ethicalabs/Research-Intent-Collab huggingface dataset:
Research on financial fraud detection by integrating latent semantic features of annual report text with accounting indicators

Purpose                     This paper aims to integrate the latent semantic features of annual report text with accounting indicators to construct a financial fraud identification model, and quantitatively analyze the impact of different corporate risks on financial fraud behavior in different industries, providing a reference for identifying financial fraud.                                                           Design/methodology/approach                     This paper obtains 3,860 corporate annual report samples and accounting indicators from 2001 to 2020 through crawlers and the CSMAR database as our experimental subjects. By integrating latent semantic features with accounting indicators and textual language features, a new indicator system group is constructed. Based on this indicator system group, multiple model identification effects are compared and a stacking-based enterprise financial fraud identification model is constructed. In addition, an econometric model is established to verify the impact of latent semantic features related to enterprises on corporate financial fraud.                                                           Findings                     The experimental results show that the constructed stacking-based enterprise financial fraud identification model performs better than other machine learning models and can effectively identify financial fraud. The econometric model established for the latent semantic information of annual reports explains the impact of different corporate trends on fraud behavior in different industries.                                                           Originality/value                     This paper combines the textual latent semantic features of annual reports with accounting indicators, expands the scope of data analysis, introduces the idea of ensemble learning, updates the financial fraud identification algorithm and constructs an econometric model for further analysis, providing a reference for financial fraud identification.
Iran's Legislative Criminal Policy Regarding the Prohibition of Witness Information Disclosure in Comparison with the Rome Statute of the International Criminal Court

Testimony is a religious obligation and a manifestation of civic participation in the administration of justice. Providing testimony may expose individuals to threats, endangering their security and that of their relatives and close associates. Therefore, in the domestic laws of many countries, protection against such threats is recognized as a fundamental right. One protective measure is ensuring the anonymity and confidentiality of witness information. This study aims to examine this protective measure in Iran's legislative criminal policy in comparison with the provisions outlined in the Rome Statute of the International Criminal Court. This research employs a descriptive-analytical method and is practical in its objectives. The research methodology is document-based and library-oriented. Measures related to the prohibition of witness information disclosure and maintaining their anonymity are considered in both Iran's legislative criminal policy and the Rome Statute. The Code of Criminal Procedure (2013), the Islamic Penal Code (2013), the Fourth Economic, Social, and Cultural Development Plan of the Islamic Republic of Iran, the Charter on Citizens' Rights, the Executive Regulation on Witness and Informant Protection, and the Executive Regulation of the Law on the Establishment of the General Inspection Organization are among the most significant laws and regulations in which the prohibition of witness information disclosure has been supported by Iranian lawmakers. In the Rome Statute, provisions for the prohibition of witness information disclosure are mentioned in articles... . An analysis of the actions and measures outlined by the Court reveals various mechanisms to prevent the disclosure of witness information. These include testimony through remote communication tools, non-disclosure of witness identities, testimony in chambers and closed sessions, and others. Among these mechanisms, the closed testimony of witnesses during preliminary investigations, testimony through electronic tools, and the necessity of ensuring their security and preventing threats are emphasized in Iranian law to counteract witness information disclosure. However, other mechanisms, such as altering identity documents, are not provided.
Explainable Ai For Environmental Decision Support: Interpreting Deep Learning Models In Climate Science

Deep learning (DL) models have demonstrated high accuracy in climate science applications but suffer from "black-box" opacity, hindering their adoption in environmental decision-making. This research bridges this gap by integrating Explainable AI (XAI) techniques with DL models to enhance transparency in climate predictions. Using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture, we forecast regional temperature anomalies and interpret outputs via SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). Our methodology is validated on ERA5 reanalysis data (1980–2025), achieving a prediction RMSE of 0.86°C. XAI analysis reveals that oceanic heat fluxes and atmospheric pressure patterns are critical drivers of anomalies. The framework empowers policymakers with actionable insights, ensuring DL models are both accurate and trustworthy for climate action.
Cosmic microwave background constraints on dark energy dynamics: Analysis beyond the power spectrum

We consider the distribution of the non-Gaussian signal induced by weak lensing on the primary total intensity cosmic microwave background (CMB) anisotropies. Our study focuses on the three point statistics exploiting an harmonic analysis based on the CMB bispectrum. By considering the three multipoles as independent variables, we reveal a complex structure of peaks and valleys determined by the re-projection of the primordial acoustic oscillations through the lensing mechanism. We study the dependence of this system on the expansion rate at the epoch in which the weak lensing power injection is relevant, probing the dark energy equation of state at redshift corresponding to the equivalence with matter or higher ($w_\infty$). We evaluate the impact of the bispectrum observable on the CMB capability of constraining the dark energy dynamics. We perform a maximum likelihood analysis by varying the dark energy abundance, the present equation of state $w_0$ and $w_\infty$. We show that the projection degeneracy affecting a pure power spectrum analysis in total intensity is broken if the bispectrum is taken into account. For a Planck-like experiment, assuming nominal performance, no foregrounds or systematics, and fixing all the parameters except $w_0$, $w_\infty$ and the dark energy abundance, a percent and ten percent precision measure of $w_0$ and $w_\infty$ is achievable from CMB data only. These results indicate that the detection of the weak lensing signal by the forthcoming CMB probes may be relevant to gain insight into the dark energy dynamics at the onset of cosmic acceleration.
Multi-index importance sampling for McKean--Vlasov stochastic differential equations

This work addresses the estimation of rare-event quantities expressed as expectations of smooth observables of solutions to a broad class of McKean--Vlasov stochastic differential equations (MV-SDEs). Building on the double loop Monte Carlo (DLMC) method with stochastic optimal control-based importance sampling (IS) introduced by Ben Rached et al. (2024a), this work extends this framework to the multi-index Monte Carlo (MIMC) setting. The resulting multi-index DLMC estimator mitigates the explosion of the coefficient of variation for rare event quantities. Moreover, it exploits the sampling efficiency of MIMC by leveraging the propagation of chaos to ensure mixed-difference variances vanish in the mean-field limit. The complexity analysis relies on assumptions on mixed-difference bias and variance decay, similar to standard MIMC assumptions. Although not rigorously proved, this work presents strong numerical evidence in support of these assumptions. The primary contribution of this work is the novel numerical integration of the MIMC method with IS for MV-SDEs. This approach reduces the computational complexity from $\mathcal{O}(\mathrm{TOL}_{\mathrm{r}}^{-4})$ for the DLMC estimator to $\mathcal{O}(\mathrm{TOL}_{\mathrm{r}}^{-2} (\log \mathrm{TOL}_{\mathrm{r}}^{-1})^2)$, enabling an accurate estimation of rare-event quantities within a prescribed relative error tolerance $\mathrm{TOL}_{\mathrm{r}}$. Numerical experiments on the Kuramoto model from statistical physics demonstrate computational savings of several orders of magnitude for the multi-index DLMC estimator with IS, compared with the standard Monte Carlo (MC) method.
Ecological planning strategies for urban parks based on biodiversity conservation and promotion: a case study of Qingxiushan Forest Park in Nanning City

Urban biodiversity plays an important role in maintaining urban ecosystem, beautifying urban environment, improving economic benefits and human well-being, and urban park, as an important habitat of urban biology, is an important part of urban biodiversity. Taking Qingxiu Mountain Park in Nanning City as an example, this paper analyzes the main problems and challenges of biodiversity conservation and promotion, and puts forward a scientific reference basis for the promotion and protection of biodiversity in Qingxiu Mountain Park in Nanning City.
Central Bank Digital Currencies in Times of Covid-19

International audience
Methods of Information Theory and Algorithmic Complexity for Network Biology

We survey and introduce concepts and tools located at the intersection of information theory and network biology. We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdos-Renyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data. We provide exact theoretical calculations, numerical approximations and error estimations of entropy, algorithmic probability and Kolmogorov complexity for different types of graphs, characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity.
Tuning Quantum Computing Privacy through Quantum Error Correction

Quantum computing is a promising paradigm for efficiently solving large and high-complexity problems. To protect quantum computing privacy, pioneering research efforts proposed to redefine differential privacy (DP) in quantum computing, i.e., quantum differential privacy (QDP), and harvest inherent noises generated by quantum computing to implement QDP. However, such an implementation approach is limited by the amount of inherent noises, which makes the privacy budget of the QDP mechanism fixed and uncontrollable. To address this issue, in this paper, we propose to leverage quantum error correction (QEC) techniques to reduce quantum computing errors, while tuning the privacy protection levels in QDP. In short, we gradually decrease the quantum noise error rate by deciding whether to apply QEC operations on the gate in a multiple single qubit gates circuit. We have derived a new calculation formula for the general error rate and corresponding privacy budgets after QEC operation. Then, we expand to achieve further noise reduction using multi-level concatenated QEC operation. Through extensive numerical simulations, we demonstrate that QEC is a feasible way to regulate the degree of privacy protection in quantum computing.
A Guide in Synthetic Biology: Designing Genetic Circuits and Their Applications in Stem Cells

Stem cells, unspecialized cells with regenerative and differentiation capabilities, hold immense potential in regenerative medicine, exemplified by hematopoietic stem cell transplantation. However, their clinical application faces significant limitations, including their tumorigenic risk due to uncontrolled proliferation and cellular heterogeneity. This review explores how synthetic biology, an interdisciplinary approach combining engineering and biology, offers promising solutions to these challenges. It discusses the concepts, toolkit, and advantages of synthetic biology, focusing on the design and integration of genetic circuits to program stem cell differentiation and engineer safety mechanisms like inducible suicide switches. This review comprehensively examines recent advancements in synthetic biology applications for stem cell engineering, including programmable differentiation circuits, cell reprogramming strategies, and therapeutic cell engineering approaches. We highlight specific examples of genetic circuits that have been successfully implemented in various stem cell types, from embryonic stem cells to induced pluripotent stem cells, demonstrating their potential for clinical translation. Despite these advancements, the integration of synthetic biology with mammalian cells remains complex, necessitating further research, standardized datasets, open access repositories, and interdisciplinary collaborations to build a robust framework for predicting and managing this complexity.
Security requirements and mechanisms for real-time serverless computing : a focus on Function-as-a-Service (FaaS)

Serverless computing, particularly its Function-as-a-Service (FaaS) offering, has emerged as a prominent cloud computing model empowering developers to craft event-driven applications by deploying custom application logic in an ephemeral runtime environment. This model abstracts away the underlying infrastructure complexities and management overhead, offering key benefits such as Pay-As-You-Go (PAYG) billing and automated scalability. Although Real-Time (RT) extensions to FaaS (RT-FaaS) have been proposed for realizing the functional requirements of industrial automation virtualization use cases, these architectures introduce distinct security challenges that generic security mechanisms cannot fully address. As a result, RT-FaaS platforms remain vulnerable to both internal and external threats and a variety of exploitation vectors that can compromise the Confidentiality, Integrity and Availability (CIA) security properties of such a platform.   The aim of this thesis is to assess the core security requirements of a RT-FaaS platform by evaluating potential vulnerabilities and risks within this environment. We further propose specifically tailored mitigation mechanisms based on the extended Berkeley Packet Filter (eBPF) and cryptographic digital signatures for a RT-FaaS platform with a WebAssembly (WASM) runtime to enhance its resilience without imposing prohibitive performance overheads. Key areas of focus for this work include RT-FaaS runtime isolation, continuous security enforcement and data protection.  We evaluated the proposed security mitigation mechanisms on the platform accounting for both effectiveness and performance. By doing so, we discuss the trade-offs between the stringent security requirements and maintaining predictable, low-latency execution essential for industrial automation use cases. While we observed some considerable jitter and latency overheads on the secure RT-FaaS platform, these did not significantly impact the functional performance of the platform. Hence adopting serverless computing for RT industrial automation use cases is feasible while ensuring robust function, system and network security.
Research on weather forecast based on neural networks

The traditional method based on aerodynamics and expertise for weather forecast focuses on present data, but the historical database is not utilized. The paper presents research on weather forecasting through the historical database. Because the atmosphere pattern is a complex, nonlinear system, the traditional method is not an effective method. The multi-layer neural network is a potent method for resolving this problem. With the backpropagation neural networks the atmosphere pattern is divided into five patterns based on the rainfall. Comments on this method and a special atmosphere pattern are given at the end of the paper.
Impact of an antimicrobial stewardship program on the incidence of carbapenem resistant gram-negative bacilli: an interrupted time-series analysis

Carbapenem-resistant Gram-negative bacilli (CR-GNB) are a critical public health threat, and carbapenem use contributes to their spread. Antimicrobial stewardship programs (ASPs) have proven successful in reducing antimicrobial use. However, evidence on the impact of carbapenem resistance remains unclear. We evaluated the impact of a multifaceted ASP on carbapenem use and incidence of CR-GNB in a high-endemic hospital. An interrupted time-series analysis was conducted one year before and two years after starting the ASP to assess carbapenem consumption, CR-GNB incidence, death rates of sentinel events, and other variables potentially related to CR-GNB incidence. An intense reduction in carbapenem consumption occurred after starting the intervention and was sustained two years later (relative effect −83.51%; 95% CI −87.23 to −79.79). The incidence density of CR-GNB decreased by −0.915 cases per 1000 occupied bed days (95% CI −1.743 to −0.087). This effect was especially marked in CR-Klebsiella pneumoniae and CR-Escherichia coli, reversing the pre-intervention upward trend and leading to a relative reduction of −91.15% (95% CI −105.53 to −76.76) and −89.93% (95% CI −107.03 to −72.83), respectively, two years after starting the program. Death rates did not change. This ASP contributed to decreasing CR-GNB incidence through a sustained reduction in antibiotic use without increasing mortality rates.
The Relationship between Business Intelligence and Analytics Use and Organizational Absorptive Capacity: Applying the DeLone &amp; Mclean Information Systems Success Model

Building on the DeLone and McLean Information Systems (IS) success model and the knowledge-based theory of absorptive capacity, this paper examines the role of the business intelligence and analytics (BI&amp;A) use for enhancing a firm’s absorptive capacity for knowledge creation. We collected survey data from an online and mail-delivered survey questionnaire with 97 respondents at the organizational level in the Slovenian medium- and large-sized firms from several industries. The results from the partial least squares SEM showed that the BI&amp;A use relates significantly to absorptive capacity enhancement, thus fostering knowledge creation. Moreover, information quality and system quality are positively linked with BI&amp;A use at the organizational level.
FFPDG: Fast, Fair and Private Data Generation

Generative modeling has been used frequently in synthetic data generation. Fairness and privacy are two big concerns for synthetic data. Although Recent GAN [\cite{goodfellow2014generative}] based methods show good results in preserving privacy, the generated data may be more biased. At the same time, these methods require high computation resources. In this work, we design a fast, fair, flexible and private data generation method. We show the effectiveness of our method theoretically and empirically. We show that models trained on data generated by the proposed method can perform well (in inference stage) on real application scenarios.
The development and implementation of a guideline-based clinical decision support system to improve empirical antibiotic prescribing

Abstract                 Background                 To describe and evaluate a clinical decision support system (CDSS) for empirical antibiotic therapy using a systematic framework.                                Methods                 A reporting framework for behavior change intervention implementation was used, which includes several domains: development, evaluation and implementation. Within the development domain a description is given of the engagement of stakeholders, a rationale for how the CDSS may influence antibiotic prescribing and a detailed outline of how the system was developed. Within the evaluation domain a technical validation is performed and the interaction between potential users and the CDSS is analyzed. Within the domain of implementation a description is given on how the CDSS was tested in the real world and the strategies that were used for implementation and adoption of the CDSS.                                Results                 Development: a CDSS was developed, with the involvement of stakeholders, to assist empirical antibiotic prescribing by physicians. Evaluation: Technical problems were determined during the validation process and corrected in a new CDSS version. A usability study was performed to assess problems in the system-user interaction. Implementation: In 114 patients the antibiotic advice that was generated by the CDSS was followed. For 54 patients the recommendations were not adhered to.                                Conclusions                 This study describes the development and validation of a CDSS for empirical antibiotic therapy and shows the usefulness of the systematic framework for reporting CDSS interventions. In addition it shows that CDSS recommendations are not always adhered to which is associated with incorrect use of the system.
Combinatorial Optimization with Quantum Computers

Quantum computers leverage the principles of quantum mechanics to do computation with a potential advantage over classical computers. While a single classical computer transforms one particular binary input into an output after applying one operator to the input, a quantum computer can apply the operator to a superposition of binary strings to provide a superposition of binary outputs, doing computation apparently in parallel. This feature allows quantum computers to speed up the computation compared to classical algorithms. Unsurprisingly, quantum algorithms have been proposed to solve optimization problems in quantum computers. Furthermore, a family of quantum machines called quantum annealers are specially designed to solve optimization problems. In this paper, we provide an introduction to quantum optimization from a practical point of view. We introduce the reader to the use of quantum annealers and quantum gate-based machines to solve optimization problems.