Search Results (1,188)

This study examines how the international harmonisation of intellectual property rules, particularly trade secret protection, reshapes the governance of artificial intelligence (AI) in ways that both enable and threaten justice. We argue that convergent standards on undisclosed information are essential for legal certainty in knowledge-intensive AI investments. Such standards are anchored in TRIPS, reinforced by WIPO guidance and digital trade agreements, and complemented by regional instruments such as the EU Trade Secrets Directive. This emerging framework facilitates cross-border technological cooperation while helping prevent the “regulatory expropriation” of code, models, and data infrastructures. At the same time, when this pro-secrecy architecture is extended to opaque algorithmic systems that mediate access to credit, employment, welfare, health and justice, it can entrench digital vulnerability: information asymmetries between firms, states and citizens; barriers to meaningful transparency and audit; and pathogenic forms of exclusion that disproportionately affect already disadvantaged groups. Building on the concept of digital and structural vulnerability, the paper defends a vulnerability-sensitive approach to harmonisation in which trade secret protection is balanced against human rights, algorithmic accountability and the regulatory space of Global South states. We conclude that only an intellectual property regime guided by an ethics and politics of vulnerability can reconcile economic integration, technological development and reducing digital vulnerability in deeply unequal societies. Full article

This study examines how institutional investors allocate trading across the earnings announcement cycle and whether industry trading concentration strengthens that activity. The analysis is motivated by two complementary ideas: public disclosures can increase the value of investors’ prior information, and even sophisticated investors face costly information processing. These perspectives imply that institutional trading need not be concentrated only before disclosure and may be strongest after earnings announcements, when investors combine newly released public information with prior firm- and industry-specific signals. Using daily institutional trading data from Ancerno, we find that institutional net trading is positively related to earnings surprises before, during, and after earnings announcements, with the strongest relation occurring in the post-announcement period. We also document a clear asymmetry: trading is strongly related to positive earnings surprises across all three stages, whereas trading related to negative earnings surprises is concentrated mainly after disclosure. In addition, industry trading concentration strengthens the relation between institutional trading and earnings news across the announcement cycle, especially for positive surprises. These findings provide an integrated view of institutional information processing around a major recurring disclosure event, show that the timing of institutional trading is informative about how earnings news is incorporated into prices, and support the view that industry specialization is linked to stronger earnings-related trading. Full article

Ukraine’s reconstruction, estimated at $524 billion over the next decade, presents an unprecedented opportunity to embed circular economy principles into industrial rebuilding, but the financial architecture currently deployed for reconstruction is structurally blind to circular outcomes. This paper examines how digital tools and innovative financing mechanisms can channel investment toward circular industrial reconstruction in Ukraine, drawing on Germany’s National Circular Economy Strategy (NCES, adopted December 2024) as a reference model. A comparative institutional analysis combines a documentary review of Ukrainian reconstruction policy frameworks (Ukraine Plan 2024–2027, RDNA4, Ukraine Facility) and German NCES instruments with the construction of a financing−technology pathway typology. Five pathways are proposed: circular bond issuance with Digital Product Passport integration; blended finance with blockchain impact verification; EU Facility conditionality with AI-driven resource management; war risk insurance with circular construction standards; and SME digitalisation credit with circular economy competency building. Each pathway is assessed against five criteria: investment scale, risk mitigation, circular measurement, digital readiness, and institutional feasibility, and applied to four industrial corridors (Dnipro region, Zaporizhzhia region, Kharkiv region, and Donetsk region). The analysis reveals that no single pathway is sufficient; a layered strategy differentiating by region is required. Digital tools, particularly the Digital Product Passport and blockchain traceability, serve as partial substitutes for institutional trust in post-conflict settings, reducing information asymmetry between investors and project operators. The paper contributes a practically oriented framework at the under-theorised intersection of post-conflict reconstruction finance and circular economy scholarship. Full article

Intensive care units (ICUs) generate heterogeneous data streams, including structured electronic health records, physiological time series, and medical imaging, that describe the same patient state through different observational forms. Effective multimodal learning in this setting requires a principled balance between representation-level symmetry and architectural asymmetry. Clinically corresponding patient states should exhibit cross-modal representational symmetry, whereas each modality retains intrinsic asymmetry in dimensionality, temporal resolution, noise characteristics, and missingness. This study proposes a modality-specific sparse autoencoder framework for efficient multimodal ICU representation learning under this symmetry–asymmetry principle. Separate sparse encoders are assigned to each modality to preserve the modality-dependent structure while suppressing redundant latent activity through adaptive gating. Representation-level symmetry is encouraged through a sparsity-aware contrastive objective that aligns paired latent embeddings across modalities only on active informative dimensions. To further model inter-patient dependencies, the framework incorporates a graph neural network (GNN) whose message-passing operations respect modality-specific sparsity patterns. Experimental results indicate that the proposed framework improves predictive performance and computational efficiency relative to conventional multimodal baselines, while also exhibiting stronger robustness under missing-modality conditions and more selective latent representations. Overall, the method provides an effective and clinically relevant multimodal learning strategy for ICU decision support while offering a measurable symmetry-aware and asymmetry-preserving formulation for heterogeneous medical data. Full article

This study examines the economic consequences associated with the mandatory adoption of International Financial Reporting Standards (IFRS) in the Iraqi banking sector. Motivated by growing evidence that the outcomes of IFRS adoption depend on institutional and market conditions, the study focuses on a bank-based emerging economy characterized by relatively underdeveloped capital markets and evolving enforcement mechanisms. Using a balanced panel of 24 banks listed on the Iraq Stock Exchange over the period 2014–2018, the analysis exploits the mandatory IFRS adoption in 2016 within a before–after regulatory framework. Panel regression techniques are employed to examine the associations between IFRS adoption and stock market liquidity, firm value, information asymmetry, and the cost of debt, while controlling for bank-specific characteristics and macroeconomic conditions. The results indicate that IFRS adoption is positively significantly associated with stock market liquidity, and negatively significantly associated with information asymmetry, consistent with improvements in the informational environment of Iraqi banks following enhanced disclosure and comparability. The findings also reveal a positive and significant relationship between IFRS adoption and the cost of debt, suggesting higher perceived financial risk by creditors. In contrast, no statistically significant association is observed between IFRS adoption and bank market valuation, highlighting the limited sensitivity of equity prices to accounting reforms in thin and institutionally constrained markets. Overall, the study contributes to the literature on the economic consequences of IFRS adoption by providing evidence from an underexplored emerging market and a highly regulated banking sector. The findings underscore the role of institutional context in shaping the outcomes of accounting standard convergence and offer policy-relevant insights for regulators and standard-setters in bank-oriented financial systems. Full article

With the rapid proliferation of large language models (LLMs), distinguishing machine-generated text from human-authored content has become increasingly critical for ensuring content authenticity, academic integrity, and trust in information systems. However, detecting text generated by LLMs remains a challenging problem, particularly in zero-shot settings where labeled data and domain-specific tuning are unavailable. To address this challenge, in this paper, we propose a novel Collaborative Multi-Agent Zero-Shot Detection framework (CMA-ZSD). In contrast to existing methods based on watermarking, statistical heuristics, or neural classifiers, our CMA-ZSD employs three functionally heterogeneous agents that perform differentiated perturbations of the input text. By jointly modeling semantic consistency, grammatical normalization, and feature-level reconstruction, our method captures intrinsic asymmetries between human-authored and LLM-generated text. A semantic similarity evaluation mechanism, combined with majority voting, enables robust and interpretable detection decisions that balance individual agent autonomy with collective consensus. Extensive experiments across 11 domains demonstrate the effectiveness of our method, with its zero-shot detection achieving accuracy comparable to domain-finetuned models in specific domains such as Finance and Reddit-dli5. Full article

Based on the panel data of Chinese A-share listed companies from 2012 to 2023, this paper takes the pilot policy of Pilot Zones for Innovative Application of Artificial Intelligence as an exogenous shock, and adopts a multi-period difference-in-differences (DID) model to systematically examine the causal effect of this policy on the quality and efficiency of enterprise innovation and its mechanism of action. It is found that the Pilot Zones for Innovative Application of Artificial Intelligence significantly improve enterprises’ innovation quality and efficiency. Mechanism tests show that the pilot policy enhances enterprise innovation quality and efficiency by driving digital transformation, eliminating information barriers, and upgrading supply chain collaboration. Heterogeneity analysis confirms that the policy dividends are more fully released in non-state-owned enterprises, high-tech enterprises, labor-intensive and technology-intensive enterprises, as well as enterprises located in cities with a higher degree of marketization. In addition, the life-cycle heterogeneity analysis shows that the pilot policy exerts the strongest and most comprehensive innovation-promoting effect on maturity-stage firms, mainly improves innovation efficiency for decline-stage firms, and does not produce significant effects for growth-stage firms. The findings offer practical insights for policymakers and local governments in refining AI-related innovation policies and pilot-zone implementation, and for enterprise managers in strategically adopting AI to strengthen innovation capability and long-term sustainable development. Full article

Skeleton-based action recognition is a critical technology for intelligent sports analysis. Although the human skeletal structure exhibits inherent bilateral symmetry, sensor noise on resource-constrained edge devices frequently induces geometric distortion and topological asymmetry. Consequently, achieving a balance between high accuracy and real-time performance remains a significant challenge. To this end, we propose EMS-GCN, an Efficient Multi-scale Shift Graph Convolutional Network that integrates geometric priors. Specifically, we design a Gaussian kernel-driven topology refinement module to mitigate structural noise inherent in sensor data. By leveraging geometric symmetry and Gaussian distances among nodes, this module dynamically constrains graph topology learning, thereby effectively rectifying the structural asymmetry and ambiguity induced by noise. Furthermore, we construct a Multi-scale Shift Linear Attention (MSLA) module to replace computationally intensive temporal convolutions. Leveraging temporal shift invariance, this module captures multi-scale contexts via parameter-free shift operations. Furthermore, we introduce a linear temporal attention mechanism to model global temporal dependencies with linear complexity, effectively resolving the information asymmetry inherent in long-range interactions. Finally, EMS-GCN incorporates a dual-branch attention structure to adaptively calibrate feature responses. Extensive experiments demonstrate that our model maintains high recognition accuracy with only 0.56 M parameters, representing a reduction of over 60% compared to mainstream baselines. These results validate the efficacy of leveraging geometric and temporal symmetries to enhance real-time sports analysis. Full article

This study explores how environmental, social and governance (ESG) disclosure relates to analysts’ forecast accuracy in Saudi Arabia, focusing on the ESG disclosure guidelines introduced by the Saudi Stock Exchange (Tadawul) in 2021. It suggests that ESG disclosure enhances corporate transparency, decreases information asymmetry, and provides analysts with additional non-financial information that can improve the earnings forecast quality. Furthermore, the introduction of ESG guidelines is likely to enhance the consistency and reliability of sustainability reporting, thereby strengthening the informational environment of the capital market. Based on a sample of listed firms from 2017 to 2024 and employing panel regression techniques, including fixed-effects and two-step system generalized method of moments (GMM) estimations, the results indicate that a higher ESG disclosure is associated with lower analyst forecast errors, reflecting an improved forecast accuracy. The findings also reveal that the forecast accuracy increased following the ESG guidelines’ introduction and that the connection between ESG disclosure and analysts’ forecast accuracy became greater after the implementation of the guidelines. Our results demonstrate the informational value of ESG disclosure and suggest that ESG reporting initiatives can boost the quality of financial information in emerging markets. Full article

This study develops a conceptual framework for reimagining Sustainable Development Goal 17 (SDG 17) in Africa through a reinterpretation of the Marshall Plan’s governance logic. The primary focus is to address persistent failures in development partnerships—namely, fragmentation, weak coordination, power asymmetries, and limited institutional capacity—by proposing a structured model of partnership governance. Using a theory-building methodology grounded in historical analysis and documentary evidence, the study applies a systematic adaptation logic in which core governance mechanisms from the Marshall Plan are re-specified to reflect African institutional realities. These mechanisms—coordination, mutual accountability, collective action, state capacity, and trust—are translated into eight operational pillars: co-development, institutional strengthening, structural transformation, regional integration, blended finance, digital public infrastructure, knowledge co-production, and resilience. The framework conceptualizes SDG 17 as a meta-governance system that aligns actors, institutions, and resources across sectors. By moving from historical abstraction to context-sensitive application, the study contributes a coherent, Africa-centered governance model that enhances partnership effectiveness and informs post-2030 development policy. Full article

Background/Objectives: Craniofacial malformations such as orofacial clefts affect ~1 in 700 births; 40–60% lack clear genetic etiology, and many exhibit asymmetry and variable expressivity unexplained by classical Sonic Hedgehog (SHH) morphogen gradient models. We investigated whether integrated molecular modules linking morphogen signaling with metabolic stress responses may better account for craniofacial developmental outcomes. Methods: Sequential UniProt gene set integration identified 186 candidate craniofacial regulators. STRING network analysis revealed modular architecture. Molecular docking profiled 17 compounds against SMO, CK1δ, PINK1, and TIE2 (control). Pathway reconstruction integrated the SHH–CK1δ–HIF1A–HEY1–PINK1 axis with in-silico-predicted CK1δ phosphorylation sites on SMO (S615, T593, S751), HIF1A (Ser247), and GLI1/2/3 transcription factors. A developmental decision tree mapped affinity profiles to node-specific phenotype hypotheses. Results: CK1δ and PINK1 emerged as candidate nodes coupling morphogen signaling with mitochondrial quality control. Cross-docking showed preferential binding to developmental kinases (CK1δ: −8.34 kcal/mol; PINK1: −8.80 kcal/mol) versus TIE2 control (−6.76 kcal/mol; p < 0.001). Pathway reconstruction suggested that CK1δ-mediated Ser247 phosphorylation of HIF1A disrupts ARNT dimerization, redirecting HIF1A toward ARNT-independent HEY1 induction and consequent PINK1 suppression. Based on computed profiles, node-specific associations were proposed as computational hypotheses: SMO perturbation → midline defects; CK1δ → facial asymmetry/clefting; PINK1 → mandibular hypoplasia. Multi-target compounds (e.g., purmorphamine, taladegib) generated composite phenotype predictions consistent with clinical complexity. Conclusions: This strictly in silico study identifies candidate integrated morphogenic modules whose multi-node perturbation may underlie anatomically specific craniofacial malformation patterns. Node–phenotype associations are prioritized computational hypotheses requiring experimental validation; if confirmed, the framework could inform developmental toxicity assessment, therapeutic design, and reclassification of idiopathic craniofacial anomalies. Full article

This paper proposes a novel covert communication paradigm in which covertness emerges from network-induced structural uncertainty, eliminating the traditional reliance on pre-shared secret pilots in multi-user cooperative networks. Unlike conventional schemes that create information asymmetry through secret training sequences, we show that structural uncertainty naturally arises from user selection in spatially dispersed networks. Specifically, we consider a public pilot aided system under a worst-case adversarial assumption where Willie possesses full knowledge of all individual channel state information (CSI) but remains uncertain about the active subset of cooperative users. We prove that this selection-induced structural uncertainty renders different transmission states statistically indistinguishable from Willie’s perspective, thereby forcing the optimal detector to reduce to an energy-based test. The proposed framework demonstrates that robust covertness can be achieved without secrecy-based coordination, providing a scalable and practically viable alternative to secret pilot management in future wireless networks. Full article

Peripheral facial palsy (PFP) causes pronounced facial asymmetry and functional impairment, highlighting the need for reliable, objective assessment. This study presents a novel, fully automated, reference-free method for quantifying facial symmetry using artificial intelligence (AI)-based facial landmark detection. A total of 405 datasets from 198 PFP patients were analyzed, each including nine standardized facial expressions covering both resting and dynamic movements. AI detected 478 landmarks per image, from which 225 paired landmarks were used to compute local asymmetry angles. Systematic evaluation identified 91 highly informative landmark pairs, primarily around the eyes, nose and mouth, which simplified the analysis and enhanced discriminatory power, while also enabling region-specific assessment of asymmetry. Statistical evaluation included Kruskal–Wallis H-tests across clinical scores and Spearman correlations, showing moderate to strong associations (0.32–0.73, p < 0.001). The fully automated pipeline produced reproducible results and demonstrated robustness to head rotation. Intuitive full-face angle maps allowed direct assessment of asymmetry without a reference image. This AI-driven approach provides a robust, objective, and visually interpretable framework for clinical monitoring, severity classification, and treatment evaluation in PFP, combining quantitative precision with practical applicability. Full article

This study investigates whether Responsible Property Investing (RPI) attributes are capitalized into condominium prices in the Bangkok Metropolitan Region. An integrated analytical framework combining Exploratory Factor Analysis (EFA) and a log–log Hedonic Price Model (HPM) was applied to a dataset of 187 condominium units derived from Environmental Impact Assessment (EIA) reports and market data. The results indicate that traditional determinants remain dominant. Unit characteristics, particularly spatial quality (β = 0.530) and interior decoration (β = 0.244), exhibit the strongest positive effects, while building amenities also contribute positively (β = 0.260). In contrast, building density (β = −0.168) and location-related distances, including transport accessibility (β = −0.323), negatively affect prices. Most RPI-related attributes are not statistically significant. Only sustainable technology (R4) shows a significant but negative effect (β = −0.206), reflecting heterogeneous valuation. These findings suggest that sustainability features are valued primarily when their benefits are directly observable, while other attributes remain weakly perceived due to information asymmetry and delayed economic returns. Overall, sustainability is only partially capitalized and context-specific in this emerging market, highlighting the need for improved market signaling, policy incentives, and greater transparency of performance information to enhance value recognition. Full article

In side-channel analysis, simple power analysis (SPA) is a widely used technique for recovering secret information by exploiting differences between operations in traces. However, in realistic measurement environments, SPA is often hindered by noise, temporal misalignment, and weak or transient leakage, which obscure secret-dependent features in single or very few power traces. In this paper, we provide a systematic analysis of moving-skewness-based trace preprocessing for enhancing asymmetric leakage characteristics relevant to SPA. The method computes local skewness within a moving window along the trace, transforming the original signal into a skewness trace that emphasizes distributional asymmetry while suppressing noise. Unlike conventional smoothing-based preprocessing techniques, the proposed approach preserves and can even amplify subtle leakage patterns and spike-like transient events that are often attenuated by low-pass filtering or moving-average methods. To further improve applicability under different leakage conditions, we introduce feature-driven window-selection strategies that align preprocessing parameters with various leakage characteristics. Both simulated datasets and real measurement traces collected from multiple cryptographic platforms are used to evaluate the effectiveness of the approach. The experimental results indicate that moving-skewness preprocessing improves leakage visibility and achieves higher SPA success rates compared to commonly used preprocessing methods. Full article