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Future 5G-Advanced and 6G systems will exploit directional wideband channels for path-based localization, yet map-free indoor positioning remains challenging because extracted multipath components (MPCs) vary widely in localization reliability. This paper presents an uncertainty-aware map-free single-anchor localization framework using 16.95 GHz indoor directional channel measurements collected at NYU WIRELESS in Brooklyn, New York. Per-MPC angular covariance is estimated from the local consistency of neighboring pattern-de-embedded power delay profile (PDP) observations and propagated into geometric localization through weighted fusion of point-type and line-type constraints. Measurements across 20 links with TX– RX separations from 11 m to 97 m show that the proposed covariance-weighted method achieves a mean localization error of 3.86 m, a median of 2.66 m, and 70% of links within 5 m using five retained MPCs. It outperforms the power-weighted and unweighted variants under identical point/line constraints, as well as the literature baselines. These results indicate that local directional consistency provides reliability information beyond received power alone and demonstrate a practical map-free approach for future indoor directional systems. 

Upper mid-band spectrum is promising for 5G and 6G cellular networks, yet concrete and low-emissivity glass impose over 20 dB of building penetration loss above 2 GHz, degrading outdoor-to-indoor (O2I) coverage. A reconfigurable intelligent surface (RIS) on a neighboring building bypasses this blockage by focusing energy onto indoor users. This paper introduces PRIMO (Parallel RIS Min-Rate Optimization), a training-free joint optimizer of base-station precoding, RIS phase shifts, and RIS placement, validated by calibrated ray tracing in a MetroTech Commons digital twin (Brooklyn, NY) at 6.75 and 16.95 GHz. At equal physical aperture, half-wavelength spacing packs 6.25 times as many elements per array at FR3 as at FR1(C), so FR1(C) leads at low indoor-user counts while FR3 dominates at higher counts, where the additional spatial degrees of freedom suppress inter-user interference. Above a 1 bps/Hz service floor, PRIMO supports 92% more indoor users at FR3 than at FR1(C), and raises the minimum user rate by up to 90% over a fully converged alternating-optimization baseline while running about 100 times faster. 

Extending standardized urban microcell (UMi) path loss (PL) models from the upper mid-band (FR3) to subterahertz (sub-THz) frequencies requires measurement-based comparisons spanning a wide spectral range within a unified framework. In this paper, we derive single- and multi-frequency omnidirectional PL models for UMi environments based on extensive NYU WIRELESS measurements at 6.75, 16.95, 28, 73, and 142 GHz in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Single-frequency close-in (CI) and floatingintercept (FI) models for each band show that CI yields more stable and physically interpretable parameters, with shadow fading standard deviations within 0.98 dB of FI across all five frequencies. We then extend the multi-frequency PL analysis to the 7–24 GHz, 0.5–100 GHz, and 0.5–150 GHz frequency ranges using CI, close-in with frequency weighting (CIF), and alpha-beta-gamma (ABG) models. Across progressively wider frequency spans, CI and CIF maintain stable distance exponents that remain closely aligned with the 3rd Generation Partnership Project (3GPP) UMi interpretations, whereas ABG offers only very modest reductions in fitting error at the cost of much greater parameter sensitivity. These results support physically anchored CI/CIF formulations with a close-in free space path loss anchor point at 1 m in order to extend 3GPP-oriented UMi PL models over the entire 0.5 to 150 GHz frequency range. 

ABSTRACT Molecular transport through polymer networks, including hydrogels and biological matrices, underpins many applications ranging from water filtration and gas separation to drug delivery and cell culture. Conventional strategies for regulating transport in polymer networks primarily focus on tuning molecular diffusion through network mesh size and polymer chemistry, whereas convection is often considered negligible because nanoscale‐mesh networks typically exhibit low fluid permeability. Although hydraulic pressure is a well‐established driving force for convection in porous media, extending pressure‐driven convection to non‐porous polymer networks has remained fundamentally challenging because they can undergo substantial deformation or fracture under pressure gradients. Here, we demonstrate that hydraulic pressure applied across mechanically tough and grid‐supported hydrogels enables robust and tunable solute transport while maintaining structural integrity. The characteristic transport time can be experimentally modulated by up to 65‐fold, consistent with a coupled diffusion–convection model. Beyond tuning transport kinetics, applied pressure enhances size‐ and charge‐dependent transport selectivity by up to 5.4‐fold compared to pressure‐free conditions. As a proof of concept, we demonstrate pressure‐programmed antimicrobial delivery that dynamically controls doxorubicin transport while blocking bacterial penetration. These findings identify pressure‐regulated convection as an underexplored mechanism for controlling transport in polymer networks. 

Sub-terahertz (sub-THz) multi-user multiple-inpu multiple-output (MU-MIMO) systems unlock immense bandwidth for 6G wireless communications. However, practical deployment of wireless systems in sub-THz bands faces critical challenges such as increased atmospheric absorption, reduced channel coherence time due to increased Doppler spread at higher carrier frequencies, and hardware bottlenecks as low-loss sub-THz phase shifters are difficult to realize. To overcome the hardware and channel estimation challenges of sub-THz systems, this paper proposes a hybrid beamforming (BF) framework that integrates reconfigurable liquid crystal (LC) antennas with a liquid neural network (LNN) for ransmitter. Specifically, we employ an LC antenna as the analog BF stage of a hybrid BF architecture, exploiting its voltage-driven permittivity tunability to achieve high gain beam steering without the need for lossy phase shifters. For digital BF, we utilize an ordinary differential equations defined LNN to learn temporal channel dynamics, and use a manifold optimization technique to compress the search space. We validated the proposed method on simulated site-specific 108 GHz ray-tracing channels in an urban scenario using NYURay, a ray-tracing simulator validated against 142 GHz propagation measurements [1], [2]. The 108 GHz carrier frequency matches the operating band of the LC antenna hardware. The proposed method achieves an 88.6% spectral efficiency (SE) gain and higher robustness to imperfect channel estimation compared to the learning-aided gradient descent and gated recurrent unit machine learning baselines [3], and 1.9 times higher SE than the 3GPP TR 38.901 standard antenna model [4], highlighting the potential of LC-based hardware for sub-THz communications. 

Sub-terahertz (sub-THz) multi-user multiple-input multiple-output (MU-MIMO) systems unlock immense bandwidth for 6G wireless communications. However, practical deployment of wireless systems in sub-THz bands faces critical challenges such as increased atmospheric absorption, reduced channel coherence time due to increased Doppler spread at higher carrier frequencies, and hardware bottlenecks as low-loss sub-THz phase shifters are difficult to realize. To overcome the hardware and channel estimation challenges of sub-THz systems, this paper proposes a hybrid beamforming (BF) framework that integrates reconfigurable liquid crystal (LC) antennas with a liquid neural network (LNN) for transmitter. Specifically, we employ an LC antenna as the analog BF stage of a hybrid BF architecture, exploiting its voltage-driven permittivity tunability to achieve highgain beam steering without the need for lossy phase shifters. For digital BF, we utilize an ordinary differential equationsdefined LNN to learn temporal channel dynamics, and use a manifold optimization technique to compress the search space. We validated the proposed method on simulated site-specific 108 GHz ray-tracing channels in an urban scenario using NYURay, a ray-tracing simulator validated against 142 GHz propagation measurements [1], [2]. The 108 GHz carrier frequency matches the operating band of the LC antenna hardware. The proposed method achieves an 88.6% spectral efficiency (SE) gain and higher robustness to imperfect channel estimation compared to the learning-aided gradient descent and gated recurrent unit machine learning baselines [3], and 1.9 times higher SE than the 3GPP TR 38.901 standard antenna model [4], highlighting the potential of LC-based hardware for sub-THz communications. 

Jailbreak attacks serve as essential red-teaming tools, proactively assessing whether LLMs can behave responsibly and safely in adversarial environments. Despite diverse strategies (eg, cipher, low-resource language, persuasions, and so on) that have been proposed and shown success, these strategies are still manually designed, limiting their scope and effectiveness as a red-teaming tool. In this paper, we propose AutoDAN-Turbo, a black-box jailbreak method that can automatically discover as many jailbreak strategies as possible from scratch, without any human intervention or predefined scopes (eg, specified candidate strategies), and use them for red-teaming. As a result, AutoDAN-Turbo can significantly outperform baseline methods, achieving a 74.3% higher average attack success rate on public benchmarks. Notably, AutoDAN-Turbo achieves an 88.5 attack success rate on GPT-4-1106-turbo. In addition, AutoDAN-Turbo is a unified framework that can incorporate existing human-designed jailbreak strategies in a plug-and-play manner. By integrating human-designed strategies, AutoDAN-Turbo can even achieve a higher attack success rate of 93.4 on GPT-4-1106-turbo.