Electrocardiogram (ECG) foundation models represent a paradigm shift from task-specific pipelines to generalizable architectures pre-trained on large-scale unlabeled waveform data. This survey presents a unified and deployment-aware review of foundation models and medical large language models (LLMs) for ECG intelligence in cardiovascular disease (CVD) diagnosis, monitoring, and clinical decision support. The central thesis of this survey paper is that next-generation cardiovascular AI systems will be inherently agentic, requiring the synergistic integration of two complementary model classes: (i) ECG foundation models that act as signal-level interpreters, learning rich electrophysiological representations via self-supervised and multimodal pretraining, and (ii) medical LLMs, trained on biomedical text corpora, that function as knowledge-based reasoning backbones for contextual inference, guideline alignment, and clinical decision support. Thus, the survey systematically reviews existing pool of generalist medical LLMs, as well as ECG foundation models that utilize techniques such as self-supervised learning, multimodal ECG-language alignment, vision transformer architectures, and possess capabilities such as zero-shot classification, automated report generation, and longitudinal risk modeling. Recognizing the constraints of consumer-grade wearable edge devices, we further examine model optimization techniques such as quantization, pruning, knowledge distillation, as well as the role of small language models in enabling low-latency, energy-efficient, and privacy-preserving ECG intelligence on edge platforms such as smartwatches. Finally, we outline future directions in multimodal ECG foundation models, agent-driven monitoring, and explainable, secure edge intelligence, with particular emphasis on real-time, on-device cardiovascular analytics in consumer electronics ecosystems.

Two-dimensional (2D) carbon networks, from pristine graphene to defect-rich and amorphous monolayers, exhibit a complex structure-energy landscape governed not only by local bonding but also by medium-range order and network topology. Capturing these multi-scale effects in a compact, interpretable, and data-efficient manner remains a major challenge for machine learning (ML) in low-dimensional materials. In this work, we introduce the CARBON-2D Topological Descriptor (C2DTD), a physically informed structural representation specifically designed for 2D carbon systems. The descriptor integrates local geometric statistics, a compact radial structural signature, and explicit primitive ring topology into a fixed-length, invariant vector that is both computationally efficient and directly interpretable. Benchmarking on diverse datasets of 2D carbon allotropes and defect-engineered graphene sheets demonstrates that C2DTD achieves robust predictive performance in small-data regimes, outperforming generic high-dimensional featurization schemes while preserving physical transparency. Unsupervised manifold analysis reveals a smoother alignment between descriptor space and the DFT energy landscape, and feature-importance and ablation studies confirm that ring topology emerges as a dominant energetic driver, particularly under vacancy-induced reconstruction. Furthermore, controlled simulations with 5-15% random vacancies show that C2DTD naturally captures the progressive transition from hexagon-dominated graphene to topologically disordered networks, enabling both dataset-level and structure-specific interpretation. Owing to its compactness, interpretability, and strong physics-based inductive bias, C2DTD provides a fast and generalizable framework for data-driven modeling, defect analysis, and high-throughput screening of 2D carbon materials.

Null forming is increasingly essential in modern wireless systems for spectrum-sharing, anti-jamming, and covert communications in contested and congested environments. Achieving deep nulls, however, is far more demanding than conventional beam steering: nulls are intrinsically narrow, and even small phase, timing, or gain mismatches across RF chains can significantly degrade suppression. This work develops and validates a self-calibrating SDR architecture tailored for high-fidelity null forming using a compact reference transmitter directionally coupled to the antenna feeds. We demonstrate the effectiveness of the approach through simulation and experimental measurements on an SDR platform operating from 3.0 to 3.5GHz, a band of growing importance for Department of Defense spectrum-sharing initiatives.

Two-Stage Vehicle Routing Problems with Stochastic Demands (VRPSDs) form a class of stochastic combinatorial optimization problems where routes are planned in advance, demands are revealed upon vehicle arrival, and recourse actions are triggered whenever capacity is exceeded. Following recent works, we consider VRPSDs where demands are given by an empirical probability distribution of scenarios. Existing approaches rely on integer L-shaped (ILS) cuts, whose coefficients are tailored for specific recourse policies. In contrast, we propose a framework that casts recourse policies as solutions of a higher-dimensional mixed-integer program, and we characterize its convex hull in the original lower-dimensional space via a new class of inequalities called scenario recourse inequalities (SRIs). We show that SRIs are valid for any recourse policy satisfying mild assumptions and are sufficient for formulating the VRPSD under a scenario-optimal recourse policy, where the recourse actions are chosen optimally for each scenario. Under this latter policy, we also show that SRIs dominate several ILS cuts. We conduct computational experiments on the VRPSD with scenarios under both the classical and the scenario-optimal recourse policies. By using the SRIs, our algorithm solves 329 more instances to optimality than the previous state-of-the-art ILS algorithm.

We introduce the concept of relational depth of a finite semigroup $S$ whose $J$-classes form a chain. It captures how far down in the ideal structure one is obliged to go in order to define the semigroup by generators and defining relations. We determine the exact value for the relational depth of an arbitrary ideal in the full transformation monoid, symmetric inverse monoid and in the partial transformation monoid.

Confocal microscopy is the cornerstone of cellular biology and biomedical research due to its non-destructive imaging, compatibility with live cells, sensitivity, optical sectioning, and subcellular resolution. To meet the demand for rapid three-dimensional imaging, we propose a novel approach using a mode-selective photonic lantern (MSPL). This fiber-based device transforms single-mode light into multiple linearly polarized modes, allowing simultaneous detection of multiple planes. Using a four-port MSPL to manipulate three group modes (LP$_{01}$, LP$_{11}$, and LP$_{21}$), we demonstrate high-throughput imaging simultaneously with multiple planes. This technique exploits differences in focus sections across modes, enabling individual multi-plane detection via a spatial division multiplexer, with some trade-off in resolution and field of view.

This tutorial contrasts probabilistic modeling and robust optimization to determine decisions in humanitarian logistics, specifically supply chains subject to adversarial (natural and human) disruptions. Natural disruptions induce dispatch of long-haul relief supply movement as storm forecasts evolve. A two-step workflow: (i) computes an initial pre-staging plan from the most likely forecast, and (ii) evaluates that fixed plan across plausible deviations in the eventual landfall location. In this way, dispatch decisions balance lead time and improved forecast information. For last-mile distribution, we propose deliveries when transportation networks must be protected against the worst case. We apply an iterative robust routing method that detects high-concentration links and increases their effective cost to promote route diversification. A case study based on Typhoon Noru (2022) shows how the combined approach identifies an optimal dispatch time and then protects last-mile delivery from difficult-to-predict network disruptions that could jeopardize the entire supply-chain operation.

We study a problem of online targets coverage by a drone or a sensor that is equipped with a camera or an antenna of fixed half-angle of view $α$. The targets to be monitored appear at arbitrary positions on a line barrier in an online manner. When a new target appears, the drone has to move to a location that covers the newly arrived target, as well as already existing targets. The objective is to design a coverage algorithm that optimizes the total length of the drone's trajectory. Our results are reported in terms of an algorithm's competitive ratio, i.e., the worst-case ratio (over all inputs) of its cost to that of an optimal offline algorithm. In terms of upper bounds, we present three online algorithms and prove bounds on their competitive ratios for every $α\in [0, π/2]$. The best of them, called \FA is significantly better than the other two for $π/6 < α< π/3$. In particular, for $α=π/4$, its worst case, \FA has competitive ratio $1.25$, while the other two have competitive ratio $\sqrt{2}$. Finally, we prove a lower bound on the competitive ratio of online algorithms for a drone with half-angle $α\in [0, π/4]$; this bound is a function of $α$ that achieves its maximum value at $α= π/4$ equal to $(1+\sqrt{2})/2 \approx 1.207$.

Large-scale online platforms and marketplace systems often evaluate new policies through experiments that randomize treatment across operational units (e.g., geographies, regions, or clusters) over many time periods. In these settings, standard A/B testing can be inefficient or unreliable due to a limited number of units, substantial cross-unit heterogeneity, non-stationarity, and potential carryover across periods. We propose Sequentially-Rerandomized Switchback Experiments (SRSB), a new experimental design that helps mitigate these challenges. SRSB re-randomizes treatment at each time period such as to enforce balance on pre-specified prognostic variables constructed from past observations. In the absence of carryover, SRSB improves precision by leveraging temporal dependence through balancing lagged outcomes and covariates; we develop finite-sample randomization inference under a sharp null as well as asymptotic inference as the number of periods grows. We then extend SRSB to settings with first-order carryover and introduce a blocked SRSB variant that rerandomizes within strata defined by the previous treatment to form stable and comparable "stay" groups. Extensive simulations demonstrate the practical gains and robustness of SRSB relative to standard switchback designs.

In sixth-generation (6G) networks, the deployment of large numbers of Internet of Things (IoT) users (IU) necessitates efficient resource utilization and reliable connectivity, making resource allocation a critical factor. Specifically, the upper mid-band (FR3) spectrum has emerged as a promising candidate for 6G systems due to its favorable balance between bandwidth availability and coverage. However, translating these spectral advantages into performance gains in dense IoT environments requires intelligent management of interference and propagation impairments. In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted IoT network operating in the FR3 band to enhance coverage and improve signal quality. Furthermore, we formulate a joint power allocation and IU-RIS association problem to maximize the achievable sum rate under practical channel conditions and power constraints. The resulting problem is nonconvex and combinatorial due to interference coupling and binary association variables. To address this challenge, we develop a multiphase resource allocation framework that integrates a successive convex approximation (SCA)-based power allocation scheme combined with a matching-theory-based user association algorithm. Simulation results demonstrate that the proposed scheme significantly outperforms conventional greedy and random search schemes in terms of sum-rate enhancement.

Quantum and classical systems can consistently be coupled via non-unitary time-irreversible mechanisms. In this paper we characterize which kind of corresponding dynamics converge in the stationary regime to a thermal hybrid state, that is, a density matrix that maximizes the hybrid arrangement entropy under the constraints of a canonical ensemble. Introducing a detailed balance condition, it is found that a specific subclass of hybrid Lindblad equations fulfill the demanded requirement. The main theoretical results are exemplified through a set of specific examples that in addition lighten how the thermal state of each subsystem in isolation is affected by their mutual coupling. In particular, a Gaussian thermal state could become a bimodal distribution when increasing the interaction strength of a classical subsystem with a quantum two-level subsystem.

Recent experiments on CeCoIn5 -- a prototypical d-wave superconductor -- indicate that its normal state lies near an unconventional quantum critical point (QCP). One intriguing hypothesis is that quantum-critical fluctuations promote fractionalization of localized 4f moments into fermionic spinons. This fractionalized Fermi liquid (FL*) scenario provides a comprehensive framework for the unconventional QCP and superconductivity, and can reconcile a "missing" Fermi-surface volume relative to the Luttinger count in the normal state of CeCoIn5. To test this possibility, we performed inelastic neutron scattering (INS) measurements on CeCoIn5 across the superconducting transition and corresponding theoretical analysis. Our high-precision spectra reveal detailed momentum and temperature dependence of the spin resonance and a structured spin excitation continuum persisting even in the normal state, placing stringent constraints on the physical picture of pairing in a d-wave superconductor. We show that a Kondo-lattice framework incorporating proximity to FL* physics and d-wave pairing reproduces key features of the data. The model suggests that both the quasi-localized nature of the f-moments above Tc and the resonance below Tc arise from common underlying gauge dynamics, implying a unifying organizing principle linking spin fractionalization and unconventional superconductivity in strongly correlated metals.

We extend the hyperplane arrangement framework for neural network expressivity from the braid to discriminantal arrangements. Compatible piecewise linear functions are characterized by circuit relations and admit a matroidal description via Mobius inversion, with dimension equal to the number of independent sets. For circuits of size three, functions are determined by values on subsets of size at most two.

In this paper, we prove a conjecture by Daniele Mundici on the sum of squared distances between consecutive elements in the $Q$-th Farey sequence for $Q\in\mathbb{Z}$ and $Q\geq 2$.

Distributed ML workloads rely heavily on collective communication across multi-GPU, multi-node systems. Emerging scale-up fabrics, such as NVLink and UALink, enable direct memory access across nodes but introduce a critical destination-side translation step: translating Network Physical Addresses (NPAs) to System Physical Addresses (SPAs), which we term Reverse Translation (Reverse Address Translation). Despite its importance, the performance impact of Reverse Address Translation remains poorly understood. In this work, we present the first systematic study of Reverse Address Translation in large-scale GPU clusters. Using an extended ASTRA-sim framework with Omnet++ as the network backend, we model Link MMUs and Link TLBs and evaluate their effect on All-to-All collective communication across varying input sizes and GPU counts. Our analysis shows that cold TLB misses dominate latency for small, latency-sensitive collectives, causing up to 1.4x performance degradation, while larger collectives benefit from warmed caches and experience diminishing returns from over sized TLBs. Based on these observations, we propose two avenues for optimization: fused pre-translation kernels that overlap Reverse Address Translation with computation and software-guided TLB prefetching to proactively populate likely-needed entries. These techniques aim to hide translation latency, particularly for small collectives, improving throughput and scalability for inference workloads. Our study establishes a foundation for designing efficient destination-side translation mechanisms in large-scale multi-GPU systems.

As Urban air mobility scales, commercial drone fleets offer a compelling, yet underexplored opportunity to function as mobile sensor networks for real-time urban traffic monitoring. In this paper, we propose a decentralized framework that enables drone fleets to simultaneously execute delivery tasks and observe network traffic conditions. We model the urban environment with dynamic information values associated with road segments, which accumulate traffic condition uncertainty over time and are reset upon drone visitation. This problem is formulated as a mixed-integer linear programming problem where drones maximize the traffic information reward while respecting the maximum detour for each delivery and the battery budget of each drone. Unlike centralized approaches that are computationally heavy for large fleets, our method focuses on dynamic local clustering. When drones enter communication range, they exchange their belief in traffic status and transition from isolated path planning to a local joint optimization mode, resolving coupled constraints to obtain replanned paths for each drone, respectively. Simulation results built on the real city network of Barcelona, Spain, demonstrate that, compared to a shortest-path policy that ignores the traffic monitoring task, our proposed method better utilizes the battery and detour budget to explore the city area and obtain adequate traffic information; and, thanks to its decentralized manner, this ``meet-and-merge" strategy achieves near-global optimality in network coverage with significantly reduced computation overhead compared to the centralized baseline.

Alladi's duality identities (1977) provide a fundamental relation between the smallest and the $k$-th largest prime factors of integers. In this paper, we establish these dualities in the setting of global function fields, extending a result of Duan, Wang, and Yi (2021) to higher orders. We apply this to study a function field analogue of the sum $\sum μ(n)ω(n)/n$, when restricted to integers whose smallest prime factor lies in an arbitrary subset of primes possessing a natural density. These results demonstrate how the second-order duality identity governs the asymptotic behaviour of these weighted Möbius sums in the function field setting.

Learning to think like a physicist (LTP) is often cited as a central goal of graduate physics education, yet what this means in practice and the extent to which physics graduate education prepares students to develop LTP and view LTP as valuable to their research and teaching remain unclear. This interview-based study, conducted with seven physics graduate students at one US public research university, explores how students define thinking like a physicist and how their coursework and research experiences correlate with this development. Students emphasized that physics uniquely requires integrating physical and mathematical concepts in ways that go beyond other science disciplines. Our findings show that physics core courses, particularly electricity and magnetism, frequently emphasize mathematical techniques and content coverage at a rapid pace at the expense of deeper conceptual engagement and development of LTP. In contrast, physics elective courses and research experiences were more synergistic with and effective in fostering conceptual understanding, problem-solving skills, and identity development as physicists. Because graduate students simultaneously take core courses, conduct research and teach introductory physics, their perspectives on LTP are particularly valuable in how physics departments may consider transforming their preparation. Their voices highlight how this transformative stage of training can either support or hinder the development of physicist thinking.

We investigate the influence of quantum-gravity (QG) induced corrections on the entanglement entropy associated with four-flavor neutrino oscillations in vacuum, incorporating an additional sterile neutrino in the (3+1) framework. Using the von Neumann entropy as a measure of quantum correlations, we analyze how Planck-scale suppressed modifications to the neutrino mass-squared differences and the extended mixing matrix affect the evolution of entanglement during successive oscillation cycles. The quantum-gravity corrections are implemented through a dimension-5 effective field theory operator that modifies the four-flavor PMNS matrix and all six mixing angles above the GUT scale. We find that the atmospheric mixing angle θ_{23} undergoes the largest deviation due to Planck-scale effects, while angles θ_{14}, θ_{24}, and θ_{34} remain essentially unchanged. The resulting QG-corrected oscillation probabilities produce characteristic deviations in the entanglement entropy profile as a function of L/E, providing a sensitive probe of Planck-scale physics within a four-flavor neutrino phenomenology framework.

We study DESI DR1 galaxies to quantify colour dependence on cosmic web environment for three tracers spanning complementary regimes: BGS ($0.15\le z<0.55$), LRG ($0.6\le z<0.9$), and ELG ($0.6\le z<1.6$). Web environments are reconstructed with the tidal-tensor (T-Web) formalism on a $256^3$ grid in an $800\,Mpc$ cube and classified into voids, sheets, filaments, and knots. Sheets and filaments dominate volume ($\sim 45$--$48\%$ and $\sim 37$--$40\%$), voids $\sim 6$--$16\%$ knots $\sim 4$--$6\%$. A mass-dependent Otsu method separates red and blue populations. The BGS red fraction evolves non-monotonically: at $z\approx0.20$, voids ($13.89\pm5.76\%$), sheets ($6.13\pm1.27\%$), filaments ($9.24\pm1.66\%$), knots ($6.12\pm3.42\%$); at $z\approx0.30$, values range from $0.63\pm0.44\%$ to $2.01\pm0.99\%$; at $z\approx0.50$, from $17.93\pm0.44\%$ to $19.63\pm1.08\%$; environmental differences are small. LRGs show environment-dependent quenching: at $z\approx0.66$, knots ($65.90\pm0.45\%$), voids ($62.40\pm1.81\%$), filaments ($60.21\pm0.48\%$), sheets ($58.37\pm3.15\%$); by $z\approx0.88$, these converge to $\sim 68$--$70\%$. ELGs exhibit strong redshift evolution: filaments drop from $55.18\pm0.31\%$ at $z\approx0.65$ to $33.22\pm0.21\%$ at $z\approx0.95$; voids and sheets show similar declines, with weak and non-monotonic. High-mass selection increases red fractions but preserves trends. Relative red and blue fractions (RRF/RBF) show filaments and sheets host the largest shares of both red and blue galaxies; knots contribute less despite elevated red fractions. The $(g-r)$ colour distributions reveal an enhanced red component in knots and bluer colours in voids, with the clearest bimodality at low redshift. Overall, stellar mass drives the primary quenching trend, while environment provides a systematic secondary modulation, strongest in dense knots and at lower stellar masses.

We explore the problem of estimating the steady state temperature in a two-dimensional domain at a point knowing the temperature to high order at another point. We find connections to the Bergman kernel of the domain, Runge's theorem, and approximate null quadrature identities.

Smart and connected mobility systems rely on 5G edge infrastructure to support real-time communication, control, and service differentiation. Achieving this requires adaptive resource management mechanisms that can react to rapidly changing traffic conditions. In this paper, we propose RL-Loop, a closed-loop reinforcement learning framework for real-time CPU resource control in 5G network slicing environments supporting connected mobility services. RL-Loop employs a Proximal Policy Optimization (PPO) agent that continuously observes slice-level key performance indicators and adjusts edge CPU allocations at one-second granularity on a real testbed. The framework leverages real-time observability and feedback to enable adaptive, software-defined edge intelligence. Experimental results suggest that RL-Loop can reduce average CPU allocation by over 55% relative to the reference operating point while reaching a comparable quality-of-service degradation region. These results indicate that lightweight reinforcement learning--based feedback control can provide efficient and responsive resource management for 5G-enabled smart mobility and connected vehicle services.

We compute explicit homological invariants of a trimmed graded double Ore extension of the quantum plane. For a pilot family of type (14641), we determine the minimal graded free resolution and graded Betti numbers of the trivial right module and also compute linear resolutions for two natural cyclic right modules. This provides a concrete link between the PBW structure of the algebra and the homological behavior of its natural quotients.

Renewable power sources have low marginal pro-duction costs, but may result in high balancing costs due to the inherent production uncertainty. Current day-ahead markets elicit only point production profiles and neglect the degree of uncertainty associated with each generating asset, preventing the market operator from accounting for balancing costs in day-ahead dispatch and ancillary service procurement. This increases total system costs and undermines market efficiency, especially in renewable-heavy power systems. To address this, we propose a new market clearing paradigm based on a two-stage mechanism, where producers report their production forecast distribution in the day-ahead stage, followed by the realized production in the real-time stage. By extending the Vickery-Clarke-Groves (VCG) payments to the two-stage setting, we show appealing properties in terms of incentive compatibility and individual rationality. An electricity market case study validates the theoretical claims, and illustrates the effectiveness of the proposed mechanism to reduce system costs.

Prior distributions must be specified for the parameters of interest in a Bayesian clinical trial. When existing evidence on the effects of the trial interventions is limited, prior distributions can be constructed with expert elicitation. However, conventional elicitation requires face-to-face interactions and intensive pre-elicitation training, which can be infeasible. Our remote elicitation was based on established expert elicitation methods. We used bivariate prior distributions for dependencies between elicited quantities. We elicited a prior distribution for the Croup Dosing Trial, which will assess the number of return visits to the emergency department within 7 days in children with croup. This trial evaluates the non-inferiority of 0.15 mg/kg of dexamethasone, compared to the standard dose of 0.60 mg/kg to treat croup. We conducted three remote workshops to elicit expert beliefs on the efficacy of the two doses of dexamethasone. Each workshop consisted of two survey rounds, separated by a group discussion. Prior to the workshop, experts reviewed provided literature on the effects of the two doses of dexamethasone. Beliefs were aggregated with expert-specific bivariate distributions. The aggregated distribution and surveyed non-inferiority margin determined the sample size. Twelve emergency medicine physicians participated in our remote elicitation exercise. The elicitation generated a prior distribution centered at 6% for the 0.60 mg/kg dose and 8% for the 0.15 mg/kg dose. The aggregated prior distribution produced a sample size of 1850, based on a non-inferiority margin of 4%. We elicited a prior distribution that incorporated past evidence and expert opinion. The elicited prior is consistent with literature on the efficacy of the dexamethasone doses in treating croup. Our approach demonstrates the feasibility of remotely eliciting bivariate distributions for clinical trials.

We study random multivariate $P$-polynomials in $\mathbb{C}^d$ with monomial supports constrained to $nP\cap\mathbb{Z}_+^d$ for a convex body $P\subset\mathbb{R}_+^d$, and deterministic coefficients admitting a uniform exponential profile $f$ on $P$. Assuming the tail condition $\mathbb{P}(\log(1+|ξ_0|)>t)=o(t^{-d})$ on the i.i.d. complex coefficients, we prove that the normalized potentials $\frac1n\log|\mathbf{P}_n|$ converge in probability in $L^1_{\mathrm{loc}}(\mathbb{C}^d)$ to a deterministic toric plurisubharmonic function $Φ_{P,f}$, and consequently the normalized zero currents $\frac1n[Z_{\mathbf{P}_n}]$ converge weakly to the closed positive $(1,1)$-current $dd^cΦ_{P,f}$. Under the stronger logarithmic moment assumption $\mathbb{E}[(\log(1+|ξ_0|))^d]<\infty$, we prove almost sure weak convergence of the zero currents along the full sequence for $d>2$, and along sparse subsequences for $d \le 2$. On $(\mathbb{C}^*)^d$, the limiting potential is given by $Φ_{P,f}(z)=I_{P,f}(\operatorname{Log} z)$, where $I_{P,f}$ is the Legendre-Fenchel transform of the profile over $P$ and $\operatorname{Log} (z)=(\log|z_1|,\dots,\log|z_d|)$. These results extend the exponential-profile mechanism of Kabluchko and Zaporozhets from one complex variable to the genuinely multivariate $P$-polynomial setting under relaxed probabilistic assumptions, directly connecting random zero hypersurfaces with convex-analytic data determined by $(P,f)$.

Interplanetary (IP) shocks efficiently modify the proton temperature anisotropy of the solar wind. Analyzing ~800 IP shocks observed by the Wind spacecraft from 1997-2024, we present a statistical study of upstream and downstream proton temperature anisotropy and its dependence on shock geometry, compression, and distance from the shock. We find that (1) quasi-perpendicular shocks produce a pronounced enhancement of perpendicular temperature downstream (Tperp > Tpara), whereas parallel shocks remain near isotropic downstream due to typically stronger upstream Tpara; (2) comparisons with the Chew-Goldberger-Low (CGL) double-adiabatic model reveal geometry-dependent deviations. CGL overestimates downstream perpendicular heating and underestimates parallel heating at quasi-perpendicular shocks, with the opposite trend at quasi-parallel shocks, highlighting the importance of non-adiabatic processes beyond simple compression; (3) Shock-driven anisotropy is strongly localized near the shock and gradually relaxes toward typical solar wind conditions farther downstream as the shock's influence diminishes; and (4) downstream anisotropy is regulated by kinetic instabilities, with quasi-perpendicular shocks constrained by proton cyclotron and mirror instabilities and quasi-parallel shocks limited by the parallel firehose instability. Together, these results show that the evolution of temperature anisotropy at interplanetary shocks is controlled by shock geometry, localized processes, and instability driven regulation.

In this work, we derive the general solutions for a cylindrically symmetric space-time filled with a cosmological perfect fluid obeying $p=γρ$ ($0\leq γ\leq 1$), where $γ=1$ represents a stiff or Zeldovich fluid. Using Marder's metric with coefficients depending on $t$ and $r$, we obtain explicit solutions of the gravitational field equations for the three cases $δ= 1, 0, -1$, corresponding to exponential, power-law, and trigonometric behaviors of the metric functions. The resulting space-times exhibit anisotropic evolution, nontrivial expansion and shear, and curvature singularities, with energy density and pressure profiles determined by the integration constants. These solutions provide a comprehensive framework for modeling cylindrically symmetric cosmologies, offering insights into early-universe dynamics and anisotropic gravitational phenomena. The versatility of the solutions also opens avenues for extensions to higher-dimensional or modified gravity scenarios, making them a valuable tool for both theoretical and phenomenological studies in general relativity.

While recent advances in large language models have significantly improved Text-to-SQL and table question answering systems, most existing approaches assume that all query-relevant information is explicitly represented in structured schemas. In practice, many enterprise databases contain hybrid schemas where structured attributes coexist with free-form textual fields, requiring systems to reason over both types of information. To address this challenge, we introduce OmniTQA, a cost-aware hybrid query processing framework that operates over both structured and semi-structured data. OmniTQA treats semantic reasoning as a first-class query operator, seamlessly integrating LLM-based semantic operations with classical relational operators into an executable directed acyclic graph. To manage the high latency and cost of LLM inference, it extends classical query optimization with data-aware planning, combining atomic query decomposition and operator reordering to minimize semantic workload. The framework also features a dual-engine execution architecture that dynamically routes tasks between a relational database and an LLM module, using operator-aware batching to scale efficiently. Extensive experiments across a diverse suite of structured and semi-structured table question answering benchmarks demonstrate that OmniTQA consistently outperforms existing symbolic, semantic, and hybrid baselines in both accuracy and cost efficiency. These gains are particularly pronounced for complex queries, large tables and multi-relation schemas.

High-pressure xenon gas TPCs with electroluminescent amplification (HPXeEL) provide detailed topological reconstruction of charged-particle trajectories, offering a distinctive two-electron signature for neutrinoless double beta decay ($0ββν$) searches. We have recently proposed ITACA, a detector concept that images both the electron track and the corresponding ion track, carried by the positive ions drifting in the opposite direction. While electrons drift rapidly to the anode for standard EL imaging, the positive ions drift slowly to the cathode with millimetre-scale diffusion, allowing time to determine the event energy and barycenter and to position a movable ion detector at the projected arrival point of the ion cloud. We present a conceptual design of the ITACA detector, addressing key feasibility questions. First, we define the detector geometry and operating parameters for a 1-tonne-scale instrument at 15 bar, including a modular tiled electroluminescent structure. Second, we present the conceptual design of the Magnetically Actuated Rotor System (MARS), the mechanism that positions the ion sensor at any $(r, θ)$ coordinate below the cathode, and show that the expected movement time is fast enough to retain $\sim95\%$ of the drift volume for ion detection, while not significantly perturbing the gas on the scales of the ion drift. Third, we propose using a Topmetal CMOS ASIC-based ion detector as an alternative to the molecular sensor approach described in our original work, enabling real-time, 3D imaging of the ion track without the need for offline laser scanning. Finally, we estimate the sensitivity of the proposed apparatus, showing that enhanced topological discrimination from the ion track, combined with an ultra-low background design, allows exploration of $0ββν$ half-lives in excess of $10^{28}$ yr.