ethicalabs/Research-Intent-Collab huggingface dataset:
Haptic Guidance for Robot-Assisted Endovascular Procedures: Implementation and Evaluation on Surgical Simulator

Vascular diseases are the most common precursors to ischemic heart disease and stroke, which are two of the leading causes of death worldwide. Endovascular intervention is a minimally invasive surgical approach to treat such diseases. Compared to open surgery, it has the advantages of faster recovery, reduced need for general anesthesia, reduced blood loss and significantly lower mortality. Endovascular procedures require high surgical skills to minimize contacts between the manipulated instruments (catheters and guidewires) and the vessel wall, which represent one of the major risks for the patient. Robotic assistance can potentially improve the precision and stability of instruments manipulation. One key limitation of current commercial robotic platforms is the lack of haptic feedback, preventing their acceptance and limiting the clinical usability. This paper proposes to bring the benefit of haptic feedback to robot-assisted endovascular intervention. Here we hypothesize that the introduction of 3D haptic guidance during robot-assisted endovascular procedure can further improve the surgical performance and safety while overcoming the limitations of currently available technology. The proposed 3D haptic guidance allows the surgeon to sense the vasculature while controlling a catheter through a robotic haptic manipulator. Validation of the system is performed through end-user experiments with vascular surgeons on a bespoke surgical simulator. The obtained results demonstrate that 3D haptic guidance has the potential of improving effectiveness, precision, and safety of endovascular intervention. Furthermore, vascular surgeons found the proposed technology safe and overall easy to use, indicating its potential on real surgical procedures.
(Invited) Electrochemistry Based and Coupled Approaches for Characterization of Lithium Ion Battery Materials and Systems

Lithium ion batteries represent a remarkable technological and commercial success, due to their lightweight and high energy density. However, to address future energy storage needs, there is a need for further research to understand and address lifetime limiting mechanisms. This presentation will highlight progress achieved via electrochemistry based and coupled methods for in-situ and operando characterization of lithium ion battery systems, spanning multiple material chemistries.
Demystifying model uncertainty and internal variability in climate change projections over the 21st century

Overall uncertainty in climate model projections is composed of scenario, model, and internal variability components. While scenario uncertainty is expressed by considering different climate scenarios, model uncertainty and internal variability components are largely ignored by climate information service providers. Instead, model projections are often expressed through the ensemble mean, which may lead to both overly optimistic assessments of risk, or on the other hand misinformed maladaptation strategies.Here, we propose a new uncertainty quantification approach that better informs end users of climate projections, showing that the multi-model internal variability, owing to its chaotic nature, is in fact virtually irreducible, and that model uncertainty grows moderately throughout the 21st century. For three future scenarios, we quantified the global internal variability of two metrics: annual precipitation (PRCP) and boreal summer average maximum daily temperature (TXJJA), by employing a single realization of each CMIP6 climate model. Our results showed that observed internal variability of the 1981-2010 period for the TXJJA metric has a negligible variation throughout the 21st century for all three scenarios. For the PRCP metric, small changes of internal variability were detected towards the end of the 21st century in the most adverse scenario (SSP3-7.0). Importantly, we observed that characterizing uncertainty in such manner produced a nuanced, and non-misleading results compared to that of the ensemble mean approach. Furthermore, the proposed uncertainty quantification approach can be expanded to similarly evaluate the uncertainty in indices of extreme weather.
Research on the Impact of Artificial Intelligence Generated Content on Consumer Willingness

This study explores the application effect of Artificial Intelligence (AI) generated content in hotel marketing. Using a 2×2 experimental design, the effects of artificial and Artificial Intelligence sources, product introduction and event promotion type copywriting on consumers’ booking intention are compared. Through the data analysis of the questionnaire, it is found that there is no significant difference in the overall impact between AI copywriting and artificial copywriting, but consumers’ trust in AI technology significantly affects their consumption preferences. Regression analysis shows that trust and acceptance of AI are important predictors of booking intention. This study shows that among Generation Z consumers, AI-generated content has a broad acceptance basis and provides a theoretical basis and practical suggestions for hotel brands to use AI for content production. At the same time, it also points out limitations such as sample limitations and situation simplification. Future research can further expand this topic by introducing real behavioral data and multi-level samples.
Correcting Misperceptions about Ethno-Racial Discrimination: The Limits of Evidence-Based Awareness Raising to Promote Support for Equal-Treatment Policies Researcher Interim Profossor of Sociology Associate Professor of Demography and Social Inequality Associate Professor Professor (wsr)

The disadvantages experienced by minorities and the lack of societal remedies for them are widely believed to be partly attributable to mainstream-majority citizens’ limited awareness of the hardships encountered by minorities. This study investigates whether informing citizens about field experiments on ethno-racial discrimination increases their recognition of the issue and support for equal-treatment policies. Based on the assumption that citizens require assistance to comprehend the significance of field-experimental evidence, the study tests three types of framing: a scientist framing the evidence as credible, a lawyer emphasizing the legal breach, or a minority expressing personal grief. The results indicate that citizens are generally aware of discrimination against ethno-racial minorities and even tend to overperceive its extent. Furthermore, communicating field-experimental results corrects and converges misperceptions but does not change recognition of the problem or support for equal-treatment policies, regardless of the framing or initial misperception. Only combining priming, correction, and framing temporarily increases recognition and donations to minority support groups, but not support for equal-treatment policies. These findings suggest evidence-based awareness campaigns have limited immediate success in promoting policy support. Citizens may donate to acknowledge minority hardships but hesitate to endorse policies that could affect their group’s privileged position.
Evolving Standards of Electronic Surveillance and the Right to Privacy in India: A Critical Analysis

The fast-evolving world of digital technology has joined the world of surveillance and privacy, posing some urgent questions on the proportions between the interests of the state security and the rights of the individual. Critically, the paper will review the changing standard of electronic surveillance and how it has impacted the right to privacy in India. Privacy as an essential right was recognized by the constitution in the case, Justice K.S. Puttaswamy v. The case of Union of India (2017) was a significant milestone in the Indian jurisprudence that allows the legal and policy frameworks that regulate surveillance to remain active based on the colonial-era legislation i.e. the Indian Telegraph Act, 1885, and the Information Technology Act, 2000. The current research paper will follow a doctrinal and analytical methodology in revealing the history of surveillance tools, analyzing modern digital mechanisms of surveillance, such as the use of Aadhaar in identification, the Central Monitoring System (CMS), and spyware usage, and evaluating them against the constitutional protection assurances.   The paper also makes a comparative study to world systems of surveillance, especially the United States and the European Union in the context of the importance of judicial oversight, transparency, and proportionality to uphold a democratic balance. It claims that the current system in India is not well equipped to discourage arbitrary intrusion as it has no independent body to regulate it, and the populace does not have much accountability. The recent adoption of the Digital Personal Data Protection Act, 2023, though a good move, does not cover the surveillance architecture and its abuse in detail  The paper has concluded that the changing principles of electronic surveillance in India require a coherent and rights-based legal framework that is based on the principles of transparency, necessity, and proportionality. It also supports the creation of an external supervisory body, judicial sanction of interception, and stringent standards of data protection to protect the privacy of citizens. These reforms are necessary so that technological governance in India is possible under the constitutional morality and international human rights.
ANALYSIS OF THE FINANCIAL PERFORMANCE ASSESSMENT IN THE CONTEXT OF BANKRUPTCY RISK MANAGEMENT

The economic environment is facing a series of transformations that are spreading rapidly and determine the appearance and manifestation of the risks, which affect the activity of the companies. Regardless of the type of activity carried out by a company, the decisions it makes regarding development and modernization, it is necessary to identify, evaluate and avoid or reduce the risks. Insufficient knowledge of the risk, as well as minimizing the importance with which it is managed, can negatively affect the performance obtained, eventually leading to the bankruptcy. In this context, determining the risk of the bankruptcy, using various mathematical models, in close correlation with the financial performance obtained, becomes particularly important. The paper presents how to evaluate the financial performance based on financial indicators and how to calculate the risk of the bankruptcy. The risk of bankruptcy is calculated using the Altman model, adapted to the field of the activity, for two companies (OMV Petrom and Rompetrol Rafinare), which operate in the same field of activity, to explain the success or failure of the activity carried out
Spatial Land Use and Land Cover (LULC) Changes for A Sustainable Biodiversity Conservation: Factors for Potential Implementation of Buffer Zone for FRIM Natural Heritage Site, Malaysia

Abstract                There will be nearly 2 billion new residents by 2030 due to human migration and population growth, yet the consequences of both current and future urbanization on biodiversity conservation are poorly known. However, an increase in the awareness of the consequences on the role of biodiversity in sustaining livelihoods and human wellbeing has been translated into the various movements, in line with the Sustainable Development Goals (SDG), especially SDG15 and SDG3. Conservation areas (CAs) have become one of the solutions to uphold ecosystem sustainability for biodiversity conservation. Thus, the study aimed to analyze the changes in land use and land cover (LULC) surrounding the CA - Forest Research Institute Malaysia (FRIM) Natural Heritage Site, Malaysia. The objectives were: 1) to quantify the changes of LULC, 2) to construct the Digital Elevation Model (DEM), of FRIM and the surroundings, thus representing the Malaysian terrestrial CAs and, 3) to suggest LULC changes- based criteria for CA buffer zone (CABAs). The data used were spatial data and satellite images and, ERDAS Imagine and ArcGIS software to execute the analysis. The study revealed a reduction in forest areas and addition in built-up (housing) and water bodies. There was an encroachment of built-up area (housing) into the FRIM. Therefore, the study suggested buffer zones for FRIM, a shield to prevent the stresses caused by urbanization and encroachment activities that may jeopardize the intactness of the conservation areas. The study also provided an effort to formulate a more effective strategy for enhancing sustainable development to the policymakers.
Impacts of social media on adolescent mental health: a rapid systematic review

The increasing use of Instagram and other social media platforms has been bringing up concerns regarding its impacts on adolescents' mental health, particularly in relation to screen time, social interaction and what is viewed, which can negatively impact their emotional wellbeing. The objective of this study is to evaluate the impacts of using Instagram and other social media platforms in adolescents mental health. For this purpose, we performed a quick systematic revision of recent studies (2020-2023) using the descriptors: Adolescents AND Instagram AND Mental Health. After analysing 113 articles, the results show that excessive usage, need for social validation and comparisons with idealistic images contribute to developing depressive symptoms. The way adolescents interact with these platforms as well as social and emotional factors also play an important role. We find that the sensible use of these technologies together with comfortable school and family environments contribute to building a sense of belonging, emotional support and safety.
Accurate Characterization of Non-Uniformly Sampled Time Series using Stochastic Differential Equations

Non-uniform sampling arises when an experimenter does not have full control over the sampling characteristics of the process under investigation. Moreover, it is introduced intentionally in algorithms such as Bayesian optimization and compressive sensing. We argue that Stochastic Differential Equations (SDEs) are especially well-suited for characterizing second order moments of such time series. We introduce new initial estimates for the numerical optimization of the likelihood, based on incremental estimation and initialization from autoregressive models. Furthermore, we introduce model truncation as a purely data-driven method to reduce the order of the estimated model based on the SDE likelihood. We show the increased accuracy achieved with the new estimator in simulation experiments, covering all challenging circumstances that may be encountered in characterizing a non-uniformly sampled time series. Finally, we apply the new estimator to experimental rainfall variability data.
Machine Learning, Big Data and the Regulation of Consumer Credit Markets: The Case of Algorithmic Credit Scoring

Recent advances in machine learning (ML) and Big Data techniques have facilitated the development of more sophisticated, automated consumer credit scoring models — a trend referred to as 'algorithmic credit scoring' in recognition of the increasing reliance on computer (particularly ML) algorithms for credit scoring. This chapter, which forms part of the 2018 collection of short essays 'Autonomous Systems and the Law', examines the rise of algorithmic credit scoring, and considers its implications for the regulation of consumer creditworthiness assessment and consumer credit markets more broadly.     The chapter argues that algorithmic credit scoring, and the Big Data and ML technologies underlying it, offer both benefits and risks for consumer credit markets. On the one hand, it could increase allocative efficiency and distributional fairness in these markets, by widening access to, and lowering the cost of, credit, particularly for 'thin-file' and 'no-file' consumers. On the other hand, algorithmic credit scoring could undermine distributional fairness and efficiency, by perpetuating discrimination in lending against certain groups and by enabling the more effective exploitation of borrowers.     The chapter considers how consumer financial regulation should respond to these risks, focusing on the UK/EU regulatory framework. As a general matter, it argues that the broadly principles and conduct-based approach of UK consumer credit regulation provides the flexibility necessary for regulators and market participants to respond dynamically to these risks. However, this approach could be enhanced through the introduction of more robust product oversight and governance requirements for firms in relation to their use of ML systems and processes. Supervisory authorities could also themselves make greater use of ML and Big Data techniques in order to strengthen the supervision of consumer credit firms.     Finally, the chapter notes that cross-sectoral data protection regulation, recently updated in the EU under the GDPR, offers an important avenue to mitigate risks to consumers arising from the use of their personal data. However, further guidance is needed on the application and scope of this regime in the consumer financial context.
Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction

Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges.
Quantum Computing in Technicolor: Topological Error Correction and the Color Code

The manipulation of topologically-ordered phases of matter to encode and pro- cess quantum information forms the cornerstone of many approaches to fault-tolerant quantum computing. Thus, it is paramount that we build a comprehensive framework of the topological objects of these phases of matter and offer descriptions on different levels. On the macroscopic level, we study the excitations and their properties ab- stractly, allowing us to draw conclusions about which types of topological objects can be realised. Zooming in to the microscopic level, we can offer concrete realisations of said objects in lattice models. To describe the dynamics of these objects, we build a space-time picture. While the space-time of topologically ordered phases is isotropic on the macroscopic level, concrete microscopic realisations require us to treat the time direction distinctly. In this thesis, we apply the program laid out above to the color code phase. We chose the color code for our comprehensive analysis as it is relatively simple to study while offering an incredibly rich set of physical properties. Furthermore, it is one of the simplest phases to implement experimentally and offers practical advantages over comparable candidate models. We begin our exploration by introducing the theory of the excitations of topo- logically ordered phases – the anyons – as well as their properties. We then turn to the example of the color code, presenting its microscopic lattice model and iden- tifying the anyonic excitations in it explicitly. We build a comprehensive catalogue of the color codes topological objects, namely the boundaries and the corners that interface boundaries, the domain walls and the twist defects that terminate domain walls, and the sub-theories and interfaces thereof. We describe said objects on both, the macroscopic-abstract, as well as the microscopic-concrete level. Furthermore, we build a static 3-dimensional model, which when interpreted as a (2+1)-dimensional model describes the dynamics of said objects. We investigate how the same topologi- cal object can be used to perform different quantum information processing tasks as its orientation in space-time is changed. Finally, we showcase the usefulness of our framework and the found objects by applying them to optimise some pressing challenges in the field. Among other things, we construct codes with high encoding rates and ones tailored to specific noise models, we reduce the temporal overhead of performing fault-tolerant gates and find a powerful description of the novel Floquet codes.
The Philosophy of Artificial Intelligence Ethics towards the Philosophy of Ki Hajar Dewantara

Artificial intelligence (Artificial Intelligence) is a simulation of human intelligence that is modeled in a machine and programmed to think like humans. The development of artificial intelligence has a great influence on the ethics and logic of human thinking in completing tasks correctly. In the world of education, the goals of artificial intelligence must be based on the philosophy of educational goals. Ibn Sina's views on educational philosophy are in line with the cultivation of ethics based on the philosophy of Ki Hajar Dewantoro. This research aims to determine the impact of artificial intelligence on ethics in the world of education which can be studied in the philosophy of Ki Hajar Dewantoro. Using the literature study method, we can examine that Ki Hajar Dewantoro's theories regarding educational philosophy are very much in line with ethical guidelines for responding to technological developments in all aspects of life
The CORA dataset: validation and diagnostics of ocean temperature and  salinity in situ measurements

Abstract. The French program Coriolis as part of the French oceanographic operational system produces the COriolis dataset for Re-Analysis (CORA) on a yearly basis which is based on temperature and salinity measurements on observed levels from different data types. The latest release of CORA covers the period 1990 to 2010. To qualify this dataset, several tests have been developed to improve in a homogeneous way the quality of the raw dataset and to fit the level required by the physical ocean re-analysis activities (assimilation and validation). These include some simple tests, climatological tests and a model background check based on a global ocean reanalysis. Visual quality control (QC) is performed on all suspicious temperature (T) and salinity (S) profiles identified by the tests and quality flags are modified in the dataset if necessary. In addition, improved diagnostic tools were developed – including global ocean indicators – which give information on the potential and quality of the CORA dataset for all applications. This Coriolis product is available on request through the MyOcean Service Desk (http://www.myocean.eu/).