Search Results (1,424)

We present Stereo-GS, a real-time system for online 3D Gaussian Splatting (3DGS) that reconstructs photorealistic 3D scenes from streaming stereo pairs. Unlike prior offline 3DGS methods that require dense multi-view input or precomputed depth, Stereo-GS estimates metrically accurate depth maps directly from rectified stereo geometry, enabling progressive, globally consistent reconstruction. The frontend combines a stereo implementation of DROID-SLAM for robust tracking and keyframe selection with FoundationStereo, a generalizable stereo network that needs no scene-specific fine-tuning. A two-stage filtering pipeline improves depth reliability by removing outliers using a variance-based refinement filter followed by a multi-view consistency check. In the backend, we selectively initialize new Gaussians in under-represented regions flagged by low PSNR during rendering and continuously optimize them via differentiable rendering. To maintain global coherence with minimal overhead, we apply a lightweight rigid alignment after periodic bundle adjustment. On EuRoC and TartanAir, Stereo-GS attains state-of-the-art performance, improving average PSNR by 0.22 dB and 2.45 dB over the best baseline, respectively. Together with superior visual quality, these results show that Stereo-GS delivers high-fidelity, geometrically accurate 3D reconstructions suitable for real-time robotics, navigation, and immersive AR/VR applications. Full article

Household waste systems are a frontline test of Australia’s circular economy transition, yet progress remains highly uneven and structurally constrained. Despite strong national targets for resource recovery and emissions reduction, local governments are expected to deliver circular outcomes without uniform access to infrastructure, funding, or technical capability. This study assesses the status, implementation, and progress of household waste management, energy recovery, and circular economy initiatives at the local government level in Australia. Using content analysis of data from 520 local government areas across six states, the study maps differences in service provision (e.g., general waste, mixed recycling, and food organics and garden organics [FOGO] collection), policy instruments, public-facing education, and participation in circular economy programs. The findings reveal that while a majority (92.5%) of councils provide general waste bins, 47% offer FOGO bins, and 78% supply mixed recyclable bins, only a small fraction (2.6%) offers a separate glass bin stream. Fewer than one in ten councils reference any form of energy recovery or waste-to-energy initiative, indicating that resource–energy integration remains emergent and geographically concentrated. Despite national policies such as the National Waste Policy Action Plan, significant regional disparities persist, particularly between metropolitan and rural councils. Guided by environmental governance theory and systems thinking, the study shows how policy fragmentation, funding limitations, and infrastructure inequities create systemic barriers to circularity. The study concludes by recommending targeted co-funding for rural councils, stronger policy support for organics and energy recovery infrastructure, and more coherent multi-level governance to achieve Australia’s 2030 waste and circular economy targets. This research contributes an evidence-based framework for understanding how governance structures and resource asymmetries shape local progress toward a circular economy. Full article

We investigate an extended Gauss iterative map by incorporating the q-exponential function, a key component of the Tsallis framework. This extension enables us to investigate the non-linear dynamics of the Gauss iterative map across a broader range of scenarios, encompassing periodic, chaotic, and bistable behaviors. Regular and chaotic phenomena have been observed in coupled systems. In this context, we propose a network of coupled extended Gauss iterative maps. In our network, we found the emergence of chimera states, characterized by the coexistence of coherent and incoherent behaviors. These states are identified within specific parameter regimes using Gopal’s metric. In this work, we show the interplay between chaos and emergent collective dynamics in coupled extended Gauss iterative maps. Full article

Generative models can generate art within a single modality with high fidelity. However, translating a work of art from one domain to another (e.g., painting to music or poem to painting) in a meaningful way remains a longstanding, interdisciplinary challenge. We propose a novel approach combining a multi-agent system (MAS) architecture with an ontology-guided semantic representation to achieve cross-domain art translation while preserving the original artwork’s meaning and emotional impact. In our concept, specialized agents decompose the task: a Perception Agent extracts symbolic descriptors from the source artwork, a Translation Agent maps these descriptors using shared knowledge base, a Generator Agent creates the target-modality artwork, and a Curator Agent evaluates and refines the output for coherence and style alignment. This modular design, inspired by human creative workflows, allows complex artistic concepts (themes, moods, motifs) to carry over across modalities in a consistent and interpretable way. We implemented a prototype supporting translations between painting and poetry, leveraging state-of-the-art generative models. Preliminary results indicate that our ontology-driven MAS produces cross-domain translations that preserve key semantic elements and affective tone of the input, offering a new path toward explainable and controllable creative AI. Finally, we discuss a case study and potential applications from educational tools to synesthetic VR experiences and outline future research directions for enhancing the realm of intelligent agents. Full article

In recent years, the European Union has played a key role in global efforts to combat climate change and the energy transition, focusing on creating fiscal, legal and regulatory policies and instruments capable of supporting the decarbonization process and ensuring a sustainable energy future. Environmental taxation has been considered not only as an essential tool to discourage pollution but also to stimulate cleaner energy production, the integration of renewable sources and energy efficiency. Our research analyses the impact of environmental tax revenues on CO₂ across 27 EU member states from 2012 to 2023. A mixed-method research approach is used, combining policy and strategy analysis, bibliometric mapping and econometric data analysis using OLS, as well as fixed and random effects models that are selected based on the Hausman test. The methodological mix approach provides empirical evidence on how fiscal instruments can simultaneously support environmental sustainability and energy resilience. The results show that environmental taxes are associated with greenhouse gas emission reductions and an increase in the share of renewable energy, especially when integrated into a coherent national policy framework. The policy analysis highlights the role of the Climate Action Budgetary Mechanism (CABM) and the Effort Sharing Regulation (ESR), underlining their importance for the European Union’s energy strategy. The bibliometric results indicate the existence of thematic clusters focused on carbon pricing, renewable energies and international comparisons, particularly with China. Finally, this study suggests that the maximum efficiency of environmental taxes is achieved when the revenues generated are reinvested in green infrastructure, innovation and sustainable jobs. Furthermore, policies should be adapted to the specificities of each Member State to ensure a fair and sustainable energy transition at the EU level. Full article

The world is far from meeting the goals of the Paris Agreement of limiting the rise of global temperature to below 1.5 °C, with dire consequences for Small Island Developing States (SIDS) in particular. If SIDS are to address their climate vulnerabilities through policy-induced resilience building, they need to have a robust governance framework in place that coherently addresses climate change adaptation and disaster risk reduction. What would such a governance framework look like? To address this question, we carried out a systematic literature review of papers published between 1992 and 2023. Our review reveals that the governance around climate change adaptation and disaster risk reduction is relatively weak in SIDS. However, the analysis of barriers and enablers unveils the contours of a proposed three-tiered governance framework, the application of which needs to be contextualised: Tier 1 comprises three key pillars: Policy Planning, Institutional Arrangements, and Laws and Regulations; Tier 2 identifies the principles of transparency, accountability, equity, legitimacy, and subsidiarity; the core pillars and the principles are nested within a broader Tier 3 comprising democratic processes (rule of law), religious and cultural values, and political commitment. In order for SIDS to fight the existential threat of climate change, the proposed framework will allow SIDS to better understand their climate governance framework and deliver low-carbon, climate resilient development within the broader ambit of sustainable development. This framework also addresses the weakness in previous studies, which consider dimensions, principles, and enabling an environment of good governance on equal footing. We illustrate this framework using the analogy of the lotus flower. Full article

Eight Views is a time-honored East Asian cultural-landscape paradigm in which eight emblematic natural—cultural scenes fuse regional character, historical memory, and aesthetic ideals into a coherent narrative. It encodes the collective memory and identity of a city (or garden/region), a premodern ‘mental map’ or proto- ‘city brand’. In China, the historic Urban Eight Views are rooted in local environments and traditions and constitute significant, high-value landscape heritage today. Yet rapid urbanization has inflicted severe physical damage on these ensembles. Coupled with insufficient holistic and systemic understanding among managers and the public, this has led, during development and conservation alike, to spatial insularization, fragmentation, and even disappearance, alongside widening divergences in cultural cognition and biases in value judgment. Taking Longmen Haoyue in Chongqing, one of the historic Urban Eight Views, as a case that manifests these issues, this study develops a holistic–relational approach for the urban, historical Eight Views and explores landscape-based pathways to protect the spatial structure and cultural connotations of the heritage that has been severely damaged and is in a state of disappearance or semi-disappearance amid modernization. Methodologically, we employ decomposition analysis to extract the historical information elements of Longmen Haoyue and its internal relational structure and corroborate its persistence through field surveys. We then apply the FAHP method to grade the conservation value and importance of elements within the Eight Views, quantitatively clarifying protection hierarchies and priorities. In parallel, a multidimensional corpus is constructed to analyze online dissemination and public perception, revealing multiple challenges in the evolution and reconstruction of Longmen Haoyue, including symbolic misreading and cultural decontextualization. In response, we propose an integrated strategy comprising graded element protection and intervention, reconstruction of relational structures, and the building of a coherent cultural-semantic and symbol system. This study provides a systematic theoretical basis and methodological support for the conservation of the urban historic Eight Views cultural landscapes, the place-making of distinctive spatial character, and the enhancement of cultural meanings. It develops an integrated research framework, element extraction, value assessment, perception analysis, and strategic response that is applicable not only to the Eight Views heritage in China but is also transferable to World Heritage properties with similar attributes worldwide, especially composite cultural landscapes composed of multiple natural and cultural elements, sustained by narrative traditions of place identity, and facing risks of symbolic weakening, decontextualization, or public misperception. Full article

With the rapid deployment of artificial intelligence (AI) data centers, demand for optical modules surges—alongside faster upgrades and stricter low-power requirements. However, traditional optical driver integrated circuits (ICs) rely on device-specific customization, which lengthens driver design cycles, delays module deployment, and raises costs, becoming a bottleneck for optical module evolution. To address these issues, this work proposes a unified optical transmitter electronic integrated circuit (EIC) design approach featuring synergistic driver-laser/modulator co-design and a versatile output driver (VOD). The VOD can be configured into three output impedance states (open-drain, differential 50-$\Omega$, or differential 100-$\Omega$), enabling it to drive various optical devices like distributed feedback lasers (DFBs), vertical-cavity surface-emitting lasers (VCSELs), electro-absorption modulated lasers (EMLs), and Mach-Zehnder modulators (MZMs) with a single design, minimizing device-specific customization. Meanwhile, its power consumption is also adjustable to maximize the power efficiency. The proposed design approach demonstrates the potential to address the critical interoperability, cost, and power challenges faced by AI data centers, providing a scalable template for next-generation coherent and 4-level pulse amplitude modulation systems and facilitating rapid deployment. Full article

We present a two-mode squeezed Jaynes–Cummings model, built upon the formalism of pair coherent states (PCSs), to investigate the dynamics of a two-level atom interacting with a two-mode quantized field. By solving the time-dependent Schrödinger equation under the rotating-wave approximation, we elucidate the system’s quantum evolution, with particular emphasis on how the squeezing degree and photon number difference modulate atomic population inversion and entanglement. We further quantify the nonclassical traits of the two-mode squeezed PCSs via Mandel’s parameter and the violation of the Cauchy–Schwarz inequality, highlighting their sensitivity to model parameters. These findings illuminate the subtle interplay of squeezing, photon statistics, and entanglement in advanced quantum optical systems. Full article

We investigate the effect of giant negative magnetoresistance in ultrahigh-mobility ($\mu \gg {10}^7{\mathrm{cm}}^2{\mathrm{V}}^{-1}{\mathrm{s}}^{-1}$) two-dimensional electron systems. These systems present a dramatic drop in the mangetoresistance at low magnetic fields ($B\sim 0.1$ T) and temperatures ($T\sim 0.1$ K). This effect is reversed by increasing the temperature or the presence of an in-plane magnetic field. The motivation for the present work is to develop a microscopical model to explain the experimental evidence, based on coherent states and Schródinger cat states of the quantum harmonic oscillator. Thus, we approach the giant negative magnetoresistance effect based on the description of ultrahigh-mobility two-dimensional electron systems in terms of Schrödinger cat states (superposition of coherent states of the quantum harmonic oscillator). We explain the experimental results in terms of the increasing disorder in the sample due to the rising temperature or the in-plane magnetic field, breaking up the Schrödinger cat states and giving rise to mere coherent states, which hold magnetoresistance in lower-mobility samples. The latter, jointly with the description of ultrahigh-mobility samples with Schrödinger cat states, accounts for the main contribution. The most interesting application of this novel description of such systems would be in the implementation of qubits for quantum computing based on bosonic models. Full article

Beyond-5G wireless systems increasingly rely on distributed massive multiple-input multiple-output (MIMO) architectures to achieve high spectral efficiency, low latency, and wide coverage. A key challenge in such networks is that cooperating base stations (BSs) often possess different levels of channel state information (CSI) due to fronthaul constraints, user mobility, or hardware limitation. In this paper, we propose two novel detectors that enable cooperation between BSs with differing CSI availability. In this setup, some BSs have access to instantaneous CSI, while others only have long-term channel information. The proposed detectors—termed the coherent/non-coherent (CNC) detector and the differential CNC detector—integrate coherent and non-coherent approaches to signal detection. This framework allows BSs with only long-term information to actively contribute to the detection process, while leveraging instantaneous CSI where available. This approach enables the system to integrate the advantages of non-coherent detection with the precision of coherent processing, improving overall performance without requiring full CSI at all cooperating BSs. We formulate the detectors based on the maximum likelihood (ML) criterion and derive analytical expressions for their pairwise block error probabilities under Rayleigh fading channels. Leveraging the pairwise block error probability expression for the CNC detector, we derive a tight upper bound on the average block error probability. Numerical results show that the CNC and differential CNC detectors outperform their respective single-BS baseline-coherent ML and non-coherent differential detection. Moreover, both detectors demonstrate strong resilience to mid-to-high range correlation at the BS antennas. Full article

The transient dynamics of electromagnetically induced transparency (EIT) are fundamental to understanding coherent light–atom interactions and the advancement of quantum technologies such as optical switching and quantum memory. However, in room-temperature atomic vapors, Doppler broadening significantly alters these dynamics, yet a comprehensive understanding of its impact on the transient EIT response remains lacking. In this study, we combine analytical and numerical methods to investigate the absorption dynamics of a weak probe field in a three-level Λ-type system driven by a strong coupling field, based on the optical Bloch equations and Laplace transform techniques. Our results show that the transient response is highly sensitive to both the atomic spontaneous emission rate and the Rabi frequency of the coupling field. Increasing the coupling field intensity not only accelerates the approach to steady state but also induces oscillatory dynamics and negative absorption. Under Doppler broadening, the time required to reach steady state increases by approximately three orders of magnitude compared to the Doppler-free case—an effect that is surprisingly insensitive to temperature variations across the 100–400 K range. Moreover, restoring a short steady-state time under broadened conditions necessitates increasing the coupling laser intensity by two orders of magnitude. These findings provide key insights into the influence of Doppler broadening on coherent transient processes and offer practical guidelines for the design of room-temperature atomic devices, including quantum memories and optical modulators. Full article

Quasi-one-dimensional (quasi-1D) topological matter Bi₄X₄ (X = Br, I) possesses versatile topological phases determined by its molar ratio of halide and the stacking mode. Establishing the intrinsic relationship between these topological orders and the quantum transport properties is extremely crucial for both of fundamental research and device applications. Here we review the recent work on the characteristic quantum transport behavior of the Bi₄X₄ system originating from various electronic states, including three-dimensional (3D) bulk states, two-dimensional (2D) surface states, and one-dimensional (1D) topological hinge states. Specifically, variable range hopping effect, Lifshitz transition, metal–insulator transition, and Shubnikov de Haas oscillations are evoked by the gapped bulk states with significant doping carriers. In 2D limits, the (100) surface states exhibit Dirac-type dispersion to produce weak antilocalization, which is a strong 1D nature due to quasi-1D crystal and electronic structure and evidenced by anomalous planar Hall effect. Last but not the least, coherent transport with Aharonov–Bohm oscillations is observed in thin-layer devices, implying the existence of 1D topological hinge states separated by the (100) surface. These unconventional quantum transport features verify the topological nature of Bi₄X₄ in different dimensions, signifying an ideal platform to design and utilize multiple topological orders in this quasi-one-dimensional material system. Full article

A Bose-Fermi mixture, consisting of both bosons and fermions, exhibits distinctive quantum coherence and phase transitions, offering valuable insights into many-body quantum systems. The ground state, as the system’s lowest energy configuration, is essential for understanding its overall behavior. In this study, we introduce the Bose-Fermi Energy-based Deep Neural Network (BF-EnDNN), a novel deep learning approach designed to solve the ground-state problem of Bose-Fermi mixtures at zero temperature through energy minimization. This method incorporates three key innovations: point sampling pre-training, a Dynamic Symmetry Layer (DSL), and a Positivity Preserving Layer (PPL). These features significantly improve the network’s accuracy and stability in quantum calculations. Our numerical results show that BF-EnDNN achieves accuracy comparable to traditional finite difference methods, with effective extension to two-dimensional systems. The method demonstrates high precision across various parameters, making it a promising tool for investigating complex quantum systems. Full article

Collaborative experiences are enriched through cross-platform interactions in the context of eXtended Reality (XR) systems. In this paper, we introduce SRVS-C (Spatially Referenced Virtual Synchronization for Collaboration), a centralized framework designed to support co-located, real-time AR (on smartphone) and VR (in headset) interactions over local networks. The framework adopts an architecture of interactive asymmetry, where the interaction roles, input modalities, and rendering responsibilities are adapted to the unique capabilities and constraints of each device. Concurrently, the framework maintains perceptual symmetry, guaranteeing a coherent spatial and semantic experience for all users. This is achieved through anchor-based spatial registration and unified data representations. Compared to prior work that relies on cloud services or symmetric platforms (e.g., VR–VR, AR–AR, and PC–PC pairings), SRVS-C supports seamless communication between AR and VR endpoints, operating entirely over TCP sockets using serialization-agnostic message formats. We evaluated SRVS-C in a dual-user scenario involving a mobile AR and a VR headset, using shared freehand drawing tasks. These tasks include simple linear strokes and geometry-rich drawing content to assess how varying interaction complexity—ranging from low-density sketches to intricate, high-vertex structures— impacted the end-to-end latency, state replication timing, and collaborative fluency. The results show that the system sustains latency between 35 ms and 175 ms, even during rapid, continuous drawing actions that generate a high number of stroke updates per second, and when handling drawings composed of numerous vertices and complex shapes. Throughout these conditions, the system maintains perceptual continuity and spatial alignment across users by applying platform-specific interactive asymmetry. Full article