To successfully implement the Sustainable Development Goals (SDGs), it is necessary to understand the process by which the achievement of one goal has a spillover effect in a development system. While existing research studies synergies and trade-offs among the SDGs, most empirical approaches operate at the goal level, treat interactions as undirected, or prioritise indicators without accounting for structural redundancy. In this paper, we propose a direction-sensitive and indicator-level network approach to detect high-impact and diversified entry points for policy intervention. By using statistically significant lagged correlations, we build a directed weighted network of SDG indicators and assign them into groups based on the balance of their positive and negative spillovers. Systemic effects are measured by weighted out-degree Opsahl centrality, and flow-based clustering is used to detect frequent paths of high positive spillovers. Applying the framework to the Indian context, it is found that the interlinkages in the SDGs are highly asymmetric and structured in specific structural subsystems. Although synergies slightly outweigh the total, trade-offs are still embedded in the sectors. Notably, the most influential indicators are focused on a single pathway of propagation, suggesting that influence-based prioritisation itself could result in redundant system-wide impacts. A cluster-based prioritisation approach leads to a more diversified set of interventions, triggering multiple structurally independent channels of beneficial spillovers. The proposed framework combines directionality, trade-off embedding, and structural propagation analysis in a single framework, providing a scalable solution for country-level SDG prioritisation under resource constraints.

Quantum circuit equivalence checking asks whether two circuits implement the same unitary. It guarantees compiler correctness and safe optimization, yet most existing approaches scale exponentially with the number of qubits or the circuit depth, or are restricted to specific circuit structures. In this work, we present an equivalence-checking method for circuits formed by arbitrary single-qubit layers interleaved with Clifford layers. This pattern is common in variational quantum algorithms and Hamiltonian simulation via Trotter decomposition. It can also represent any unitary with sufficient depth. We prove the existence of an efficient classical algorithm that determines whether a pair of circuits with shared single-qubit layers are equivalent for every possible choice of the shared single-qubit unitaries. The same algorithm can also certify their non-equivalence for fixed assignments of single-qubit unitaries. Our framework supports the validation of emerging quantum compilers and facilitate the discovery of novel circuit optimization passes.

Semantic communication is emerging as a key paradigm for 6G networks, where the goal is not to perfectly reconstruct bits but to preserve the meaning that matters for a given task. This shift can improve bandwidth efficiency, robustness, and application-level performance. However, most existing studies focus solely on encoder-decoder design and ignore network-wide decision-making. As data traverses multiple hops, semantic relevance may decrease, routing may overlook meaningful information, and semantic distortion can increase under dynamic network conditions. To address these challenges, this paper proposes a management-oriented semantic communication framework built upon Knowledge-Defined Networking (KDN). The framework comprises three core modules: a semantic-reasoning module that computes relevance scores by mapping semantic embeddings onto a knowledge graph that encodes task concepts and contextual relationships; a semantic-aware routing mechanism that forwards data along paths that preserve meaning; and a semantic-distortion controller that adaptively adjusts encoding and routing to preserve semantic fidelity. Our ns-3 results show clear benefits: semantic delivery success improves by 12%, semantic distortion decreases by 22%, re-routing events drop by 44%, and throughput efficiency rises by 14% compared to baseline methods (shortest-path, load-based, and distortion-only routing). These results indicate that meaning-aware and feedback-driven control is essential for reliable and scalable semantic communication in future 6G networks.

Despite time-reversal breaking in momentum space, several altermagnets remain electrically silent to the primary characterization tool anomalous Hall effect, due to crystalline symmetries, jeopardizing their experimental identification. Here, we show that time-reversal odd magnetoresistance exhibiting butterfly-like hysteresis with linear magnetic field dependence near the zero field provides a robust transport signature of altermagnetism even when the anomalous Hall effect vanishes. Using semiclassical theory and symmetry analysis, we demonstrate that this effect is generic across altermagnets and validate it through first-principles calculations in CrSb. Our results establish linear magnetoresistance as an alternative detection of the Berry curvature and Neel order in unconventional antiferromagnets.

Small scale clumps of ionized gas have been suggested by observations in interstellar medium and circumgalactic medium. The propagation of radio signals can be deflected by these plasma clumps, i.e. plasma lensing. One observable consequence is the magnification and demagnification of background sources. These effects distort the observed luminosity function and potentially introduce bias into population studies. In this work, we investigate these effects on fast radio bursts using Gaussian plasma clumps distributed across multiple lens planes within a small field of view. The central electron density for each clump is sampled from uniform, log-normal, and Gaussian distributions. Two analytical models are employed to mimic the intrinsic luminosity function. Our results show that plasma lensing can modify the observed luminosity functions. On one hand, our model shows that radio sources may be demagnified below the detection threshold, the strength varies between ~1-15% depending on the ionized gas model and the source redshift. On the other hand, magnification can produce anomalously bright sources at the high luminosity end. Both effects introduce potential biases in inferred source properties. The lensing strength correlates with the power spectrum of free electron density. However, scattering effect in the host galaxy or in the Milky Way can suppress the plasma lensing effects.

The relaxation of stochastic systems after sudden perturbations is constrained by speed limits and often reveals memory effects that hinder attempts to accelerate their dynamics. Here we demonstrate Kovacs-type nonmonotonic relaxation in the electro-orientation of non-spherical nanoparticles and show how this memory effect limits simple acceleration protocols. Experimentally, the orientational dynamics is monitored optically through field-induced birefringence, which is proportional to the nematic order parameter. When an AC electric field is first set to an extreme value until the birefringence reaches its target and is then switched to the target field (matched two-step protocol), the relaxation exhibits a characteristic Kovacs shoulder. We interpret this behavior within a theoretical framework based on the Smoluchowski equation for the orientational probability density. In the high-frequency AC regime, orientational relaxation is governed by induced dipoles, and the observed memory effect originates from polydispersity, which generates a spectrum of rotational diffusion coefficients and hence multiscale relaxation. Building on this insight, we design protocols that mitigate the detrimental effect of memory by sequentially suppressing the slowest active relaxation mode. Experiments on nanoparticle suspensions with different properties confirm these mechanisms, and we demonstrate substantial reductions in relaxation time compared with single quenches and matched two-step protocols with NaMt suspensions. More broadly, these results illustrate how memory effects emerge when many degrees of freedom are steered with a single control parameter and provide an experimentally accessible strategy for controlling multiscale stochastic dynamics.

We consider large time asymptotics for damped nonlinear Schrödinger equations. It is known that the nonlinear solution asymptotically behaves like a linear solution when time $t$ tends to infinity in the energy space. We prove that its convergence rate can be refined and the obtained rate is sharp if initial data belong to certain function spaces. This result partially solves open problems concerning the optimal decay rate of scattering.

Discrete-variable (DV) entanglement is crucial for numerous quantum applications, yet its deterministic generation in many bosonic systems remains experimentally challenging. In contrast, continuous-variable (CV) entanglement can be produced efficiently. We propose two optimal schemes for converting CV bipartite entanglement into DV entanglement using only local operations and classical communication. The first scheme extracts maximally entangled qubit pairs at the theoretically maximal rate, while the second probabilistically produces a maximally entangled qudit pair with the highest average entanglement. In both schemes, we quantify the optimal performance and identify the measurement operators required for implementation. Notably, using only a sequence of binary measurements, our approach can succeed in a finite number of measurement rounds on average, even though the CV resource is infinite-dimensional. Our schemes improve the feasibility of implementing DV-based quantum technologies on bosonic platforms.

Neural Structural Obfuscation (NSO) (USENIX Security'23) is a family of ``zero cost'' structure-editing transforms (\texttt{nso\_zero}, \texttt{nso\_clique}, \texttt{nso\_split}) that inject dummy neurons. By combining neuron permutation and parameter scaling, NSO makes a radical modification to the network structure and parameters while strictly preserving functional equivalence, thereby disrupting white-box watermark verification. This capability has been a fundamental challenge to the reliability of existing white-box watermarking schemes. We rethink NSO and, for the first time, fully recover from the damage it has caused. We redefine NSO as a graph-consistent threat model within a \textit{producer--consumer} paradigm. This formulation posits that any obfuscation of a producer node necessitates a compatible layout update in all downstream consumers to maintain structural integrity. Building on these consistency constraints on signal propagation, we present \textsc{Canon}, a recovery framework that probes the attacked model to identify redundancy/dummy channels and then \textit{globally} canonicalizes the network by rewriting \textit{all} downstream consumers by construction, synchronizing layouts across \texttt{fan-out}, \texttt{add}, and \texttt{cat}. Extensive experiments demonstrate that, even under strong composed and extended NSO attacks, \textsc{Canon} achieves \textbf{100\%} recovery success, restoring watermark verifiability while preserving task utility. Our code is available at https://anonymous.4open.science/r/anti-NSO-9874.

We investigate the thermodynamics and thermodynamic geometry of several Anti--de Sitter black hole solutions arising from nonlinear electromagnetic theories, namely the ModMax, nonlinear electrodynamics (NED), and Euler--Heisenberg AdS black holes, together with their holographically dual conformal field theory (CFT) descriptions. The analysis is carried out within three entropy frameworks: the standard Bekenstein--Hawking entropy and the generalized Rényi and Kaniadakis entropies. For each system, we analyze the phase structure through the behavior of temperature, specific heat, and the scalar curvature obtained from geometrothermodynamics (GTD). We find that thermodynamic critical points correspond to extrema in the temperature--entropy relation and coincide with divergences of the specific heat. These locations are reproduced by singularities in the Legendre--invariant GTD curvature, demonstrating a consistent geometric interpretation of the phase transitions. A comparison between the bulk black hole systems and their dual CFT counterparts shows that the number and structure of critical points are preserved under the holographic correspondence. Our results further reveal that the Euler--Heisenberg AdS black hole exhibits a more intricate phase structure compared with the ModMax and NED cases, while the Kaniadakis entropy consistently generates an additional critical point across all systems considered. These findings highlight the combined influence of nonlinear electromagnetic dynamics and generalized entropy formalisms on the critical behavior of AdS black holes and their dual CFTs.

Many-body localization (MBL) provides a mechanism by which interacting quantum systems evade thermalization, leading to persistent memory of initial conditions and slow entanglement growth. Probing these dynamical signatures in large systems and at long evolution times remains challenging for both classical simulations and current quantum devices. Here we experimentally investigate the ergodic-MBL crossover in quasiperiodic Floquet Ising systems using up to 144 qubits on an IBM Quantum processor. By implementing deep Floquet circuits reaching up to 5000 cycles, we access long-time many-body dynamics beyond the regime explored in previous quantum computing experiments. Measurements of autocorrelation functions reveal a smooth crossover from rapid thermalization at weak quasiperiodic potential strength to persistent correlations in the strong-disorder regime. Notably, in addition to the one-dimensional system, we also observe clear signatures consistent with localization behavior in the two-dimensional system. Furthermore, the quantum Fisher information exhibits logarithmic growth over thousands of Floquet cycles, providing evidence for slow entanglement spreading characteristic of the MBL regime. These results demonstrate that programmable quantum processors can serve as experimental platforms for probing nonergodic quantum many-body dynamics and exploring localization phenomena in regimes beyond the reach of classical simulations.

The integrability or non-integrability of a spacetime usually refers to whether the motion of massive or massless particles in the spacetime is integrable or not. The standard black hole spacetimes such as the Schwarzschild and Kerr metrics are always integrable for both timelike and null geodesics. They belong to a first type of spacetime. However, the Melvin type spacetimes as a second type of spacetime are non-integrable, regardless of whether they are for massive or massless particle motion. In this paper, we discover the possibility of a third type of spacetime with non-integrable dynamics of timelike geodesics and integrable dynamics of null geodesics. In fact, conformal transformations may transform type one solutions into type three. This is due to the conformal factors preventing the separation of variables from the Hamilton-Jacobi equation and leading to the absence of a fourth constant of motion for the massive particle dynamics. Nevertheless, the massless particle motion still remains integrable in these metrics for any conformal factors because the conformal factors have no effect on the null geodesics whatsoever. The conformal Kerr metric is an example of the third type of spacetime. In addition to the conformal transformation method, other paths may yield the third type of spacetime. The Kerr-Bertotti-Robinson black hole metric and the accelerating Schwarzschild spacetime are two examples of non-conformal solutions that are also of type three.

This study presents a new procedure for necessary tests of multivariate normality based on the uniform distribution on the Stiefel manifold. We demonstrate that the test statistic, which is formed by the product of the scaled residual matrix and the symmetric square root of a Wishart matrix, is exactly distributed as a matrix-variate normal distribution under the null hypothesis. Monte Carlo simulations are conducted to assess the Type I error rate and power in non-asymptotic settings.

This paper presents a general neural network framework for solving quantum few-body systems, extending prior methods to handle diverse particle masses, interaction types, and system configurations. Our architecture, which combines an adaptive step size with the Metropolis-Adjusted Langevin Algorithm for Monte Carlo sampling, accurately approximates the ground-state wave functions of systems featuring harmonic confinement, Gaussian two-body interactions, and including three-body forces. In ten-particle systems, it achieves lower relative energy errors (with respect to the reference values) than previous machine-learning methods. Leveraging GPU-accelerated computation, the method scales favorably with system size while maintaining robust convergence, reduced hyperparameter sensitivity, and stable training. Beyond accurate energy estimation, the model captures spatial distributions and correlation structures, offering physical insights about inter-particle structure. By unifying applicability across identical and nonidentical particles, the proposed approach establishes a versatile computational tool for exploring complex few-body quantum systems, with significant implications for advancing computational models in few-body quantum systems.

In this brief review, the historical aspects of the generalization of the Grad--Shafranov equation to the case of anisotropic plasma are discussed.

The doubly charmed tetraquark $T_{cc}$ has been reported by the LHCb experiment in 2022, and a lot of theoretical studies has been conducted. The small binding energy measured from $D^{\ast + }D^0$ threshold indicates that $T_{cc}$ is a $DD^\ast$ molecule. On the other hand, the superflavor symmetry, which relates heavy antiquarks to heavy diquarks, provides a useful framework for predicting the existence of partner exotic hadrons associated with $T_{cc}$. By replacing $\bar{D}^{(*)}$ with $Ξ_{cc}^{(*)}$ within this symmetry, $\bar{D}^{(*)} Ξ_{cc}^{(*)}$ and $Ξ_{cc}^{(*)}Ξ_{cc}^{(*)}$ are expected to form partner structures of $T_{cc}$. In this paper, we investigate bound and resonant states of $\bar{D}^{(*)} Ξ_{cc}^{(*)}$ and $Ξ_{cc}^{(*)}Ξ_{cc}^{(*)}$ based on the one boson exchange potential, where $π$, $ρ$, $ω$ and $σ$ are considered as bosons. The cutoff parameter and the coupling constants for $\bar{D}^{(*)} Ξ_{cc}^{(*)}$ and $Ξ_{cc}^{(*)}Ξ_{cc}^{(*)}$ are taken to be the same as those for $T_{cc}$ due to superflavor symmetry. We also discuss the $σ$ coupling constant, which is uncertain, dependence of these mass spectra. A lot of bound and resonant states with some quantum numbers are obtained for each $σ$ coupling constant, but these mass spectra depend on the $σ$ coupling constant significantly.

Mission-critical healthcare applications including real-time intensive care monitoring, ambulance-to-hospital orchestration, and distributed medical imaging inference require workflow-level, time-bounded coordination across heterogeneous devices, edge servers, and network control entities. While current 3GPP and O-RAN standards excel at per-device control and quality-of-service enforcement, they do not natively expose abstractions for workflow-level coordination under strict clinical timing constraints, leaving this capability to fragile, application-specific overlays. This article outlines the Collective Adaptive Intelligence Plane (CAIP) as a standards-aligned coordination framework that addresses this abstraction gap without introducing new protocol layers. CAIP is realized through minimal, backward-compatible coordination profiles anchored to existing RRC, QoS/SDAP, and O-RAN E2 interfaces, enabling workflow-scoped coordination context binding, deadline-aware coordination pacing, semantic flow association, and privacy-preserving data locality across distributed clinical entities. We analyze the structural limitations of existing standards, present a concrete interface mapping to 3GPP and O-RAN mechanisms, illustrate deployment through a representative ICU coordination scenario, and outline a phased standardization roadmap from proof-of-concept xApp deployment to AI-native 6G specification evolution. The proposed framework is incrementally deployable on current 5G Advanced infrastructure and provides a principled migration path toward workflow-level coordination abstraction as a first-class capability in future 6G healthcare networks.

The cessation of star formation in galaxies, known as 'quenching', is a complex, multi-scale process which has been theorized to be linked to galaxy mergers. In this paper, we investigate the potential role of mergers in quenching galaxies in the IllustrisTNG cosmological hydrodynamical simulation. We track the evolution of over 11,000 central galaxies in the simulation with stellar mass $M_\star \ge 10^9 M_\odot$ at $z = 0$ throughout the entirety of cosmic history. We compare their star formation and merger histories to test whether mergers are necessary or sufficient for inducing quenching in the simulation. Only a very small fraction of mergers (about 3 per cent of major mergers and about 12 per cent of all mergers) lead to quenching within 1 Gyr, indicating that mergers are not sufficient by themselves to cause quenching. Furthermore, the vast majority of quenching events are not preceded by a merger within 1 Gyr. Once random coincidences are accounted for and a stellar mass-matched control sample is applied, no merger excess is observed. Hence, mergers are clearly not necessary for quenching to occur in the simulation. Finally, we perform a series of random forest classification and regression analyses to assess the integrated role of mergers in galaxy quenching and supermassive black hole growth in IllustrisTNG. We determine that secular processes dominate the growth of supermassive black holes and the quenching of central galaxies in this simulation, in stark contrast to prior theoretical expectations from idealized hydrodynamical simulations.

We study the optimal upper $X_U$ and lower $X_L$ sequence spaces that can be assigned to each Banach lattice $X$. These spaces are symmetric, have the Fatou property and the unit vector basis has in these spaces very special properties. Determined by the order structure of $X$ the spaces $X_U$ and $X_L$ turn out to be very useful when studying Banach lattices. Among other results, in terms of these constructions, we identify Banach lattices that satisfy equal-norm upper and lower $p$-estimates, give a characterization of $L_p(μ)$-spaces, derive some properties of the tensor product operator in Lorentz and Orlicz spaces, identify Orlicz spaces in which the unit vector basis is upper (resp. lower) semi-homogeneous.

The rapid evolution of Large Language Models (LLMs) into autonomous, tool-calling agents has fundamentally altered the cybersecurity landscape. Frameworks like OpenClaw grant AI systems operating-system-level permissions and the autonomy to execute complex workflows. This level of access creates unprecedented security challenges. Consequently, traditional content-filtering defenses have become obsolete. This report presents a comprehensive security analysis of the OpenClaw ecosystem. We systematically investigate its current threat landscape, highlighting critical vulnerabilities such as prompt injection-driven Remote Code Execution (RCE), sequential tool attack chains, context amnesia, and supply chain contamination. To systematically contextualize these threats, we propose a novel tri-layered risk taxonomy for autonomous Agents, categorizing vulnerabilities across AI Cognitive, Software Execution, and Information System dimensions. To address these systemic architectural flaws, we introduce the Full-Lifecycle Agent Security Architecture (FASA). This theoretical defense blueprint advocates for zero-trust agentic execution, dynamic intent verification, and cross-layer reasoning-action correlation. Building on this framework, we present Project ClawGuard, our ongoing engineering initiative. This project aims to implement the FASA paradigm and transition autonomous agents from high-risk experimental utilities into trustworthy systems. Our code and dataset are available at https://github.com/NY1024/ClawGuard.

A promising pathway towards scalable quantum photonic processors involves the simultaneous integration of deterministic single-photon sources, low-loss photonic circuitry, and fast reconfigurability. Thin-film lithium tantalate on insulator (LTOI) offers an exceptional electro-optic response and low optical loss at 900 nm wavelength band, yet its lack of efficient quantum emitters has hindered progress toward fully integrated quantum technologies. Here, we demonstrate heterogeneous integration of indium arsenide quantum dots (QDs) with low-loss reconfigurable LTOI waveguides using micro-transfer printing. By directly butt-coupling tapered gallium arsenide waveguides with inversely tapered LTOI waveguides, we achieve robust and alignment-tolerant inter-waveguide coupling. The hybrid chip operates at cryogenic temperatures, enabling deterministic routing of successively emitted single photons from the QDs with a halfwave voltage-length product, confirming the cryogenic stability of LTOI's electro-optic coefficient. These results establish the first demonstration of high-speed on-chip routing of single photons with hybrid QD-LTOI circuits, providing a scalable pathway toward integrated quantum photonic processors.

Integrated sensing and communication (ISAC) is widely regarded as a key enabling technology for 6G wireless networks. While extensive research has explored the coexistence of sensing and communication functionalities, the use of orthogonal frequency-division multiplexing (OFDM) waveforms for monostatic ISAC remains underexplored. In this article, we present practical approaches for enabling monostatic sensing on wireless communication devices and illustrate how OFDM signals can provide radar-like sensing capabilities such as ranging, Doppler estimation, and environmental perception. We hope this article will stimulate further research on OFDM-based monostatic ISAC and accelerate its adoption in 6G networks.

Lightweight block cipher design has largely focused on incremental optimization of established paradigms such as substitution--permutation networks, Feistel structures, and ARX constructions, where security derives from the algebraic complexity of individual components. We propose a different approach based on \emph{expander-graph interaction networks}, where diffusion and security arise from sparse structural connectivity rather than component sophistication. We present \textbf{ExpanderGraph-128 (EGC128)}, a 128-bit block cipher constructed as a 20-round balanced Feistel network. Each round applies a 64-bit nonlinear transformation governed by a 3-regular expander graph whose vertices execute identical 4-input Boolean functions on local neighborhoods. Security analysis combines MILP-based differential bounds, proven optimal through 10 rounds via SCIP, establishing 147.3-bit differential security and conservatively extrapolating to 413 bits for the full cipher. Linear analysis provides MILP bounds of $\geq 2^{145}$, while related-key evaluation shows no free rounds for any nonzero key difference. Additional tests confirm rapid algebraic degree growth and the absence of invariant affine subspaces. Implementation results demonstrate practical efficiency. FPGA synthesis on Xilinx Artix-7 achieves 261~Mbps at 100~MHz using only 380 LUTs, while ARM Cortex-M4F software requires 25.8~KB Flash and 1.66~ms per encryption. These results show that expander-graph-driven diffusion provides a promising design methodology for lightweight cryptography.

While it is known that North Korean defectors (NKDs) struggle with South Korea's healthcare system, the specific challenges of their patient journey remain underexplored. To investigate this, we conducted interviews with 10 NKDs about an 8-step patient journey and identified the clinical consultation step as a critical barrier for all participants, marked by three key challenges: expressing symptoms, managing social and cultural concerns, and overcoming language differences. In response, we developed Medibridge, a mobile prototype that allows users to rehearse with an AI doctor before a real hospital visit to generate a tangible ``Helper Note'' for their actual consultation. Our evaluation with 15 NKDs showed improvements in perceived communication capability, including greater expression clarity, reduced social and cultural concerns, and enhanced linguistic confidence. Our contributions include an empirical understanding of NKDs' healthcare challenges, a novel AI-powered rehearsal system that prepares users for real-world clinical communication, and design implications for inclusive technologies for displaced populations.

Quantum many-body systems coupled to out-of-equilibrium reservoirs can behave as active matter and exhibit signs of flocking. However, the resulting steady states are highly mixed and carry only weak quantum signatures. We show that signatures of active matter also arise in ensembles of pure states undergoing monitored quantum dynamics. We consider a spinful Luttinger liquid subject to measurement processes that shuffle spin-up particles to the left and spin-down particles to the right. For weak monitoring strengths and ferromagnetic spin interactions, we find power-law quantum correlations between spin current and charge density, which we identify as a hallmark of active quantum matter. The monitoring plays a dual role, generating the quantum active correlations for weak strengths while driving a Berezinskii-Kosterlitz-Thouless (BKT) phase transition to a shortrange correlated state at larger strengths.

We study the participation and stabilizer entropy of non-unitary quantum circuit dynamics, focusing on the critical line that separates the low-entanglement spin-glass phase and the paramagnetic phase. Along this critical line, the entanglement has a logarithmic scaling, which enables us to access the critical regime using large-scale matrix product state simulations with modest bond dimension. We find that both the participation entropy and stabilizer entropy exhibit critical slowing down: their saturation time scales linearly with the system size, in stark contrast to purely unitary dynamics, where saturation occurs on logarithmic time scales. In addition, we study bipartite participation and stabilizer mutual information, and find that it shows similar scaling behavior to the entanglement entropy. Finally, by analyzing the participation entropy of several paradigmatic Clifford circuits, we identify similar slow dynamical behavior near their respective critical points.

Let $k$ be an algebraically closed field and $G$ a connected reductive group over $k((t))$ satisfying some conditions. We define a stratification by conjugacy classes of twisted Levi subgroups of $G$ on each Moy-Prasad quotient $\mathfrak{k}_{x,r}/\mathfrak{k}_{x,r+}$ of $G$. We then calculate the strata in terms of the associated twisted Levi subgroups. This calculation is necessary for several followup papers on the local geometric Langlands program.

We develop a practical framework for semi-device-independent (SDI) certification under operational deviations from the ideal protocol model. Apparent violations of classical benchmarks need not signal genuinely non-classical behaviour; they can arise from misalignment between (i) the scoring rule, (ii) the finite-sample statistical bound applied to that score, and (iii) the operational model realised in the experiment, including bias, memory, drift, and selection effects. We formalise a protocol-agnostic alignment principle based on a martingale-safe lower confidence bound and an operationally consistent effective classical ceiling. This yields a quantitative diagnostic, the \emph{robustness gap} $Δ_{\mathrm{rob}} = S_{\mathrm{low}} - S_{C,\mathrm{eff}}$, which separates statistical fluctuations from structural modelling errors. Statistical deviations vanish asymptotically, whereas model misalignment can produce persistent false certification unless the benchmark is corrected. Using the $2\!\to\!1$ random access code as a minimal SDI testbed, we show that postselection can inflate conditional scores, whereas unconditional scoring restores the correct operational meaning of the witness. We further show that adaptive learning-based classical agents do not enlarge the admissible classical set; rather, they recover the effective classical ceiling implied by the operational model. The resulting framework provides a systematic diagnostic for certification in realistic quantum communication and measurement settings with embedded classical control, adaptive processing, and nonideal data acquisition.

AI agents increasingly act through external tools: they query databases, execute shell commands, read and write files, and send network requests. Yet in most current agent stacks, model-generated tool calls are handed to the execution layer with no framework-agnostic control point in between. Post-execution observability can record these actions, but it cannot stop them before side effects occur. We present AEGIS, a pre-execution firewall and audit layer for AI agents. AEGIS interposes on the tool-execution path and applies a three-stage pipeline: (i) deep string extraction from tool arguments, (ii) content-first risk scanning, and (iii) composable policy validation. High-risk calls can be held for human approval, and all decisions are recorded in a tamper-evident audit trail based on Ed25519 signatures and SHA-256 hash chaining. In the current implementation, AEGIS supports 14 agent frameworks across Python, JavaScript, and Go with lightweight integration. On a curated suite of 48 attackinstances, AEGIS blocks all attacks in the suite before execution; on 500 benign tool calls, it yields a 1.2% false positive rate; and across 1,000 consecutive interceptions, it adds 8.3 ms median latency. The live demo will show end-to-end interception of benign, malicious, and human-escalated tool calls, allowing attendees to observe real-time blocking, approval workflows, and audit-trail generation. These results suggest that pre-execution mediation for AI agents can be practical, low-overhead, and directly deployable.

Large curved displays are ideal for viewing 360 degree content, such as 3D maps, but typically restrict users to a 180 degree viewport, leaving information off-screen. Since users naturally direct their heads toward regions on-screen before interacting, head movements offer a promising alternative for workspace manipulation to bring off-screen content into view. We explore rate control functions (linear, sigmoid, polynomial) and zone control functions (continuous, friction, interrupted, additive) to translate head rotations into workspace control, enabling users to access off-screen content. Polynomial rate control emerges as the best choice, achieving the fastest trial times and highest subjective ratings. Using a map navigation task, our second study demonstrates that users perform better with the polynomial head-based technique than with the industry-standard controller-based methods, click-and-drag and joystick-push, for 360\degree workspace navigation. Based on these findings, we provide guidelines to inform the design of future 360\degree workspace navigation techniques for large curved displays.