ethicalabs/Research-Intent-Collab huggingface dataset:
Positive-Unlabeled Learning for Control Group Construction in Observational Causal Inference

In causal inference, whether through randomized controlled trials or observational studies, access to both treated and control units is essential for estimating the effect of a treatment on an outcome of interest. When treatment assignment is random, the average treatment effect (ATE) can be estimated directly by comparing outcomes between groups. In non-randomized settings, various techniques are employed to adjust for confounding and approximate the counterfactual scenario to recover an unbiased ATE. A common challenge, especially in observational studies, is the absence of units clearly labeled as controls-that is, units known not to have received the treatment. To address this, we propose positive-unlabeled (PU) learning as a framework for identifying, with high confidence, control units from a pool of unlabeled ones, using only the available treated (positive) units. We evaluate this approach using both simulated and real-world data. We construct a causal graph with diverse relationships and use it to generate synthetic data under various scenarios, assessing how reliably the method recovers control groups that allow estimates of true ATE. We also apply our approach to real-world data on optimal sowing and fertilizer treatments in sustainable agriculture. Our findings show that PU learning can successfully identify control (negative) units from unlabeled data based only on treated units and, through the resulting control group, estimate an ATE that closely approximates the true value. This work has important implications for observational causal inference, especially in fields where randomized experiments are difficult or costly. In domains such as earth, environmental, and agricultural sciences, it enables a plethora of quasi-experiments by leveraging available earth observation and climate data, particularly when treated units are available but control units are lacking.
Air pollution exposure monitoring using portable low-cost air quality sensors

Los entornos urbanos con un alto grado de industrialización están infestados de productos químicos peligrosos y contaminantes en el aire. Estos contaminantes pueden tener efectos devastadores en la salud humana, causando enfermedades agudas y crónicas como infecciones respiratorias, cáncer de pulmón y enfermedades cardíacas. El monitoreo de la contaminación del aire es vital no solo para los ciudadanos, advirtiéndoles sobre los riesgos para la salud de los contaminantes del aire, sino también para los responsables políticos, ayudándoles a redactar reglamentos y leyes que tengan como objetivo minimizar esos riesgos para la salud. Actualmente, el monitoreo de la contaminación del aire se basa principalmente en costosas estaciones de sensores estáticos de alta gama. Estas estaciones producen solo información agregada sobre los contaminantes del aire y no pueden capturar las variaciones en la exposición individual a la contaminación del aire. Como alternativa, este artículo desarrolla un sistema de monitoreo de la contaminación del aire basado en la ciudadanía que captura los niveles de exposición individual a los contaminantes del aire durante las actividades diarias en interiores y exteriores. Presentamos un sensor portátil de bajo costo y realizamos una campaña de medición utilizando los sensores para demostrar la validez y los beneficios de las mediciones de contaminación ciudadana. Específicamente, (i) clasificamos con éxito los datos en interiores y exteriores, y (ii) validamos la consistencia y precisión de nuestros datos clasificados en exteriores con las mediciones de una estación de monitoreo de referencia de alta gama. Nuestros resultados experimentales prueban aún más la efectividad de nuestra campaña al (i) proporcionar información detallada sobre la contaminación del aire en una amplia área geográfica, (ii) identificar las causas probables de la contaminación del aire que dependen del área y (iii) proporcionar a los ciudadanos información personalizada sobre los contaminantes del aire en su viaje diario.
A Benchmark Study of Machine Learning Models for Online Fake News Detection

The proliferation of fake news and its propagation on social media has become a major concern due to its ability to create devastating impacts. Different machine learning approaches have been suggested to detect fake news. However, most of those focused on a specific type of news (such as political) which leads us to the question of dataset-bias of the models used. In this research, we conducted a benchmark study to assess the performance of different applicable machine learning approaches on three different datasets where we accumulated the largest and most diversified one. We explored a number of advanced pre-trained language models for fake news detection along with the traditional and deep learning ones and compared their performances from different aspects for the first time to the best of our knowledge. We find that BERT and similar pre-trained models perform the best for fake news detection, especially with very small dataset. Hence, these models are significantly better option for languages with limited electronic contents, i.e., training data. We also carried out several analysis based on the models' performance, article's topic, article's length, and discussed different lessons learned from them. We believe that this benchmark study will help the research community to explore further and news sites/blogs to select the most appropriate fake news detection method.
A 4-case study of alloplastic cranioplasty by additive manufacturing of 3D printed modified mold

Additive manufacturing of 3D printed modified mold has been initiated lately as a method for creating an alloplastic implant for the repair of a skull bone defect. To achieve the best surgical and aesthetic outcome, the ideal implant is expected to be well-built and robust enough, as well as appropriate for the entire bone defect. Hereby, the authors evaluate 3D additive manufacturing of prefabricated mold as a procedure to create an implant used for personalized cranioplasty of large bone defects.
Accountability and Transparency to Fight against Corruption: An International Comparative Analysis

The paper analyzes whether transparency, the quality of budgetary management and the strength of audit institutions have a positive effect on corruption and can increase trust in governments from a...
Illusion or Persuasion: A Study on the Factuality of Generative Artificial Intelligence (GAI) Content and Rhetorical Communication Effects

(GAI)to investigate the role of rhetorical expressions on the trust of the public towards GAI-generated content, this paper utilizes control-group experiments. It is shown that intemperate rhetorical expressions in GAI-generated content are conducive to increasing the public’s perception of trust towards it and increasing the likelihood of information sharing. At the same time, these intemperate rhetorical expressions can help to ease the credibility crisis caused by inaccuracies contained in the GAI-generated information to a certain extent, especially toward low-competent groups in terms of media literacy. In this way, GAI hallucinations cannot be just seen as a technological error; they are also a communicative effect, and such findings offer empirical evidence and actionable ways of how to counter the misleading mechanisms of AIGC and governance measures.
TQU-SLAM Benchmark Dataset for Comparative Study to Build Visual Odometry Based on Extracted Features from Feature Descriptors and Deep Learning

The problem of data enrichment to train visual SLAM and VO construction models using deep learning (DL) is an urgent problem today in computer vision. DL requires a large amount of data to train a model, and more data with many different contextual and conditional conditions will create a more accurate visual SLAM and VO construction model. In this paper, we introduce the TQU-SLAM benchmark dataset, which includes 160,631 RGB-D frame pairs. It was collected from the corridors of three interconnected buildings comprising a length of about 230 m. The ground-truth data of the TQU-SLAM benchmark dataset were prepared manually, including 6-DOF camera poses, 3D point cloud data, intrinsic parameters, and the transformation matrix between the camera coordinate system and the real world. We also tested the TQU-SLAM benchmark dataset using the PySLAM framework with traditional features such as SHI_TOMASI, SIFT, SURF, ORB, ORB2, AKAZE, KAZE, and BRISK and features extracted from DL such as VGG, DPVO, and TartanVO. The camera pose estimation results are evaluated, and we show that the ORB2 features have the best results (Errd = 5.74 mm), while the ratio of the number of frames with detected keypoints of the SHI_TOMASI feature is the best (rd=98.97%). At the same time, we also present and analyze the challenges of the TQU-SLAM benchmark dataset for building visual SLAM and VO systems.
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning

With the growth of computer vision applications, deep learning, and edge computing contribute to ensuring practical collaborative intelligence (CI) by distributing the workload among edge devices and the cloud. However, running separate single-task models on edge devices is inefficient regarding the required computational resource and time. In this context, multi-task learning allows leveraging a single deep learning model for performing multiple tasks, such as semantic segmentation and depth estimation on incoming video frames. This single processing pipeline generates common deep features that are shared among multi-task modules. However, in a collaborative intelligence scenario, generating common deep features has two major issues. First, the deep features may inadvertently contain input information exposed to the downstream modules (violating input privacy). Second, the generated universal features expose a piece of collective information than what is intended for a certain task, in which features for one task can be utilized to perform another task (violating task privacy). This paper proposes a novel deep learning-based privacy-cognizant feature generation process called MetaMorphosis that limits inference capability to specific tasks at hand. To achieve this, we propose a channel squeeze-excitation based feature metamorphosis module, Cross-SEC, to achieve distinct attention of all tasks and a de-correlation loss function with differential-privacy to train a deep learning model that produces distinct privacy-aware features as an output for the respective tasks. With extensive experimentation on four datasets consisting of diverse images related to scene understanding and facial attributes, we show that MetaMorphosis outperforms recent adversarial learning and universal feature generation methods by guaranteeing privacy requirements in an efficient way for image and video analytics.
Study protocol: Addressing evidence and context to facilitate transfer and uptake of consultation recording use in oncology: A knowledge translation implementation study

The time period from diagnosis to the end of treatment is challenging for newly diagnosed cancer patients. Patients have a substantial need for information, decision aids, and psychosocial support. Recordings of initial oncology consultations improve information recall, reduce anxiety, enhance patient satisfaction with communication, and increase patients' perceptions that the essential aspects of their disease and treatment have been addressed during the consultation. Despite the research evidence supporting the provision of consultation recordings, uptake of this intervention into oncology practice has been slow. The primary aim of this project is to conduct an implementation study to explicate the contextual factors, including use of evidence, that facilitate and impede the transfer and uptake of consultation-recording use in a sample of patients newly diagnosed with breast or prostate cancer.Sixteen oncologists from cancer centres in three Canadian cities will participate in this three-phase study. The preimplementation phase will be used to identify and address those factors that are fundamental to facilitating the smooth adoption and delivery of the intervention during the implementation phase. During the implementation phase, breast and prostate cancer patients will receive a recording of their initial oncology consultation to take home. Patient interviews will be conducted in the days following the consultation to gather feedback on the benefits of the intervention. Patients will complete the Digital Recording Use Semi-Structured Interview (DRUSSI) and be invited to participate in focus groups in which their experiences with the consultation recording will be explored. Oncologists will receive a summary letter detailing the benefits voiced by their patients. The postimplementation phase includes a conceptual framework development meeting and a seven-point dissemination strategy.Consultation recording has been used in oncology, family medicine, and other medicine specialties, and despite affirming evidence and probable applications to a large number of diseases and a variety of clinical contexts, clinical adoption of this intervention has been slow. The proposed study findings will advance our conceptual knowledge of the ways to enhance uptake of consultation recordings in oncology.
Contrastive Masked Feature Modeling for Self-Supervised Representation Learning of High-Resolution Remote Sensing Images

As an emerging learning paradigm, self-supervised learning (SSL) has attracted extensive attention due to its ability to mine features with effective representation from massive unlabeled data. In particular, SSL, driven by contrastive learning and masked modeling, shows great potential in general visual tasks. However, because of the diversity of ground target types, the complexity of spectral radiation characteristics, and changes in environmental conditions, existing SSL frameworks exhibit limited feature extraction accuracy and generalization ability when applied to complex remote sensing scenarios. To address this issue, we propose a hybrid SSL framework that integrates the advantages of contrastive learning and masked modeling to extract more robust and reliable features from remote sensing images. The proposed framework includes two parallel branches: one branch uses a contrastive learning strategy to strengthen global feature representation and capture image structural information by constructing positive and negative sample pairs; the other branch adopts a masked modeling strategy, focusing on the fine analysis of local details and predicting the features of masked areas, thereby establishing connections between global and local features. Additionally, to better integrate local and global features, we adopt a hybrid CNN+Transformer architecture, which is particularly suitable for intensive downstream tasks such as semantic segmentation. Extensive experimental results demonstrate that the proposed framework not only exhibits superior feature extraction ability and higher accuracy in small-sample scenarios but also outperforms state-of-the-art mainstream SSL frameworks on large-scale datasets.
Central Bank Digital Currency: A Multivocal Literature Review

PurposeSeveral terms are interchangeably employed by researchers and practitioners to refer to central bank digital currency (CBDC), resulting in potential mistakes in the CBDC description. This study aims to survey the conceptualization of the CBDC and its utilization context to propose a list of CBDC terminologies.Design/methodology/approachThe research method used is the multivocal literature review, which covers the state-of-the-art with scientific papers and state-of-the-practice with practitioners' reports of the CBDC terminology.FindingsThe finding reveals that the terminologies used to mention a digital currency (DC) issued by a central bank are digital money, official DC, DC, centrally banked cryptocurrencies, digital cash, digital central bank money, CBDCs, central bank-issued cryptocurrency, central bank cryptocurrency, digital fiat currency, central bank-issued digital cash and sovereign digital currencies. The authors who proposed CBDC with distributed ledger technology-based infrastructure named it central bank cryptocurrency, and the others who didn’t specify clearly the infrastructure called it CBDC or another synonym of the DC.Originality/valueWe propose a CBDC concept map to clarify the CBDC understanding, which lists all terminologies found in the literature in a logical structure. The proposed CBDC concept map elucidates the linguistic landscape and clarifies the interpretation nuances across different CBDC terminologies, provides a comprehensive blueprint of the multi-conceptualization nature of CBDCs and contributes with an accessible tool for economists, technologists and lawyer researchers.
Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through             in silico             theoretical studies with the aim to guide and complement further             in vitro             and             in vivo             experimental efforts. Clearly, what counts is the result             in vivo             , not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways.
Origin of facies zonation along the slope of the Middle Triassic Latemar carbonate platform (Dolomites, Italy): clues from trace elements and stable isotope geochemistry

Many Paleozoic and Mesozoic high-relief carbonate platforms show a subdivision of the slope facies. The upper slopes are comprised of massive to poorly clinostratified microbial boundstone, whereas clinostratified deposits of grainstone, rudstone, and breccia are common lower slope facies. At present, the reasons why such slope facies subdivision exist are not fully understood, and the environmental conditions along these slopes are only poorly constrained. In order to shed light on the origin of the facies zonation of microbial platforms, marine fibrous cements lining cm-scale primary cavities were sampled along a well exposed slope of the Latemar platform at different paleo-water depths, and were analyzed for their C and O stable isotope composition. Rare earth element (REE), Y, Ca, Mg, Fe, Mn, Cr, Co, Cd, Cu, Ba and Sr contents were also analyzed, using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). The fibrous cements are characterized by superchondritic Y/Ho ratios, negative Ce anomalies and PAAS-normalized patterns that are depleted in light REE, indicating precipitation in oxygenated seawater. The highest 13C values were recorded in samples from shallow depths, and then reach a minimum at paleo-water depths of around 50-200 m. Cadmium (Cd), a trace metal with a nutrient-like behavior, is most abundant within this same depth range. A correlation between 13C and Cd points to an active biological pump in the water column flanking the slope. Photosynthesizing primary producers were active in the first few tens of m of the water column, as reflected by relatively higher 13C carbonate values. A respiration maximum is indicated between 50-200 m water depth by relatively high Cd, and a 13C which is ca. 0.5 ‰ lower with respect to the surface water. Below 200 m water depth, Cd and 13C values rebound, and slope facies switch from microbialitic to detrital (grainstone and rudstone). The correlation of nutrient tracers with the downslope facies transition suggests that the microbial communities that constitute the boundstone facies were likely nutrient-limited at Latemar, and could only proliferate at shallow water depths, where nutrients were not yet depleted by respiration. This could explain the subdivision of the slope into a upper (microbial) and a lower (“detrital”) facies belt, and suggests that this facies transition, commonly observed in Paleozoic and Mesozoic microbial platforms, may mark the depth to the oxygen minimum zone in coastal water columns.
Climate Change Projections for the Portuguese Viticulture Using a Multi-Model Ensemble

Portuguese vitiviniculture represents an extremely important economic activity for the agricultural sector, particularly for some renowned winemaking regions, such as Alentejo, Dão, Douro, and Minho. Viticultural zoning allows tying the suitability of a given grapevine variety to the local soil and climatic conditions. Given the existing climate variability in the Portuguese territory and its likely changes in the future, this zoning is thus of utmost interest. In this study, the current viticultural zoning in Portugal is discussed, as well as changes induced by climate change in the period 2011-2070. For this purpose, daily temperatures and precipitation rates were used to calculate the Huglin, cool night, dryness and hydrothermal indices. A composite index based on the previous indices was also calculated. For the assessment of the recent past conditions (1961-2000), an observational dataset (E-OBS) was used, while for future climate projections, a dataset comprising 16 simulations of regional climate models (produced by the ENSEMBLES project) was considered. In the future climate, statistically signiﬁcant increases in the thermal indices are projected to occur in the next decades, while for the precipitation-based indices decreases might be expected, particularly over the south and innermost regions of Portugal. A reshaping of the main Portuguese winemaking regions is likely to occur in the upcoming decades, therefore emphasizing the need for the development of appropriate measures for the adaptation to or mitigation of these climatic changes at the level of varieties and rootstocks used, as well as at the implemented cultural practices, keeping the typicity and wine styles.