We consider a sparse i.i.d.\ non-Hermitian random matrix model $X_n$ (with sparsity parameter $K_n$) and a deterministic finite-rank perturbation $E_n$. Assuming biorthogonality for $E_n$ and a growth condition on $K_n$, we outline a finite-rank resolvent reduction leading to asymptotics for the overlap between an outlier eigenvector of $Y_n:=X_n+E_n$ and the corresponding spike eigenspace. In particular, for an outlier spike $μ$ with $|μ|>1$, the squared projection of the associated (right) eigenvector onto the spike eigenspace converges in probability to $1-|μ|^{-2}$. Our result generalizes Theorem 1.6 of [HLN26] to general finite rank case solving Open Problem 5.

Nominal techniques provide a mathematically principled approach to dealing with names and variable binding in programming languages. This paper explores an attempt to make nominal techniques accessible as an Agda library. We aim for a technical victory of implementing nominal ideas; we further require a moral victory that the overhead be acceptable for practical systems. The results of this paper have been mechanised and are publicly accessible at https://omelkonian.github.io/nominal-agda/.

Let $\mathcal{G}=\{G_1, G_2, \ldots , G_k\}$ be a family of bipartite graphs on the same vertex set. A rainbow Hamilton path (cycle) in $\mathcal{G}$ is a path (cycle) that visits each vertex precisely once such that any two edges belong to different graphs of $\mathcal{G}.$ In this paper, by adopting the technique of bi-shifting, we present tight sufficient conditions in terms of the spectral radius for a family $\mathcal{G}$ to admit a rainbow Hamilton path and cycle, respectively. Meanwhile, we completely characterize the corresponding spectral extremal graphs.

We obtain new lower and upper bounds for the numerical radius of a bounded linear operator $A$ on a complex Hilbert space, which refine the existing ones. In particular, if $w(A)$ and $\|A\|$ denote the numerical radius and operator norm of $A$, respectively, then we show that \begin{eqnarray*} ν(A) + \frac{1}{4} \left\||A|^2+|A^*|^2\right\| \leq w^2(A) \leq \frac12 w\left(\frac{|A|+|A^*|}{2}A \right)+ \frac14 \left\| |A|^2+ \left( \frac{|A|+|A^*|}{2}\right)^2 \right\|, \end{eqnarray*} where $ν(A)\geq 0$ is a real number involving the operator norm of the Cartesian decomposition of $A$. We also develop several new numerical radius inequalities for the products and sums of operators via Euclidean operator radius of $2$-tuples of operators. In addition, we deduce equality characterizations for the inequalities. As an application, we obtain numerical radius inequalities for the commutators of operators, which improves the Fong and Holbrook's inequality $w(AB\pm BA) \leq 2\sqrt{2} w(A) \|B\|$ [Canadian J. Math. 1983].

Motivated by ill-posed PDEs such as $\mathrm{div} (v) = F$ we study locally convex topologies $\mathcal{T}_{\mathcal{C}}$ on real vector spaces $X$ that are a ``localized'' version of a locally convex topology $\mathcal{T}$ to members of a family $\mathcal{C}$ of convex subsets of $X$. The distributions $F$ arising as $\mathrm{div} (v)$ are expected to be the members of the dual of well-chosen $X$ with respect to an appropriate localized topology $\mathcal{T}_{\mathcal{C}}$. In this work, the emphasis is on studying the functional analytic properties of $\mathcal{T}_{\mathcal{C}}$, according to those of $\mathcal{T}$ and $\mathcal{C}$. For instance, we show that in all foreseen applications, $\mathcal{T}_{\mathcal{C}}$ is sequential but none of Fréchet-Urysohn, barrelled, and bornological. These awkward phenomena are illustrated explicitly on a specific example corresponding to the distributional divergence of continuous vector fields in $\mathbb{R}^m$. We also show that, essentially, $\mathcal{T}_{\mathcal{C}}$ is semireflexive if and only if members of $\mathcal{C}$ are $\mathcal{T}$-compact. This leads to an abstract existence theorem, thereby establishing a general scheme for characterizing those $F$ such that $\mathrm{div} (v) = F$ for various classes of regularity of $v$, various classes of domains, and various boundary conditions.

Multiple seasonalities have been widely studied in continuous time series using models such as TBATS, for instance in electricity demand forecasting. However, their treatment in categorical time series, such as air quality index (AQI) data, remains limited. Categorical AQI often exhibits distinct seasonal patterns at multiple frequencies, which are not captured by standard models. In this paper, we propose a framework that models multiple seasonalities using Fourier series and indicator functions, inspired by the TBATS methodology. The approach accommodates the ordinal nature of AQI categories while explicitly capturing daily, weekly and yearly seasonal cycles. Simulation studies demonstrate the empirical consistency of parameter estimates under the proposed model. We further illustrate its applicability using real categorical AQI data from Kolkata and compare forecasting performance with Markov models and machine learning methods. Results indicate that our approach effectively captures complex seasonal dynamics and provides improved predictive accuracy. The proposed methodology offers a flexible and interpretable framework for analyzing categorical time series exhibiting multiple seasonal patterns, with potential applications in air quality monitoring, energy consumption and other environmental domains.

Effects of neoclassical toroidal viscosity (NTV) on plasma flow evolution in the presence of resonant magnetic perturbation (RMP) in a tokamak have been evaluated using a cylindrical theory model. Calculations show that the introduction of NTV has almost no effect on the flow on the resonant surface, so the locked or unlocked state on the resonant surface remains unchanged, but it impacts the rotation profile in the core region. The toroidal, poloidal, and parallel flows in the core region are slightly reduced with uniform pressure. For non-uniform pressure profiles, elevated $β$ enhances the global amplitude of NTV torque but suppresses that of electromagnetic (EM) torque. These two driving terms collectively maintain the locked mode state.

We study the impact of supermassive black hole (SMBH) growth, $\langle \dot{M}_\mathrm{SMBH}\rangle$, major and minor galaxy mergers, and gas processes, on the average gas metallicity of galaxies, with the aim to uncover which of these processes drive the scatter in the gas metallicity-stellar mass relation (MZR) at different redshifts in nodes, filaments and voids. At $z=5$, minor mergers produce the largest differential in $\log[Z_g/Z_\odot]$ for all environments, where the node population displays a maximum $0.38$ dex increase in the average $\log[Z_g/Z_\odot]$ compared to non-merging galaxies. The node population also displays a consistent $0.1$ dex reduction in $δ\log[Z_g/Z_{\odot}]$ across all redshifts, whilst filament and void galaxies show a lower magnitude of reduction. Major mergers show little influence on these same properties. This suggests minor mergers regulate metallicity and contribute to over galaxy mass growth concurrently, accelerating chemical evolution post merger. Between $z=1-3$, a high $\langle \dot{M}_\mathrm{SMBH}\rangle$ leads to a reduction in $δ\log[Z_g/Z_{\odot}]$ for all environments. Here, node galaxies show the largest reduction of approximately $0.25$ dex, suggesting that metal-rich outflows strongly drive the MZR at intermediate times. Finally, galaxies with low $M_{gas}/{M_{tot}}$ show increased $δ\log[Z_g/Z_{\odot}]$ across all redshifts and environments, again a $0.25$ dex maximum for node galaxies. These galaxies also spike in $δ\log[Z_g/Z_{\odot}]$ at late times, below $z=1$. At this time, galaxies in the nodes show negative $\langle \dot{M}_\mathrm{gas} \rangle$ whilst also showing the largest $δ\log[Z_g/Z_{\odot}]$ values we observe of $0.2$ dex, suggesting the importance of the balance between gas accretion and starvation in driving MZR scatter at low redshifts.

We present a semiclassical phase-space method to calculate thermal and ground states of large interacting spin systems. To this end, we extend the recently developed truncated Wigner approximation for spins (TWA) to the imaginary time, termed iTWA. The evolution of the canonical density matrix in imaginary time is mapped to a partial differential equation of its Wigner function. Truncation at the Fokker-Planck level leads to a set of stochastic differential equations, which can be efficiently simulated. We show that the iTWA can provide very good approximations to the ground state of a random and in general frustrated anti-ferromagnetic Ising Hamiltonian on a 3-regular graph, for which finding the exact ground state and approximations to it beyond a certain accuracy is NP hard. Furthermore in order to assess the ability of the method to properly account for leading-order quantum effects, we analyze the ground-state quantum phase transition of the nearest-neighbor, transverse-field Ising model in one and two spatial dimensions, finding very good agreement with the exact behaviour. The critical behavior obtained in iTWA follows the quantum-classical correspondence.

In cell-free massive MIMO, centralized precoding is {theoretically known} to {remarkably} outperform its distributed counterparts, albeit {with} high implementation complexity. However, this letter highlights a practical limitation {often overlooked:} {widely used closed-form} centralized {precoders} are typically derived under a sum-power constraint, which often demands unrealistic power allocation that exceeds hardware capabilities. {When two simple heuristics (global power scaling and local normalization) are applied to enforce the per-AP instantaneous power constraint}, the centralized performance superiority disappears, making distributed precoding {a robust option}.

Spintronic nano-neurons offer a promising route towards energy-efficient, high-performance hardware neural networks thanks to their inherent low-input nonlinear dynamics. However, training such networks remains a major bottleneck as it depends on oversimplified models of device behaviour and is highly sensitive to device variability. Here, we introduce a hardware architecture that overcomes these limitations by enabling on-device generation of gradients. First, we introduce theoretically and demonstrate experimentally that magnetic tunnel junctions can generate tunable and complex nonlinear responses. Building on this, we implement an analogue finite-difference approach to enable on-chip training in spintronic neural networks with one and two hidden layers. We experimentally implemented device in the loop backpropagation in a magnetic tunnel junction based neural network, achieving a classification accuracy of 93.3% despite pronounced device variability. During training, the gradients generated by the proposed analog neurons closely match the values derived numerically, without incurring computational overhead. Via physical simulations, we also demonstrate that this approach can be scaled up to support training in deep architectures. Our results pave the way for reliable, trainable and fully analogue spintronic neural networks, opening up new possibilities for next-generation, energy-efficient artificial intelligence hardware.

We propose a method to identify nonlinear acyclic networks in continuous time when the dynamics are located on the edges and all the nodes are excited. We show that it is necessary and sufficient to measure all the sinks to identify any tree in continuous time when the functions associated with the dynamics are analytic and satisfy $f(0)=0$, which is analogous to the discrete-time case. For general directed acyclic graphs (DAGs), we show that it is necessary and sufficient to measure all sinks, assuming that the dynamics are not linear (a condition that can be relaxed for trees). Then, based on the measurement of higher order derivatives and nonzero initial conditions, we introduce a method for the identification of trees, which allows us to recover the nonlinear functions located in the edges of the network under the assumption of dictionary functions. Finally, we propose a method to identify multiple parallel paths of the same length between two nodes, which allow us to identify any DAG when combined with the algorithm for the identification of trees. Several examples are added to illustrate the results.

Beamforming is a fundamental technology that not only enhances communication efficiency but also lays the foundation for massive multiple-input multiple-output~(MIMO) systems. However, its reliance on accurate channel state information (CSI) estimation introduces significant training overhead and feedback costs, especially in large-scale antenna systems. In this paper, we investigate positioning-assisted beamforming as a competitive alternative to the CSI-based methods, which circumvents the complicated CSI estimation. In particular, we analyze the outage probability of positioning-assisted systems with joint Gaussian beams and derive its closed-form expressions for both two-dimensional~(2D) and three-dimensional~(3D) scenarios. Based on these results, we also derive closed-form expressions for the optimal joint Gaussian beam pattern. The optimal solution is independent of the positioning error distribution in 2D scenarios but depends on it in 3D cases. Subsequently, the asymptotic performance of the approximation error is analyzed. Numerical results verify the derived outage probability expressions, and show the effectiveness of the beam pattern optimization.

The explosive growth of short video platforms has generated a massive surge in global traffic, imposing heavy financial burdens on content providers. While Peer-to-Peer Content Delivery Networks (PCDNs) offer a cost-effective alternative by leveraging resource-constrained edge nodes, the limited storage and concurrent service capacities of these peers struggle to absorb the intense temporal demand spikes characteristic of short video consumption. In this paper, we propose to minimize transmission costs by exploiting a novel degree of freedom, the inherent flexibility of server-driven playback sequences. We formulate the Optimal Video Ordering and Transmission Scheduling (OVOTS) problem as an Integer Linear Program to jointly optimize personalized video ordering and transmission scheduling. By strategically permuting playlists, our approach proactively smooths temporal traffic peaks, maximizing the offloading of requests to low-cost peer nodes. To solve the OVOTS problem, we provide a rigorous theoretical reduction of the OVOTS problem to an auxiliary Minimum Cost Maximum Flow (MCMF) formulation. Leveraging König's Edge Coloring Theorem, we prove the strict equivalence of these formulations and develop the Minimum-cost Maximum-flow with Edge Coloring (MMEC) algorithm, a globally optimal, polynomial-time solution. Extensive simulations demonstrate that MMEC significantly outperforms baseline strategies, achieving cost reductions of up to 67% compared to random scheduling and 36% compared to a simulated annealing approach. Our results establish playback sequence flexibility as a robust and highly effective paradigm for cost optimization in PCDN architectures.

Continuous efforts have been devoted to integrate millimeter wave (mmWave) and terahertz (THz) bands into future communication standards in order to overcome the bandwidth shortage problem and achieve high data rates, primarily through developing accompanying technologies that can overcome the severe propagation loss and blockage associated with increased carrier frequency. One of the most notable accompanying technologies is reconfigurable intelligent surface (RIS), which uses a large number of low-cost passive reflecting elements to reconfigure the propagation environments for improved communication performance and coverage. Despite its numerous benefits, RIS can make channel estimation more difficult due to its lack of radio frequency (RF) chains that can perform baseband signal processing. In addition, the cascaded channel structure of RIS-aided communication systems, which differs from that in conventional systems, brings about significant challenges in both channel estimation and beamforming. In this paper, we propose the joint channel estimation and beamforming optimization algorithm for RIS-aided multiple-input multipleoutput (MIMO) communication systems. By carefully exploiting the angular sparsity of mmWave/THz channels, our proposed algorithm successfully designs the RIS matrices that not only facilitate the channel estimation process but also achieve the passive beamforming gain through increased channel capacity. Simulation results demonstrate that our proposed algorithm provides the systems of interest with significant improvement in spectral efficiency.

Data approximation is essential in fields such as geometric design, numerical PDEs, and curve modeling. Moving Least Squares (MLS) is a widely used method for data fitting; however, its accuracy degrades in the presence of discontinuities, often resulting in spurious oscillations similar to those associated with the Gibbs phenomenon. This work extends the integration of MLS with the Weighted Essentially Non-Oscillatory (WENO) method and with an innovative partition of unity approach to higher dimensions. We propose a data-dependent operator using the novel Non-Linear Partition of Unity based on Moving Least Squares method in $\mathbb{R}^n$, which improves accuracy near discontinuities and maintains high-order accuracy in smooth regions. We demonstrate some theoretical properties of the method and perform numerical experiments to validate its effectiveness.

This study explores singularity-free solutions to the static, spherical symmetric Einstein equations with the standard Schwarzschild solution as a boundary condition. Imposing the absence of curvature singularities and requiring differentiability of the time component of the metric leads to a sign change across the horizon, violating the Principle of Equivalence locally. We find a solution within the event horizon with a simple ``cosmological constant'' stress-energy tensor. Considering the impact of sign change to a compact stellar remnant, modeled by an incompressible perfect fluid obeying the Tolman-Oppenheimer-Volkoff equation, we rediscover the same geometry, indicating both mathematical and physical feasibility of the model. We also find a new theoretical limit M/R=3/8, which is lower than the Buchdahl limit of M/R=4/9 for the density of a perfect fluid that will recede behind an event horizon. The equation of state is discussed, and we propose that the final state is described by a Higgs-like free scalar field.

Two-dimensional tin halide perovskites provide a highly tunable platform for exciton phonon coupling and local lattice distortions, enabled by their intrinsically soft lattice. We report a combined temperature and pressure dependent photoluminescence study of the layered perovskite (4FPEA)$_{2}$SnBr$_{4}$. At room temperature, its optical response is dominated by near band edge (NBE) excitons, which redshift linearly under hydrostatic pressure up to $\sim$3 GPa, indicating a rigid band edge behavior without phase transitions. Cooling reveals a broad, strongly Stokes shifted self-trapped exciton (STE) emission, evidencing a crossover from delocalized to self localized excitonic states. Strikingly, while NBE emission redshifts under pressure, STE emission exhibits an anomalous blueshift, reflecting pressure induced modification of the exciton phonon energy landscape. In contrast, the iodide analogue (4FPEA)$_{2}$SnI$_{4}$ shows no STE emission under identical conditions, highlighting the critical role of lattice rigidity and dielectric screening in stabilizing self-trapped excitons.

This paper develops a harmonic-domain framework for systems with variable fundamental frequency. A variable-frequency sliding Fourier decomposition is introduced in the phase domain, together with necessary and sufficient conditions for time- domain realizability. An exact harmonic-domain differential model is derived for general nonlinear systems under variable frequency, without assumptions on the frequency variation. An explicit parameter-varying approximation is then obtained, along with a tight error bound expressed in terms of local relative frequency variation, providing a non-conservative validity criterion and clarifying the limitations of classical heuristics. A main result shows that, for linear phase-periodic systems with affine frequency dependence, stability analysis and control synthesis can be carried out without approximation and without assumptions on the frequency variation, provided the frequency evolves within a prescribed interval. As a consequence, both problems reduce to harmonic Lyapunov inequalities evaluated at the two extreme frequency values, yielding a convex LMI characterization. The framework is illustrated on a variable-speed permanent magnet synchronous motor.

The excitation functions of proton induced reactions on natMo targets are measured using the activation technique followed by off-line γ-ray spectroscopy with improved precision. The experiment was performed at the BARC-TIFR Pelletron Linac Facility, Mumbai. Thin samples of natMo were irradiated with a proton beam of energies ranging from 13 to 22 MeV for measurements of various (p,x) cross sections, with an emphasis on the production of medical isotopes. The present data for 93mTc and 94gTc address and resolve the discrepancies in the existing data. The 93gTc radioisotope production is extracted with appropriate corrections for 93mTc contribution. While the measured cross sections for the production of 95gTc, 96g+mTc, 99mTc and 99Mo are consistent with the reported data, significant differences are observed for 89g+mZr. A comprehensive uncertainty analysis, including the correlation coefficients of the measured reaction cross sections is also presented.

Chiral edge states in Chern insulators are theoretically predicted to propagate unidirectionally along the sample boundary with inherent robustness against local perturbations, which manifests as the immunity to impurity-induced backscattering, a key factor for the development of robust, high-performance quantum devices. However, the direct experimental verification of the robustness of chiral edge states remains scarce. Here, we experimentally validate the robustness of the chiral edge states in MnBi2Te4 devices featuring engineered geometric defects introduced via atomic force microscope (AFM) nanomachining. Specifically, under a moderate perpendicular magnetic field, the MnBi2Te4 devices exhibit the Chern insulator state, characterized by a quantized Hall plateau and simultaneously vanishing longitudinal resistance. To verify the robustness of this topological state, we modify the device geometry by cutting a slit using AFM nanomachining that severs the original edge channel. Remarkably, the quantization behavior survives this drastic modification. The robust nature of the chiral edge transport is further confirmed by two-terminal, three-terminal and non-local measurements, fully demonstrating that the edge currents can bypass the artificial cut without dissipation. Our results unambiguously demonstrate the robustness of chiral edge states against geometric disruption and establish AFM nanomachining as a promising technique for topological quantum devices engineering.

State-of-the-art ensemble Kalman filtering (EnKF) algorithms require incorporating localization techniques to cope with the rank deficiency and the inherited spurious correlations in their error covariance matrices. Localization techniques are mostly ad-hoc, based on some distances between the state and observation variables, requiring demanding manual tuning. This work introduces a new ensemble filtering approach, which is inherently localized, avoiding the need for any auxiliary localization technique. Instead of explicitly applying localization on ensembles, the idea is to first localize the continuous analysis probability density function (pdf) before ensemble sampling. The localization of the analysis pdf is performed through an approximation by a product of independent marginal pdfs corresponding to small partitions of the state vector, using the variational Bayesian optimization. These marginals are then sampled following stochastic EnKF and deterministic ensemble transform Kalman filtering (ETKF) procedures, using ensembles larger than the partitions' size. The resulting filters involve the same forecast steps as their standard EnKF and ETKF counterparts but different analysis steps, iteratively adjusting the EnKF and ETKF updates of each partition based on the ensemble means of the other partitions. Numerical experiments are conducted with the Lorenz-96 model under different scenarios to demonstrate the potential of the proposed filters. The new filters' performances are comparable to those of the EnKF and ETKF with already tuned localization, both in terms of computational burden and estimation accuracy.

Quantum technologies rely on high-quality resource states, such as maximally entangled or private states, which are indispensable for quantum communication and cryptography. In practice, however, these states are inevitably degraded by noise. Distillation protocols aim to recover high-resource states from multiple imperfect copies, and while stabilizer-based methods have demonstrated high performance in entanglement purification, they have yet to be established for broader tasks such as secret-key distillation. This work introduces a unified framework for stabilizer-based resource distillation in systems of prime local dimension. By formulating stabilizer routines as quantum channels and deriving closed-form expressions for their output, we enable the application of stabilizer operations to general input states and diverse distillation objectives. We identify key invariances in resource measures, such as coherent and private information, and demonstrate how they can be leveraged to significantly reduce the numerical complexity of channel optimization. To illustrate the framework's versatility, we introduce several protocols: gF-IMAX for general fidelity optimization, and (S)CI-IMAX and (S)PI-IMAX for optimizing (smooth) coherent and private information in both asymptotic and one-shot regimes. Our numerical results confirm that these protocols effectively tailor stabilizer channels to specific operational tasks, establishing them as a robust and flexible tool for quantum resource distillation.

In this work, we construct a locally inertial reference system adapted to a geodesic observer in stationary, axisymmetric dust solutions of the Einstein equations employed as effective models of a portion of a galactic disc. To ensure a consistent spatial orientation among different local observers, we also introduce the radially locked reference system, in which one spatial axis is aligned with the radial direction defined by null geodesics passing through the galactic center. Within this framework, we analyze how the dust configuration is described by such observers by computing the frequency shift of photons exchanged between pairs of dust geodesics. Building on this construction, we outline a procedure to reconstruct spectroscopic and astrometric relative velocities with respect to locally inertial observers, providing a coherent foundation for the study of galactic kinematics in a fully general relativistic context.

Speech deepfake detection (SDD) is essential for maintaining trust in voice-driven technologies and digital media. Although recent SDD systems increasingly rely on self-supervised learning (SSL) representations that capture rich contextual information, complementary signal-driven acoustic features remain important for modeling fine-grained structural properties of speech. Most existing acoustic front ends are based on time-frequency representations, which do not fully exploit higher-order spectral dependencies inherent in speech signals. We introduce a cyclostationarity-inspired acoustic feature extraction framework for SDD based on spectral correlation density (SCD). The proposed features model periodic statistical structures in speech by capturing spectral correlations between frequency components. In particular, we propose temporally structured SCD features that characterize the evolution of spectral and cyclic-frequency components over time. The effectiveness and complementarity of the proposed features are evaluated using multiple countermeasure architectures, including convolutional neural networks, SSL-based embedding systems, and hybrid fusion models. Experiments on ASVspoof 2019 LA, ASVspoof 2021 DF, and ASVspoof 5 demonstrate that SCD-based features provide complementary discriminative information to SSL embeddings and conventional acoustic representations. In particular, fusion of SSL and SCD embeddings reduces the equal error rate on ASVspoof 2019 LA from $8.28\%$ to $0.98\%$, and yields consistent improvements on the challenging ASVspoof 5 dataset. The results highlight cyclostationary signal analysis as a theoretically grounded and effective front end for speech deepfake detection.

Retrieval-Augmented Generation (RAG) enhances the capabilities of large language models (LLMs) by incorporating external knowledge, but its reliance on potentially poisonable knowledge bases introduces new availability risks. Attackers can inject documents that cause LLMs to refuse benign queries, attacks known as blocking attacks. Prior blocking attacks relying on adversarial suffixes or explicit instruction injection are increasingly ineffective against modern safety-aligned LLMs. We observe that safety-aligned LLMs exhibit heightened sensitivity to query-relevant risk signals, causing alignment mechanisms designed for harm prevention to become a source of exploitable refusal. Moreover, mainstream alignment practices share overlapping risk categories and refusal criteria, a phenomenon we term alignment homogeneity, enabling restricted risk context constructed on an accessible LLM to transfer across LLMs. Based on this insight, we propose TabooRAG, a transferable blocking attack framework operating under a strict black-box setting. An attacker can generate a single retrievable blocking document per query by optimizing against a surrogate LLM in an accessible RAG environment, and directly transfer it to an unknown target RAG system without access to the target model. We further introduce a query-aware strategy library to reuse previously effective strategies and improve optimization efficiency. Experiments across 7 modern LLMs and 3 datasets demonstrate that TabooRAG achieves stable cross-model transferability and state-of-the-art blocking success rates, reaching up to 96% on GPT-5.2. Our findings show that increasingly standardized safety alignment across modern LLMs creates a shared and transferable attack surface in RAG systems, revealing a need for improved defenses.

Testing Ultra-Wideband (UWB) systems is challenging, as multiple devices need to coordinate over lossy links and the systems' behavior is influenced by timing, synchronization, and environmental factors. Traditional testing is often insufficient to capture these complex interactions, highlighting the need for an overarching testbed infrastructure that can manage devices, control the environment, and make measurements and test scenarios repeatable. In this work, we present a highly automated testbed architecture built on Robot Operating System Version 2, integrating device management with environmental control and measurement systems. It includes an optical reference system, a controllable Autonomous Guided Vehicle to position devices within the environment, and time synchronization via Network Time Protocol (NTP). The testbed achieves a Root Mean Squared Error of 4.8 mm for positioning repeatability and 0.493$°$ for the orientation, and our NTP-based synchronization approach achieves a timing accuracy of below 1 ms. All testbed functionality can be controlled remotely through simple Python scripts to allow automated orchestration tasks such as conducting complex measurement scenarios. We demonstrate this with a measurement campaign on UWB localization, showing how it enables repeatable, observable, and fully controlled wireless experiments.

Following any quantum information processing protocol, it is essential to reset a mixed state of a many-body interacting spin-network to the computational-zero pure state. This task is challenging, both theoretically and experimentally, because of the quantum correlations. There is currently no effective cooling strategy for both high and low temperatures in such networks. Here we put forth a universal cooling strategy for multi-spin interacting networks. The strategy is based on the collective coupling of the system to an ancilla spin that intermittently dumps part of its entropy into an ultracold bath. Yet this strategy should overcome the symmetry-imposed correlations that impede the cooling. To avoid the prohibitive complexity of computing the dynamics, we resort to graph analysis of the network. %To approach the desired state, We show that a unique choice of alternating, non-commuting system-ancilla interaction Hamiltonians exists that breaks the symmetry constraints and allows the network to approach the desired pure state. We illustrate this universal purification strategy in diverse experimental settings.

Despite advancements in surgical techniques, hernia recurrence rates remain high, underscoring the need for improved understanding of abdominal wall behaviour. While surgeons are aware of many factors contributing to hernia occurrence (e.g obesity, smoking, surgical technique or site infection), it would be of interest to consider it as a biomechanical pathology. Indeed, an abdominal hernia arises from an imbalance between abdominal wall deformability and applied forces. This review article discusses how biomechanics offer a quantitative framework for assessing healthy and damaged tissue behaviour, guiding personalised surgical strategies throughout the pre-, intra-, and post-operative periods. The abdominal wall is a dynamic, load-bearing structure, continuously subjected to intra-abdominal pressure and mechanical stress. Its biomechanical properties, including elasticity and resistance to loading forces, dictate its function and response to surgical intervention. The linea alba is the stiffest component experiencing the highest stress, while the abdominal wall's anisotropic nature influences deformation patterns. Various experimental and computational methods enable biomechanical characterisation. Hernias represent mechanical failures at anatomical weak points. While surgeons qualitatively evaluate abdominal wall's biomechanics by estimating deformation and closure forces, functional imaging (elastography, dynamic acquisitions) could provide objective biomechanical insights. Hernia formation alters abdominal wall biomechanics, inducing greater mobility and elasticity. Surgical repair fundamentally alters the biomechanics of the abdominal wall. The choice of defect's suturing technique, mesh properties, placement, overlap and fixation methods (e.g. suture, tacks) significantly influence mechanical outcomes. Surgical repair tends to restore physiological biomechanics by re-establishing force transmission and hernia-induced excessive mobility. Suturing techniques, mesh selection and placement influence mechanical outcomes. However, optimal results require implants with mechanical properties mimicking native tissue. Lightweight meshes (<70 g/m2) placed in a retrorectus position, combined with a small-bite suture technique, have been associated with lower recurrence rates and improved post-operative function. By bridging biomechanics with surgical practice, this review highlights how mechanical principles shape hernia formation, diagnosis, and repair. A deeper integration of biomechanical principles into surgical decision-making could refine hernia management and lead to patient-specific, mechanics-informed strategies. For surgeons, this knowledge is not just academic - it is practical and can make a difference to patient outcomes.

Competing electronic phases in two-dimensional transition metal dichalcogenides constitute a fertile platform for uncovering emergent ground states and elucidating the control parameters that govern the correlated electron phases. Among these materials, vanadium diselenide is particularly compelling: while the bulk hosts a well-established charge density wave (CDW), monolayers exhibit markedly different electronic behavior. Here, we identify three distinct electronic regimes in mechanically exfoliated VSe$_2$ flakes on Au(111) substrates, where interfacial hybridization, charge transfer, and strain act as primary tuning parameters of electronic order. Monolayers strongly coupled to gold show complete suppression of the CDW, accompanied by the emergence of moiré modulations. In contrast, bilayers preserve the in-plane $4a \times 4a$ CDW characteristic of the bulk limit. Strained, electronically decoupled monolayers formed in suspended membrane and bubble regions stabilize a $\sqrt{3}a\times\sqrt{7}a$ CDW phase, underscoring the reversible role of substrate interaction and hybridization.