a student at Cambridge. Darwin’s work on HMS Beagle was in large measure an exploration of replacement patterns of “allied forms” of endemic species in time and in space. His earliest discussions of transmutation, in his essay February 1835, as well as the Red Notebook and the early pages of Notebook B (the latter two written in 1837 back in England), contain Brocchi’s analogy, including the idea of inherent species longevities. Darwin’s first theory of the origin of species was explicitly saltational, invoking geographic isolation as the main cause of the abrupt appearance of new species. We conclude that Darwin was testing the predicted patterns of both Brocchian and Lamarckian transmutation as early as 1832 at the outset of his work on the Beagle.
Integrated Hydrological Modeling to Analyze the Effects of Precipitation on Surface Water and Groundwater Hydrologic Processes in a Small Watershed

The main objective of this study is to evaluate the performance of the integrated hydrological model, MIKE SHE in a small watershed to analyze the effect of two different precipitation sources on model outputs (groundwater elevation and surface water flows). The model was calibrated and validated with observed groundwater elevations and surface water flows measured at the United States Geological Survey (USGS) gage stations in the basin. The model calibration performance for surface water flows (R = 0.80, MAE= 0.20 m3/s, BIAS = −0.14 m3/s, NSE = 0.59) and groundwater elevations (R = 0.74, MAE = 0.45 m, BIAS = 0.08 m, NSE = 0.35) showed that the model was able to predict hydrological processes based on forcing variables in a small watershed. The analysis did not show the model with precipitation at the nearer (NOAA-Edwardsville) gauge station has better performance than the farther gauge station (NOAA-St. Louis). The quantitative analyses for the most sensitive model output variable suggested that precipitation uncertainties had noticeable impacts on surface water flows (0.81% to 11.19%), than groundwater elevations (0.06% to 0.07%), with an average of 6.71% and 0.66%, respectively. Our results showed noticeable differences in simulated surface water flows in spring (12.9%) and winter (36%) seasons compared to summer (11.4%) and fall (4.6%) as a result of difference (6% to 18%) in precipitation, which indicated that uncertainties in precipitation impact simulated surface water flows in a small watershed vary with different seasons. Our analyses have shown that precipitation affects the simulated hydrological processes and care should be taken while selecting input datasets (i.e., precipitation) for better hydrological model performance, specifically for surface water flows.
Diversity entropy-based Bayesian deep learning method for uncertainty quantification of remaining useful life prediction in rolling bearings

Remaining useful life (RUL) prediction of rolling bearings plays a critical role in reducing unplanned downtime and improving machine productivity. The existing prediction methods primarily provide point estimates of RUL without quantifying uncertainty. However, uncertainty quantification of RUL is crucial to conduct reliable risk analysis and make maintenance decision, which can significantly decrease the maintenance costs. To solve the uncertainty quantification problem and improve prediction accuracy at the same time, a novel diversity entropy-based Bayesian deep learning (DE-BDL) method is proposed. First, start degradation time (SDT) of bearings is adaptively determined using diversity entropy, which can extract early degradation information. Then, multi-scale diversity entropy (MDE) is developed to extract dynamic characteristics over multiple scales. Third, the obtained features using MDE are fed into the BDL model for degradation tracking and prediction. By doing this, the proposed DE-BDL method has merits in subsequent decision making, which can not only provide point estimation but also offer uncertainty quantification with epistemic uncertainty and aleatoric uncertainty. The superiority of the proposed method is validated using run-to-failure data. The experimental results and comparison with state-of-art prediction methods have demonstrated that the proposed DE-BDL method is promising for RUL of rolling bearings.
Cross-Country Macroeconomic Dynamics: Inflation, Growth, and Monetary Policy - India, Singapore, and the United Kingdom

This paper presents a comparative macroeconomic analysis of India, Singapore, and the United Kingdom, examining how inflation dynamics, monetary policy frameworks, and growth cycles interacted across the post-pandemic period. OLS persistence regressions confirm inflation is highly self-propagating across all three economies (β ranging from 0.95 to 0.99), with the UK exhibiting near-unit persistence (β = 0.99) consistent with unanchored expectations. Pairwise Granger causality tests reveal an asymmetric transmission structure: India's CPI Granger-causes both Singapore (p = 0.028) and UK inflation (p = 0.018), suggesting a sequential emerging-toadvanced economy transmission chain. Singapore's exchange-rate-centred framework produced a comparatively smoother disinflation path, consistent with its direct import price adjustment mechanism. The findings offer insights into the comparative advantages of exchange-rate versus interest-rate targeting frameworks under conditions of recurring global supply shocks.
Interrelationship of a custom and a treaty in international law

https://www.ester.ee/record=b5361411
Angular power spectrum of the fastica cosmic microwave background component from Background Emission Anisotropy Scanning Telescope data

We present the angular power spectrum of the cosmic microwave background (CMB) component extracted with FASTICA from the Background Emission Anisotropy Scanning Telescope (BEAST) data. BEAST is a 2.2-m off-axis telescope with a focal plane comprising eight elements at Q (38‐45 GHz) and Ka (26‐36 GHz) bands. It operates from the UC (University of California) White Mountain Research Station at an altitude of 3800 m. The BEAST CMB angular power spectrum has already been calculated by O’Dwyer et al. using only the Q-band data. With two input channels, FASTICA returns two possible independent components. We found that one of these two has an unphysical spectral behaviour, while the other is a reasonable CMB component. After a detailed calibration procedure based on Monte Carlo (MC) simulations, we extracted the angular power spectrum for the identified CMB component and found a very good agreement with the already published BEAST CMB angular power spectrum and with the Wilkinson Microwave Anisotropy Probe (WMAP) data.