ethicalabs/Research-Intent-Collab huggingface dataset:
DLPGEN: Preparing Molecular Dynamics Simulations with Support for Polarizable Force Fields

Preparing input files for molecular dynamics (MD) simulations can be a tedious task, particularly if different MD programs need to be used. Most simulation packages are accompanied by applications that handle this task, and, in some cases, software to perform interconversion between MD programs can be found. However, the conversion between different types of files is not always foolproof or the force field may not be fully supported, as quite often observed with polarization models. This work describes the program DLPGEN, which produces input files for the MD programs GROMACS, CHARMM, DL_POLY, and LAMMPS. The program can prepare polarizable force fields using a self-consistent field approach or with a dual thermostat-extended Lagrangian model. For GROMACS, a new polarization scheme suitable for the simulation of molecules containing more than one virtual particle is described. Results obtained for ethanol in the liquid state revealed that the system configurational energy, liquid density, and self-diffusion coefficient obtained with GROMACS are in good agreement with the data found with LAMMPS and CHARMM. In the case of DL_POLY, a problem with the Shells temperature control was found, suggesting that this program may not be suitable for the simulation of molecules containing multiple Drude particles.
The judicial function of the African Court on Human and Peoples’ Rights in default judgments: the developments set forth in the Léon Mugesera case

This case discussion focuses on the judicial function of the African Court on Human and Peoples’ Rights (Court) in default judgments. The discussion brings to the foreground the changes introduced by the Court in its recently revised Rules of Procedure and how these changes are put in practice in its case law. The revised provision on default judgments includesthe extended power of the Court to decide on its own motion on whether to issue a default judgment and the introduction of a legal innovation in international dispute settlement, namely, a remedy for the defaulting party to set aside a default judgment. The recent default judgment in Léon Mugesera v Rwanda serves as a focal point in the analysis since it is the first instance that the revised Rules of Procedure were put into practice and since the Mugesera case illustrates the difficulties encountered by the Court in cases of non-appearance of the respondent state. The Court’s Rules of Procedure and case law are also placed within the corpus of international (human rights) law and the jurisprudence of its two regional counterparts on matters pertaining to default judgments so as to shed light on different approaches. The analysis makes two main arguments. The first concerns theprocedural requirements for rendering a judgment in default. Until the Mugesera decision, the Court was not consistent in meeting these requirements, and had been known to decide in default on its own motion without having a textual basis in the Protocol or the Rules of Procedure to do so. The recently revised Rule of Procedure grants the Court the power to decide on its own motion. Second, the analysis argues that the Mugesera decision cements an alarming trend in the case law, whereby in cases of nonappearance the Court does not satisfy itself that the applicant’s submissions are well-founded.
A Study on Public Perception on the Adoption of Central Bank Digital Currency (CBDC) in Ireland

The advent of blockchain technology and its application within the financial system has spurred debates, especially with regards to the implication of digital currencies. As a reactive response to the effects of privately issued digital currencies and the absence of regulatory implications and government power/control, researchers have proposed justifications for the issuance of central bank digital currencies as well as suggestions regarding its design. However, there is the absence of sufficient study regarding public perception and adoption of CBDCs and their willingness to trade-off financial freedom and privacy for national and economic benefits. The study focused on Ireland and sought to identify trust drivers and determine the public’s perception of CBDC. It adopted a quantitative analysis using the Unified Theory of Acceptance and Use of Technology (UTAUT) model and obtained responses via a survey. The paper adopted statistical approaches such as Descriptive Statistics, Frequency, Chi-Square Test, Cronbach’s Alpha Test and Factor Analysis. The results showed that individuals are knowledgeable about digital currencies and that higher education and employment status influence the decision to adopt the CBDC. Also, the study hints that anonymity, reversibility, security and privacy are trust drivers for CBDC adoption.
CAD Interoperabiity for Navy Reuse in Additive Manufacturing (AM), 3D Printing, Maintenance and Training

Naval Research Program Poster
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We design white-box inference attacks to perform a comprehensive privacy analysis of deep learning models. We measure the privacy leakage through parameters of fully trained models as well as the parameter updates of models during training. We design inference algorithms for both centralized and federated learning, with respect to passive and active inference attackers, and assuming different adversary prior knowledge.   We evaluate our novel white-box membership inference attacks against deep learning algorithms to trace their training data records. We show that a straightforward extension of the known black-box attacks to the white-box setting (through analyzing the outputs of activation functions) is ineffective. We therefore design new algorithms tailored to the white-box setting by exploiting the privacy vulnerabilities of the stochastic gradient descent algorithm, which is the algorithm used to train deep neural networks. We investigate the reasons why deep learning models may leak information about their training data. We then show that even well-generalized models are significantly susceptible to white-box membership inference attacks, by analyzing state-of-the-art pre-trained and publicly available models for the CIFAR dataset. We also show how adversarial participants, in the federated learning setting, can successfully run active membership inference attacks against other participants, even when the global model achieves high prediction accuracies.
Optimal design and experimental validation of a three-zone simulated moving bed process based on the Amberchrom-CG161C adsorbent for continuous removal of acetic acid from biomass hydrolyzate

Abstract   In the production process of bio-ethanol from biomass, acetic acid is recognized as the key impurity to be removed from the sugar components that are generated by hydrolyzing biomass. In regard to this issue, it has recently been confirmed that the Amberchrom-CG161C resin was highly qualified as the adsorbent of a simulated moving bed (SMB) process for continuous separation of acetic acid from the biomass hydrolyzate, i.e., sugars. However, the previous study on the Amberchrom-CG161C SMB with the aforementioned separation goal has been limited to only a theoretical work, including some batch-chromatography tests. The experimental validation of such an Amberchrom-CG161C SMB process, including its optimal design, was attempted in this article. This task began by assembling the experimental unit of the SMB process with three zones. Its operating conditions were then optimized by using genetic algorithm. Under the optimized operating conditions, the relevant three-zone SMB experiment was conducted. The assay of all the resultant product samples verified that the SMB separation of interest was performed successfully as designed. The experimental data were also found to agree closely with the model predictions. Finally, a partial-discard strategy was applied to maintain the sugar product concentration as high as possible.
Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression

We consider the problem of predicting how the likelihood of an outcome of interest for a patient changes over time as we observe more of the patient data. To solve this problem, we propose a supervised contrastive learning framework that learns an embedding representation for each time step of a patient time series. Our framework learns the embedding space to have the following properties: (1) nearby points in the embedding space have similar predicted class probabilities, (2) adjacent time steps of the same time series map to nearby points in the embedding space, and (3) time steps with very different raw feature vectors map to far apart regions of the embedding space. To achieve property (3), we employ a nearest neighbor pairing mechanism in the raw feature space. This mechanism also serves as an alternative to data augmentation, a key ingredient of contrastive learning, which lacks a standard procedure that is adequately realistic for clinical tabular data, to our knowledge. We demonstrate that our approach outperforms state-of-the-art baselines in predicting mortality of septic patients (MIMIC-III dataset) and tracking progression of cognitive impairment (ADNI dataset). Our method also consistently recovers the correct synthetic dataset embedding structure across experiments, a feat not achieved by baselines. Our ablation experiments show the pivotal role of our nearest neighbor pairing.
South-South Cooperation in the World Trade Organization's Dispute Settlement Body

The South–South Cooperation (SSC) strategy advocates that Global South states cooperate within international organizations to gain bargaining leverage and advance their interests in global governance. The SSC agenda advances a multipolar international system, multilateral negotiations, and mutual economic development, especially for less developed states. This research examines qualitative and quantitative documentation of SSC leadership of Brazil and India in the World Trade Organization’s Dispute Settlement Body. The analysis uses case studies of each actor’s profile in trade disputes and cross-tabulations of their success, both individually and collectively against Global North states. The findings support the proposition that Global South states have relatively high rates of success, especially when they collaborate in disputes against the Global North. This research extends the institutional analysis of SSC to the Dispute Settlement Body as an arena for rule-based decision making and adds a qualitative analysis of the SSC leadership and a quantitative analysis of categorical data of trade disputes. The conclusion summarizes the findings and suggests areas for future research.
Environmental Rights are a Human Right to a Healthy Environment: A Brief Review

Environmental Rights is a branch of law that deals with the protection and safeguarding of the environment; and, is a set of legal rules that regulate people's relationships with nature, and it can be said that it is an interdisciplinary right, based on rules of private law, public law, administrative law, and criminal law. Therefore, it regulates the creation, modification, and fulfillment of legal relationships that condition the enjoyment, conservation, and improvement of the environment, inducing actions and abstentions in favor of the environment and the common good.  As such, it is a fundamental human right to a safe, clean and healthy environment; and sustainable environmental governance cannot exist without the establishment of and respect for human rights.
SynQP: A Framework and Metrics for Evaluating the Quality and Privacy Risk of Synthetic Data

The use of synthetic data in health applications raises privacy concerns, yet the lack of open frameworks for privacy evaluations has slowed its adoption. A major challenge is the absence of accessible benchmark datasets for evaluating privacy risks, due to difficulties in acquiring sensitive data. To address this, we introduce SynQP, an open framework for benchmarking privacy in synthetic data generation (SDG) using simulated sensitive data, ensuring that original data remains confidential. We also highlight the need for privacy metrics that fairly account for the probabilistic nature of machine learning models. As a demonstration, we use SynQP to benchmark CTGAN and propose a new identity disclosure risk metric that offers a more accurate estimation of privacy risks compared to existing approaches. Our work provides a critical tool for improving the transparency and reliability of privacy evaluations, enabling safer use of synthetic data in health-related applications. % In our quality evaluations, non-private models achieved near-perfect machine-learning efficacy \(\ge0.97\). Our privacy assessments (Table II) reveal that DP consistently lowers both identity disclosure risk (SD-IDR) and membership-inference attack risk (SD-MIA), with all DP-augmented models staying below the 0.09 regulatory threshold. Code available at https://github.com/CAN-SYNH/SynQP
Knowledge Discovery for Biology with Tavema: Producing and Consuming Semantics in the Web of Science

Life Science research has extended beyond in vivo and in vitro bench-bound science to incorporate in silico knowledge discovery, using resources that have been developed over time by different teams for different purposes and in different forms. The myGrid project has developed a set of software components and a workbench, Taverna, for building, running and sharing workflows that link third party bioinformatics services, such as databases, analytic tools and applications. Intelligently discovering prior services, workflow or data is aided by a Semantic Web of annotations, as is the building of the workflows themselves. Metadata associated with the workflow experiments, the provenance of the data outcomes and the record of the experimental process need to be flexible and extensible. Semantic Web metadata technologies would seem to be well-suited to building a Semantic Web of provenance. We have the potential to integrate and aggregate workflow outcomes, and reason over provenance logs to identify new experimental insights, and to build and export a Semantic Web of experiments that contributes to Knowledge Discovery for Taverna users and for the scientific community as a whole.
The Extractive Industry Transparency Initiative and Corruption in Nigeria: Rethinking the Links between Transparency and Accountability

The identification of corruption as the development problem in resource-rich African countries has in recent years led to the emergence of a dominant perspective that suggests the link between transparency and accountability as a strategy to address corruption and other aspects of 'the resource curse'. This chapter makes two inter-related contributions to the literature on transparency. First, it suggests that the strategy of linking transparency and accountability as a cure for the resource curse is both problematic and potentially misleading. Secondly, MoI points out that a core challenge confronting the analysis of transparency - and therefore its usefulness - is whether the assumed link between transparency and actual improvement on the wide range of issues for which it is often adapted on the ground is incorrect. The chapter suggests that the Nigerian Extractive Industry Transparency Initiative (NEITI) has at best been ineffective in the fight against corruption in Nigeria.   Keywords:accountability; Nigerian Extractive Industry Transparency Initiative (NEITI); resource-rich African countries; transparency
Legal Risk Analysis for Artificial Intelligence Generated Content (AIGC)

Artificial Intelligence Generated Content (AIGC), exemplified by ChatGPT, has experienced significant advancements in recent years. These advancements are attributed to training language models on extensive datasets, resulting in the acquisition of distinct learning and creative abilities that differentiate them from earlier AI tools. Generative AI has enhanced the productivity of human society's work and education. However, it has also brought up several legal hazards, such as challenges in regulating it under public law and issues of infringement under private law. China's regulatory approach to generative AI should prioritize caution and inclusivity on a broad scale. It should progressively establish a flexible regulatory and governance structure, as well as a comprehensive liability governance system, on a smaller scale.
Developing a multi-center registry of peripheral artery disease using natural language processing: unlocking the power of the electronic health record

Abstract                                   Background                   Although peripheral artery disease (PAD) is common and associated with a high risk of mortality, contemporary PAD research remains limited due to poor accuracy of diagnosis codes in the electronic health record (EHR). We previously developed and validated a novel natural language processing (NLP) algorithm that can extract ankle brachial index (ABI) and toe-brachial index (TBI) values from clinical documents and identify patients with PAD with high accuracy (positive predictive value 92.4%).                                                   Purpose                   Scale the NLP system for developing a nationwide registry of PAD in the Veterans Health Administration (VHA), the largest integrated health system in the U.S. that provides care to ~9 million Veterans across 130 sites. Describe the baseline characteristics and mortality outcomes among participants and identify predictors of long-term mortality.                                                   Methods                   A total of 1,182,025 documents from vascular and radiology procedures during 2013-2020 were processed by the NLP system. After excluding non-ABI vascular studies, patients with a known diagnosis of PAD prior to 2015, patients younger than 40 years of age, and those who do not regularly follow-up in the VHA, we identified 107,711 patients with newly diagnosed PAD during 2015-2020. Patients were followed longitudinally using the VA’s electronic health record data. The primary outcome was all-cause mortality. We used Cox proportional hazards regression to examine predictors of long-term mortality.                                                   Results                   The mean age was 70.5 years; 97.3% were males, and 18.5% were of Black race. The mean ABI value was 0.78 (SD: 0.26) and the mean TBI value was 0.51 (SD: 0.19). There was a high prevalence of hypertension (83.6%), heart failure (20.8%), diabetes (53.6%), renal failure (22.1%) and chronic obstructive pulmonary disease (32.4%, Table 1). At 1-year, 9.6% of patients had died and this proportion increased to 25.4% at 3 years. Although nearly all patient variables were significantly associated with mortality, older age, severe PAD at diagnosis, diabetes, heart failure, renal failure and COPD were the strongest predictors of long-term mortality. Black race was associated with lower hazard of long-term mortality.                                                   Conclusions                   Using a previously validated novel NLP algorithm that extracts ABI and TBI values from clinical documents, we have successfully developed a registry of newly diagnosed PAD in the VHA. Ongoing studies from our cohort will yield important insights into the association between clinical management of PAD and clinical outcomes, with the goal of identifying opportunities for improving care.
An Integrated Pest Management Tactic for Quagga Mussels: Site-Specific Application of Fish Biological Control Agents

Abstract                The quagga mussel Dreissena bugensis is a harmful aquatic pest that invaded the southwestern USA in 2007. Challenges with managing this pest have been encountered because the invaded systems are primarily open-water sources used for human consumption and/or are connected to freshwater habitats containing threatened and endangered species. Existing chemical and physical control methods are undesirable, and the use of some methods is restricted or prohibited because they pose risks to humans and ecosystems more broadly. To address this problem, we investigated the efficacy of using resident fishes as biocontrol agents for managing different life stages of quagga mussels on different spatial scales in a site-specific manner. We conducted field experiments to test whether planktivorous Bluegill Lepomis macrochirus reduced mussel infestations on substrates of varying orientations in small and large pens through predation on larval mussels. We also performed an experiment to evaluate whether carnivorous Redear Sunfish L. microlophus reduced mussel infestations established on substrates of varying orientations in small pens through predation on juvenile and adult mussels. Bluegill significantly reduced mussel infestations on all substrates in the pens through predation on larvae and small juvenile mussels. Redear Sunfish reduced existing juvenile and adult mussel populations in some cases, with consumption varying among individuals and substrate orientations. Our results indicate that fishes, specifically Bluegill, may represent effective site-specific biocontrol agents for quagga mussels, reducing impacts on targeted infrastructure (e.g., water towers, docks, and pipes) and habitats having different surface orientations by controlling more than one life stage of the pest. Development of an integrated pest management strategy that considers application of this tactic in combination with others would undoubtedly improve the management of quagga mussels—and potentially that of congeneric zebra mussels D. polymorpha—within lake and reservoir ecosystems.
UN Peacekeeping, Responsibility to Protect and Humanitarian Intervention

The question of armed intervention on behalf of the international community, in the internal affairs of a state against the wishes of the government of that state, in order to prevent widespread death or suffering among the population, is not a new one. Indeed Imperial Rome grappled with the same problems in Dalmatia and Judaea 2,000 years ago, as the international community does in those same regions today. How effective are peacekeeping operations in preventing and stopping violence? Is there an alternative to United Nations (UN) and regional peacekeeping operations? The practice of UN peacekeeping is evolving in many instances into robust peacemaking actions with a positive responsibility to protect (R2P) civilians within the field of operations. The R2P (and ‘responsibility while protecting’ (RwP)) concept sets out a key principle to enable the international community to prevent atrocity crimes. Since its emergence, however, there have been intense discussions over how to put the principle into practice. Some aspects of the concept remainf unclear, including how to undertake, as the last resort, the use of military force. These issues must be considered within the boundaries set by R2P which seek at all costs to avoid the use of force for other reasons than ceasing mass atrocity crimes. The use of force, therefore – including possible military action by the international community, given growing international reluctance to accept grave threats to peace and security, including mass crimes against defenceless populations – has to be thoroughly analysed and comprehended. This article presents an analysis of the development of civilian peacekeeping, its relevance in the field of conflict resolution and its autonomy from multidimensional peacekeeping, championed by the UN.