Digital-driven scaling poses significant problems to analog circuits because scaling severely deteriorates transistor current saturation, significantly degrading the intrinsic gain. Special material properties of emerging low-dimensional semiconductors trigger the possibility of providing solutions. We report complementary carbon nanotube thin-film transistors with negative differential resistance-induced current super-saturation for high, exponentially variable intrinsic gain with immunity against degradation during scaling. Current super-saturation at the negative-to-positive differential resistance transition boundary provides intrinsic gain singularities. The large-window, gate-modulated negative differential resistance behavior derived from carbon nanotube’s characteristics enables its practical utilization in circuits. When approaching the singularity, we record that the intrinsic gain varies by orders of magnitude, ranging from 102 to 106 at different operation points. We further demonstrate high and exponentially variable gain in an operational amplifier, showing a tunable single-stage gain ranging from 35 to 60 decibels.
As artificial intelligence-generated content (AIGC) continues to evolve, video-to-audio (V2A) generation has emerged as a key area with promising applications in multimedia editing, augmented reality, and automated content creation. While Transformer and Diffusion models have advanced audio generation, a significant challenge persists in extracting precise semantic information from videos, as current models often lose sequential context by relying solely on frame-based features. To address this, we present TA-V2A, a method that integrates language, audio, and video features to improve semantic representation in latent space. By incorporating large language models for enhanced video comprehension, our approach leverages text guidance to enrich semantic expression. Our diffusion model-based system utilizes automated text modulation to enhance inference quality and efficiency, providing personalized control through text-guided interfaces. This integration enhances semantic expression while ensuring temporal alignment, leading to more accurate and coherent video-to-audio generation.
The asymmetrical global higher education and knowledge systems ordered by Euro–American hegemony have been increasingly interrogated, especially by scholars in the humanities and social sciences (HSS). With gathering awareness, growing HSS scholars from non-Western backgrounds have called for global intellectual pluriversality. Responding to such a trend, this article sheds new light on the status quo of East Asian and other non-Euro–American intellectual traditions by taking Chinese intellectual traditions as a case. Since the nineteenth century, generations of Chinese intellectuals have strived to transform their intellectual traditions into modern resources. This historical mission has been carried on by contemporary scholars and become even more complex in the current global era. By unpacking the real perceptions and recent experiences of Chinese HSS scholars, this study demonstrates that Chinese intellectual traditions deeply influence today’s knowledge production and have been transformed into three kinds of academic resources: approaches, methodologies/paradigms, and theories. However, the transformation process has never been smooth. Domestically, the great endeavours of Chinese HSS scholars are often impeded by the dominant intellectual extraversion and coercive audit culture; internationally, they feel constrained by epistemic injustice. This article proposes an empirical approach to examining and presenting intellectual traditions in the individual experiences of scholars. It reveals the high complexities of navigating through asymmetrical globalisation to achieve intellectual pluriversality.
This paper develops a unified theory integrating the three pillars of the pension system—public, occupational, and private pensions—within a heterogeneous-agent overlapping generations (OLG) model. By incorporating income heterogeneity and institutional features unique to each pillar, the model captures how individuals across the income distribution participate in the pension system and derive utility. We characterize the distinct yet interactive roles of each pillar in providing risk sharing and retirement security and identify fundamental trade-offs in pension design. Our model provides a laboratory for analyzing the coordination of the three pillars that aims at enhancing equity and fiscal sustainability.
This study aims to identify the associations between teacher mental health and student mental health. Cross-sectional data were collected from 127,877 students aged 9–20 years and 2,759 teachers across 31 provinces in China. The mental health of students and teachers were assessed by well-being (life satisfaction and positive mental health), and psychological distress (depression and anxiety). Controlling for demographic variables, multilevel regression analyses suggest that higher teacher positive mental health was linked to higher student positive mental health and lower student depression; higher teacher depression were correlated with higher student depression; and teacher life satisfaction and anxiety were not correlated with any indicators of student mental health. The study highlights the significant association between teacher mental health and student mental health.
Trust in artificial intelligence (AI) has become a central issue due to the opacity and unpredictability of AI decision-making processes. However, existing studies often produce inconsistent results and fail to provide a unified understanding of the underlying factors, making a comprehensive review necessary. To address this gap, we conducted a systematic review of 562 empirical studies to explore the antecedents and consequences of human trust in AI. The review identified key antecedents of trust, including AI capability, anthropomorphism, individual factors, and explainability, and associated consequences, such as behavioral intention, attitude, and acceptance. A cross-cultural analysis revealed significant differences in how cultural contexts influence the perception and prioritization of factors, such as capability, transparency, and anthropomorphism. These findings emphasize the need for a multidimensional approach to developing trustworthy AI systems, highlighting the importance of cultural sensitivity in AI design. The review also suggests several promising avenues for future research, including dynamic trust formation, reciprocal trust relationships, and the evolution of trust over time. Addressing these areas will enhance our understanding of trust in AI and contribute to the development of culturally adapted and ethically sound AI technologies.