Inflaton decay inevitably emits gravitons through bremsstrahlung during reheating. We show that the soft part of this emission amplitude, fixed by Weinberg's soft-graviton theorem, becomes an irreducible stochastic gravitational-wave (GW) background after accounting for cosmological evolution. The theorem fixes the infrared branch of the spectrum, $Ω_{\rm GW}\propto f$, independently of the microscopic operator responsible for inflaton decay, while the normalization is controlled by the hard inflaton decay rate and by a phase-space factor. We carry this out for inflaton $n$-body decays, including the phase-space integrals, finding that the maximum of the spectrum scales as $2/n$ relative to the $n=2$ case. The signal can reach $Ω_{\rm GW}h^2\sim \mathcal O(10^{-17})$ at frequencies above the GHz scale. This predicts a stochastic graviton floor from perturbative reheating: a larger signal would require either other processes beyond perturbative bremsstrahlung or inflationary scenarios beyond conventional single-field slow roll.

Stellar streams from disrupted globular clusters are excellent probes of dark matter (DM) subhalos. Observed Milky Way streams display a remarkable diversity of features: spurs, gaps, kinks, cocoons, and density variations, many attributed to subhalo encounters. But how much of this diversity arises from the host itself? We simulate $\sim$15,000 globular cluster streams across four Milky Way-mass halos from the FIRE-2 cosmological simulations, evolved in basis function expansion potentials capturing the evolving disk, halo, and large-scale structure while excluding small-scale perturbers such as DM subhalos and giant molecular clouds. We find that roughly three quarters of streams develop complex features from the host potential, such as spurs, kinks, and cocoon-like envelopes. Even the smoothest streams exhibit 10--25\% width variation along their track and host overdensities and gaps at scales of ${\sim}2^\circ$, squarely in the $1^\circ$--$5^\circ$ range predicted for subhalo-induced gaps. Pericentric distance is the primary predictor of stream morphology, with ${\sim}15$ kpc separating smooth from disturbed streams and circular orbits beyond $\sim$20 kpc producing the smoothest streams. Only $\sim$70 out of $\sim$15,000 streams are free of detectable wiggles in the track at any scale. Analogs to observed features, such as the GD-1 spur and the ATLAS--Aliqa Uma kink, emerge even without the presence of subhalos. As next-generation surveys (LSST, Euclid, and Roman) resolve stream structure across hundreds of streams, the baseline established here, streams evolved without small-scale perturbers, becomes essential for extracting DM substructure constraints.

Quasinormal modes (QNMs) of black holes can exhibit avoided crossings (ACs), in which specific QNM frequencies approach each other while their amplitudes are enhanced and acquire nearly opposite phases, leading to characteristic interference. Resolving such closely spaced modes through black hole spectroscopy is observationally challenging. In this paper, we investigate the detectability of nearly degenerate QNMs in the presence of an AC within a Bayesian framework using three waveform models. We examine how the inference of the complex frequencies and amplitudes depends on the separation between the two QNM frequencies and on the choice of template waveform. We find that resolving the individual QNM frequencies is difficult even under optimistic conditions. On the other hand, collective waveform signatures associated with ACs may still be identified through complementary waveform descriptions, provided that the AC-related modes dominate the observed ringdown signal and contamination from more slowly damped modes is negligible or can be removed.

This position paper argues that safety and alignment cannot be achieved by constraining an external system: they must emerge from the co-regulatory design of the human--AI cognitive system as a whole ("AI as Part of Self"). Contemporary AI increasingly participates in attention allocation, reasoning, synthesis, and decision-making, shaping the very cognitive processes through which humans form beliefs, make decisions, and constitute their sense of self. Humans and AI occupy complementary epistemic roles under mutual constraint, forming a symbiotic cognitive unit whose co-regulation -- not the external control of either party alone -- is the proper locus of alignment. We identify the risks of unstructured delegation: deskilling, automation bias, transfer of epistemic authority, and oracle-style centralization of knowledge. Drawing on System~0 cognition theory, we further show that AI operates prior to conscious deliberation, shaping the pre-attentive infrastructures through which agency and trust are negotiated -- a level that conventional oversight cannot reach. We conclude with design principles for cognitive co-regulation addressed to ML engineers and governance bodies. The goal of this work is to guide human cognition toward resilience and epistemic agency at the foundation of human selfhood.

In this paper, we investigate a bistatic multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system over block-fading channels, focusing on the scenario where the sensing and communication receivers (Rxs) are co-located. Under the assumption of unknown channel state information (CSI) at the Rx, two schemes are considered: pilot sensing (PS) and data-aided sensing (DAS). The communication rate-sensing distortion functions for both schemes are characterized. For the DAS scheme, a closed-form asymptotic expression for the sensing distortion is derived by using random matrix theory (RMT). The asymptotic performance analysis explicitly quantifies the significant gains of the DAS scheme, revealing a strict $3$ dB effective SNR improvement in the low-SNR regime and a strictly faster performance scaling rate in the high-SNR limit compared to the PS scheme.

We present an efficient, nearly optimal quantum algorithm for solving linear matrix differential equations, with applications to the simulation of open quantum systems and beyond. For unitary or dissipative dynamics, the algorithm computes an entry of the solution matrix with query complexity $\widetilde{\mathcal{O}}(ν\mathcal{L} t/ε)$, where the constant $ν$ depends on the problem parameters, $\mathcal{L}$ involves a time integral of upper bounds on the norms of evolution operators, and $ε$ is the error. In particular, $ν\mathcal{L}$ is linear in $t$ for unitary dynamics and can be a constant for dissipative dynamics. Our result contrasts prior quantum approaches for differential equations that typically require exponential time for this problem due to the encoding in a quantum state, which can lead to exponentially small amplitudes. We demonstrate the utility of the algorithm through an end-to-end application, namely the simulation of dissipative dynamics for non-interacting fermions, which can be extended to other quantum and classical systems. We compare with classical algorithms and give evidence of polynomial quantum speedups for systems in a lattice, which become more pronounced for systems with long-range interactions and can be shown to be exponential in general. We also provide a lower bound of $Ω(ν\mathcal{L} t/ε)$ for unitary or dissipative dynamics that proves our algorithm is optimal up to logarithmic factors.

We perform hydrodynamical simulations with radially varying resolution to study the effects of stellar feedback on the radial inflow of gas from the Central Molecular Zone (CMZ, $R\sim200$ pc) to the Circumnuclear Disk (CND, $R\sim5$ pc) of the Milky Way. The simulations include a realistic Milky Way barred gravitational potential, a cooling function coupled to a non-equilibrium chemical network, gas self-gravity, star formation, supernova feedback, and radiation feedback from massive stars computed via on-the-fly radiative transfer. Our main findings are as follows: 1) Stellar feedback drives a radial inflow that decreases monotonically with decreasing Galactocentric radius. The time-averaged inflow rate in our fiducial SNRad simulation, which includes both supernova and radiation feedback, declines from $\langle \dot{M} \rangle\sim5\times10^{-3}$ Msun/yr at $R\sim100$ pc, to $\langle\dot{M}\rangle\sim10^{-4}$ Msun/yr at $R\sim10$ pc, to $\langle\dot{M}\rangle\sim10^{-6}$ Msun/yr at $R\sim1$ pc. 2) The total inflow rate can be broken down into two components driven by two distinct mechanisms. First, feedback-driven turbulence redistributes the angular momentum of gas clouds, producing a smooth (secular) transport of mass inward, similar to a Shakura-Sunyaev viscous accretion disk. This component contributes inflow rates that vary from $\dot{M}\sim5\times10^{-4}$ Msun/yr at $R\sim100$ pc to $\dot{M}\sim10^{-7}$ Msun/yr at $R\sim1$ pc. Second, episodic inflow events can transiently increase the inflow rate by several orders of magnitude, reaching $\dot{M}\sim10^{-3}$ Msun/yr over timescales of $Δt\sim3$-$5$ Myr at $R=10$ pc. 3) The stellar feedback model significantly affects the episodic inflow but has little impact on the smooth component. Simulations including radiation feedback produce substantially more episodic events than those with supernova feedback alone.

Enabling continued data-center growth under increasing grid stress motivates closer coordination between flexible computing demand and co-located battery energy storage systems (BESS) to improve site operations and provide grid services. This paper develops a robust co-optimization framework for day-ahead operation of data centers with co-located BESS under utility-imposed interconnection limits on peak load and ramping. The model jointly considers deadline-constrained computing workloads, managed through workload scheduling and dynamic voltage and frequency scaling (DVFS), together with degradation-aware BESS dispatch to enable cost optimization and participation in ancillary-service markets. Case studies based on real-world market and workload data show that the proposed framework yields feasible day-ahead schedules across a range of operating conditions, with substantially larger benefits when interconnection constraints become binding. Under baseline conditions, BESS value is derived from both ancillary-service participation and improved workload and energy management. Under stressed peak-load and ramping limits, however, the daily value of BESS increases by a factor of two or more, driven primarily \revise{by BESS actions to reduce the potential incompletion in the schedulable workload while complying with interconnection constraints}. Under tight peak-load caps, workload composition also matters where a higher share of non-schedulable jobs can increase operating cost by more than 25\% relative to more flexible workload mixes. \revise{Additionally, DVFS studies further show that processor-level control is a material flexibility lever under tight load limits.} These results demonstrate that coordinated compute-storage flexibility can materially expand the operational headroom and grid value of data centers, especially under increasingly scarce grid capacity.

We present a quantum algorithm for solving algebraic Riccati equations, with applications to quantum-chemical random-phase approximation (RPA) and higher-order RPA theories. Our method block-encodes stabilizing Riccati solutions via Riesz projectors onto invariant subspaces of an associated non-normal matrix, implemented using contour-integral resolvents and quantum singular value transformations. Applied to $m$-particle, $m$-hole RPA, our algorithm yields a block-encoding of the amplitude solution and estimates the electronic correlation-energy density with it. Under localized-orbital sparsity assumptions, the end-to-end cost scales linearly with system size and polynomially with excitation rank $m$, suggesting an exponential advantage in $m$ over plausible classical local-correlation heuristics. More broadly, this work provides a framework for quantum algorithms for nonlinear matrix equations in quantum chemistry and opens a possible route toward developing quantum algorithms for coupled-cluster theory.

We study the \textit{Kaluza--Klein} descent of non-invertible higher-form symmetry defects from eleven-dimensional Supergravity to Type IIA Supergravity. Starting from the eleven-dimensional construction of non-invertible Supergravity defects, we show that the full defect, including its auxiliary topological sector, admits a pushforward along the M-theory circle in the zero-mode Supergravity regime. The seven-dimensional \textit{Chern--Simons}-like auxiliary theory descends to a six-dimensional \(BF\)-type sector. We also show that the compactification of the eleven-dimensional \textit{Bianchi} sector splits into an invertible \(H_{[3]}\)-sector and a twisted non-invertible \(\widetilde F_{[4]}\)-sector, controlled by \(d\widetilde F_{[4]}+H_{[3]}\wedge F_{[2]}=0\). The resulting Type IIA defect algebra contains both an invertible Picard subgroup and non-invertible \(BF\)-dressed defects, with charged probes identified through the standard M-theory/Type IIA brane dictionary.

Most deployed WAN Traffic Engineering (TE) systems use a logically centralized controller that periodically gathers traffic demands, runs a TE optimization or heuristic, and then programs the network. At scale, these solutions can be sub-optimal, and can take minutes to react to demand changes or failures. In this paper, we introduce OnlineTE, a system that reacts immediately to demand changes and failures, and delivers near-optimal solutions within seconds of a change. OnlineTE builds on the theory of optimization decomposition to devise scalable, near-optimal, distributed TE solvers for path-based MLU and Max-flow problems. In OnlineTE, each switch solves part of the optimization, and a central coordinator orchestrates the progress of the switches. As such, a switch can trigger a re-optimization as soon as it notices a demand change or failure, enabling high reactivity. OnlineTE scales to large WANs, and its compute requirements are well below the capabilities of modern WAN switches. It also enables a new opportunity, edge-based TE, which can utilize resources more efficiently than today's path-based approaches. On a testbed emulation of a 750-node WAN topology, OnlineTE can outperform the state-of-the-art by up to an order of magnitude.

Accurate estimation of the covariance matrix of cosmic shear statistics is essential for cosmological analyses using current and upcoming wide-area weak lensing surveys. In this work, we investigate analytical methods for computing the Gaussian covariance matrix of the cosmic shear two-point correlation function (2PCF), taking into account the effects of finite survey geometry. We compute the covariance of 2PCF based on the improved Narrow Kernel Approximation (iNKA), with a projection using the Legendre transformation. We also consider other analytical covariance estimators, the $f_{\mathrm{sky}}$ approximation and the weighted quartic-counts method. We evaluate the accuracy of those analytical methods using the convergence fields with the HSC Year 3 survey mask as a test case. We find that the covariance of the 2PCF obtained by using the iNKA does not reproduce the covariance measured directly from Gaussian simulations. Although the iNKA accurately models the diagonal structure of the harmonic-space covariance, residual inaccuracies in the off-diagonal components propagate through the Legendre transformation and significantly affect the real-space covariance. In contrast, the weighted quartic-counts method shows better agreement with the simulations. Our results demonstrate that accurate modeling of the off-diagonal structure of the harmonic-space covariance is crucial for obtaining reliable covariance estimates of real-space weak lensing statistics in the presence of survey window effects.

In this paper, the conditions of monogenicity are weakened for functions with values in a three-dimensional commutative algebra over the field of complex numbers. By monogenicity we mean continuity and the existence of the Gâteaux derivative.

We present the Data Quality Report Builder toolkit, DQRbuild, a suite of data quality tools that have been developed to vet gravitational-wave events in preparation for the fourth LIGO-Virgo-KAGRA observing run. We explain the main functionality and the many scientific tests that we support. To validate the performance of the tools included in the toolkit, we run a series of tests on all significant candidates shared as public alerts in the third observing run to compare against what was manually reported using human intervention. We find that these automated tools can now identify 96% of the problems identified by humans during this previous observing run, with a 24% false alarm rate. We conclude with a commentary on the prospects and potential challenges for fully automating the process of vetting the data quality for gravitational-wave events identified in future observing runs.

Temporal random walks, which sample causality-preserving paths, are widely used to analyze time-stamped interactions in domains such as microservices, finance, and online platforms. Generating such walks at scale is challenging because real-world graphs evolve as high-volume streams, making continuous ingestion, efficient memory usage, and strict temporal ordering essential for practical deployment. We present Tempest (TEMPoral nEtwork Streaming Traversals), a GPU-accelerated engine for streaming temporal random walks. Tempest combines a GPU-native dual-index organization over a shared edge store with a hierarchical cooperative scheduler that dispatches walks at thread, warp, or block granularity based on per-step node convergence, enabling efficient start-edge selection, hop-by-hop causality enforcement, and window-based eviction without synchronization. It further provides closed-form constant-time samplers for common temporal bias functions. Our evaluation demonstrates sustained real-time processing of billion-edge streams under sliding windows, outperforming prior systems in ingestion and walk generation throughput while preserving causal correctness.

We consider 3D micropolar flows with possible vanishing spin viscosity and investigate the decay of the energy for large times. We compute first the exact $L^2$-asymptotic profile, as $t\to+\infty$, for solutions to the linear 3D micropolar equations, up to the second order. For the nonlinear micropolar system, we first establish the existence of restricted Leray solutions. This new notion of solutions is required because it is not known whether the weak finite energy solutions verify a strong energy inequality. Next, we study the large-time behavior of restricted Leray solutions, and prove that they behave asymptotically in $L^2$ like their linear counterpart, up to the critical algebraic decay rate $O(t^{-5/2})$ for the energy. Applying a remarkable linear enstrophy identity, we show that the microrotation field exhibits faster decay in $L^2$ than the velocity field, allowing us to impose our hypothesis on the velocity field only and not on the angular velocity.

Accurate seasonal runoff forecasts are critical for managing California's reservoirs and water supply for millions of its residents. Winter snow accumulation provides a strong source of predictability of snowmelt-based runoff in the spring and summer months, but progressive hydroclimatic changes in the Sierra Nevada are altering its timing and volume. These changes reduce the skill of statistical forecasts trained on historical data, highlighting the need for improved forecasting systems that can capture the changing dynamics of snowmelt. Here we demonstrate that a collaborative workflow between an agentic AI assistant and an automated code-mutation system, both powered by large language models, can accelerate the development of competitive seasonal runoff forecasting systems. In our framework, the AI agent discovers relevant datasets, synthesizes domain knowledge from prior forecasting competitions and the scientific literature, and explores the space of model architectures, while the code-mutation system refines each of the solutions explored by the agent through Monte Carlo Tree Search over the code space. The resulting system forecasts monthly Full Natural Flow (FNF) at 1- to 6-month lead times across 23 Sierra Nevada watersheds using an adaptive ensemble of three XGBoost quantile regression sub-models with physics-informed feature engineering. Evaluated against California's operational Bulletin 120 forecasts over 2021-2025, the agent-evolved model achieves superior skill for early-season cumulative April-July runoff predictions, reducing watershed-averaged quantile forecast error by up to 29%, and offering a new paradigm for AI-driven scientific model development in the geosciences.

We investigate systems containing objects with negative mass (NMOs). In a system consisting of one object with positive mass and one NMO, a bound state exists even though the force exerted by the NMO on the object with positive mass is repulsive. Unlike a standard system consisting of two objects with positive mass, the gravitational waves emitted from this system exhibit a decrease in frequency and amplitude over time. We propose a model of the time evolution of the Ellis-Bronnikov wormhole, along with a formulation that eliminates the ghost that appears when constructing the Ellis-Bronnikov wormhole, a candidate for an NMO. Furthermore, numerical simulations are performed to obtain the optical appearance of such NMOs. The observed luminosity is also compared with the Schwarzschild black hole and with the Simpson-Visser wormhole, leading to clear differences in the photon ring substructure around the central object.

The groundbreaking metric age of information (AoI) has been introduced to measure information freshness in communication networks. As transformational as it is, AoI metric falls short in some applications, such as remote monitoring, since it is a semantic-agnostic metric which does not consider the dynamics of the random process. There is a need to quantify the performance of a remote estimator via a metric that combines freshness and semantic aspects. To this end, in this paper, we introduce a novel metric coined age of staleness (AoS) that measures when the last time that the current estimation was correct. First, we analyze a simple scenario where an $n$-ary symmetric Markov source is observed by a monitor via a constant sampling rate, obtain a closed-form expression for the AoS, and show that it is a monotonically decreasing function of the sampling rate. Next, we consider multiple distinct Markov sources, and formulate an optimization problem, where the remote monitor allocates the total sampling rate to tracking the sources. Although the optimization problem is non-convex, its structure is suitable for obtaining a near-optimal solution using the polyblock algorithm, which leverages the monotonicity of the objective function. While the new AoS metric could be applicable in many scenarios, we believe it is particularly well-suited for a digital twin network (DTN) where multiple physical objects (POs) are monitored with a total sampling rate constraint to maintain a digital representation of them, namely, their digital twin (DT).

Achieving a just and sustainable transition requires the pursuit of multiple social and environmental targets. Two primary barriers impede this process: (1) targets are often in conflict with each other, and (2) policies aimed at these targets are commonly planned in isolation, neglecting complex interdependencies in the system. To address these challenges, we propose a general modeling framework that evaluates the holistic impact of policies and decision-making on sustainability targets while capturing system interdependencies in a policy-target network. Inspired by Kauffman's NK fitness landscape, our framework takes the form of a multi-objective optimization model that employs a dynamic evolutionary algorithm in conjunction with network analysis. Our algorithm accounts for tradeoffs between conflicting targets by dynamically reallocating resources to the most impactful and efficient policies. One key finding indicates that increasing resources generally enhances performance, but marginal gains stagnate at a point of diminishing returns. Sensitivity analysis reveals that the system is primarily driven by three factors: budget constraint, network density (interconnectivity), and policy efficacy. This study serves as a foundational step towards developing a decision-support tool that assists policymakers in achieving optimal outcomes for problems with a large number of dynamically interacting targets.

We investigate the role of the four viscosity parameters, in fluids where the particles possess a microstructure (micropolar flows) and are allowed to rotate in a two-dimensional setting. We first establish the existence of global finite energy solutions, satisfying the classical energy equality, for arbitrary initial data in $L^2$, in the case of a spin viscosity $γ\ge0$, and we construct the asymptotic profiles of the solution as $t\to+\infty$. We deduce the remarkable fact that the large time behavior only depends on the kinematic viscosity $μ$, and not on the other parameters $χ$ (vortex-viscosity), $γ$ (spin viscosity) and $κ$ (gyroviscosity) of the model. Our primary tool is a new enstrophy-like identity of independent interest, involving the difference between the fluid vorticity and the micro-angular velocity. Another consequence of our analysis is the identification of scenarios where the presence of micro-rotational effects significantly enhances dissipation, thereby slowing down the fluid motion at large times.

In this work, we construct a BRST-exact quartet mechanism in $SU(3)$ Yang-Mills theory in the Landau gauge. The quartet sector is cohomologically trivial in the standard vacuum, ensuring equivalence to pure Yang-Mills theory. The transformation rules carry both commutator and anticommutator structures, enlarging the field content from eight to nine degrees of freedom. Working in a prescribed Cartan-oriented background (compatible with the classical equations of motion), the theory induces a mass matrix reproducing the distinct $i$-particle propagator structure of earlier replica models without explicit breaking terms. To respect the BRST doublet theorem, we separate background generation from observable cohomology. Introducing a background-equivariant covariant Cartan frame, we show the filtered $i$-particle bilinear is the lowest perturbative component of an all-orders off-shell BRST cocycle. Despite the complex poles of elementary propagators, its leading two-point function retains a Källén--Lehmann representation with a real positive threshold and positive spectral density. The fully quantized action provides a consistent framework for renormalizability, establishing a systematic mechanism for recovering $i$-particle propagators and identifying BRST-controlled composite observables from a BRST-exact quartet extended to $SU(3)$.

The Brunauer--Emmett--Teller (BET) method is a standard tool for estimating surface areas from adsorption isotherms, yet practical implementations involve multiple algorithmic steps whose correctness is rarely made explicit. In this work, we present a fully executable and formally verified BET analysis pipeline implemented in the Lean~4 theorem prover. Our formalization covers the complete BET Surface Identification (BETSI)-style workflow, including window enumeration, monotonicity checks, knee selection, and linear regression. We carry out computations in floating-point arithmetic and develop the corresponding correctness proofs over the real numbers, using a shared polymorphic implementation that supports both. On the proof side, we show that the regression coefficients returned by the algorithm agree with their specification-level definitions and minimize the least-squares error under the stated assumptions. We also formalize the algebraic derivation of the BET linearized expression and connect that result directly to the executable analysis pipeline. We further prove that the window enumeration is sound and complete, and that the admissibility checks and knee-based selection satisfy their formal specifications. We evaluate the implementation against the BETSI reference method on benchmark adsorption isotherms. Compared to BETSI, LeanBET agrees to machine precision for 18 of the 19 isotherms, with only a 0.03\% deviation for the UiO-66 dataset. This demonstrates that a scientific computing workflow can be built in Lean, yielding both formal verification guarantees and numerical agreement with an established Python reference implementation.

Ransomware recovery in critical manufacturing infrastructure is not only a backup-restoration problem. Production capability depends on coupled information-technology, operational-technology, physical-process, quality, logistics, identity, and supplier systems. After ransomware, a plant may rebuild servers yet remain unable to schedule work, authenticate operators, trust engineering workstations, release product, reconnect OT assets, or coordinate suppliers. This paper reframes manufacturing ransomware recovery as a critical-infrastructure continuity and interdependency problem. We conduct a PRISMA-guided multivocal review of academic literature, standards and government guidance, threat frameworks, public incident material, and verified full-text/source-page evidence anchors. The review identifies nine evidence-backed recovery failure modes: dependency blindness, untrusted restore point and backup over-trust, identity trust collapse, lack of proof-of-recovery, unsafe OT reconnection, segmentation assumption failure, capability mismatch, unmanaged degraded operation, and supplier dependency failure. We then introduce Minimum Viable Factory Recovery (MVF Recovery): the smallest safe, trusted, and operationally meaningful production capability that can be resumed under current dependency, evidence, identity, data, network, OT, and supplier constraints. MVF Recovery is an analytical objective rather than a claim of full recovery, implementation, or safety certification. The paper derives a recovery lifecycle and benchmarking directions as secondary outputs. The contribution is an evidence-calibrated foundation for capability-centric ransomware recovery in critical manufacturing infrastructure.

Transparent conducting oxides (TCOs) are essential for the optoelectronics industry, but there is a critical gap in cost-effective methods to rapidly deposit low sheet resistance, high transmittance films without damaging delicate materials, including emerging soft semiconductors like metal-halide perovskites. In this work, atmospheric pressure chemical vapor deposition (AP-CVD) is used to synthesise H:In2O3 films with 7.20+/-0.01 Ohm/sq sheet resistance (0.50+/-0.06 mOhm.cm resistivity) and transmittance up to 89% in the near-infrared (NIR), surpassing commercial sputter-deposited indium tin oxide. The growth rate is 40x higher than atomic layer deposition (ALD), and the AP-CVD films are fully processed under atmospheric conditions at only 140 C. Comparison of secondary ion mass spectrometry and time-of-flight elastic recoil detection analysis with changes in carrier concentration indicate that H dopants are introduced from the water oxidant. There is an increase in mobility form 40+/-10 cm2/Vs to 160+/-30 cm2/Vs when changing from O2 to H2O as the oxidant, which is attributed to H dopants passivating oxygen vacancies that act as carrier scattering centers. This work establishes AP-CVD as a promising method for manufacturing high figure-of-merit TCOs in a rapid, scalable and cost-effective manner, using mild growth conditions compatible with thermally-sensitive materials.

We make rigorous the physics prediction that lattice Yang-Mills theories with gauge groups which have trivial centers do not satisfy Wilson's criterion for quark confinement. Specifically we prove that $\mathrm{SO}(3)$ lattice Yang-Mills theory does not satisfy Wilson's criterion in a strong coupling regime.

This paper presents a novel architecture utilizing a 10T SRAM cell for XNOR-based in-memory computing, aimed at mitigating the extensive routing challenges typically encountered in conventional in-memory computing systems. By integrating a full adder between in-memory multiplication cells, the proposed design achieves a 50% reduction in routing complexity. The architecture performs multiply-accumulate (MAC) operations using XNOR computation optimized for binary neural networks (BNNs). Additionally, a 14T-based full adder is employed to construct an N-bit ripple carry adder in the adder tree, significantly reducing the area compared to traditional 28T-based CMOS designs. The 10T SRAM XNOR computation further enhances the latency for MAC operations. The proposed approach reduces the latency and area overhead, improving the overall hardware's area efficiency by 2.67x compared to the state-of-the-art.

We establish the joint $*$-convergence of a random circulant matrix and a specific deterministic diagonal matrix. We also show that the empirical spectral distributions of skew-circulant and left skew-circulant random matrices converge weakly a.s.~to complex Gaussian and symmetrized Rayleigh distributions, respectively. The $*$-convergence of symmetric Toeplitz and Hankel random matrices is well known. So is the weak convergence of their random spectrum. However, not much is known about the limits. We exploit the connections of circulant, reverse circulant, and left skew-circulants with the Hankel and Toeplitz matrices, to show the $*$-convergence of the random symmetric Toeplitz matrix to the sum of two non-commutative self-adjoint variables, each having a real Gaussian distribution. A similar result holds for the non-symmetric Toeplitz matrix, but the variables are not self-adjoint and have a complex Gaussian distribution. The random Hankel matrix is shown to converge in $*$-distribution to a sum of two self-adjoint variables, each of which has a symmetrized Rayleigh distribution. As a consequence of these results, we also obtain a different proof of the convergence of the empirical spectral distribution of symmetric Toeplitz and Hankel matrices, and a slightly different way of expressing the moments of the limit spectral distribution.

Real-time event detection in IoT mesh sensor networks must balance sensitivity against false-positive load on a constrained mesh radio. We present a Monte Carlo comparison of the Temporal Spectral Noise-Floor Adaptation (TSNFA) detector against four classical comparators drawn from the radar Constant False Alarm Rate (CFAR) family and from sequential change detection: the Lipski FFT energy detector, Cell-Averaging CFAR (CA-CFAR), Ordered-Statistic CFAR (OS-CFAR), and state-machine Cumulative Sum (CUSUM). All five detectors are implemented to fit a Cortex-M0+ class envelope, process a 1-D 100 Hz time series in 128-sample frames, and use temporal reference windows in place of the spatial reference cells of conventional radar CFAR. Across a factorial set of four configurations (10 and 50 nodes; 12 dB and 18 dB SNR), each replicated five times over 24 hours, TSNFA achieves 99.97 to 100% event detection rate with 100% event precision and zero false-positive clusters per node. The classical comparators each succeed on one quality dimension and fail on another. Lipski FFT (k = 3), CA-CFAR, and OS-CFAR all maintain near-perfect detection rate but with event precision below 3% and per-node bandwidth between 145 kB/h and 1.2 MB/h. CA-CFAR and OS-CFAR are indistinguishable in false-alarm performance, both saturating the same broadband-statistic failure mode. CUSUM shows an SNR-dependent detection-rate drop from about 70% at 18 dB to 51% at 12 dB. TSNFA is the only algorithm tested that simultaneously achieves high detection rate, high precision, and low per-node bandwidth.

Standard Gaussian splatting methods rely on heuristic densification and pruning to adaptively allocate primitives during training, and the resulting Gaussian count strongly influences both reconstruction quality and runtime. This makes comparisons across methods fragile: improvements can stem from higher representational capacity rather than algorithmic design. A common and naive workaround for this is hard-stopping or budgeting densification/pruning once a target count is reached, which biases training because different methods hit the cap at different times, yielding non-uniform densify/prune exposure across views and uneven point distributions. We propose a target point control scheme that preserves the standard densification window and cadence, but adjusts only the existing densification and opacity-culling hyper-parameters to track a quadratic target count trajectory. This quota-governor reaches the desired count by 15k iterations without abrupt cutoffs, ensuring that all methods and views receive equal densification and pruning cycles, enabling fairer, capacity-matched evaluation.