ethicalabs/Research-Intent-Collab huggingface dataset:
The Impact of Financial Technologies on Digital Transformation of Accounting, Audit and Financial Reporting

FinTech is a combination of Finance and Technology which involves use of modern smart technology like big data analytics, cloud computing, artificial intelligence, machine learning, and robotics, for doing finance. Financial technology development has influenced nearly all financial services industry, from granting to insurance, from accounting to consultancy, from consumer finance to investment banking. As a result of digital transformation, preparation of XBRL-based structured digital Financial Reports, block-chain trading, accounting of new digital assets, cryptocurrency has assigned more strategic functions to accountants, while simultaneously supported the auditing to reduce the risk at the minimum level.  In order to obtain or retain competitive advantage it is essential to invest money, time into implementing in innovations. A cloud system is part of the basic infrastructure now. When the entire business model changes so do change the ways of accounting, auditing and financial reporting that are inevitable components of business. The integration of modern digital technologies into finances, has enabled finance professionals to achieve greater efficiency, work speed, and financial transparency. Keywords: Financial technologies; Digital Accounting and IFRS; Cryptocurrency; Blockchain; XBRL-standard; Audit; Financial Reporting;
Ultralow-threshold dual-wavelength optical bistability from a perovskite hyperbolic metasurface and its application in a photonic neural network

In a hyperbolic metamaterial or metasurface (HMM or HMS), strong light-matter interaction occurs at the phase transition wavelength where the material behavior changes from metal to dielectric. As a result, the enhanced electric field can generate non-linear phenomena, such as optical bistability. In this work, we numerically investigate polarization-dependent dual-wavelength optical bistability from perovskite HMS consisting of alternating layers of MAPbBr3 perovskite and Au. Benefiting from the anisotropic property, the bistable operation wavelength for TE and TM polarizations are different. In particular, for TE polarization, strong light matter interaction resulting from the increment of incident light power not only shifts the phase transition wavelength but also enhances the transmission. Consequently, the bistable on-off contrast ratio is unprecedentedly enlarged. For TM polarization, strong light-matter interaction gives rise to an ultra-low bistable threshold. The proposed optical bistable states can serve as an activation function for optical neurons in a photonic neural network, with similar prediction accuracy across a variety of learning tasks as the classic activation functions ReLU and Sigmoid. Our work suggests a novel avenue towards the insertion of perovskite HMS into optical computing networks.
All-Optical Nonlinear Activations Using Saturable Absorbers on a Generic InP Platform for Photonic Neural Networks

We experimentally demonstrate all-optical nonlinear activation functions using standalone saturable absorbers, as well as saturable absorbers integrated into microring resonators and Mach–Zehnder interferometer structures on a monolithic InP platform. These configurations enable programmable nonlinear responses and support both passive and active operation modes. The measured activation characteristics exhibit softplus- and ReLU-like behaviors, suitable for neuromorphic computing. When incorporated into a convolutional neural network for MNIST digit classification, the system achieves over 97% accuracy, highlighting the potential of SA-based photonic components for compact, low-power, and scalable optical neural networks.
Masked Momentum Contrastive Dynamic Transformer for Self-Supervised Functional Connectivity Representation Learning

Functional connectivity (FC) derived from functional MRI (fMRI) shows significant promise in predicting behavior and demographics using deep learning techniques. Incorporating vertex-wise FC maps, which capture fine-grained spatial details of neural activity, offers the potential to enhance FC-based prediction accuracy. However, fMRI data is inherently limited and noisy, challenging neural networks to reliably identify patterns within high-dimensional cortical vertices. Therefore, we design a novel Masked Momentum Contrastive Dynamic Transformer, which utilizes masked momentum contrastive pre-training to explore subject-specific features and enhances prediction accuracy by leveraging the temporal dynamics of FCs with a dynamic transformer. Specifically, our framework 1) learns effective subject-specific representations by treating vertex-wise FCs from different runs of an individual as distinct views and maximizing their affinity, and 2) employs a vertex-wise masking strategy to promote learning from limited data. Extensive experiments on gender classification and cognition prediction validate its superior performance on the Human Connectome Project dataset.
Topological Phase Transitions in Condensed Matter — Experimental Predictions

&#9888;&#65039; SUPERSEDED: This paper has been superseded by Paper XXVIII: Condensed Matter Analogues of Hopf Soliton Physics (DOI: 10.5281/zenodo.19349018) and Paper XCI: From Electrons to Ferrofluids &mdash; Condensed Matter Realizations of Hopf Soliton Physics (DOI: 10.5281/zenodo.19555616), which together provide more complete and updated treatments. This paper is retained for historical reference. Please cite the successor papers instead.            Abstract     The topological soliton framework developed in this series identifies particles as Hopf-fibred field configurations with topological invariants $\pi_3(S^2) = \mathbb{Z}$. While the fundamental solitons operate at the Compton scale ($\bar{\lambda}_C \sim 10^{-13}$ m), the same mathematical structures — Hopf fibrations, linking numbers, topological charges — appear in accessible condensed matter systems. We present detailed experimental protocols for observing and characterizing Hopf solitons in three platforms: cholesteric liquid crystals (CLCs), spinor Bose-Einstein condensates (BECs), and frustrated magnetic materials (spin ice). For each platform, we specify sample preparation, creation protocols, imaging techniques, and quantitative predictions including soliton size ($1$--$50\,\mu$m in CLCs, $5$--$20\,\mu$m in BECs, $1$--$10$ nm in spin ice), energy barriers for topological transitions ($10$--$100\,k_BT$ in CLCs), and expected lifetimes ($10^2$--$10^6$ s in CLCs at room temperature). We identify cholesteric liquid crystals as the most promising near-term platform, building on the pioneering observations of Ackerman and Smalyukh [1]. The key experimental signature distinguishing Hopf solitons from other topological textures is the linking number $H = 1$ of preimage curves, measurable via three-dimensional polarization microscopy reconstruction.       Keywords          physics       topology       solitons       condensed matter       experiments       liquid crystals       BEC       spin ice                   Type       Preprint                 License       CC BY 4.0                 Date       2026-03-28                 Subject       Theoretical Physics                 DOI       10.5281/zenodo.19295021                  &copy; 2026 Alexander Novickis. Licensed under     Creative Commons Attribution 4.0 International.
Vision-Language Pre-training: Basics, Recent Advances, and Future Trends

This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. We group these approaches into three categories: ($i$) VLP for image-text tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding; ($ii$) VLP for core computer vision tasks, such as (open-set) image classification, object detection, and segmentation; and ($iii$) VLP for video-text tasks, such as video captioning, video-text retrieval, and video question answering. For each category, we present a comprehensive review of state-of-the-art methods, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies. In addition, for each category, we discuss advanced topics being actively explored in the research community, such as big foundation models, unified modeling, in-context few-shot learning, knowledge, robustness, and computer vision in the wild, to name a few.
Social Responsibility, Corporate Governance and Lack of Financial Reporting Transparency In Tehran Stock Exchange

Transparency helps create market confidence and supports economic sustainability. The concept of social responsibility is quite close to the concept of sustainable development. The consequence of the sustainable development approach is special attention to the concept of disclosure and reporting of corporate social responsibility. The growing interest in disclosing corporate accountability reflects an increasing demand for transparency. Therefore, social responsibility and corporate governance are expected to play an effective role in reducing opacity in Financial Reporting. This study examines the moderating effect of corporate governance quality on the relation between corporate social responsibility and opacity in financial reporting. The sample consists of 114 firms listed in Tehran Stock Exchange during the years 2014 to 2019. Results show that social responsibility reduces the opacity in financial reporting and corporate governance reduces the severity of this negative relation.
Influence of curricular changes in health promotion and disease prevention on residentsʼ behaviors over a five-year period

Residents' compliance with guidelines for health promotion and disease prevention (HPDP) often fall short of the ideal.After a year in which faculty developed clinical practice HPDP guidelines, an intervention consisting of curricular changes and chart modifications was planned and pilot-tested in 1985-86 at an ambulatory care clinic of the Family Practice Residency Program, University of Louisville School of Medicine. Approximately 24 residents per year (eight at each level of training) worked in the clinic throughout the study years. The HPDP curriculum required residents to attend 48 one-hour conferences conducted over a three-year period. Four retrospective chart audits were used to measure residents' compliance with the HPDP guidelines during (1) the year before the pilot test (104 charts), (2) the pilot year (113 charts), (3) the year after the pilot test--the first year of full implementation (100 charts), and (4) five years after the pilot test (100 charts). Data from the chart audits were analyzed by using a permutation test for decreasing trend.Although there was an increase in the residents' compliance with the HPDP guidelines during the pilot year, the residents' HPDP activities tended to return to baseline levels in the following years, despite the ongoing curriculum.The authors conclude that because the frequency of the residents' HPDP activities tended to return to baseline levels, clinical education alone is not enough to sustain residents' compliance with HPDP guidelines.
The Fatty Acid Profile of the Deep‐Sea Gastropod <i>Parvaplustrum wareni</i> Indicates a Dominant Role of Chemosynthesis in the Nutrition of the Hydrothermal Vent Ecosystem (Piip Volcano)

ABSTRACTThe gastropod Parvaplustrum wareni (Parvaplustridae) is an important faunal component in the ecosystem of deep‐sea hydrothermal vents of the Piip submarine volcano, Bering Sea. To highlight trophic relationships in this ecosystem, the fatty acid (FA) composition of the hydrothermal gastropod P. wareni has been studied. This is one of the few studies analyzing FA compositions of gastropods in a hydrothermal vent ecosystem. The major polyunsaturated FAs of this mollusk are represented by n‐3 and n‐6 FAs: arachidonic acid (20:4n‐6), eicosapentaenoic acid (20:5n‐3), docosatetraenoic acid (22:4n‐6), and docosapentaenoic acid (22:5n‐3). The low level of FA markers of phytoplankton suggests an insignificant role of organic matter created by photosynthesis in the nutrition of P. wareni. A high level of monounsaturated FAs (more than 70% of total FAs) dominated by palmitoleic (16:1n‐7) and cis‐vaccenic (18:1n‐7) FAs indicates the bacterial nutrition of the mollusk. In general, the analysis of the P. wareni FA composition has allowed a conclusion that the organic matter created by chemosynthesis provides a favorable basis for the P. wareni population to thrive in the hydrothermal vents of the Piip volcano.
A Rational View of Insurance and Genetic Discrimination

The Human Genome Project has committed a portion of its annual budget to the study of the ethical, legal, and social implications (ELSI) of genetics research. The assertion that discrimination in health insurance is a real problem pervades the ELSI literature despite compelling evidence that at worst the threat is theoretical. The     author     reviews this evidence and also argues that genetic discrimination in the area of life insurance is unlikely to become a significant social problem. He contends that by perpetuating unsubstantiated fears, many connected with the ELSI program have inappropriately engaged in advocacy at the expense of science.
Multi-sparse signal recovery for compressive sensing

Signal recovery is one of the key techniques of Compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one domain to formulate the L0 norm optimization. Recent investigation shows that some signals are sparse in multiple domains. To further improve the signal reconstruction performance, we can exploit this multi-sparsity to generate a new convex programming model. The latter is formulated with multiple sparsity constraints in multiple domains and the linear measurement fitting constraint. It improves signal recovery performance by additional a priori information. Since some EMG signals exhibit sparsity both in time and frequency domains, we take them as example in numerical experiments. Results show that the newly proposed method achieves better performance for multi-sparse signals.
How to engage in alternative publication models? The example of Peer Community In Animal Science

Retour d'expérience sur l'adoption de Peer Community In pour la publication d'articles en sciences animales.
Force field model and molecular dynamics simulation of polyynes

Abstract   The molecular force field model for polyynes is developed by fitting the empirical force field potential function into intermolecular interaction potentials of various configurations including side-by-side, parallel, crossover, and head-to-head configurations calculated using the Moller–Plesset perturbation theory at MP2/6-311G(d, p) level of theory. And the force field model is applied to the molecular dynamics simulation of polyyne–methanol mixture. The calculations reveal that the polyyne molecules aggregate in bundles, and hydrogen bonds are formed between the polyyne hydrogen and methanol oxygen.
Social and legal consequences of Ukraine’s ratification of the Rome Statute of the International Criminal Court

Although the International Criminal Court opened an investigation when russia launched its aggression against Ukraine, numerous obstacles still hinder the restoration of justice and complicate the prosecution of those responsible. This study aimed to examine the challenges of harmonising domestic criminal and procedural law with international norms in the light of the social and legal repercussions of ratification. The research applied doctrinal, institutional and normative and legal analyses, together with comparative methods and case studies. The findings showed that, even though Ukraine’s journey towards full membership of the Rome Statute culminated in ratification, effective harmonisation of national legislation remains incomplete. One explanation identified is inaccuracy in the official Ukrainian translation of the Statute. Consequently, several issues have emerged, including a legislative narrowing of the recognised forms of genocide and the incorrect use of formulaic expressions such as “crimes against humaneness”, among others. The study identified specific features of detention and captivity arising from differing legal grounds that determine the status of such individuals under the norms of international humanitarian law. It was concluded that the proper regulated operation of Joint Investigation Teams is essential for the admissibility of collected evidence, particularly concerning the qualification of the individuals involved, whose professional competence is crucial. It was demonstrated that the use of open-source digital information in the documentation of international crimes must comply not only with the Berkeley Protocol but also with the requirements of jurisdictional and national admissibility. The practical value of this research lies in its potential to serve as a reference point for national legislators in the pursuit of thorough and proportionate legal harmonisation, as well as for courts and other law enforcement bodies
Computer Vision: A Review of Detecting Objects in Videos – Challenges and Techniques

Traffic safety aims to change the attitude of citizens towards careless traffic on the roads, making this the first step towards changing behavior. Also, teach the rules of safe pedestrian behavior and minimize the risks of road accidents. So many regulations have been set to avoid road accidents and traffic jams, which is the study scope of this paper using IT technology. With the expanding interests in Computer vision use cases such as vehicles self-driving, face recognition, intelligent transportation frameworks and so on individuals are hoping to assemble custom AI models to recognize and distinguish specific objects. Object detection is part of a computer's vision where objects that can be observed externally and are found in videos can be identified and tracked by computers. Therefore, object tracking is an important part of video analysis. There are many proposed methods such as Tracking, Learning, Detection, Mean shift and MIL. In this paper, the computer vision state in object detecting domain along with its challenges are discussed, also we address some requirements and techniques to overcome these challenges. Finally, TensorFlow technology is presented as a recommended solution to support Lane’s violation.
Uncertainty quantification on small angle x-ray scattering measurement using Bayesian deep learning

Small angle x-ray scattering (SAXS) is a widely recognized solution for measuring complex nanostructures. With the increasing demand for accurately assessing structural characteristics and optimizing manufacturing processes, uncertainty quantification in SAXS inverse problems has become a critical issue. However, traditional methods face challenges such as slow computation speed and inaccurate estimation of multidimensional parameters. To overcome these issues, we propose an uncertainty quantification approach suitable for SAXS measurement that approximates the posterior using Bayesian deep learning. The effectiveness and reliability of our method are illustrated by assessing structural parameters of synthetic 2D Si grating samples. The uncertainty quantification takes only about 2.3 s, thousands of times faster than the conventional Markov Chain Monte Carlo (MCMC) methods. Also, our method has superior repeatability for parameter measurement compared to the MCMC approaches. It provides the potential of efficient and reliable SAXS measurement in increasingly intricate semiconductor manufacturing.
Experimental Investigation of Optimal Nanoparticle Inclusion for Enhanced Flexural Performance in Continuous Fiber Ceramic Nanocomposites

This work reports the effects of using varying weight percentage of nanoparticle inclusions on mechanical performance of continuous fiber ceramic composites. The ceramic fiber reinforcement was Nicalon                     TM                     , and KiON CERASET                     ®                     preceramic polymer was mixed with nanoparticle inclusions in the presence of a surfactant agent, which gave good dispersion of the particles within the matrix. Yttrium oxide nanoparticles with an average size of 29 nm was used as the inclusion with varying weight percentages of 5, 10, 15, and 20%. For comparison, samples without nanoparticles were also manufactured. Two different types of nanoparticle filled composites were manufactured. The first one followed neat preceramic polymer reinfiltration cycle, whereas the second system was manufactured with corresponding nanoparticle dispersed preceramic polymer reinfiltration. A characterization analysis of the samples using scanning electron microscopy revealed proper dispersion of nanoparticle along with good quality of the parts. In general, the weight gain percentage at each stage of reinfiltration/pyrolysis for both types of nanoparticle filled ceramic composites is consistently less than that for ceramic composites manufactured without nanoparticle. This indicates the compactness of the material and retention of shape during the B-staging. Four-point bending test was also conducted to evaluate the mechanical performance of the ceramic composite samples at room temperature. Nanoparticle filled samples consistently showed significant improvement in flexural strength compared to their counterparts without nanoparticle reinforcement.
Cancer systems biology in the genome sequencing era: Part 2, evolutionary dynamics of tumor clonal networks and drug resistance

A tumor often consists of multiple cell subpopulations (clones). Current chemo-treatments often target one clone of a tumor. Although the drug kills that clone, other clones overtake it and the tumor reoccurs. Genome sequencing and computational analysis allows to computational dissection of clones from tumors, while singe-cell genome sequencing including RNA-Seq allows to profiling of these clones. This opens a new window for treating a tumor as a system in which clones are evolving. Future cancer systems biology studies should consider a tumor as an evolving system with multiple clones. Therefore, topics discussed in Part 2 of this review include evolutionary dynamics of clonal networks, early-warning signals for formation of fast-growing clones, dissecting tumor heterogeneity, and modeling of clone-clone-stroma interactions for drug resistance. The ultimate goal of the future systems biology analysis is to obtain a whole-system understanding of a tumor and therefore provides a more efficient and personalized management strategies for cancer patients.
High-Performance Passive Macromodeling Algorithms for Parallel Computing Platforms

This paper presents a comprehensive strategy for fast generation of passive macromodels of linear devices and interconnects on parallel computing hardware. Starting from a raw characterization of the structure in terms of frequency-domain tabulated scattering responses, we perform a rational curve fitting and a postprocessing passivity enforcement. Both algorithms are parallelized and cast in a form that is suitable for deployment on shared-memory multicore platforms. Particular emphasis is placed on the passivity characterization step, which is performed using two complementary strategies. The first uses an iterative restarted and deflated rational Arnoldi process to extract the imaginary Hamiltonian eigenvalues associated with the model. The second is based on an accuracy-controlled adaptive sampling. Various parallelization strategies are discussed for both schemes, with particular care on load balancing between different computing threads and memory occupation. The resulting parallel macromodeling flow is demonstrated on a number of medium- and large-scale structures, showing good scalability up to 16 computational cores.