Full Motion State Localization with Extra Large Aperture Arrays

Conventional localization techniques typically assume far-field (FF) propagation characterized by planar wavefronts and simplified spatial relationships. The use of higher carrier frequencies has given rise to the paradigm of extra large aperture arrays (ELAAs) which consist of a large number of tightly packed antenna elements. These arrays have a large electrical aperture which increases the Fraunhofer distance making the FF assumption restrictive. As a result, near-field (NF) effects, such as spherical wavefront curvature, direction dependent gains, and spatial variations in Doppler and delay, become significant even at distances previously regarded as FF. This paradigm shift opens up new opportunities: the rich multi-parametric structure of NF models if properly exploited can enable superior localization accuracy. In this work, we investigate the potential of multi-snapshot, full-motion state (3D position, 3D velocity, and 2D orientation) estimation using delay and Doppler measurements for a mobile receiver equipped with a linear ELAA in an environment comprising a number of wideband anchors. We develop a signal model that captures both the NF propagation geometry and spatially varying Doppler effects. We perform an information-theoretic analysis to establish Cramer-Rao lower bounds (CRLB) on the achievable position error bound (PEB), velocity error bound (VEB), and orientation error bound (OEB), respectively. We reveal that delay measurements carry richer information than Doppler measurements, and standalone Doppler measurements cannot overcome information losses due to unknown channel gains and frequency offsets, enabling only coarse estimation capabilities. We also propose a maximum-likelihood (ML) approach to jointly estimate the 8D position parameters from measured channel characteristics.

TAAC: A gate into Trustable Audio Affective Computing

With the emergence of AI techniques for depression diagnosis, the conflict between high demand and limited supply for depression screening has been significantly alleviated. Among various modal data, audio-based depression diagnosis has received increasing attention from both academia and industry since audio is the most common carrier of emotion transmission. Unfortunately, audio data also contains User-sensitive Identity Information (ID), which is extremely vulnerable and may be maliciously used during the smart diagnosis process. Among previous methods, the clarification between depression features and sensitive features has always serve as a barrier. It is also critical to the problem for introducing a safe encryption methodology that only encrypts the sensitive features and a powerful classifier that can correctly diagnose the depression. To track these challenges, by leveraging adversarial loss-based Subspace Decomposition, we propose a first practical framework \name presented for Trustable Audio Affective Computing, to perform automated depression detection through audio within a trustable environment. The key enablers of TAAC are Differentiating Features Subspace Decompositor (DFSD), Flexible Noise Encryptor (FNE) and Staged Training Paradigm, used for decomposition, ID encryption and performance enhancement, respectively. Extensive experiments with existing encryption methods demonstrate our framework's preeminent performance in depression detection, ID reservation and audio reconstruction. Meanwhile, the experiments across various setting demonstrates our model's stability under different encryption strengths. Thus proving our framework's excellence in Confidentiality, Accuracy, Traceability, and Adjustability.

Designing Any Imaging System from Natural Language: Agent-Constrained Composition over a Finite Primitive Basis

Designing a computational imaging system -- selecting operators, setting parameters, validating consistency -- requires weeks of specialist effort per modality, creating an expertise bottleneck that excludes the broader scientific community from prototyping imaging instruments. We introduce spec.md, a structured specification format, and three autonomous agents -- Plan, Judge, and Execute -- that translate a one-sentence natural-language description into a validated forward model with bounded reconstruction error. A design-to-real error theorem decomposes total reconstruction error into five independently bounded terms, each linked to a corrective action. On 6 real-data modalities spanning all 5 carrier families, the automated pipeline matches expert-library quality (98.1 +/- 4.2%). Ten novel designs -- composing primitives into chains from 3D to 5D -- demonstrate compositional reach beyond any single-modality tool.

The Semantic Ladder: A Framework for Progressive Formalization of Natural Language Content for Knowledge Graphs and AI Systems

Semantic data and knowledge infrastructures must reconcile two fundamentally different forms of representation: natural language, in which most knowledge is created and communicated, and formal semantic models, which enable machine-actionable integration, interoperability, and reasoning. Bridging this gap remains a central challenge, particularly when full semantic formalization is required at the point of data entry. Here, we introduce the Semantic Ladder, an architectural framework that enables the progressive formalization of data and knowledge. Building on the concept of modular semantic units as identifiable carriers of meaning, the framework organizes representations across levels of increasing semantic explicitness, ranging from natural language text snippets to ontology-based and higher-order logical models. Transformations between levels support semantic enrichment, statement structuring, and logical modelling while preserving semantic continuity and traceability. This approach enables the incremental construction of semantic knowledge spaces, reduces the semantic parsing burden, and supports the integration of heterogeneous representations, including natural language, structured semantic models, and vector-based embeddings. The Semantic Ladder thereby provides a foundation for scalable, interoperable, and AI-ready data and knowledge infrastructures.

Mechanisms of Introspective Awareness

Recent work shows that LLMs can sometimes detect when steering vectors are injected into their residual stream and identify the injected concept, a phenomenon cited as evidence of "introspective awareness." But what mechanisms underlie this capability, and do they reflect genuine introspective circuitry or more shallow heuristics? We investigate these questions in open-source models and establish three main findings. First, introspection is behaviorally robust: detection achieves moderate true positive rates with 0% false positives across diverse prompts. We also find this capability emerges specifically from post-training rather than pretraining. Second, introspection is not reducible to a single linear confound: anomaly detection relies on distributed MLP computation across multiple directions, implemented by evidence carrier and gate features. Third, models possess greater introspective capability than is elicited by default: ablating refusal directions improves detection by 53pp and a trained steering vector by 75pp. Overall, our results suggest that introspective awareness is behaviorally robust, grounded in nontrivial internal anomaly detection, and likely could be substantially improved in future models. Code: https://github.com/safety-research/introspection-mechanisms.

Homodyne vs. Heterodyne Architectures in Sub-THz Transceivers: A Phase Noise Perspective

This letter examines the impact of oscillator phase noise on sub-terahertz OFDM transceiver architectures, with a focus on the comparison between homodyne and heterodyne designs. Using a Hexa-X compliant phase noise model, we analytically show that heterodyne architectures reduce the total accumulated phase noise variance by distributing frequency translation across lower-frequency oscillators under realistic phase-noise scaling laws, thereby shifting the dominant impairment from inter-carrier interference to common phase error. OFDM simulations at 70 GHz and 140 GHz demonstrate that while homodyne architectures remain competitive at mmWave frequencies, heterodyne designs provide improved robustness to phase noise at higher sub-THz carriers. These results highlight transceiver architecture as a key design lever for relaxing oscillator and phase-locked loop constraints in future sub-THz wireless systems.

4D Fresnel Space-Time Modulation for Near-Field ELAA: Kinematic Multiplexing and O(N log N) Precoding at Sub-THz Frequencies

Extremely Large Antenna Arrays (ELAA) operating at sub-terahertz frequencies introduce a regime where near-field Fresnel propagation and high-mobility carrier Doppler interact simultaneously, creating a four-dimensional signal space that existing schemes exploit only partially. This paper proposes \textbf{4D Fresnel Space-Time Modulation (4D-FSM)}, a unified framework encoding information jointly across angle, depth, synthetic velocity, and QAM amplitude through a structured symbol manifold $\mathcal{S}$. Synthetic velocity is introduced via Space-Time Modulation (STM): a linear phase ramp $u(ξ,t) = \exp(j[Ωt + g_kξ])$ induces a Doppler-equivalent shift without physical motion, creating velocity-orthogonal bubbles that resolve co-located users. We derive the joint orthogonality surface governing simultaneous user separability in depth and velocity, revealing that users separated in depth require strictly less velocity separation to remain orthogonal -- a multiplexing gain with no counterpart in OTFS or LDMA. The Discrete Fresnel Transform (DFnT) factorization $\mathbf{H} = \mathbf{F}_D \mathbf{C}(z) \mathbf{P}$ reduces precoder complexity from $\mathcal{O}(N^3)$ to $\mathcal{O}(N\log N)$, completing within \SI{500}{\nano\second} against a \SI{5.4}{\micro\second} coherence window. Monte Carlo evaluation at $f_c = \SI{140}{\giga\hertz}$, $N = 4096$ confirms $ρ\approx 0.998$ across the full velocity range, \SI{6.16}{\bit\per\second\per\hertz} spectral efficiency where all baselines collapse, and $K_{\max} = 64$ orthogonal users -- a $248\times$ sum-rate advantage over TTD at $K = 50$.

Lean Learning Beyond Clouds: Efficient Discrepancy-Conditioned Optical-SAR Fusion for Semantic Segmentation

Cloud occlusion severely degrades the semantic integrity of optical remote sensing imagery. While incorporating Synthetic Aperture Radar (SAR) provides complementary observations, achieving efficient global modeling and reliable cross-modal fusion under cloud interference remains challenging. Existing methods rely on dense global attention to capture long-range dependencies, yet such aggregation indiscriminately propagates cloud-induced noise. Improving robustness typically entails enlarging model capacity, which further increases computational overhead. Given the large-scale and high-resolution nature of remote sensing applications, such computational demands hinder practical deployment, leading to an efficiency-reliability trade-off. To address this dilemma, we propose EDC, an efficiency-oriented and discrepancy-conditioned optical-SAR semantic segmentation framework. A tri-stream encoder with Carrier Tokens enables compact global context modeling with reduced complexity. To prevent noise contamination, we introduce a Discrepancy-Conditioned Hybrid Fusion (DCHF) mechanism that selectively suppresses unreliable regions during global aggregation. In addition, an auxiliary cloud removal branch with teacher-guided distillation enhances semantic consistency under occlusion. Extensive experiments demonstrate that EDC achieves superior accuracy and efficiency, improving mIoU by 0.56\% and 0.88\% on M3M-CR and WHU-OPT-SAR, respectively, while reducing the number of parameters by 46.7\% and accelerating inference by 1.98$\times$. Our implementation is available at https://github.com/mengcx0209/EDC.

Experimental Analysis of Microbubble Propagation for In-Body Data Transmission

In-body communication is an upcoming field with significant implications for medical diagnostics and therapeutic interventions. Microbubbles have gained attention due to their distinct physical properties, making them promising candidates to facilitate communication within the human body. This work explores the use of microbubbles as communication carriers, with a particular focus on their detection and the application of a modulation scheme. Through experimental analysis the feasibility and effectiveness of microbubble-based communication is tested. Filtering and peak detection methods are applied to accurately identify the presence of microbubbles despite noise, demonstrating the feasibility of microbubble-based communication systems for future biomedical applications. The results offer insights into signal integrity, noise challenges, and the optimization of detection algorithms, providing a foundation for future advancements in this field.

LEO-based Carrier-Phase Positioning for 6G: Design Insights and Comparison with GNSS

The integration of non-terrestrial networks (NTN) into 5G new radio (NR) enables a new class of positioning capabilities based on cellular signals transmitted by Low-Earth Orbit (LEO) satellites. In this paper, we investigate joint delay-and-carrier-phase positioning for LEO-based NR-NTN systems and provide a convergence-centric comparison with Global Navigation Satellite Systems (GNSS). We show that the rapid orbital motion of LEO satellites induces strong temporal and geometric diversity across observation epochs, thereby improving the conditioning of multi-epoch carrier-phase models and enabling significantly faster integer-ambiguity convergence. To enable robust carrier-phase tracking under intermittent positioning reference signal (PRS) transmissions, we propose a dual-waveform design that combines wideband PRS for delay estimation with a continuous narrowband carrier for phase tracking. Using a realistic simulation framework incorporating LEO orbit dynamics, we demonstrate that LEO-based joint delay-and-carrier-phase positioning achieves cm-level accuracy with convergence times on the order of a few seconds, whereas GNSS remains limited to meter-level accuracy over comparable short observation windows. These results establish LEO-based cellular positioning as a strong complement and potential alternative to GNSS for high-accuracy positioning, navigation, and timing (PNT) services in future wireless networks.