High-capacity associative memories based on Kernel Logistic Regression (KLR) achieve strong retrieval performance but typically require substantial computational resources. This paper investigates the compressibility of KLR Hopfield networks to clarify the geometric principles underlying their robust representations. We present a geometric interpretation based on spontaneous symmetry breaking and Walsh analysis, and examine it through compression experiments involving quantization and pruning. The experiments reveal a clear asymmetry: the network remains robust under low-precision quantization while exhibiting strong sensitivity to pruning. We interpret this behavior through a "sparse function, dense representation" principle, in which a sparse input mapping is implemented through a dense bimodal parameterization. These findings suggest a practical route toward hardware-efficient kernel associative memories and provide insight into the geometric principles underlying robust representation in neural systems.

Semantic communication (SemCom) is accelerating its momentum to catch up with the massive increase in users' demands in both quantity and quality, with the assistance of advanced deep learning (DL) techniques. Specifically, SemCom can actively embed the semantic meaning of the data into the transmission process, while eliminating statistical redundancy to preserve bandwidth resources for other users. Therefore, the transmitter encodes the message in the most concise way, while the receiver tries to interpret the message with the DL model and its knowledge of the transmitter's intended meaning. Most existing works only consider one transmitter and one receiver, which limits their ability to address the diversity in users' models and capabilities. Therefore, in this paper, we propose a multi-user semantic communication system where each user is equipped with a distinct DL-based joint source-channel decoder architecture, reflecting the diversity in computing capacity. The challenging issue with the proposed system is the catastrophic forgetting property of neural networks, where the DL-based encoder fails to encode the data for the previous user when being trained with a new user. To address this, we propose an anchor decoder with an architecture that is symmetric to the encoder. The symmetric decoder has the same computational capacity as the encoder, providing feedback that aligns with the encoder's extraction capabilities and enhances optimization efficiency. The parameters of the optimized encoder are then frozen and used to train decoders for various users, aligning them with the encoder outputs. Finally, we conduct a series of simulation experiments to validate the proposed framework against other benchmarks.

While bulk silicon has long been understood to exhibit relatively weak spin-orbit coupling (SOC), confinement of electrons to quantum dots (QDs) at a silicon heterointerface results in significantly larger SOC. This is a concern for electron spin qubit performance, as intravalley and intervalley SOC can significantly perturb the operation of electron spin qubits. While these interactions can be harnessed to drive coherent rotations in a singlet-triplet qubit, coupling to low-lying excited valley states can lead to undesirable spin relaxation when valley splitting is on resonance with the Zeeman energy. In this work, we measure the angular dependence of the interfacial spin-orbit interaction as a function of the direction and magnitude of an applied external magnetic field in SiMOS and Si/SiGe heterostructures, two common material platforms for silicon spin qubits. We construct a physical model that accurately infers intra- and inter-valley SOC physics from fits to the data, allowing for a direct comparison between these two material systems. For the devices measured we find that, while the $g$-factor differences are comparable, the SiMOS QDs exhibit an order of magnitude larger spin-valley coupling than for Si/SiGe. Moreover, we find that the angular dependence of the spin-valley coupling is similar for both devices, with similar magnetic field orientations minimizing the spin-valley coupling. Our work points towards operational schemes for optimizing spin-valley coupling to avoid or exploit this mechanism for qubit operation.

We present an updated overview of the symmetry preserving Contact Interaction model in hadronic physics, developed a little over a decade ago to describe the mass spectrum and internal structure of mesons and diquarks composed of light and heavy quarks. Over the years, the Contact Interaction has evolved into a framework capable of treating both ground and excited states, providing a simple yet consistent approach to nonperturbative QCD. In this review, we examine the mass spectrum and elastic form factors of forty mesons with different spins and parities, together with their corresponding diquark partners. Importantly, we update the comparison of Contact Interaction predictions using recent results from the literature, offering a fresh perspective on the model's performance, strengths, and limitations. The analysis presented here refines previous conclusions and supports the Contact Interaction as a practical tool for hadron structure studies, with potential applications to baryons and multiquark states. We also present comparisons with other theoretical models and approaches, including lattice quantum chromodynamics, and comment on future prospects in view of ongoing and planned hadron structure experimental programs. In particular, forthcoming measurements at FAIR, together with future studies at Jefferson Lab and the Electron Ion Collider, are expected to provide key insights into hadron structure, with FAIR offering indirect constraints via hadron spectroscopy, hadronic interactions, and in-medium properties, while high-precision data on meson structure and form factors from Jefferson Lab and the Electron Ion Collider will provide valuable benchmarks to confront Contact Interaction based predictions.

We develop a rigid $\mathscr{N} =(1,1)$ superspace framework for spatially inhomogeneous impurity deformations in $D=1+1$ dimensions by embedding the impurity profile into a real background superfield (spurion). This spurionic completion provides a manifestly supersymmetric description at the level of the action and offers a systematic route to identify which inhomogeneous backgrounds preserve a nontrivial subset of supercharges. Focusing on static interface-type configurations, we determine the half-BPS condition on the spurion background and the corresponding supersymmetry projector. In the resulting half-BPS sector we derive the associated first-order BPS equation for static bosonic matter configurations and establish an exact Bogomol'nyi completion of the static energy, yielding a sharp bound saturated by BPS solutions. We further comment on how explicit coordinate dependence and derivative-dependent impurity couplings can obstruct the Bogomol'nyi structure, thereby motivating spurionic extensions that retain supersymmetric control over inhomogeneous deformations.

Tunable bound states in the continuum (BICs) in photonic crystal slabs are highly sensitive to substrate-induced mirror-symmetry breaking and typically degrade into finite-$Q$ quasi-BICs in realistic integrated platforms. Here we show that such degradation can be deterministically reversed. Using temporal coupled-mode theory and full-wave simulations, we demonstrate that the radiation channel opened by the substrate can be exactly cancelled by introducing a second, independent odd-parity perturbation inside the slab. This dual-asymmetry strategy restores the singularity of the radiation matrix and thereby recovers a tunable BIC in a substrate-supported photonic crystal slab. The recovered state regains both the polarization vortex and the characteristic $Q\propto Δk^{-2}$ scaling. The recovery points further follow a linear relation in the two-asymmetry parameter space, revealing a simple mode-dependent compensation law. The same mechanism also restores merging-BIC configurations, showing that it applies not only to isolated tunable BICs but also to higher-order topological resonance states built from them. Our results establish a practical route for preserving tunable topological resonances in substrate-supported nanophotonic systems.

Finite-Hamiltonian impurity solvers provide direct real-frequency spectra and a natural route to enlarged impurity Hamiltonians, but their applicability is limited by the rapid Hilbert-space growth with the number of bath or other added one-particle orbitals. We introduce an active-learning extension of adaptive-truncation configuration interaction (AL-ATCI) that identifies the determinant manifold relevant to the correlated state. The approximation is systematically controlled by the query size N_query, which also provides an internal convergence parameter when no external benchmark is available. Over the benchmark range studied here, the computational cost grows only weakly with bath size, because enlarging the bath mainly expands the combinatorial determinant space rather than the physically relevant manifold. In dynamical mean-field-theory benchmarks for the one-dimensional Hubbard model, AL-ATCI reproduces exact-diagonalization accuracy and extends cellular calculations to clusters as large as N_c = 10. For a three-orbital rotationally invariant Sr2RuO4 impurity problem, we demonstrate systematic convergence of dynamical quantities and a highly compressed configuration space as N_b is increased from 9 to 18. These results substantially alleviate the bath-discretization bottleneck of exact-diagonalization- and configuration-interaction-based impurity solvers and make large-bath and enlarged-orbital calculations more practical.

We prove that two half-space models in the KPZ universality class, exponential last-passage percolation and a family of Poisson-avoiding metrics generalizing colored TASEP, converge to a common scaling limit. This scaling limit is the directed landscape in half-space, a random directed metric in the half-plane indexed by a parameter which determines the strength of the boundary interaction. As part of our analysis, we characterize the half-space directed landscape in terms of the half-space KPZ fixed point, and prove convergence of geodesics. We also give an explicit construction of joint stationary measures (or horizons) in half-space for the log-gamma polymer, the KPZ equation, exponential and geometric last passage percolation, and the directed landscape itself.

Optical readout of MicroPattern Gaseous Detectors (MPGDs) makes use of the high granularity of imaging sensors to achieve good spatial resolution for radiation imaging and particle detection. CF4 is widely used as a scintillating gas because its emission lies in the visible range, where optical sensors are most sensitive. However, to reduce reliance on greenhouse gas, such as CF4, which also has limited availability, alternative gas mixtures emitting scintillation light in the ultraviolet range can be used in combination with wavelength shifters. We investigate the spatial resolution achievable with optically read out Gaseous Electron Multipliers (GEMs) and Micromegas when using solid wavelength shifter layers such as Tetraphenyl butadiene (TPB). TPB coatings on the anode of glass Micromegas achieve the best spatial resolution of 0.22 mm owing to the minimal distance between the origin of the scintillation light and the wavelength shifter. Nevertheless, TPB layers were also shown to achieve moderate spatial resolution in combination with optically read out GEMs.

We prove positivity of a weighted holographic energy for four-dimensional spacetimes with negative cosmological constant whose conformal boundary at infinity is conformally static and admits either spherical sections, or toroidal sections with compatible spin structure.

Chang'E-4 (CE4), the first mission to soft-land on the lunar farside, provides a unique opportunity for astronomical observations from an environment shielded from terrestrial radio interference, and thus serves as pathfinder for lunar farside radio search for extraterrestrial intelligence (SETI) studies. We present a search for periodic technosignatures using low-frequency radio observations from the CE-4 mission, the first radio SETI study based on data from on the observation in lunar farside. We analyze the CE4 dynamic spectra with a component-level framework that combines principal component analysis (PCA), cross-antenna basis alignment, as well as temporal periodicity and frequency comb structure diagnostics. No final periodic candidate signal is found after the selection procedure, and we therefore find no evidence in the present CE4 sample for a credible periodic artificial signal. This study serves as a pathfinder and provides a practical framework for lunar radio SETI analysis. As more future lunar missions begin to incorporate radio instrumentation, lunar farside may become a promising site for expanding radio SETI research.

We report our new lattice results for the sphaleron rate calculated within a thermal effective field theory of soft SU(N) gluons whose momenta are below the magnetic scale, where $N=2,3$, for a wide range of temperatures spanning from $0.6$-$10^{15}$ GeV at sufficiently large volumes. Comparing these results with sphaleron rates in a non-thermal SU(N) plasma where the infrared gluons are over-occupied, we estimate the typical thermalization time for these ultra-soft soft gluons during the early stages of reheating after inflation. We also calculate the thermal production rate of relativistic axions due to these non-perturbatively interacting soft gluons which shows a significant deviation from its perturbative estimate even at the electroweak scale.

In this paper, we establish the global well-posedness of the one-phase Muskat problem with surface tension for small initial data. This problem describes the motion of the interface separating a wet region from a dry region within a porous medium, a process governed by Darcy's law. Although physically essential, the inclusion of surface tension introduces an additional challenge. We prove that if the initial free boundary is sufficiently small in $H^s$, $s>d/2+1$, then the problem admits a unique global strong solution. Moreover, the solution converges to zero in Lipschitz norm as $t\rightarrow\infty$. To the best of our knowledge, this work constitutes the first global well-posedness result for the one-phase Muskat problem with surface tension.

We show that every group $G$ embeds malnormally into a simple, complete co-Hopfian group $H$. This implies that a non-trivial endomorphism of $G$ extends to $H$ if and only if it is an inner automorphism, strengthening a theorem of Schupp and answering a question of Bergman.

Strict minimum message length (SMML) is an information-theoretic coding principle that represents a continuous statistical model by a finite set of assertions and a partition of the sample space. We show that the SMML objective decomposes into assertion entropy and conditional cross-entropy, balancing the cost of identifying an assertion against the cost of encoding data under the assigned model. For any fixed partition, the optimal codepoint for each cell is the model distribution that minimises Kullback-Leibler divergence from the data distribution restricted to that cell. Using the local Fisher-Rao geometry of regular parametric models, we show that, under high-resolution regularity conditions, optimal SMML partitions are asymptotically the pullback, through the maximum likelihood estimator, of weighted Fisher-Rao Voronoi tessellations in parameter space, with assertion probabilities appearing as additive weights. For regular exponential families, SMML codepoints satisfy a moment-matching condition and admit an interpretation as KL/Bregman centroids, while exact SMML cells are pullbacks of convex polyhedra in sufficient-statistic space. Together, these results show that SMML induces a natural information-geometric quantisation linking entropy-based coding, KL projection, and divergence-based Voronoi geometry.

This paper develops a cooperative fault-tolerant tracking framework for heterogeneous networked linear systems subject to sensor faults and external disturbances. Each unit employs an augmented $\mathcal{H}_\infty$ observer that jointly reconstructs the system state and unknown sensor fault, providing disturbance-attenuated estimation guarantees. An inner state-feedback gain is synthesized through convex $\mathcal{H}_\infty$ Linear Matrix Inequalities (LMIs) to ensure robust closed-loop stabilization and disturbance rejection, while an outer distributed integral action eliminates steady-state tracking offsets and enables cooperative tracking of a setpoint source. The resulting cooperative error dynamics are shown to satisfy an Input-to-State Stability (ISS) property with respect to disturbances and residual estimation uncertainty, and converge exponentially to zero in the disturbance-free case. Furthermore, vanishing cooperative error guarantees network-wide consensus tracking of the desired setpoint. Numerical studies on heterogeneous DC-motor networks with star, cyclic, and path communication topologies demonstrate accurate state and fault estimation, robust cooperative tracking, and resilience against disturbances and time-varying sensor faults. The proposed framework provides a scalable and robust coordination strategy for interconnected systems operating under sensing imperfections and uncertain environments.

We used the FourStar near-IR camera on Magellan-Baade to obtain high S/N H-Band imaging of 66 galaxies with radial velocities of 2000 < V < 5000 km/s. Our goal was to use the superior distance measurements of surface-brightness-fluctuations (SBF) to derive ``peculiar velocities'' to test claims that the CMB dipole anisotropy, equivalent to $\approx$600 km/s with respect to the Local Group, arises from a 'local' overdensity in the galaxy/dark-matter distribution -- the Great Attractor. SBF's ability to measure distances with 5% accuracy confirms a strong flow over a steradian of the sky peaking at Vpec $\sim$ 1000 km/s and converging to zero at D $\approx$70 Mpc from the Local Group. The modest spatial extent of this flow $R_V$ $\sim$ 5000 km/s is consistent with the original Great Attractor model (a diameter D $\sim$ 140 Mpc), as well as the magnitude and direction of the CMB dipole anisotropy, and the power spectrum of CMB fluctuations -- the latter two arguably the most secure measurements in astrophysics. In contrast, our results are at-odds with reports of comparable amplitude 'bulk flows' on scales of hundreds of Mpc that themselves may be inconsistent with the expected fluctuations in the CMB for a $Λ$CDM universe. We contend that only distance-estimators as accurate as SBF are able settle the question of whether the CMB dipole arises from the gravitational influence of large-scale structure within, or without $\sim$100 Mpc of the Local Group.

Reliable approximations for correlation functions at intermediate and strong coupling remain hard to obtain for general quantum field theories. Perturbative expansions are often asymptotic or have a finite radius of convergence, which limits their applicability beyond weak coupling. Here we combine weak- and strong-coupling expansions and propose to use two-point Padé schemes to construct approximants. For lattice $ϕ^4$ theory, we show that this two-point interpolation strategy yields accurate global approximations to the two-point correlation function across broad coupling regimes and compares favorably with standard one-point resummation methods. We also provide heuristic explanations for the observed convergence behavior and discuss the practical range of validity of the approach.

The orbifold lattice has been proposed as a route to practical quantum simulation of Yang--Mills theory, with claims of exponential speedup over all known approaches. Through analytical derivations, Monte Carlo simulation, and explicit circuit construction, we identify compounding costs entirely absent in Kogut--Susskind formulations: a mass-dependent Trotter overhead that scales as $m^4$, non-singlet contamination that grows as $m^2$ and worsens with penalty terms, and a mandatory mass extrapolation. Monte Carlo simulations of SU(3) establish a universal scaling: the continuum limit forces $m^2 \propto 1/a$, binding the Trotter step to the lattice spacing through a cost unique to orbifolds. For a fiducial $10^3$ calculation, the orbifold is $10^4$--$10^{10}$ times more expensive than every published alternative. These results indicate that the claimed computational advantages do not at present survive quantitative scrutiny.

Variable-length Markov chains (VLMCs) are a flexible class of higher-order Markov models that admit a natural representation as context trees. Existing Bayesian methods for specifying prior distributions on tree structures rely on branching processes, but these suffer from a fundamental limitation. The connection between branching probabilities at individual nodes and the structural properties of the induced tree distribution is not straightforward, making it difficult to construct priors encoding specific structural beliefs. We address this limitation by introducing a novel representation of prior distributions on tree space based on context-tree functions. By directly specifying weights for individual contexts through a function on nodes, our approach provides an intuitive mechanism for incorporating structural hypotheses into the prior. This class of distributions maintains computational tractability, allowing marginal likelihoods and posterior mode trees to be computed exactly via generalizations of the Context Tree Weighting (CTW) and Context Tree Maximizing (CTM) algorithms. Exact Bayes factor computation enables rigorous model comparison and hypothesis testing. We demonstrate the flexibility and effectiveness of our approach through simulation studies comparing different prior specifications, and develop practical algorithms for selecting the maximal depth and performing model selection based on Bayes factors.

Quantum computers promise transformative speedups, but environmental noise destroys their fragile states. Conventional quantum error correction (QEC) encodes information redundantly across physical qubits, yet fails above a threshold of about $1\%$ and incurs polynomial qubit overhead. A recent theorem [Shiraishi2024] from the resource theory of coherence shows that catalytic covariant operations amplify coherence at an unbounded rate, but this result has never been cast as an operational protocol. The challenge is to turn an asymptotic theorem into a recovery scheme that works at any noise strength with realistic resources. Here we show that catalytic coherence amplification can be cast as an error-correction primitive, Catalytic Quantum Error Correction (CQEC), which recovers a known target state from noisy copies without any error \emph{magnitude} threshold whenever the target's coherent modes are preserved. Whereas existing QEC degrades above its threshold, CQEC maintains $F > 0.999$ across 200~configurations spanning $d = 4$--$64$, and the impractical $n^{*} \sim d^{4} e^{2γ}$ copy requirement is reduced by nine orders of magnitude via a three-stage pipeline combining CPMG dynamical decoupling, Clifford twirling, and recursive swap-test purification, yielding $F_\mathrm{cat} > 0.96$ from only eight noisy copies. These results turn an abstract resource-theoretic statement into a concrete tool complementary to stabilizer- and purification-based QEC. By replacing a quantitative threshold with a qualitative condition on the support of coherence, CQEC enables ancillary modules within surface-coded processors to be repaired far beyond the conventional threshold; an open-source package reproducing all results in $\sim$30\,s accompanies this work (arXiv:2603.25774, https://github.com/deeptell-inc/cqec).

The ability to simulate human privacy decisions has significant implications for aligning autonomous agents with individual intent and conducting cost-effective, large-scale privacy-centric user studies. Prior approaches prompt Large Language Models (LLMs) with natural language user statements, data-sharing histories, or demographic attributes to simulate privacy decisions. These approaches, however, fail to balance individual-level accuracy, human auditability, token efficiency, and population-level representation. We present Narriva, an approach that generates text-based synthetic privacy personas to address these shortcomings. Narriva grounds persona generation in prior user privacy decisions, such as those from large-scale survey datasets, rather than purely relying on demographic stereotypes. It compresses this data into concise, human-readable summaries structured by established privacy theories. Through benchmarking across five diverse datasets, we analyze the characteristics of Narriva's synthetic personas in modeling both individual and population-level privacy preferences. We find that grounding personas in past privacy behaviors achieves up to 87% predictive accuracy, improving over a non-personalized LLM baseline by 6-17 percentage points across datasets, while yielding an 80-95% reduction in prompt tokens compared to in-context learning with raw examples. Finally, we demonstrate that personas synthesized from a single survey can reproduce the aggregate privacy behaviors and statistical distributions of entirely different studies.

Experimental implementations of Hamiltonian dynamics are often affected by dissipative noise arising from interactions with the environment. This raises the question of whether one can detect the presence or absence of such dissipation using only access to the observed time evolution of the system. We consider the following decision problem: given black-box access to the time-evolution channels $e^{t\mathcal{L}}$ generated by an unknown time-independent Lindbladian $\mathcal{L}$, determine whether the dynamics are purely Hamiltonian or contain dissipation of magnitude at least $ε$ in normalized Frobenius norm. We give a randomized procedure that solves this task using total evolution time $\mathcal{O}(ε^{-1})$, which is information-theoretically optimal. This guarantee holds under the assumptions that the Lindblad generator has bounded strength and its dissipative part is of constant locality with bounded degree. Our work provides a practical method for detecting dissipative noise in experimentally implemented quantum dynamics.

Cavity optomechanics has enabled slow-to-fast light conversion, but traditional optomechanic systems suffer from limited tunability due to fixed mechanical frequencies. To address this constraint, we introduce a magnon degree of freedom into an optomechanical system, constructing a system that integrates photons, phonons, and magnons. We establish the theoretical model of the optomagnonic-Laguerre-Gaussian rotational system, and present numerical simulations of Fano resonances and group delay. By manipulating the magnon degree of freedom, we not only achieve slow-to-fast light conversion associated with magnons but also successfully realize such conversion effects associated with mechanical rotation-this achievement effectively overcomes the inherent tunability limitations of pure optomechanical systems and expands the frequency coverage of light conversion effects. Notably, we numerically demonstrate bidirectional light speed conversion (slow-to-fast and fast-to-slow) via continuous control field frequency modulation to tune cavity mode detuning. Additionally, our results show that adjusting optomagnonic parameters enables dynamic switching between slow light and fast light at multiple frequencies. This work provides a flexible platform for multi-frequency light speed control, with potential applications in all-optical networks and quantum communications.

In this work we investigate Weighted Model Enumeration (WME): given a Boolean formula and a weight function over its satisfying assignments, enumerate models while accounting for their weights. This setting supports weight-driven queries, such as producing the top-k models or all models above a threshold. While related to AllSAT, Weighted Model Counting, and MaxSAT, these paradigms do not treat selective enumeration under weights as a native solver task. We present CDCL-based algorithms for WME that integrate weight propagation, weight-based pruning, and weight-aware conflict analysis into both chronological and non-chronological backtracking frameworks. Chronological backtracking exploits implicit blocking and keeps the clause database compact, thereby reducing memory footprint and enabling efficient propagation. In contrast, non-chronological backtracking with clause learning supports explicit blocking and restarts. We show that both approaches are feasible and complementary, highlighting trade-offs in pruning effectiveness with weights and clarifying when each performs best.

Cryogenic Rayleigh-Benard convection (RBC) at very high Rayleigh numbers (Ra) serves as a key system for understanding buoyancy-driven industrial and large scale natural flows and for testing theories of turbulent convective heat transport. Cryogenic helium experiments allow one to reach extremely high Ra under well-controlled laboratory conditions; however, interpretation of the resulting heat-transfer scalings remains sensitive to non-Oberbeck-Boussinesq (NOB) effects, experimental uncertainties, as well as a number of corrections that ought to be applied to raw data, including corrections for the adiabatic temperature gradient, parasitic heat leaks, or finite thermal conductivity of plates and sidewalls of RBC cells. We present an analysis of experimental uncertainties and data corrections procedures applicable to cryogenic RBC experiments, specifically to those performed in cylindrical RBC cells in Brno: measurement uncertainties, parasitic effects, choice of 4He working points in the p-T diagram and evaluation of relevant properties of the particular working fluid in connection with the available thermophysical property databases. In particular, our study highlights the necessity of rigorous uncertainty analysis for assessing experimental evidence suggesting either transition to the ultimate regime of RBC due to intrinsic ultimate-regime dynamics or as a manifestation of NOB effects and experimental imperfections.

Analog circuit design remains highly dependent on expert knowledge due to the complexity of device-level interactions and topology design. Recent transformer-based approaches for device-level topology generation have shown promise, yet they suffer from low electrical validity without human-in-the-loop (HITL) training and severe memorization caused by sequence-based circuit representations. In this work, we propose AnalogToBi, a framework for device-level analog circuit topology generation. AnalogToBi introduces circuit-type conditioning for categorizing heterogeneous multi-type topology datasets, device renaming augmentation to mitigate memorization, a bipartite graph representation for improved structural generalization, and grammar-guided decoding to enforce structural validity during bipartite graph generation. Experimental results demonstrate that AnalogToBi achieves high validity and novelty without HITL training while effectively avoiding memorization of training topologies. Our code is available at https://github.com/Seungmin0825/AnalogToBi.

We propose a unified model for jet formation applicable to active galactic nuclei, young stellar objects, and X-ray binaries. In this model, the binding energy released from the accretion disk is primarily stored as turbulence rather than being radiated away, leading to the formation of advection-dominated accretion flows. Near the central object, a thick accretion disk with funnel-like structures develops. Within the turbulent flows, the smallest stable blobs can be accelerated beyond the escape velocity through the combination of two mechanisms - the Gaussian-like velocity distribution within the turbulence and a mechanism involving the combined effects of inward pressure force and angular momentum conservation.These rapidly moving blobs may exit through the funnels, collectively forming two opposing jets. This model predicts that jets originate from the innermost region of the thick disk surrounding the central object. The formation of jet is directly related a parameter ηthat describes the energy fraction stored in turbulence in units of the binding energy of local Keplerian energy. η> 0.5 is a minimal condition for jet to form. This model can be extended to account for jet formation in active galactic nuclei, young stellar objects, X-ray binaries, and other analogous astronomical systems.

In realistic pursuit-evasion scenarios, abrupt target maneuvers generate unavoidable periods of elevated uncertainty that result in estimation delays. Such delays can degrade interception performance to the point of causing a miss. Existing delayed-information guidance laws fail to provide a complete remedy, as they typically assume constant and known delays. Moreover, in practice they are fed by filtered estimates, contrary to these laws' foundational assumptions. We present an overarching strategy for tracking and interception that explicitly accounts for time-varying estimation delays. We first devise a guidance law that incorporates two time-varying delays, thereby generalizing prior deterministic formulations. This law is driven by a particle-based fixed-lag smoother that provides it with appropriately delayed state estimates. Furthermore, using semi-Markov modeling of the target's maneuvers, the delays are estimated in real-time, enabling adaptive adjustment of the guidance inputs during engagement. The resulting framework consistently conjoins estimation, delay modeling, and guidance. Its effectiveness and superior robustness over existing delayed-information guidance laws are demonstrated via an extensive Monte Carlo study.

Uranium borides are promising candidate fuel forms for use in advanced nuclear reactors due to their high thermal conductivity and potential for dual use as both fuel and burnable absorber materials. In this work, uranium tetraboride (UB$_4$) and uranium monoboroncarbide (UBC) composites were synthesized using an industrially scalable borocarbothermic reduction method. The high-temperature structural evolution of the as-synthesized borides was investigated using in situ synchrotron X-ray diffraction (SXRD). The oxidation behavior was further characterized using a combination of SXRD and thermogravimetric analysis (TGA), allowing direct comparison with other potential accident-tolerant fuels such as UB$_2$, U$_3$Si$_2$, UC, and UN. The UB$_4$-UBC composite shows higher uranium loading than monolithic UB$_4$ and demonstrates promising oxidation behavior at elevated temperature, pointing to its potential as an improved uranium boride-based fuel form.