ethicalabs/Research-Intent-Collab huggingface dataset:
Topological data analysis and topological deep learning beyond persistent homology: a review

Topological data analysis (TDA) is a rapidly evolving field in applied mathematics and data science that leverages tools from topology to uncover robust, shape-driven insights in complex datasets. The main workhorse is persistent homology, a technique rooted in algebraic topology. Paired with topological deep learning (TDL) or topological machine learning, persistent homology has achieved tremendous success in a wide variety of applications in science, engineering, medicine, and industry. However, persistent homology has many limitations due to its high-level abstraction, insensitivity to non-topological changes, and reliance on point cloud data. This paper presents a comprehensive review of TDA and TDL beyond persistent homology. It analyzes how persistent topological Laplacians and Dirac operators provide spectral representations to capture both topological invariants and homotopic evolution. Other formulations are presented in terms of sheaf theory, Mayer topology, and interaction topology. For data on differentiable manifolds, techniques rooted in differential topology, such as persistent de Rham cohomology, persistent Hodge Laplacian, and Hodge decomposition, are reviewed. For one-dimensional (1D) curves embedded in 3-space, approaches from geometric topology are discussed, including multiscale Gauss-link integrals, persistent Jones polynomials, and persistent Khovanov homology. This paper further discusses the appropriate selection of topological tools for different input data, such as point clouds, sequential data, data on manifolds, curves embedded in 3-space, and data with additional non-geometric information. A review is also given of various topological representations, software packages, and machine learning vectorizations. Finally, this review ends with concluding remarks.
Mesoscale Front Tracking Simulation of a Microgravity-based Isothermal Equiaxed Solidification Experiment of an Al-20wt.%Cu Alloy performed on board the MASER 13 Sounding Rocket

Abstract                The ability to accurately predict microstructural evolution in response to metallic alloy composition and thermal processing parameters is of critical importance in preventing defects and maximising sustainability in advanced liquid metal processing manufacturing, e.g. casting, welding, and additive manufacturing. Sophisticated solidification models are used widely in industry to optimise mould designs and processing parameters. To ensure validity of simulation predictions, similarly sophisticated solidification experiments are required. Since its development, real-time in situ X-ray radiography of solidification has become the benchmark for solidification experimentation, providing significant insight into nucleation, primary phase evolution, solutal rejection, soft and hard impingement, and isothermal transformations. The compact nature of lab-based X-ray diagnostic equipment and solidification furnaces have enabled metal alloy solidification observation in real time under microgravity conditions. One such experiment was performed on board the MASER 13 sounding rocket showing, for the first time, complete isothermal equiaxed solidification metallic alloy in microgravity. In this work, mesoscale Front Tracking (FT) was used to simulate that microgravity experiment on the grain-refined Al-20wt.%Cu disc-shaped sample. Experimental data was used to initialise the FT algorithm with grain envelope development predicted based on the applied cooling rate. Latent heat release during solidification was shown to have a negligible impact on solidification overall, owing to the small volume of metal and the relatively low cooling rate. Soft impingement, i.e. solutal awareness, of neighbouring grains was shown to be the dominant mode of primary dendrite growth restriction and inoculant particle poisoning.
Privacy, Surveillance, and Constitutional Rights in the Digital State

The transformation of Indonesia into a digital state has intensified the use of digital surveillance technologies, including big data analytics, biometric systems, and algorithmic monitoring, in the name of public administration, security, and governance efficiency. While these developments promise administrative effectiveness, they simultaneously pose serious constitutional challenges to the protection of privacy as a fundamental right. This article examines the normative position of privacy within Indonesia’s constitutional framework and analyzes the problem of normative ambiguity surrounding state surveillance in the digital era. Employing normative juridical research with statute, conceptual, and case approaches, this study identifies the absence of clear constitutional limits on surveillance authority, vague standards for public interest and national security justifications, and weak mechanisms of accountability and oversight. The findings demonstrate that such ambiguity risks legitimizing an over-surveillance state and undermines legal certainty and substantive constitutional protection. This article argues that privacy in the digital state must be reconstructed as a core constitutional safeguard through clear legal bases, proportionality requirements, mandatory judicial authorization, and independent supervisory mechanisms, ensuring a balanced relationship between state power and fundamental rights.
'Hacking' Assistive Technology: Creating Personal AI Tools for Access

This dissertation contributes a motivation for and approaches to 'hack' assistive software: to move beyond one-size-fits all assistive technology design, and towards enabling anyone to create and customize AI tools for accessibility that meet their unique needs. AI-based assistive technologies have been increasingly adopted by blind people to gain visual access in scenarios such as reading printed text, identifying objects, and navigating. These services are typically designed to maximize broad applicability and usability, focusing on simple and common use cases under the assumption of universal needs. Yet, this results in inflexibility, leaving a long tail of diverse and situational needs that AI assistive technologies cannot account for. My dissertation argues that in addition to 'lowering the floor' and making AI tools that are usable for everyone, we also need to 'raise the ceiling' and create systems that enable people to create and do more. Building on a foundation of 'making' and DIY cultures, I bring those philosophies into the assistive software space, and develop new approaches for people to make and adapt software to their needs. I first present work that studies the challenges with existing assistive applications, contributing evidence that blind people already put a significant amount of effort into working around gaps in these tools, and that they engage in design and making to do so, through three strategies which I term hacking, switching, and combining. I demonstrate that there is a significant gap between the assistive technology solutions that people desire to create, and the approaches that can support them in doing so. I then contribute two novel tools that leverage end-user programming approaches in order to close this gap. First, I present ProgramAlly, an end-user programming application for creating customized filters for visual information, using existing models as building blocks that can be rearranged. Next, I present AllyExtensions, a system leveraging mobile automation tools to augment already deployed applications with new features in a flexible way. Through designing and evaluating these systems, I demonstrate end-user creation processes that are approachable, accessible, and expressive for blind people, as in ProgramAlly, and that can also be applied practically to real-world assistive technology workflows, as in AllyExtensions. Overall, this dissertation presents motivation and methods for the democratization of assistive technology creation, and supports people in having greater control over AI-based technologies in their lives.
Error mitigation increases the effective quantum volume of quantum computers

Quantum volume is a single-number metric which, loosely speaking, reports the number of usable qubits on a quantum computer. While improvements to the underlying hardware are a direct means of increasing quantum volume, the metric is "full-stack" and has also been increased by improvements to software, notably compilers. We extend this latter direction by demonstrating that error mitigation, a type of indirect compilation, increases the effective quantum volume of several quantum computers. Importantly, this increase occurs while taking the same number of overall samples. We encourage the adoption of quantum volume as a benchmark for assessing the performance of error mitigation techniques.
From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data

Audio-aware large language models (ALLMs) have recently made great strides in understanding and processing audio inputs. These models are typically adapted from text-based large language models (LLMs) through additional training on audio-related tasks. This adaptation process presents two major limitations. First, ALLMs often suffer from catastrophic forgetting, where crucial textual capabilities like instruction-following are lost after training on audio data. In some cases, models may even hallucinate sounds that are not present in the input audio, raising concerns about reliability. Second, achieving cross-modal alignment between audio and language typically relies on large collections of task-specific question-answer pairs for instruction tuning, making it resource-intensive. To address these issues, previous works have leveraged the backbone LLMs to synthesize general-purpose, caption-style alignment data. In this paper, we propose a data generation framework that produces contrastive-like training data, designed to enhance ALLMs' ability to differentiate between present and absent sounds. We further extend our approach to multi-audio scenarios, enabling the model to either explain differences between audio inputs or produce unified captions that describe all inputs, thereby enhancing audio-language alignment. We refer to the entire ALLM training framework as bootstrapping audio-language alignment via synthetic data generation from backbone LLMs (BALSa). Experimental results indicate that our method effectively mitigates audio hallucinations while reliably maintaining strong performance on audio understanding and reasoning benchmarks, as well as instruction-following skills. Moreover, incorporating multi-audio training further enhances the model's comprehension and reasoning capabilities. Overall, BALSa offers an efficient and scalable approach to developing ALLMs.
The Right Not to be Subject to Automated Individual Decision-Making/Profiling Concerning Big Health Data. Developing an Algorithmic Culture

Τhis paper explores the legal issues that arise from the collection and processing of Big Health Data in the light of the EU legislation on Data Protection, placing particular emphasis on the General Data Protection Regulation. Whether Big Health Data can be characterised as “personal data” or not is really the crux of the matter. The legal ambiguity is compounded by the fact that, even though the processing of Big Health Data is premised on the de-identification of the data subject, the possibility of a combination of Big Health Data with other data circulating freely on the web or from other data files cannot be excluded. Moreover, data subject’s rights, e.g., the right not to be subject to a decision based solely on automated processing, are heavily impacted by the use of AI, algorithms and technologies that reclaim health data for further use, resulting in sometimes ambiguous results that have substantial impact on individuals. On the other hand, as the COVID-19 pandemic has revealed, Big Data analytics can offer crucial source of information. In this respect, this paper identifies and systematises the legal provisions concerned, offering interpretative solutions that tackle dangers concerning data subjects’ rights while embracing the opportunities that Big Health Data have to offer.
From Definition to Regulation: How Social Media Platforms Govern Synthetic Media and Deepfakes​

Generative AI has rapidly expanded the production and circulation of synthetic media, bringing renewed urgency to questions of how deepfakes are governed. In the absence of comprehensive public regulation, major online social media platforms increasingly function as de facto regulators through their governance policies. This paper examines how platforms govern synthetic media by analysing publicly available policies on deepfakes issued by X, YouTube, TikTok, and Instagram. Drawing on a qualitative research design that combines document analysis with interpretive discourse analysis, the study compares how platforms define synthetic media, frame associated risks and harms, and operationalise governance mechanisms such as labelling, removal, and disclosure. The analysis reveals both convergence and divergence across platform approaches, particularly in how responsibility for governing synthetic media is distributed among creators, platforms, and users. By conceptualising platforms as regulatory actors, the paper provides a systematic comparative analysis of how platform policies construct synthetic media as a governance problem and demonstrates the shared but internally fragmented logic through which platform-led governance operates. Together these contributions offer an empirical basis for co-regulatory policy design.
Indigenous Peoples' Land Rights Under the International Covenant on Civil and Political Rights

his paper presentes the evolving understanding of indigenous peoples' land rights under the International Covenant on Civil and Political Rights (ICCPR), as reflected in the practice of the Human Rights Committee (HRC), the monitoring body established under the ICCPR. The discussion is based on cases decided under the Optional Protocol to the Covenant, on the Committee's general comments and on the Committee's consideration of periodic reports by States parties. As to the points of entry to the discussion on land rights, two provisions of the ICCPR are identified: the right of all peoples to self- determination (Article 1) and the protection afforded, under the notions of 'culture' and 'minority' in Article 27, to indigenous peoples' rights related to lands and resources.
Depth of Ultra High Energy Cosmic Ray Induced Air Shower Maxima Measured by the Telescope Array Black Rock and Long Ridge FADC Fluorescence Detectors and Surface Array in Hybrid Mode

Abstract                The Telescope Array (TA) observatory utilizes fluorescence detectors and surface detectors (SDs) to observe air showers produced by ultra high energy cosmic rays in Earth’s atmosphere. Cosmic-ray events observed in this way are termed hybrid data. The depth of air shower maximum is related to the mass of the primary particle that generates the shower. This paper reports on shower maxima data collected over 8.5 yr using the Black Rock Mesa and Long Ridge fluorescence detectors in conjunction with the array of SDs. We compare the means and standard deviations of the observed                                                                                                                                   X                                                                                       max                                                                                                                     distributions with Monte Carlo                                                                                                                                   X                                                                                       max                                                                                                                     distributions of unmixed protons, helium, nitrogen, and iron, all generated using the QGSJet II-04 hadronic model. We also perform an unbinned maximum likelihood test of the observed data, which is subjected to variable systematic shifting of the data                                                                                                                                   X                                                                                       max                                                                                                                     distributions to allow us to test the full distributions, and compare them to the Monte Carlo to see which elements are not compatible with the observed data. For all energy bins, QGSJet II-04 protons are found to be compatible with TA hybrid data at the 95% confidence level after some systematic                                                                                                                                   X                                                                                       max                                                                                                                     shifting of the data. Three other QGSJet II-04 elements are found to be compatible using the same test procedure in an energy range limited to the highest energies where data statistics are sparse.
Natural Policy Gradients In Reinforcement Learning Explained

14 pages, 3 figures
First results from the Very Small Array -- III. The cosmic microwave background power spectrum

We present the power spectrum of the fluctuations in the cosmic microwave background detected by the Very Small Array (VSA) in its first season of observations in its compact configuration. We find clear detections of first and second acoustic peaks at l≈ 200 and ≈550, plus detection of power on scales up to l= 800. The VSA power spectrum is in very good agreement with the results of the BOOMERanG, DASI and MAXIMA telescopes despite the differing potential systematic errors.
Statistical mechanics of phase transitions in elastic media with vanishing thermal expansion

We consider the elastic theory for Ising transitions in an isotropic elastic medium in the zero thermal expansion (ZTE) limit. We use this theory to study the nature of the fluctuations in the system near the second phase transitions at $T_c$ in the ZTE limit given by $dT_c/dV=0$, where $V$ is the system volume, and explore anomalous elasticity. Allowing for the local strain to couple {\em asymmetrically} with the states of the order parameter, we uncover the dramatic effects of these couplings on the fluctuations of the local displacements near $T_c$, and also on the nature of the phase transition itself. Near second order phase transitions and with weak asymmetry in the order parameter - strain couplings, the variance of the displacement fluctuations in two dimensions scale with the system size $L$ in a universal fashion as $[\ln (L/a_0)]^{2/3}$; $a_0$ is a small-scale cutoff. Likewise, the correlation functions of the difference of the local displacements at two different points separated by $r$ scale as $[\ln (r/a_0)]^{2/3}$ for large $r$. For stronger asymmetry, this variance diverges as $L$ exceeds beyond a (nonuniversal) size, determined by the model parameters, signaling a transition to a phase with only short range order or the loss of the positional order of the elastic medium. At dimensions higher than two, for sufficiently weak selectivity, the variance of the displacement fluctuations is $L$-independent corresponding to long range order. However, if the selectivity parameters rise beyond a dimension-dependent threshold values, again the positional order is lost with a concomitant transition to a phase with short range order. Large values of the order parameter - strain couplings can turn the phase transition into a first order as well.
Building public trust and confidence in secondary use of health data for healthcare improvement and research: a qualitative study pre-protocol

Background                                      Secondary use of health data provides opportunities to drive improvements in healthcare provision, personalised medicine, comparative effectiveness research, health services innovation, and policy and practice. However, secondary data use requires compliance with relevant legislation, implementation of technical safeguards, ethical data management, and respect for data sharers. Existing evidence suggests widespread support for secondary use of health data among the public, which co-exists with concerns about privacy, confidentiality and misuse of data. Balancing the protection of individuals’ rights against the use of their health data for societal benefits is of vital importance, and trust underpins this process. The study protocol explores how to build public trust and confidence in the secondary use of health data through all key stakeholder groups in Ireland, towards developing a culture that promotes a safe and trustworthy use of data.                                        Methods                                                           This study will adopt a qualitative cross-sectional approach conducted in accordance with the Consolidated Criteria for Reporting Qualitative Research COREQ guidelines. Participants in the study will include academics and researchers; healthcare professionals, data protection, ethics and privacy experts and data controllers; pharmaceutical industry and patients and public. Purposive and convenience sampling techniques will be utilised to recruit the participants, and data will be collected utilizing focus groups that may be supplemented with semi-structured interviews. Data will be coded by themes using reflexive thematic analysis (TA) and collective intelligence (CI) will be convened post-analysis to explore the preliminary findings with the participants.                                        Ethics and Dissemination                                      Ethical approval was obtained from the Royal College of Surgeons in Ireland Research Ethics Committee (REC202208013). Final data analysis and dissemination is expected by Q1 2024. Findings will be disseminated through peer-reviewed journal publications, presentations at relevant conferences, and other academic, public and policy channels. Lay summaries will be designed for Public and Patient Involvement (PPI) contributors and general public.
Information Design for Cultural Heritage. Data-Driven Dashboard Prototype - Eni Case

This research report documents a project developed for the Information Design & Data Research + Storytelling course. Using a corporate archive as the primary source, the study explores the historical evolution of a selected topic through archival and secondary research, situating it within the broader economic, territorial, and social transformations of its time.  Through a structured research process — including archive exploration, data collection, and narrative development — the team identified key patterns and stories embedded in the archival materials, which informed both the content and the design of the final output.  The result is a digital storytelling platform that translates archival complexity into an interactive, multi-layered experience. The platform combines data visualizations, maps, timelines, and narrative nodes to allow users to explore the topic at different levels of depth, balancing historical context with visual clarity.