ethicalabs/Research-Intent-Collab huggingface dataset:
Covariate Adjustment in Bayesian Adaptive Randomized Controlled Trials

In conventional randomized controlled trials, adjustment for baseline values of covariates known to be at least moderately associated with the outcome increases the power of the trial. Recent work has shown particular benefit for more flexible frequentist designs, such as information adaptive and adaptive multi-arm designs. However, covariate adjustment has not been characterized within the more flexible Bayesian adaptive designs, despite their growing popularity. We focus on a subclass of these which allow for early stopping at an interim analysis given evidence of treatment superiority. We consider both collapsible and non-collapsible estimands, and show how to obtain posterior samples of marginal estimands from adjusted analyses. We describe several estimands for three common outcome types. We perform a simulation study to assess the impact of covariate adjustment using a variety of adjustment models in several different scenarios. This is followed by a real world application of the compared approaches to a COVID-19 trial with a binary endpoint. For all scenarios, it is shown that covariate adjustment increases power and the probability of stopping the trials early, and decreases the expected sample sizes as compared to unadjusted analyses.
Balanced Partitioning for Optimizing Big Graph Computation: Complexities and Approximation Algorithms

Graph partitioning is a key fundamental problem in the area of big graph computation. Previous works do not consider the practical requirements when optimizing the big data analysis in real applications. In this paper, motivated by optimizing the big data computing applications, two typical problems of graph partitioning are studied. The first problem is to optimize the performance of specific workloads by graph partitioning, which lacks of algorithms with performance guarantees. The second problem is to optimize the computation of motifs by graph partitioning, which has not been focused by previous works. First, the formal definitions of the above two problems are introduced, and the semidefinite programming representations are also designed based on the analysis of the properties of the two problems. For the motif based partitioning problem, it is proved to be NP-complete even for the special case of $k=2$ and the motif is a triangle, and its inapproximability is also shown by proving that there are no efficient algorithms with finite approximation ratio. Finally, using the semidefinite programming and sophisticated rounding techniques, the bi-criteria $O(\sqrt{\log n\log k})$-approximation algorithms with polynomial time cost are designed and analyzed for them.
‘The right to privacy and the future of mass surveillance’

This article considers the feasibility of the adoption by the Council of Europe member states of a multilateral binding treaty, called the Intelligence Codex, aimed at regulating the working methods of state intelligence agencies. The Codex is the result of deep concerns about mass surveillance practices conducted by the United States’ National Security Agency and the United Kingdom Government Communications Headquarters. The article explores the reasons for such a treaty. To that end, it identifies the discriminatory nature of the United States’ and the United Kingdom’s domestic legislation, pursuant to which foreign cyber surveillance programmes are operated, which reinforces the need to broaden the scope of extraterritorial application of the human rights treaties. Furthermore, it demonstrates that US and UK foreign mass surveillance interferes with the right to privacy of communications and cannot be justified under Article 17 ICCPR and Article 8 ECHR. As mass surveillance seems set to continue unabated, the article supports the calls from the Council of Europe to ban cyber espionage and untargeted cyber surveillance. The response to the proposal of a legally binding Intelligence Codex from the 47 Council of Europe governments has been so far muted, however a soft law option may be a viable way forward.
Thermodynamic Space of Chemical Reaction Networks

Living systems operate out of equilibrium, continuously consuming energy to sustain organised, functional states. Their emergent behaviour usually relies on a set of interconnected chemical reaction networks (CRNs) driven by external fluxes that keep some species at fixed concentrations. Hence, uncovering the principles governing the functioning of these CRNs is crucial to understand how living systems generate and regulate complexity. While kinetics plays a key role in shaping detailed dynamical phenomena, the range of operations of a CRN is fundamentally constrained by thermodynamics. Here, we introduce and analytically derive the "thermodynamic space" of a CRN, i.e., the range of accessible stationary concentrations that can be realized under a given energetic budget. We establish analogous bounds for reaction affinities, shedding light on how global thermodynamic properties, such as the total non-equilibrium driving, can limit local non-equilibrium quantities. We illustrate our results in various paradigmatic examples, demonstrating how the onset of complex behaviors is intimately tangled with the presence of non-equilibrium conditions. By providing a general tool for analysing CRNs, the presented framework constitutes a stepping stone to deepen our ability to predict complex out-of-equilibrium phenomena and design artificial chemical systems, starting from the sole knowledge of the underlying thermodynamic properties.
Modeling and Optimization of Phenylalanine Ammonia Lyase Stabilization in Recombinant Escherichia coli for the Continuous Synthesis of <scp>l</scp>-Phenylalanine on the Statistical-Based Experimental Designs

Some approaches for improving recombinant phenylalanine ammonia lyase (PAL) stability in Escherichia coli during the enzymatic methods of l-phenylalanine (l-Phe) production were developed following preliminary studies by means of statistical-based experiment designs (response surface method). The traditional non-statistical technology was used to screen four critical factors for PAL stability during the bioconversion process, viz., glycerin, sucrose, 1,4-dithiothreitol (DTT), and MgSO(4). The central composite design (CCD) was applied to optimize the combined effect of critical factors for recombinant PAL stability and understand the relationship between the factors and PAL stability. The optimum values for testing variables were 13.04 mM glycerin, 1.87 mM sucrose, 4.09 mM DTT, and 69 mM Mg(2+). A second-order model equation was suggested and then validated experimentally. The model adequacy was very satisfactory because the coefficient of determination was 0.88. The maximum PAL activity was retained as 67.73 units/g after three successive cycles of bioconversion. In comparison to initial PAL activity, the loss of PAL activity was only 22%. PAL activity was enhanced about 23% in comparison to the control (without any stabilizer additives). PAL stability was significantly improved during successive bioconversion. The results obtained here verified the effectiveness of the applied methodology and may be helpful for l-Phe production on an industrial scale.
A Corpus-based Comparison of Human Interpreting and AI Machine Translation in Korean-English: A Discourse-level Analysis

This study provides a corpus-based comparative analysis of discourse features between human interpretation and AI-driven machine translation (MT) for the Korean-English language pair. As AI translation technologies, particularly Neural Machine Translation(NMT) and Large Language Models (LLMs), have become increasingly sophisticated, their performance is often compared to that of professional human translators. However, most evaluations have focused on sentence-level accuracy, leaving discourse-level characteristics underexplored. To address this gap, we constructed a parallel corpus using 100 sentences from the Multitarget TED Talks Task dataset, comparing the official human translations with translations generated by GPT-4.1-mini. We quantitatively analyzed three key discourse features: lexical diversity(TTR), sentence length, and lexical density. The results of independent samples t-tests showed no statistically significant differences between human and AI translations in TTR (p=.742), sentence length (p=.889), or lexical density (p=.983). These findings suggest that current high-performance AI models can produce translations that are structurally and lexically comparable to professional human translations at the discourse level. This study contributes to a more nuanced understanding of AI translation quality and suggests that future research should focus on more subtle semantic, pragmatic, and stylistic dimensions to differentiate between human and machine-generated texts.
Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies

Artificial neural network (ANN) approach has been applied to estimate the density functional theory (DFT) energy with large basis set using lower-level energy values and molecular descriptors. A total of 208 different molecules were used for the ANN training, cross validation, and testing by applying BLYP, B3LYP, and BMK density functionals. Hartree–Fock results were reported for comparison. Furthermore, constitutional molecular descriptor (CD) and quantum-chemical molecular descriptor (QD) were used for building the calibration model. The neural network structure optimization, leading to four to five hidden neurons, was also carried out. The usage of several low-level energy values was found to greatly reduce the prediction error. An expected error, mean absolute deviation, for ANN approximation to DFT energies was 0.6±0.2 kcal mol−1. In addition, the comparison of the different density functionals with the basis sets and the comparison of multiple linear regression results were also provided. The CDs were found to overcome limitation of the QD. Furthermore, the effective ANN model for DFT/6-311G(3df,3pd) and DFT/6-311G(2df,2pd) energy estimation was developed, and the benchmark results were provided.
An automated pipeline integrating AlphaFold 2 and MODELLER for protein structure prediction

The ability to predict a protein's three-dimensional conformation represents a crucial starting point for investigating evolutionary connections with other members of the corresponding protein family, examining interactions with other proteins, and potentially utilizing this knowledge for the purpose of rational drug design. In this work, we evaluated the feasibility of improving AlphaFold2's three-dimensional protein predictions by developing a novel pipeline (AlphaMod) that incorporates AlphaFold2 with MODELLER, a template-based modeling program. Additionally, our tool can drive a comprehensive quality assessment of the tertiary protein structure by incorporating and comparing a set of different quality assessment tools. The outcomes of selected tools are combined into a composite score (BORDASCORE) that exhibits a meaningful correlation with GDT_TS and facilitates the selection of optimal models in the absence of a reference structure. To validate AlphaMod's results, we conducted evaluations using two distinct datasets summing up to 72 targets, previously used to independently assess AlphaFold2's performance. The generated models underwent evaluation through two methods: i) averaging the GDT_TS scores across all produced structures for a single target sequence, and ii) a pairwise comparison of the best structures generated by AlphaFold2 and AlphaMod. The latter, within the unsupervised setups, shows a rising accuracy of approximately 34% over AlphaFold2. While, when considering the supervised setup, AlphaMod surpasses AlphaFold2 in 18% of the instances. Finally, there is an 11% correspondence in outcomes between the diverse methodologies. Consequently, AlphaMod's best-predicted tertiary structures in several cases exhibited a significant improvement in the accuracy of the predictions with respect to the best models obtained by AlphaFold2. This pipeline paves the way for the integration of additional data and AI-based algorithms to further improve the reliability of the predictions.
The Concept of Artificial Intelligence and its Prospects from the Perspective of the Living Ethics’ Philosophy

The article considers the concept of artificial intelligence (AI) using categories and basic principles of the consciousness theory developed in the Living Ethics (LE). The latter is a modern form of the ancient tradition of exploring conscious­ness in Indian philosophy and spiritual practice. Categorial apparatus of Indian philosophy contains rich variety of distinction which may be successfully imple­mented also in the modern cognitive researches. The article shows that more pre­cise definitions of the basic concepts allow to fulfill strict delimitation between “strong” and “weak” AI as well as between what is possible and what completely impossible regarding AI. Strong AI in a sense of possessing “subjective presenta­tions” appears to be impossible. But, a deeper understanding of nature of con­sciousness in LE allows to move the limits of what is considered as possible for the weak AI. First, LE asserts mechanical mode of operation underlying the most of intellectual operations. Hence, even “weak AI” may fulfill many functions which were before attributed only to “strong AI”. Second, LE defines conscious­ness and intelligence as inherent inner potential powers of material systems, manifesting also in their ability and tendency to self-organization. Therefore, some features of “artificial” intelligence may be reconsidered as manifestations of intrinsic “intelligence” of matter, which also implies wider possibilities for AI systems. Some parallels appear with major Western philosophers such as G. Bruno, Leibnitz, H. Bergson, E. Husserl, including recent approaches of N. Luhmann’s systems theory and B. Latour’s actor-network theory
Acceptance of health technology assessment submissions with incremental cost-effectiveness ratios above the cost-effectiveness threshold

In health technology assessment (HTA) agencies where cost-effectiveness plays a role in decision-making, an incremental cost-effectiveness ratio (ICER) threshold is often used to inform reimbursement decisions. The acceptance of submissions with ICERs higher than the threshold was assessed across different agencies and across indications, in order to inform future reimbursement submissions.All HTA appraisals from May 2000 to May 2014 from National Institute for Health and Care Excellence (NICE), Scottish Medicines Consortium (SMC), Pharmaceutical Benefits Advisory Committee (PBAC), and Canadian Agency for Drugs and Technologies in Health (CADTH) were assessed. Multiple technology appraisals, resubmissions, vaccination programs, and requests for advice were excluded. Submissions not reporting an ICER, or for which an ICER could not be determined were also excluded. The remaining appraisals were reviewed, and the submitted ICER, recommendation, and reasoning behind the recommendation were extracted.NICE recommended the highest proportion of submissions with ICERs higher than the threshold (34% accepted without restrictions; 20% with restrictions), followed by PBAC (16% accepted without restrictions; 4% with restrictions), SMC (11% accepted without restrictions; 14% accepted with restrictions), and CADTH (0% accepted without restrictions; 26% with restrictions). Overall, the majority of higher-than-threshold ICER submissions were classified into the "malignant disease and immunosuppression" therapeutic category; however, there was no notable variation in acceptance rates by disease area. Reasons for accepting submissions reporting ICERs above the threshold included high clinical benefit over the standard of care, and addressing an unmet therapeutic need.Acceptance of submissions with higher-than-threshold ICERs varied by HTA agency and was not significantly influenced by disease category. Such submissions must be accompanied by robust, concrete, and transparent evidence in order to achieve patient access.
Species Trees are Recoverable from Unrooted Gene Tree Topologies Under a Constant Rate of Horizontal Gene Transfer

Reconstructing the tree of life from molecular sequences is a fundamental problem in computational biology. Modern data sets often contain a large number of genes, which can complicate the reconstruction problem due to the fact that different genes may undergo different evolutionary histories. This is the case in particular in the presence of horizontal genetic transfer (HGT), where a gene is inherited from a distant species rather than an immediate ancestor. Such an event produces a gene tree which is distinct from, but related to, the species phylogeny.   In previous work, a natural stochastic models of HGT was introduced and studied. It was shown, both in simulation and theoretical studies, that a species phylogeny can be reconstructed from gene trees despite surprisingly high rates of HGT under this model. Rigorous lower and upper bounds on this achievable rate were also obtained, but a large gap remained. Here we close this gap, up to a constant. Specifically we show that a species phylogeny can be reconstructed correctly from gene trees even when, on each gene, each edge of the species tree has a constant probability of being the location of an HGT event. Our new reconstruction algorithm, which relies only on unrooted gene tree topologies, builds the tree recursively from the leaves and runs in polynomial time.   We also provide a matching bound in the negative direction (up to a constant) and extend our results to some cases where gene trees are not perfectly known.
Corporate social responsibility disclosure in corporate communication : A content analysis of the automotive industry’s sustainability reports

Problems related to sustainable development such as environmental issues, human rights, orlabor conditions, are nowadays deeply integrated in our society. It became primordial forcompanies to take into consideration these problems in their business development. For morethan a decade now, car companies started to publish official documents summarizing theircommitments in favor of Corporate Social Responsibility (CSR) actions. Through thesesustainability reports, car producers disclose information concerning their sustainable policyand bring details to stakeholders on performed sustainable actions. Because publication of sustainability reports is a relatively new phenomenon, a lot of studies still have to be made in this sector. In this way, no main studies were made regarding howCSR actions performed and disclosed by car companies evolved in the past decade. Didcompanies keep focusing on the same type of sustainable actions or did some changes occuron the way they conduct their CSR policies? No trends were available to try to understandhow car companies CSR actions evolved. This thesis is an attempt to fulfill this gap and bringa first set of useful findings on this topic. Based on three different car producers which are BMW Group, Ford Motor Company andMitsubishi Motors Corporation, our study analyses these companies 2002, 2006 and 2010sustainability reports and looks at how CSR actions are disclosed. The purpose is firstly todescribe how companies’ CSR commitment disclosure is evolving between 2002 and 2010and secondly to find explanation to the key trends. In order to achieve this, we used six mainCSR categories (Economic, Environment, Human Rights, Labor Practices and Decent Work,Product Responsibility and Society) and classified disclosed information in relation with thesecategories to create trends. For each of these six categories, sub-categories were used in orderto be more precise in the analysis process of the documents. Results showed an important interest of car producers for the environment category whichrepresents, on average, more than 40% of disclosed information in sustainability reports.Society category trend shows an important increase between 2006 and 2010 which led it tobecome the second most represented category in reports. Other categories trends present amore stable evolution with time. As it is explained more in detail in our analysis part, thisattraction toward environmental issues for car producers can be explained by the badreputation given to car producers in terms of emissions, energy consumption and moreglobally environmental protection. Cultural background and business mindset are alsoinfluencing companies’ choices. Responsive behaviors resulting from stakeholder’s pressureas well as adaptation to a weak regulatory framework are additional elements to explain the key disclosure trends.
Reflection vs. Refraction Seismic: Tiziano Rocco and the Development of Applied Geophysics in Italy

Tiziano Rocco was a mining engineer involved since his early career in a key and new activity for oil companies around the world in the 1930s: applied geophysics. Hired in 1929 by the Italian state-owned company AGIP (founded in 1926) under the guidance of Guido Bonarelli and Arnaldo Belluigi, then manager of the geophysical section, after a short period of training he led gravimetric, magnetic and electrical survey teams in Italy, during the 1934 campaign in eastern Sicily. Rocco eagerly studied the scientific literature and soon learned that a new geophysical method for oil exploration, seismic reflection, had been successfully tested in the USA since the early 1930s (Guidi, 1998). In 1937, after some trials with German equipment and the in-house construction of rudimentary seismic units, AGIP asked Francesco Vercelli (Trieste Institute of Geophysics) to visit Berlin to verify the state of the art of the German allies, and look for a more convenient alternative than that of the American Western Geophysical, already contacted by Rocco. Vercelli and Rocco visited Berlin, and they verified the lack of satisfactory technical standards, which instead they found in the USA in another joint visit (1938). Upon their return, the Board of AGIP approved Western’s offer (although with great political worries), and in 1939 two seismic groups arrived in Italy. The first survey was carried out on 10 June 1940, the very day Italy entered WW2, in the Po Valley basin (Pozzi, 2009). Good results arrived quickly: in late 1940, the structures of Ripalta, Caviaga, Cortemaggiore and Piadena had already been outlined. Rocco located the first exploratory wells, which led to the first success in 1944, the discovery of Caviaga gas field. Rocco foresaw the potential of geophysics, and in particular of reflection seismic, at least a decade in advance in Europe, in times of poor collaboration between Companies, Universities and Research Institutes, when scientific and technological advances were shared with difficulty. Rocco left AGIP in 1943, due to the turbulent events of the armistice and the German occupation of Italy; from 1945 he worked with SPI (Società Petrolifera Italiana) and from 1948 with Western Geophysical. He returned to AGIP in 1951, as Director of exploration. In the 1950s and ‘60s he significantly contributed on the company’s successes in Italy and foreign countries (Iran, Egypt, Nigeria, Tunisia and Libya), as well as for the detailed definition of the geological structures of the Po Valley basin (Rocco, 1942; Rocco, 1949). He retired in 1968, and in the same year he was elected President of the Italian Geological Society (SGI) for the two-year period 1968-69. Rocco had been SGI member since 1940, and held the position of Vice President under the chairmanship of Roberto Malaroda (1966-67). With his appointment, SGI intended to open up to the applicative aspects of geosciences and intensify the relationships with the industrial sector, and particularly with AGIP.