ethicalabs/Research-Intent-Collab huggingface dataset:
Fault Tree Analysis to Compute the Probability of an Event: A Case Study in Oil and Gas Industry

The aim of this study is establishing Fault Tree Analysis (FTA) with using experts’ opinions to compute the probability of an event (as a main factor of risk value). Thus, in order to find the probability of top event, all of the basic event’s probability should be available when FTA is drawn for an event. In this case, several experts will be employed to express their opinions in qualitative terms for all basic events instead of using probabilities from handbooks. In real life, participated experts have subjective weights based on their background and experiences. Therefore, they should be weighted by a standard method like as AHP. Then the experts’ opinions will be collected. In this case, fuzzy set theory will be employed. All experts’ opinion which is expressed in qualitative terms (crisp value) is transferred to fuzzy set number (triangular or trapezoidal). Accordingly, in fuzzy environment their opinions will be aggregated in a set of fuzzy number form. So, the fuzzy number requires to be defuzzified to crisp value. Finally probability of basic events will be computed, and subsequently the probability of top event will be calculated using Boolean algebra. Keywords: Fault tree analysis, Fuzzy logic, Chemical complex plant, Expert judgment
Law and Practice of the International Criminal Tribunals—Specific Contexts

In this chapter the law and practice of the international criminal tribunals (ICTs) with respect to the question of how to address procedural violations committed in the pre-trial phase of the proceedings is addressed in two specific contexts: arrest and detention, and disclosure. Indeed, this book is concerned with the judicial response to procedural violations committed in the pre-trial phase of international criminal proceedings, which covers not only what at the national level would be described as ‘police illegality’ or ‘unlawfulness’, of which unlawful arrest or detention is an obvious example, but also the violation by the prosecution of its pre-trial obligations, of which disclosure to the defence is a prime example. In this chapter also, the law and practice of the ad hoc Tribunals and that of the ICC are compared. The purpose of this ‘contextual’ chapter is to complement the (general) overview provided in the previous chapter, and thereby provide a fuller picture of the law and practice of the ICTs with respect to the question of how to address pre-trial procedural violations.
Dark Patterns, Enforcement, and the emerging Digital Design Acquis: Manipulation beneath the Interface

The term ‘dark patterns’ is commonly used to describe manipulative techniques implemented into the user interface of websites and apps that lead users to make choices or decisions that would not have otherwise been taken. Legal academic and policy work has focussed on establishing classifications, definitions of dark patterns, constitutive elements, and typologies of dark patterns across different fields. Regulators have responded to this issue with several enforcement decisions related to data protection and privacy violations, and with rulings protecting consumers. Accordingly, this article analyses the appropriateness of regulatory oversight of designers and platforms that deploy dark patterns inside digital technologies. By further analysing design techniques, we conclude this type of deceptive design is inappropriately attributed to the user interface when some patterns are embedded in the system architecture. With this in mind, the article also analyses the emerging digital design acquis of the European Union. The Digital Markets Act and Digital Services Act, the proposals for a new Data Act and AI Act are critiqued for suitability of regulating deceptive design over the entirety of, what we coin, the deceptive design visibility spectrum.
Knowledge Utilization and Open Science Policies: Noble aims that ensure quality research or Ordering discoveries like a pizza?

This is a pre-print version. The original paper was published at the International Astronautical Congress in Washington DC (USA), October 2019. arXiv admin note: text overlap with arXiv:1801.08033
Formation of Paleoarchean-Mesoarchean Na-rich (TTG) and K-rich granitoid crust of the Singhbhum craton, eastern India: Constraints from major and trace element geochemistry and Sr-Nd-Hf isotope composition

Abstract   The Singhbhum craton in eastern India is underlain by Paleo- to Mesoarchean Tonalites-Trondhjemites-Granodiorites (TTGs) and granites that formed in two pulses at 3.45–3.44 Ga and 3.35–3.32 Ga, followed by potassic granite activity at 3.1 Ga. The TTGs are metaluminous to slightly peraluminous and highly evolved with low Ni, Cr, and MgO. Low heavy-REE concentrations, Sr/Y and Eu/Gd, and high Nb, Ta, and Y concentrations indicate they belong to the low HREE, medium- to low-pressure TTG type. Their compositions are similar to TTGs derived from chondritic source with amphibolitic residue, consistent with rutile-free and garnet-bearing metabasic source. The granites have similar REE and multielement patterns as the TTGs, albeit with pronounced negative Eu, Sr, and Ti anomalies. High K2O/Na2O, higher heavy-REE concentration, low Sr/Y and Eu/Gd, and negative Eu anomalies of the 3.05 Ga Mayurbhanj potassic granites are consistent with shallow plagioclase-bearing crustal sources. The increasing K2O/Na2O of the granitoids from 3.45 Ga to 3.05 Ga is related to gradual thickening of the continental crust and increasing contributions of intracrustal melts in younger granitoids. Neodymium and Hf isotope data indicate the 3.45–3.44 Ga granitoids have juvenile as well as Eoarchean TTG crust-like signature. The 3.35–3.32 Ga granitoids were produced by partial melting of the same mafic crust that gave rise to the 3.45–3.44 Ga TTGs with some components requiring up to 50% crustal contribution. The Mayurbhanj granites represent intracrustal melts of more potassic members of the Paleoarchean TTGs. The trace-element characteristics of the granitoids reflect melt generation at different depths in a tectonic setting producing both TTGs as well as granites contemporaneously. Plausible geodynamic settings could be very hot subduction or “dripduction” regime involving shallow melting of delaminated mafic crust producing the 3.45 Ga juvenile TTGs and minor granites. Large scale delamination and melting of mafic lower crust and felsic proto-crust in a thickened crustal pile in a subduction-like regime could have produced granitoids with juvenile as well as recycled isotopic signatures. The Nd-Hf isotope data indicate that the Paleoarchean upper mantle was moderately depleted in response to a spurt in Paleoarchean continental growth.
Vision-Language-Vision Auto-Encoder: Scalable Knowledge Distillation from Diffusion Models

Building state-of-the-art Vision-Language Models (VLMs) with strong captioning capabilities typically necessitates training on billions of high-quality image-text pairs, requiring millions of GPU hours. This paper introduces the Vision-Language-Vision (VLV) auto-encoder framework, which strategically leverages key pretrained components: a vision encoder, the decoder of a Text-to-Image (T2I) diffusion model, and subsequently, a Large Language Model (LLM). Specifically, we establish an information bottleneck by regularizing the language representation space, achieved through freezing the pretrained T2I diffusion decoder. Our VLV pipeline effectively distills knowledge from the text-conditioned diffusion model using continuous embeddings, demonstrating comprehensive semantic understanding via high-quality reconstructions. Furthermore, by fine-tuning a pretrained LLM to decode the intermediate language representations into detailed descriptions, we construct a state-of-the-art (SoTA) captioner comparable to leading models like GPT-4o and Gemini 2.0 Flash. Our method demonstrates exceptional cost-efficiency and significantly reduces data requirements; by primarily utilizing single-modal images for training and maximizing the utility of existing pretrained models (image encoder, T2I diffusion model, and LLM), it circumvents the need for massive paired image-text datasets, keeping the total training expenditure under $1,000 USD.
Algorithmic aesthetics: Cognitive perspectives on AI-generated visual art

In recent years, the use of generative artificial intelligence has proliferated across various domains, ranging from advertising and social media to the generation of visual artwork for presentation at esteemed art exhibitions and for sale on the art auction market. Despite its growing prevalence, empirical data show that people maintain a clear bias against AI-generated visual art when they are aware of its artificial origins. Here, we explore why this bias might exist and whether its existence dooms the eventual acceptance and even celebration of art generated by artificial means. We do this by bringing together recent empirical evidence from the disciplines of neuroaesthetics, social cognition, art history, and human-machine interaction to develop several new perspectives that are ripe for empirical evaluation. Given the growing momentum and outlook of AI-generated art, particularly image generation, in today’s society, our aim with this paper is to provide novel evidence-based propositions for understanding the relationship between visual art and artificial intelligence with a clear focus on social cognitive neuroscience perspectives.
Quantum anomalous Hall effect in magnetic topological insulators

The search for topologically non-trivial states of matter has become an important goal for condensed matter physics. Here, we give a theoretical introduction to the quantum anomalous Hall (QAH) effect based on magnetic topological insulators in two-dimension (2D) and three-dimension (3D). In 2D topological insulators, magnetic order breaks the symmetry between the counter-propagating helical edge states, and as a result, the quantum spin Hall effect can evolve into the QAH effect. In 3D, magnetic order opens up a gap for the topological surface states, and chiral edge state has been predicted to exist on the magnetic domain walls. We present the phase diagram in thin films of a magnetic topological insulator and review the basic mechanism of ferromagnetic order in magnetically doped topological insulators. We also review the recent experimental observation of the QAH effect. We discuss more recent theoretical work on the coexistence of the helical and chiral edge states, multi-channel chiral edge states, the theory of the plateau transition, and the thickness dependence in the QAH effect.
General Principles of Law as Applied by International Criminal Courts and Tribunals

AbstractInternational lawyers usually disregard the vital functions that general principles of law may play in the decisions of international courts and tribunals. The relative indifference in these principles should lead to a renewed attention in the legal regime of the general principles of law as a source of public international law. As far as international criminal law is concerned, general principles of law may be crucial to the outcome of an international trial, inter alia because the conviction of an accused in respect of a particular charge may depend on the existence of a given defence under this source. This article examines the role that general principles of law have played in the decisions of international criminal courts and tribunals. In particular, it aims at analysing their alleged 'subsidiary' nature, their process of determination, and their transposition from national legal systems into international law. It concludes that general principles of law have played a significant role not only by filling legal gaps, but also by being a fundamental means for the interpretation of legal rules and the enhancement of legal reasoning.
Multipose Audio-Visual Speech Recognition

Publication in the conference proceedings of EUSIPCO, Barcelona, Spain, 2011
Million Atom Electronic Structure and Device Calculations on Peta-Scale Computers

Semiconductor devices are scaled down to the level which constituent materials are no longer considered continuous. To account for atomistic randomness, surface effects and quantum mechanical effects, an atomistic modeling approach needs to be pursued. The Nanoelectronic Modeling Tool (NEMO 3-D) has satisfied the requirement by including emprical $sp^{3}s^{*}$ and $sp^{3}d^{5}s^{*}$ tight binding models and considering strain to successfully simulate various semiconductor material systems. Computationally, however, NEMO 3-D needs significant improvements to utilize increasing supply of processors. This paper introduces the new modeling tool, OMEN 3-D, and discusses the major computational improvements, the 3-D domain decomposition and the multi-level parallelism. As a featured application, a full 3-D parallelized Schrödinger-Poisson solver and its application to calculate the bandstructure of $δ$ doped phosphorus(P) layer in silicon is demonstrated. Impurity bands due to the donor ion potentials are computed.
Serum matrix metalloproteinase-7 (MMP-7) as a Biomarker for differentiating biliary atresia from neonatal hepatitis: evidence synthesis through systematic review and meta-analysis

Biliary atresia (BA) requires timely differentiation from neonatal hepatitis (NH) and early surgery for optimal outcomes. This systematic review and meta-analysis evaluated the diagnostic performance of serum Matrix metalloproteinase-7 (MMP-7) as a biomarker for differentiating BA from NH.The PRISMA-DTA compliant synthesis was based upon primary data available till date, assessing the role of MMP-7 in diagnosing BA. Studies reporting diagnostic accuracy metrics were included. Pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratio (DOR) and area under the summary receiver operating characteristic (SROC) curve were calculated.Twenty-four studies (7298 participants: 3301 BA, 2,930 NH and 1067 controls) were analysed. The serum MMP-7 values ranged from 1.2 to 10.3 ng/mL in controls, 1.88-12.57 ng/mL in NH and 10.26-121.1 ng/mL in BA, with interquartile ranges spanning 1.08-25.3, 0.78-34.0 and 3.38-368.0 ng/mL, respectively. Median serum MMP-7 levels were consistently higher in BA vis-a-vis NH and the control population. Pooled sensitivity and specificity of MMP-7 as a diagnostic biomarker were 0.94 and 0.88, respectively. The DOR was 120.09 and the SROC AUC was 0.967.Serum MMP-7 demonstrates excellent diagnostic accuracy for differentiating BA from NH. Further research should focus on assay standardization and age-specific reference ranges to optimize clinical utility.
PROBING THE INFLATON: SMALL-SCALE POWER SPECTRUM CONSTRAINTS FROM MEASUREMENTS OF THE COSMIC MICROWAVE BACKGROUND ENERGY SPECTRUM

In the early universe, energy stored in small-scale density perturbations is quickly dissipated by Silk damping, a process that inevitably generates μ- and y-type spectral distortions of the cosmic microwave background (CMB). These spectral distortions depend on the shape and amplitude of the primordial power spectrum at wavenumbers k ≲ 104 Mpc-1. Here, we study constraints on the primordial power spectrum derived from COBE/FIRAS and forecasted for PIXIE. We show that measurements of μ and y impose strong bounds on the integrated small-scale power, and we demonstrate how to compute these constraints using k-space window functions that account for the effects of thermalization and dissipation physics. We show that COBE/FIRAS places a robust upper limit on the amplitude of the small-scale power spectrum. This limit is about three orders of magnitude stronger than the one derived from primordial black holes in the same scale range. Furthermore, this limit could be improved by another three orders of magnitude with PIXIE, potentially opening up a new window to early universe physics. To illustrate the power of these constraints, we consider several generic models for the small-scale power spectrum predicted by different inflation scenarios, including running-mass inflation models and inflation scenarios with episodes of particle production. PIXIE could place very tight constraints on these scenarios, potentially even ruling out running-mass inflation models if no distortion is detected. We also show that inflation models with sub-Planckian field excursion that generate detectable tensor perturbations should simultaneously produce a large CMB spectral distortion, a link that could potentially be established with PIXIE.
An Upper Bound to the Number of Gates on Single Qubit within One Error-Correction Period of Quantum Computation

Based on the amplitude behavior of quantum Rabi oscillation driven by a coherent field we show that there exists an upper bound to the number of logical operation performed on any single qubit within one error-correction period of a quantum computation. We introduce a parameter to depict the maximum of this number and estimate its decoherence limit. The analysis shows that a generally accepted error-rate threshold of quantum logic gates limits the parameter to so small a number that even a double of fault-tolerant Toffoli gates can hardly be implemented reliably within one error-correction period. This result suggests that the design of feasible fault-tolerant quantum circuits is still an arduous task.
Gradient Surgery for One-shot Unlearning on Generative Model

Recent regulation on right-to-be-forgotten emerges tons of interest in unlearning pre-trained machine learning models. While approximating a straightforward yet expensive approach of retrain-from-scratch, recent machine unlearning methods unlearn a sample by updating weights to remove its influence on the weight parameters. In this paper, we introduce a simple yet effective approach to remove a data influence on the deep generative model. Inspired by works in multi-task learning, we propose to manipulate gradients to regularize the interplay of influence among samples by projecting gradients onto the normal plane of the gradients to be retained. Our work is agnostic to statistics of the removal samples, outperforming existing baselines while providing theoretical analysis for the first time in unlearning a generative model.
Evaluating Range Value at Risk Forecasts

The debate of what quantitative risk measure to choose in practice has mainly focused on the dichotomy between Value at Risk (VaR) -- a quantile -- and Expected Shortfall (ES) -- a tail expectation. Range Value at Risk (RVaR) is a natural interpolation between these two prominent risk measures, which constitutes a tradeoff between the sensitivity of the latter and the robustness of the former, turning it into a practically relevant risk measure on its own. As such, there is a need to statistically validate RVaR forecasts and to compare and rank the performance of different RVaR models, tasks subsumed under the term 'backtesting' in finance. The predictive performance is best evaluated and compared in terms of strictly consistent loss or scoring functions. That is, functions which are minimised in expectation by the correct RVaR forecast. Much like ES, it has been shown recently that RVaR does not admit strictly consistent scoring functions, i.e., it is not elicitable. Mitigating this negative result, this paper shows that a triplet of RVaR with two VaR components at different levels is elicitable. We characterise the class of strictly consistent scoring functions for this triplet. Additional properties of these scoring functions are examined, including the diagnostic tool of Murphy diagrams. The results are illustrated with a simulation study, and we put our approach in perspective with respect to the classical approach of trimmed least squares in robust regression.
Optical Diffusion Models for Image Generation

17 pages, 10 figures
Responsibility to Protect and Militarized Humanitarian Intervention

AbstractMy essay “Responsibility to Protect and Militarized Humanitarian Intervention: When and Why the Churches Failed to Discern Moral Hazard” (JRE 40.2) called for more questioning engagement with R2P than the broadly uncritical welcome given by the churches to the doctrine between September 2003 and September 2008. In response to Luke Glanville's reply, this essay identifies further reasons for caution before accepting R2P and so‐called humanitarian wars alongside defensive wars as paradigmatically justified. It is structured with reference to the tests in A More Secure World: Our Shared Responsibility, the Report by the Secretary‐General's High‐level Panel on Threats, Challenges and Change, as recommended to the General Assembly when considering whether to authorize or apply military force: seriousness of risk, intention, last resort, proportional means, balance of consequences.
A crisis in science literacy and communication: Does reluctance to engage the public make academic scientists complicit?

The impetus for this commentary was the news that I was chosen to receive the 2018 Canadian Association of University Teachers (CAUT) Lee Lorch Award (the CAUT Distinguished Academic Award), which recognizes contributions to the three pillars of academia: teaching, research, and service. Although delighted by the news, I admit that it felt odd to receive a reward for doing something that I love. I have always believed that it is a privilege to be a professor. I love universities. I love universities because they are keepers, interpreters, and disseminators of our collective knowledge. I love universities because they are also the place where we create new knowledge: knowledge that can be used to improve our lives.
Use of Semantic Similarity and Web Usage Mining to Alleviate the Drawbacks of User-Based Collaborative Filtering Recommender Systems

One of the most famous methods for recommendation is user-based Collaborative Filtering (CF). This system compares active user’s items rating with historical rating records of other users to find similar users and recommending items which seems interesting to these similar users and have not been rated by the active user. As a way of computing recommendations, the ultimate goal of the user-based collaborative filtering is recommending items with the high accuracy and coverage degree. Nevertheless, some famous limitations are obstacles to meet them. They are Scalability, Sparseness and new item problems. Scalability problem can be handled with the use of Data Mining techniques like clustering. However, use of this technique often leads to the lower recommendation accuracy. Nevertheless, two other problems still remain. Involving Semantic knowledge can increase the performance of recommendation in sparseness and New-Item Problem conditions as well. This paper presents a new approach to deal with the drawbacks of user-based CF systems for web pages recommendation by Combination of Semantic Knowledge with Web Usage Mining (WUM). Semantic knowledge of web pages are extracted and subsequently incorporated into the navigation patterns of each cluster which obtained from clustering the access sessions to get the Semantic Patterns of each cluster. The cluster with the most relevant semantic pattern is chosen with the comparison of semantic representation of the active user session with the semantic patterns and the proper web pages are recommended based on a switching recommendation engine. This engine recommends a list of appropriate recommendations. Results of the implementation of this hybrid web recommender system indicates that this combined approach yields better results in both accuracy and coverage metrics and also has a considerable capability to handle collaborative filtering recommender system for its typical shortcomings .