ethicalabs/Research-Intent-Collab huggingface dataset:
Bayesian Parameter Estimation for Dynamical Models in Systems Biology

Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and '-omics' studies that have helped populate protein-protein interaction networks in great detail, modeling in systems biology lacks systematic methods to estimate kinetic parameters and quantify associated uncertainties. This is because of multiple reasons, including sparse and noisy experimental measurements, lack of detailed molecular mechanisms underlying the reactions, and missing biochemical interactions. Additionally, the inherent nonlinearities with respect to the states and parameters associated with the system of differential equations further compound the challenges of parameter estimation. In this study, we propose a comprehensive framework for Bayesian parameter estimation and complete quantification of the effects of uncertainties in the data and models. We apply these methods to a series of signaling models of increasing mathematical complexity. Systematic analysis of these dynamical systems showed that parameter estimation depends on data sparsity, noise level, and model structure, including the existence of multiple steady states. These results highlight how focused uncertainty quantification can enrich systems biology modeling and enable additional quantitative analyses for parameter estimation.
The Content and Function of Cosmopolitanism in a Selection of International Criminal Law Tribunals

Reasoning and justifications before the international criminal tribunals and courts (ICTs) have not always strictly kept to legal rules and/or legal standards. At times, philosophical structures were used directly in argument. Consequently, this article is, in particular, concerned with cosmopolitanism in arguments and judgments from a selection of counsel and judges emanating from the Nuremberg and Tokyo International Military Tribunals as well as the ad hoc International Criminal Tribunal for the former Yugoslavia (ICTY) and the International Criminal Tribunal for Rwanda (ICTR).  It aims to provide an analysis of how the different generations of ICT and the different role-players therein used cosmopolitanism in their arguments. While not a treatise on cosmopolitanism as such, the article will identify and consider several broad cosmopolitan strands relevant to the selected arguments. The ensuing analysis will propose a taxonomy of specific arguments from the selected role-players addressing (1) the existence of a homogeneous civilisation and/or common morality as a descriptive truth or falsity, (2) the progressive realisation of the international society as a normative aspiration and (3) the relationship of the duty to the international community versus that owed to the domestic community of birth. Thereafter, the article will discuss plausible functions, in addition to persuasion, served by these arguments (including winning the case, undermining or supporting the legitimacy of the ICL proceedings in their entirety and/or controlling the subsequent historical narrative). Finally, the article will situate these investigations within the broader study of philosophy at the selected ICTs.
Pseudodifferential multi-product representation of the solution operator of a parabolic equation

By using a time slicing procedure, we represent the solution operator of a second-order parabolic pseudodifferential equation on $\R^n$ as an infinite product of zero-order pseudodifferential operators. A similar representation formula is proven for parabolic differential equations on a compact Riemannian manifold. Each operator in the multi-product is given by a simple explicit Ansatz. The proof is based on an effective use of the Weyl calculus and the Fefferman-Phong inequality.
Interactions Between Dark Matter and Dark Energy: Simulations and Cosmological Implications Alberto Mir´o Mor´an, University of Extremadura, mail:albertomiro1991@gmail.com

Understanding the evolution of the universe is deeply rooted in ex-  ploring the intricate interplay between dark matter and vacuum energy.  While traditional cosmology treats these components as independent, new  frameworks propose dynamic interactions that transcend the standard  CDM paradigm. This idea is compelling because it challenges the no-  tion of a static vacuum energy and introduces a fluid, evolving picture  of the cosmos, where energy exchange between dark matter and vacuum  energy influences cosmic transitions, structure formation, and expansion  dynamics.  This approach is significant for several reasons. First, it provides a  unifying perspective that links the universe’s accelerating expansion with  the growth of large-scale structures, bridging two seemingly disparate phe-  nomena under a single theoretical framework. Second, by incorporating  interactions, we can address persistent tensions in cosmology, such as dis-  crepancies in the Hubble constant, which traditional models struggle to  resolve. Finally, this theory opens a new avenue for testing cosmological  predictions through observational data, including gravitational lensing,  fast radio bursts, and galaxy clustering.  The abstract nature of this idea is its strength it invites a reconsid-  eration of the fundamental forces shaping the cosmos, positioning dark  matter and vacuum energy not as isolated entities but as dynamic, in-  terdependent agents. By embracing this conceptual shift, we not only  refine our understanding of the universe’s past and future but also create  a fertile ground for theoretical and observational breakthroughs.
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning

A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents. In particular, each agent perceives the environment as effectively non-stationary due to the changing policies of other agents. Moreover, each agent is itself constantly learning, leading to natural non-stationarity in the distribution of experiences encountered. In this paper, we propose a novel meta-multiagent policy gradient theorem that directly accounts for the non-stationary policy dynamics inherent to multiagent learning settings. This is achieved by modeling our gradient updates to consider both an agent's own non-stationary policy dynamics and the non-stationary policy dynamics of other agents in the environment. We show that our theoretically grounded approach provides a general solution to the multiagent learning problem, which inherently comprises all key aspects of previous state of the art approaches on this topic. We test our method on a diverse suite of multiagent benchmarks and demonstrate a more efficient ability to adapt to new agents as they learn than baseline methods across the full spectrum of mixed incentive, competitive, and cooperative domains.
Between Precaution and Prevention: Scientific Uncertainty and Implementation of the Precautionary Principle

[Abstract] The cause and effect of risks like the destruction of the environment are global and multiplied now, and international efforts are needed to tackle new aspects of global environmental risks. However, to address the potential risks, international law should incorporate some degree of "uncertainty" and probable "risks" into the normative picture. The precautionary principle is an important instrument in tackling this "grey area" of uncertainty. Having its roots in German domestic law, this principle saw its widespread acceptance in international documents since the 1980s. The legal nature and status of the precautionary principle are under discussion among international legal scholars, while international courts and tribunals have been reluctant to apply this principle or to recognize its normative status explicitly. But this principle has a firm relationship with other existing norms and concepts for environmental protection, and its existence as a tool for initiating proactive measures in the face of uncertain risk is valuable. Although the precautionary principle shares a common continuum of timeframe with the principle of prevention, the dividing line between these two principles is rather vague. A continuous assessment as a part of risk management becomes necessary for both principles to operate effectively. It is for the decision-makers to draw the line, but this by no means leaves them in a free-hand position: prudent and cautious decision-making should be made in the face of scientific uncertainty for the effective implementation of the precautionary principle. 論説 Articles Departmental Bulletin Paper application/pdf
Modeling Characteristics of an Operational Probabilistic Safety Assessment (PSA)

Probabilistic Safety Assessments (PSAs) are increasingly being used as a tool for supporting the acceptabilty of design, procurement, construction, operation, and maintenance activities at nuclear power plants. Since the issuance of Generic Letter 88-20 [1] and subsequent Individual Plant Examinations (IPEs)/Individual Plant Examinations for External Events (IPEEEs) [2], the NRC has issued several Regulatory Guides such as RG 1.182 [3] to describe the use of PSA in risk informed regulation activities. The PSA models developed for the IPEs were typically based on a “snapshot” of the the risk profile at the nuclear power plant. The IPE models contain implicit assumptions and simplifications that limit the ability to realistically assess current issues. For example, IPE modeling assumptions related to plant configuration limit the ability to perform online equipment out-of-service assessments. The lack of model symmetry results in skewed risk results. IPE model simplifications related to initiating events have resulted in non-conservative estimates of risk impacts when equipment is removed from service. The IPE models also do not explicitly address all external events that are potentially risk significant as equipment is removed from service.
Is there a quantum gravity effect on the cosmic microwave background power spectrum?

4 pages, prepared for the AT1 Session of MG14 in Rome
Data Integration and Knowledge Discovery in Life Sciences

Recent advances in various forms of omics technologies have generated huge amount of data. To fully exploit these data sets that in many cases are publicly available, robust computational methodologies need to be developed to deal with the storage, integration, analysis, visualization, and dissemination of these data. In this paper, we describe some of our research activities in data integration leading to novel knowledge discovery in life sciences. Our multistrategy approach with integration of prior knowledge facilitates a novel means to identify informative genes that could have been missed by the commonly used methods. Our transcriptomics-proteomics integrative framework serves as a means to enhance the confidence of and also to complement transcriptomics discovery. Our new research direction in integrative data analysis of omics data is targeted to identify molecular associations to disease and therapeutic response signatures. The ultimate goal of this research is to facilitate the development of clinical test-kits for early detection, accurate diagnosis/prognosis of disease, and better personalized therapeutic management.
Dynamic social network analysis and recommender technologies in scientific communities: the case of computer science.

The Web in general, and the Web 2.0, in particular, have changed the way in which scientific information is created, exchanged and consumed. Online accessible digital libraries bring the access to large collection of scientific literature to the research community. Information sharing and research results dissemination are much faster than before. The large amount of scientific data available on the Web gives unique opportunities and also raise challenges for understanding the organization and diffusion of scientific knowledge, and the ability to quickly locate information needed to perform research. The basic interest of this thesis is to study the structure of scientific knowledge domains from a social network view. Techniques developed in knowledge mapping typically focus on purely mental perspectives of scientific knowledge, while ignoring its social aspects. We study the social structures that shape the scientific knowledge domains. We propose a framework based on social network analysis, that allows us to analyze knowledge domains and the development pattern of research communities. To demonstrate, we analyze the dynamics of the computer science knowledge domain. We find interesting patterns regarding its interdisciplinary nature, the collaboration behavior and the development of communities in sub-disciplines. The results are useful for research funding agencies, scientific policy management, institution leaders, librarians, collection managers and research community members. Another important aspect of understanding knowledge domains and their behavior, especially research communities, is the community-based recommendation. We propose a framework that takes into account community recommendations for their members. The framework discovers communities from the research interaction between scholars as well as librarial objects (publications, conferences/journals, etc.). The applications and evaluations of this framework in two recommendation problems, community recommendation and talk/collaborator recommendation for conference participants, demonstrate the effectiveness and benefit of communities in supporting their members.
Risk‐benefit perceptions, preferences for solutions, and gaining trust: Listening to New Jersey's Atlantic Ocean port communities

AbstractWe designed a survey to measure public preferences for management actions at ports that would reduce negative risk perceptions, increase competence‐based trust, and overall provide port managers with opportunities to build communication channels with surrounding populations. A total of 511 people who lived in a 45‐square mile area of New Jersey containing over 300,000 people and a dozen large and small ports responded to solutions that offered independent science support, provided ongoing public health surveillance, and economic assistance for local communities. Environmental health concerns about air, noise, fires, port security, and protection against climate‐related impacts were major correlates of these preferences. Yet many residents recognized economic benefits associated with nearby ports and many either trusted port management or were neutral and felt not well informed. Those who had a personal or family connection to the port were stronger port supporters. The most important finding and challenge for port managers is building ongoing relationships with the majority of nearby residents who are not aware of port activities, which presents an opportunity for increasing trust through proactive outreach and listening to nearby community representatives.
Assessing the value of health information exchange organizations to hospital interoperability

Abstract                                   Introduction                   Various federal efforts have supported the development of local, regional, and state health information exchange organizations (HIOs), a type of network that promotes secure electronic exchange of patient health information among hospitals, healthcare providers, community-based organizations, and public health authorities. With the emergence of complementary and alternative modes to facilitate exchange, we sought to assess the value of HIOs to hospital interoperability.                                                   Methods                   We leveraged nationally representative survey data from a unique linked dataset of N  = 2200 hospitals linked to HIOs to examine the relationship between hospitals’ engagement in a range of interoperability activities and participation in an HIO that offers services to support those activities.                                                   Results                   We found that hospital participation in an HIO was significantly associated with greater engagement in clinical information exchange, public health reporting, and health-related social needs information exchange. However, among hospitals participating in an HIO, we did not find strong evidence of a relationship between specific HIO capabilities or services and hospital engagement in exchange.                                                   Conclusion                   Our findings indicate HIOs offer a useful avenue for exchange but are only one path through which exchange takes place as there are various alternatives available to support interoperability activities.
Profiling and (automated) decision-making under the GDPR: A two-step approach

This paper examines profiling and decision-making under the GDPR and analyses how these two processes are interconnected. The GDPR's definition of profiling is analysed and put in relation to both automated and human decision-making. This contribution works with a two-step approach. It can be derived from the structure and wording of the GDPR and provides for an enhanced level of legal certainty. Within this approach, profiling is considered to be step 1 and decision-making to be step 2. The two steps are treated as distinct, yet logically interconnected. This helps understand how profiling and decision-making are conducted. It makes it possible to identify the legal implications of these two steps and to allocate who is legally responsible, no matter how many parties are involved. The approach might be particularly helpful in the context of joint controllership, as it makes it possible to delineate whether joint controllership is given in the first place and to allocate the respective responsibilities of the parties concerned. Profiling (step 1) leads to implications of primary relevance for the data subjects’ right to the protection of personal data. Decision-making (step 2) regularly does not lead to such data protection implications but is primarily relevant from a personal autonomy and (economic) freedom perspective. A notable exception is the rare scenario of solely automated decision-making falling under Art. 22(1) GDPR. The two-step approach is eventually applied to a use case that concerns profiling and automated decision-making in the context of credit scoring conducted by a social network.
Quantum Computing: Vision and Challenges

The recent development of quantum computing, which uses entanglement, superposition, and other quantum fundamental concepts, can provide substantial processing advantages over traditional computing. These quantum features help solve many complex problems that cannot be solved otherwise with conventional computing methods. These problems include modeling quantum mechanics, logistics, chemical-based advances, drug design, statistical science, sustainable energy, banking, reliable communication, and quantum chemical engineering. The last few years have witnessed remarkable progress in quantum software and algorithm creation and quantum hardware research, which has significantly advanced the prospect of realizing quantum computers. It would be helpful to have comprehensive literature research on this area to grasp the current status and find outstanding problems that require considerable attention from the research community working in the quantum computing industry. To better understand quantum computing, this paper examines the foundations and vision based on current research in this area. We discuss cutting-edge developments in quantum computer hardware advancement and subsequent advances in quantum cryptography, quantum software, and high-scalability quantum computers. Many potential challenges and exciting new trends for quantum technology research and development are highlighted in this paper for a broader debate.
The impact of the full-scale invasion of Ukraine on attitudes towards the United Nations Security Council reform: the case of the permanent five (P5)

This bachelor thesis aimed to investigate the impact of the full-scale invasion of Ukraine on the perspectives and discourses of the permanent five members (P5) of the United Nations Security Council (UNSC) towards the UNSC reform. To conduct the study, research questions were posed to investigate changes that occurred after the invasion began. Firstly, changes in the levels of assigned urgency to the topic were explored. Secondly, changes in the perspectives and discourse of the P5 were examined. To reach the conclusions two qualitative content analyses have been conducted with various speeches made by the P5 countries at the United Nations General Assembly. The analyses revealed an increase in the level of urgency shown by France and the U.S. towards the topic, but since this is not the majority of the P5, the change is insufficient to conclude that there was a change in the view of the P5 as a whole, showing a limited impact by the outbreak of the war. The analyses also found out that significant change was observed in discourses and perspectives of the P5, but since the changes did not help reach a consensus across the P5, once again the limited impact of the war was observed. Based on the theoretical framework of the work, it was also concluded that the impact of the war did not help in overcoming the inertia and that the war did not serve as a possible critical juncture.
Image Classification using Federated Learning with Differential Privacy : A Comparison of Different Aggregation Algorithms

The objective of this thesis was to investigate how the addition of a privacy-preserving mechanism to a federated learning model was affecting the performance of the model for an image classification task. Further, it was to get knowledge on how the outlook to use federated learning in the biotech industry is and what possible threats and attacks that could obstruct the utilization of federated learning among competitors. In the project four different aggregation algorithms for federated learning were examined. The methods were weighted fedAvg, unweighted FedAvg, weighted FedProx and unweighted FedProx. The experiment was using tensorflow federated to simulate the different methods. They were evaluated using accuracy, loss, recall, precision and F1 score. The result of this study shows that the performance of the deep neural network model is decreasing as differential privacy is introduced to the process. Out of the four aggregation algorithms used, weighted fedProx was the one that performed the best despite the added noise. It was also concluded that federated learning has potential to be used in the biotechnology industry among competitors, but that there are still security threats and attacks to avoid.