Riemannian Hamiltonian Monte Carlo (RHMC) is a promising MCMC methodology thanks to its ability to accommodate position-dependent preconditioning and multi-step proposals. While RHMC performs well in low dimensions, it becomes infeasible in high dimensions due to its $O(d^3)$ cost per fixed-point iteration, where $d$ is the dimension of the target density. Even when the position-dependent preconditioner is based on the diagonal of the Hessian, the cost is still $O(d^2)$ per fixed-point iteration. In this paper, we propose a computational method to reduce the computational complexity of RHMC fixed-point iterations with diagonal preconditioners from $O(d^2)$ to $O(d)$ for targets with ``coordinate friendly'' structures. This distribution class includes generalized linear models as well as other dense and sparse graphical models. The method is expressed as manipulating the compute graph and can therefore be automated to work on black box targets. Finally, we show empirically that our implementation of RHMC results in better sample quality per unit of compute time for various target distributions compared to state-of-the-art HMC NUTS algorithms with both position-independent and position-dependent preconditioners.

Binary systems composed of stars with similar parameters should have identical chemical composition. However, many chemically anomalous pairs have been found in the literature, such as the binary HD 129171/HD 129209. It is still unclear whether these anomalies originate from inhomogeneities of protostellar clouds, with important implications for chemical tagging and theories of star formation, or if they are caused by a planet engulfment event suffered by one binary component. In this work, we measure precise differential abundances for the system HD 129171/HD 129209 to explore the planet engulfment hypothesis proposed in the literature. We focus particularly on the Be abundance, showing that this element can serve as a diagnostic of engulfment events for solar-type stars. Atmospheric parameters were determined imposing spectroscopic equilibrium of iron lines. Masses and ages were estimated with the isochronal method. Li, Be, N and O abundances were determined via spectral synthesis. Other elemental abundances (up to Zn) were determined by equivalent width measurements. The spectra adopted in the analysis were gathered using UVES/ESO. We confirm the large difference in [Fe/H] (0.120 $\pm$ 0.004 dex) and A(Li) (-1.00 $\pm$ 0.02 dex) among the members of the pair, and the trend between differential abundances and condensation temperature of the elements. The binary system also shows detectable differences in Be abundances (-0.20 $\pm$ 0.04 dex). The abundance pattern of the pair is reasonably reproduced by an engulfment model of 11.2 M$_\oplus$ of rocky material. The difference in chemical abundances of the HD 129171/HD 129209 pair provides strong evidence in favor of the planet engulfment scenario. In this context, the detection of a Be difference among chemically inhomogeneous binary systems can be used as a diagnostic of rocky material ingestion suffered by a member of the pair.

With the rapid growth of interactive applications in large language model (LLM) online services, maintaining high system throughput while ensuring user-perceived latency has become a key issue in inference scheduling. Existing LLM service systems rely on coarse-grained output constraints, making it difficult to effectively handle resource contention among multiple requests, resulting in low resource utilization efficiency and limited support for fine-grained quality of service (QoS) differentiation. We present SlidingServe, a sliding-window-driven SLO-Aware scheduling system for online LLM inference. SlidingServe designed a lightweight batch latency predictor to estimate the execution time of a batch. Based on this, SlidingServe uses SlidingChunker to combine information from the current iteration and the next iteration to achieve dynamic chunking and improve the overall system throughput while maintaining strict QoS guarantees. SlidingServe introduces Multi-Level Priority Sorter to sort candidate requests in order to balance fairness and efficiency. Additionally, when multiple requests within the same batch are at risk of SLO violating,SlidingServe introduces BatchConstructor, which uses dynamic programming to select the set of requests to execute in the current round, mitigating the SLO violation risk of critical requests.Our evaluation demonstrates that SlidingServe can improve service capacity by up to 30% compared to advanced scheduling systems under various load conditions, and further reduces the rate of SLO violation by 16%-53% under heavy-load inference mode.

When a horizontal plate vibrates strongly enough, it causes small particles such as sand grains to continually bounce on it and, over time, to diffuse across its surface. This phenomenon is the cause of the well-known Chladni figure, which is drawn by a higher density of grains gathering along the nodal lines of a resonating elastic plate. Using a heterogeneous, non-resonating plate, we investigate experimentally this type of diffusion. We find that, for the most part, is it comparable to classical molecular diffusion. We can define a temperature for the bouncing grains, and the system then obeys the fluctuation-dissipation theorem. We also recover Maxwell-Boltzmann statistics at equilibrium, when temperature is uniform. However, when temperature varies across the vibrating plate, the microscopic details of the grains' dynamics affect their macroscopic behavior: Fick's law, for instance, no longer applies. Instead, our experiments support a new transport relation that was recently proposed to represent diffusion in Chladni's experiment. Finally, we propose an expression for the heat flux associated to the non-equilibrium steady state predicted by this new relation, and test it against observations.

Computer Science education fundamentally depends on intensive laboratory hours to foster true programming mastery and logical reasoning. However, the widespread adoption of Generative Artificial Intelligence (AI) has made it virtually impossible to distinguish authentic student effort from instant AI code synthesis by evaluating final submissions alone. To preserve pedagogical integrity, educators must enforce authentic coding discipline, guiding students through unassisted, iterative development cycles. While centralized environments like JupyterHub provide instructors with a platform to host and monitor the learning process step-by-step, they introduce severe operational vulnerabilities; because Jupyter environments inherently allow arbitrary shell command execution, they expose the underlying shared host to unauthorized system manipulation and lateral movement. This paper presents VISMATIC, a secure, low-cost framework designed to resolve this tension between process-oriented monitoring and infrastructure security. By pairing robust environment isolation with explicit user-interaction tracking at the API level, VISMATIC captures authentic programming behaviors without exposing the underlying host system. Evaluation from a pilot student cohort demonstrates that our macro-level behavioral metrics successfully flag statistical anomalies indicative of automated or off-platform workflows while preserving student anonymity, offering a scalable blueprint for safeguarding educational discipline in the AI era.

The nitrogen-vacancy (NV) center in diamond has attracted considerable attention as a highly sensitive quantum sensor that can operate at room temperature. In particular, continuous-wave optically detected magnetic resonance (CW-ODMR) is promising for a wide range of applications because of its simplicity. However, conventional AC magnetic-field sensing schemes based on CW-ODMR suffer from a limited detection bandwidth: the detectable frequency is either fixed by intrinsic physical parameters of the NV center or, even when tunable, restricted to a narrow range of only a few MHz. Here, we propose a broadband AC magnetometry scheme based on CW-ODMR with NV centers using microwave-driven dressed states.Through theoretical analysis and numerical simulations, we show that the proposed scheme enables the detection of AC magnetic fields with frequencies up to the order of 100 MHz, which has been difficult to achieve using conventional CW-ODMR-based methods.

We introduce an auxiliary gradient-flow framework for variational problems with generalized Newtonian structure governed by an N-function. The key idea is to replace the nonlinear constitutive dependence on the gradient, or symmetric gradient, by an auxiliary scalar variable representing its squared magnitude. This shifts the nonlinearity from the state equation to the auxiliary variable, yielding a sequence of uniformly elliptic weighted linear problems. At the continuous level, we construct an auxiliary energy on a metric space adapted to the growth of the underlying N-function. In this topology, we prove lower semicontinuity, geodesic $λ$-convexity, and exponential convergence of the associated minimizing-movement scheme. At the finite element level, we derive a metric gradient flow through an explicit Riesz map, prove global well-posedness of the resulting semi-discrete ODE, and establish convergence to the finite element solution of the Euler--Lagrange equations of the generalized Newtonian energy. For the $p$-Laplacian and $p$-Stokes models, this gives a rigorous convergence result for $4/3\le p\le 4$, $p\ne2$, with asymptotic rate estimates beyond this range. We also propose practical time discretizations, including an operator-splitting scheme that gives the \kac iteration as a special case, and an adaptive pseudo-transient method that can be implemented using scalable linear solvers. Numerical experiments for power-law, Carreau--Yasuda, regularized Bingham, and optimal-design models demonstrate robustness, mesh-independent iteration counts in the tested regimes, and performance that matches or outperforms Newton's method.

The continuous regulation of transport properties through softness remains a longstanding challenge in active matter. Here, we show that encasing a programmable active particle within a deformable membrane naturally gives rise to intermittent stop-and-go dynamics, with ballistic motion at short times crossing over to diffusion at long times. Crucially, membrane softness acts as a single control parameter that continuously tunes persistence, intermittency, and long-time transport, linking the internal driving to the emergent locomotion of the synthetic cell. Combining experiments, simulations, and a run-and-tumble theoretical framework, we identify the minimal physical ingredients underlying this behavior and establish design principles for programmable soft active transport, opening new avenues at the interface of active matter physics and synthetic robotics.

The study of synchronization in complex systems has recently been revolutionized by incorporating higher-order interactions through simplicial complexes. Building in particular upon the higher-order Kuramoto model, which considers oscillators on nodes, links, and higher-dimensional simplices. This work extends the monolayer framework of the higher-order Kuramoto model to multilayer networks where the layers are adaptively coupled through order parameters of the oscillators placed on the simplices. We propose two multilayer architectures: one that allows interactions between signals of the same dimension across layers and the other that permits cross-dimensional interactions. We observe that a higher coupling strength is required for synchronization transitions of the node signals and the projected uplink and downlink signals during adaptation. For example, incorporating node dynamics into link evolution delays the onset of synchronization. This study opens an avenue for understanding complex dynamical processes within interconnected higher-order structures. Finally, we present a comprehensive theoretical framework, first for a bilayer network where layers are random networks treated under the annealed approximation, and then extend the analysis to the case of fully connected layers. The theoretical predictions align remarkably well with numerical simulations, accurately capturing the dynamics of the original model in a globally coupled scenario.

Sensitive health data should preferentially be analysed on site. In typical bioinformatics workows, public databases are duplicated and used by specialised tools to enrich the local datasets. In the case of genomic variation data, this process is called variant annotation. In this session we demonstrate variant annotation using federated SPARQL queries. We rst overview how clinico-genomic data can be modelled as a knowledge graph (KG), leveraging state-of-the-art biomedical ontologies. We then perform variant annotation by querying UniprotKB, a massive curated KG for gene and proteins. Our approach avoids public data duplication while maintaining genomic data on site and aligning it with FAIR principles. Our use-case is based on the ICAN project, a research program aimed at studying the physiopathology of cerebral berry aneurysms.

This paper provides and analyzes a dataset detailing the characteristics and execution data of all jobs submitted to the IN2P3 Computing Center (Villeurbanne, France), a national research and support unit of the CNRS, in 2024. The main additional value of this contribution compared to previously available datasets consists in the combination of an extended time interval considered, the inclusion of memory usage data and its recency, on top on improving the diversity of datasets provenance. This allows researchers to simulate and evaluate scheduling algorithms on a real workload over a large time window. Thus, specificities due to seasonal, monthly, and weekly user behaviors can be taken into account, which is not possible with smaller or synthetic datasets. It is composed of 44M jobs submitted by 1k users running on a cluster of a maximum of 312 machines supporting 46k concurrent threads and providing 105To of RAM.

Random walkers usually diffuse according to Fick's law. On average, they move down the gradient of their concentration and, in the absence of external force, tend to distribute themselves uniformly. In some experiments, however, this familiar notion is at odds with observation. Sand grains, for instance, gather along the nodal lines of a vibrated elastic plate to form a Chladni figure, thus accumulating where fluctuations are weak -- a fact that escapes the reach of Fick's law. On theoretical grounds, Büttiker [Zeitschrift für Physik B, 68, 1987] and Landauer [J Stat Phys, 53, 1988] proposed that particles submitted to a non-uniform temperature field would indeed gather where the temperature is low. They also predicted that, in the presence of a potential force, a non-uniform temperature could drive a steady current of particles, powered only by noise. Here, we present an experimental realization of these phenomena in a macroscopic system, which confirms the quantitative predictions of Büttiker.

The radiation mechanism of gamma-ray burst (GRB) prompt emission remains uncertain. Although the fast-cooling synchrotron model in a decaying magnetic field can account for the characteristic nonthermal spectral shape, its computational cost has limited its use in systematic observational fitting and statistical model comparison. We develop a convolutional neural network (CNN)-based spectral emulator for this physical model and train it on a large synthetic data set generated over a physically motivated parameter space. The trained network reproduces the numerical spectra with high fidelity while reducing the cost of spectral evaluation to the millisecond level. We then incorporate the emulator into a Bayesian spectral-analysis framework and apply it to the time-resolved spectra of GRB 231020A observed by Fermi/GBM. In most time intervals, the decaying-field fast-cooling synchrotron model provides better fits and smaller Bayesian information criterion values than the standard fast-cooling synchrotron model. These results suggest that a radially decaying magnetic field provides a plausible and more physically motivated interpretation of the prompt-emission spectrum of this burst, while also indicating that the emulator offers a practical route for large-sample Bayesian inference and systematic comparisons of GRB prompt-emission models.

We investigate star formation activity in galaxies belonging to two Hickson Compact Groups (HCGs), HCG 56 and HCG 92 (Stephan's Quintet), both of which show clear evidence of interactions, using spectral energy distribution (SED) analysis across the near- to far-infrared range. By combining data from the Infrared Satellite AKARI, the Spitzer Space Telescope, and the Herschel Space Observatory, we examine how galactic interactions influence the physical conditions and the evolution of group members. The observed SEDs of member galaxies are compared with model SEDs representing both star-forming galaxies and active galactic nuclei (AGN). Star formation rates (SFRs) are estimated using two independent methods: (i) the strength of mid-infrared polycyclic aromatic hydrocarbon (PAH) bands and (ii) far-infrared luminosities attributed to star formation, as derived from the models. Although both methods yield generally consistent results, SFRs based on PAH features are systematically lower, possibly due to the PAH destruction in some interacting galaxies. When plotted against the stellar mass, all member galaxies are found below the main sequence of star-forming galaxies in the SDSS field, suggesting that interaction-induced starbursts are not seen in HCG 56 and HCG 92.

We study an assignment problem where a set of agents and a set of facilities lie on a line metric. The goal is to compute an assignment of agents to facilities to approximately minimize the social cost (the total distance of agents from their assigned facilities) given only partial information regarding the metric. Unlike previous work which focused solely on algorithms with access to the ordinal preferences of the agents over the facilities (ORD), we also consider the value of information regarding approval preferences (APP), and inter-facility distances (DIST). For different combinations of these three information types, we establish tight bounds on the distortion of deterministic algorithms, showing that it is possible to improve over the optimal bound of $3$ that can be achieved using only ORD information. Among other results, we show a tight bound of $1+\sqrt{2}$ for APP+DIST which holds even for general metrics, and a tight bound of $2$ for ORD+APP+DIST.

We show the emergence of memory effects in the dynamics of a driven two-level system interacting with a composite environment, and analyze their influence on work, heat and entropy production. We further investigate how the interplay between driving, dissipation and memory effects, stemming from the finiteness of the environment, shapes the thermodynamic response of the system, thus providing insight into quantum thermodynamics beyond the Markovian approximation.

Service discovery is a fundamental process in wireless networks, enabling devices to find and communicate with services dynamically, and is critical for the seamless operation of modern systems like 5G and IoT. This paper introduces PriSrv+, an advanced privacy and usability-enhanced service discovery protocol for modern wireless networks and resource-constrained environments. PriSrv+ builds upon PriSrv (NDSS'24), by addressing critical limitations in expressiveness, privacy, scalability, and efficiency, while maintaining compatibility with widely-used wireless protocols such as mDNS, BLE, and Wi-Fi. A key innovation in PriSrv+ is the development of Fast and Expressive Matchmaking Encryption (FEME), the first matchmaking encryption scheme capable of supporting expressive access control policies with an unbounded attribute universe, allowing any arbitrary string to be used as an attribute. FEME significantly enhances the flexibility of service discovery while ensuring robust message and attribute privacy. Compared to PriSrv, PriSrv+ optimizes cryptographic operations, achieving 7.62* faster for encryption and 6.23* faster for decryption, and dramatically reduces ciphertext sizes by 87.33%. In addition, PriSrv+ reduces communication costs by 87.33% for service broadcast and 86.64% for anonymous mutual authentication compared with PriSrv. Formal security proofs confirm the security of FEME and PriSrv+. Extensive evaluations on multiple platforms demonstrate that PriSrv+ achieves superior performance, scalability, and efficiency compared to existing state-of-the-art protocols.

Financial decision systems require fast surrogate models for pricing, calibration, hedging, XVA, stress testing, and portfolio optimization. Standard neural surrogates reproduce prices or risk quantities, but downstream tasks depend as much on derivatives: deltas, vegas, curve and credit-spread sensitivities, exposure and objective gradients. We formulate a derivative-informed operator-learning framework in which the learned map -- a neural operator, random-feature operator, or finite-dimensional surrogate -- is trained both to match a high-fidelity pricing or risk operator and to match directional Fréchet derivatives generated on the fly. The framework combines operator learning, adjoint algorithmic differentiation, tangent sensitivity equations, random sketching of Jacobian actions, and no-arbitrage constraints. We derive error bounds showing derivative accuracy controls local stress errors, hedging error, and optimizer instability, and that discrete-time hedging error is also governed by second-order (gamma) accuracy. A Black--Scholes network over eight seeds shows a tuned derivative weight cuts vega error by 40\% and delta error by 15\% while modestly improving prices, but not an unsupervised second-order Greek. Heston and Bates random-feature experiments reduce stochastic-volatility and jump-parameter sensitivity errors by 60--76\%. A random-feature DeepONet/Galerkin operator mapping instantaneous-volatility curves to dense price surfaces reduces out-of-sample JVP error by 44\% and price RMSE by 23\% over eight seeds; it also shows derivative consistency alone does not remove no-arbitrage violations, so economic constraints must be imposed explicitly. The framework gives a disciplined route from value-only surrogates to derivative-aware engines that output differentiable instruments for hedging, risk, and control.

To improve the accuracy of Monte Carlo estimation of expectations, a set of zero-mean feature functions, known as control variates, can be used. They can be used as feature functions for linear regression of the target function, and we can obtain an unbiased and variance-reduced estimate using its residual. One known way to construct such functions is a method using an equality called Stein's identity, but these functions are not sufficient for the case where the target distribution is multimodal. We propose a different approach to constructing these zero-mean functions based on distribution approximation and the density ratio. We demonstrate that combining the functions constructed by these two strategies can effectively reduce the estimation variance for a bimodal distribution.

Recent measurements of Higgs boson properties using proton-proton collision data recorded at $\sqrt{s}=13$ TeV and $\sqrt{s}=13.6$ TeV with the ATLAS and CMS detectors at the LHC are presented. CMS results on the Higgs boson mass and width in the $H\toγγ$ channel are reported, together with a measurement of the Higgs boson width in the $H\to WW^*$ final state. A combined measurement of the charge-parity (CP) nature of the Higgs-vector boson couplings by the ATLAS collaboration, as well as improved results in individual channels by both experiments are summarised. Finally, the latest CMS analysis of the CP structure of the Higgs-tau lepton Yukawa coupling is reported.

We describe fully coupled simulations that bridge atomistic cathode dynamics and plasma formation during the earliest stages of vacuum arcing. The model combines molecular dynamics, finite element electrothermal calculations, electron emission and particle-in-cell plasma simulations via dynamic transfer of particles between the surface and plasma domains. Simulations of Cu nanoprotrusions reveal two routes to thermal runaway: direct Joule heating-driven instability and a novel nanoparticle-assisted mechanism, where detached nanoparticles generate neutral vapor that becomes ionized.

Neural speech codecs are key to speech transmission and storage, but most use uniform quantization across frames, allocating the same bitrate regardless of content and wasting bits. We propose VoCodec, a low-bitrate streamable neural speech codec with voicing-driven quantization that assigns higher bitrate to voiced frames and lower bitrate to unvoiced frames according to perceptual sensitivity. VoCodec embeds a voicing detector in a fully causal encoder-quantizer-decoder neural coding framework, using residual scalar-vector quantization for voiced frames and simple scalar quantization for unvoiced ones. Experiments show that on the LibriTTS dataset at a 16 kHz sampling rate, VoCodec outperforms baseline neural speech codecs even at a bitrate as low as 1.1 kbps. Our further experiments also confirm that introducing voicing-driven quantization can effectively reduce the bitrate by approximately 27% compared with uniform quantization strategy.

\abstract{Errors in observatory coordinates directly impact the precision of pulsar time-scale construction. Using the pulsar timing software TEMPO2, this study simulates various station position errors within the three-dimensional terrestrial reference frame for three different types of millisecond pulsars, over periods of 13 days and 5 years, and analyzes their effects on pulsar timing results.The findings demonstrate that,for both 13-day and 5-year observation spans, station coordinate errors substantially reduce the accuracy of pulsar timescale construction when the zenith angle exhibits long-term variations. This effect is independent of pulsar type and the daily observable time of the station antenna for the pulsar. A linear relationship is found between station coordinate errors and the Root-Mean-Square (RMS) of pulsar timing residuals, with fitted linear coefficients ranging from $1.36 \times 10^{-11}$ to $1.61 \times 10^{-9}$ for the three pulsars. The Roemer delay error caused by coordinate inaccuracies is notably larger than other delay and correction terms. Errors along the x- and y-axes have comparable influences on timing precision, whereas errors along the z-axis have a relatively smaller effect. Kendall correlation analysis between station error-induced Roemer delay and RMS yields a correlation coefficient $r = 1.67\%$ and $p = 100\%$ in all cases, indicating that, at current timing precision levels, coordinate errors primarily affect the Roemer delay term and thus the pulse arrival times, which is highly consistent with theoretical models.While these findings offer valuable insights into the key factors influencing pulsar timescale accuracy and related applications, they may not hold under conditions of a constant zenith angle or limited elevation angles, such as those at FAST.}

We develop a polylogarithmic structure for Bragg diffraction based on a weighted multi-plane interference model. Within this kind of construction, the scattering amplitude is expressed as a polylogarithmic generating function. By introducing extra contributions with power-law and the usual exponential decay, it takes the form $F(θ) = \mathrm{Li}_m\left(e^{iθ_{\mathrm{eff}} - ε}\right)$, where $ε$ is a finite coherence length. In the limit where $ε\rightarrow 0$, the argument of the polylogarithm approaches the unit circle and the classical Bragg condition corresponds to the approach of the polylogarithm argument toward its branch point $z=1$. This formulation provides a compact analytical framework for describing diffraction line shapes within a generalized correlation model in which peak positions, widths, and line shapes arise from a single analytic structure. Although we are able to recover the standard Bragg law for ideal crystals, the polylogarithm model captures deviations due to finite correlation length, disorder and non-uniform lattice coherence. We show that if Bragg peaks correspond to boundary singularities of the polylogarithm, a connection between diffraction theory and complex analysis arise. The proposed theoretical model may be particularly relevant for disordered or partially coherent materials, where conventional diffraction models often require additional phenomenological broadening assumptions.

Uncertainty principle, one of the most iconic features of quantum mechanics, was originally viewed as a fundamental limitation. Since the inception of quantum information science, researchers began to use it to achieve quantum advantages. To better understand the origin of these advantages, an essential question is: To what extent can the uncertainty principle's signatures be simulated by a single measurement? As a single measurement clearly cannot demonstrate the uncertainty principle, such a simulation, if exists, implies the claimed advantages may either stem from other quantum features, or just be reproducible in a less resourceful way. In this work, we report a series of noise-robust no-go theorems, showing that strong enough signatures of uncertainty principle cannot be simulated by a single measurement, even when assisted by quantum pre- or post-processing. This signature is modelled by complementary instruments. We completely characterise complementary instruments by a numerically feasible measure and show that they are necessary and sufficient resources for the advantage in an operational task that aims to unambiguously send classical information.

We construct non-projective complete log canonical algebraic surfaces whose canonical divisors are semi-ample over an algebraically closed field of any characteristic other than the algebraic closure of a finite field. We provide a unified framework to construct such surfaces for any given non-negative Kodaira dimension, namely, zero, one, or two. Furthermore, we show that any complete log canonical algebraic surface with Kodaira dimension minus infinity is automatically projective. This projectivity result confirms that our construction covers all possible values for the Kodaira dimension of non-projective complete log canonical surfaces.

In this paper we consider an implicit semi-discrete approximation of a degenerate reaction-cross-diffusion system. Due to the symmetry in the parabolic part, this system is known to preserve segregation of densities -- initially non-overlapping densities belonging to different species remain segregated for all times, which leads to internal layers between different species. We show that time-discrete approximations exist and converge to a weak solution, as the timestep goes to zero.

Mirror symmetry can strongly enhance the transmission of waves through a barrier inside a complex medium. We recently showed that this phenomenon enables quantitative through-barrier sensing: by tuning programmable scatterers on one side of the barrier to maximize the broadband total transmission through the barrier, the characteristics of scatterers at mirror-symmetric positions on the other side of the barrier can be determined. Considering a sufficiently large bandwidth was crucial to ensure that no accidental narrowband asymmetric resonance can outperform the symmetry-induced transmission enhancement. Here, we overcome this scheme's vexing need for a large bandwidth by replacing the underlying frequency diversity with configurational diversity. Specifically, we introduce auxiliary tunable scatterers at mirror-symmetric positions on either side of the barrier and sweep their characteristics through a series of random mirror-symmetric configurations. We tune the programmable main scatterers on one side of the barrier to maximize the average of the total through-barrier transmission over a series of configurations of the auxiliary scatterers at a single frequency, in order to sense the characteristics of the main scatterers on the other side of the barrier. We systematically study the accuracy of our single-frequency sensing scheme based on a multiport-network system model that cascades two mirror-related wave-chaotic cavities with a weakly transmitting barrier in between. We further examine an extension to non-reciprocal chaotic cavities involving circulators. Altogether, our results establish configurational diversity as a route to single-frequency, symmetry-empowered through-barrier sensing in reconfigurable complex media.

Most neural speech codecs use residual vector quantization (RVQ), in which later VQs contribute less but consume the same bitrate, leading to inefficiency. We propose P2PSynCodec, an ultra-low-bitrate neural speech codec with a plain-to-pseudo synergistic vector quantizer (P2PSVQ). P2PSVQ consists of one plain VQ and multiple pseudo VQs. The plain VQ produces basic tokens by quantization, while the pseudo VQs generate auxiliary tokens by neural prediction and incur zero transmitted bitrate. Thus, speech is decoded from the plain-VQ tokens together with predicted pseudo-VQ tokens, greatly reducing bitrate. Experiments show that P2PSynCodec achieves speech reconstruction quality comparable to competing codecs at 2.0 kbps while operating at only 0.5 kbps, demonstrating high efficiency for ultra-low-bitrate speech coding.

We study the topological zeta function of a loopless matroid $\mathsf{M}$ and its Möbius transform. We provide a novel and manifest description (a ``Master Theorem'') for both functions and all of their coefficients, which can be used to give transparent solutions to several open questions and conjectures on topological zeta functions of matroids, even in greater generality than what was anticipated. As applications we solve conjectures of van der Veer (2019), Kutler (2023), and Mengesha, Miranda, and Sun (2026).