Peak breaking Matrix Multiplication is a promising technique to improve the performance of DL, especially in LLM training and inference. We present FalconGEMM, a cross-platform framework that automates the deployment, optimization, and selection of Lower-Complexity Matrix Multiplication Algorithms (LCMAs) across diverse hardware. There are three key innovations: (1) a Deployment Module that enables portable execution across various hardware and input configurations through code generation; (2) an Execution Module with Group-Parallel Optimizations that maximizes on-chip data reuse, utilizes parallel resources, and reduces bandwidth overhead; and (3) a Decision Module featuring a lightweight analytical performance model to select the optimal strategy based on matrix shapes and hardware profiles. Extensive evaluation is conducted on LLM workloads across GPU (H20, A100) and CPU (ARM, x86) architectures with multiple data types. FalconGEMM succeeds in delivering peak breaking performance and outperforms GEMM libraries (e.g., cuBLAS, CUTLASS, Intel MKL, etc) by 7.59%-17.85% and LCMA competitors like AlphaTensor by 12.41%-55.61%. Our framework makes the theoretical promise of LCMAs practical for production deployment across the heterogeneous landscape of modern hardware.

Near-threshold $J/ψ$ photoproduction has been used to infer scalar gluonic structure from integrated cross-section data. We test whether such an inference is stable against the assumed non-forward continuation of the OPE kernel. With an exponential diffractive slope fixed, the threshold fit favors an additional scalar $G_S(t)$-like contribution. However, the residuals show a systematic $W$-dependent trend. When either the effective slope or the kernel shape is replaced by a physically motivated form-factor continuation, the trend disappears and the fitted scalar coefficient is driven to zero. Thus integrated $σ(W)$ data cannot determine the non-forward OPE kernel or isolate a scalar $G_S(t)$ contribution. Differential $dσ/dt$ data are required.

Eclipse shadow bands are transient intensity patterns that can appear on the ground near solar eclipse totality. This study presents a reusable image-processing framework for analyzing shadow-band video recordings collected with consumer-grade cameras. The framework quantifies band prominence, band orientation, band separation, band velocity and power spectral density from video recordings. Applied to two eclipse datasets, the method detected statistically significant shadow-band activity during eclipse windows that align with the scintillation theory for shadow bands. The findings also indicate that eclipse shadow bands can consist of simultaneous superimposed band modes with different orientations and velocities. This study demonstrates that consumer-grade cameras can support quantitative analysis of shadow bands and may support future observational and atmospheric studies

Subnational monitoring of public health and development indicators often relies on household surveys where data are sparse at the desired spatial resolution. Small area estimation (SAE) methods address this challenge by borrowing strength across areas and incorporating auxiliary information. However, comparing these estimators remains difficult in the absence of ground truth. We propose a design-based cross-validation framework for evaluating small area estimators that accommodates complex survey designs. Our approach enables model-agnostic comparisons between area-level and unit-level SAE models. We derive a decomposition of the conditional mean squared error that yields a consistent cross-validation score, show that finite-sample comparisons carry an unidentifiable bias that can be bounded, and use this bound as a principled threshold for ranking models. We further show that leave-one-area-out cross-validation, a popular alternative, targets extrapolation rather than smoothing error and can reverse the correct ranking. We evaluate the framework through extensive design-based simulations. We apply the framework to compare subnational female literacy estimators in Zambia using the 2024 Demographic and Health Survey. The framework applies broadly across prevalence mapping and other SAE problems and is applicable to any small area estimator irrespective of the underlying model class.

Code generation is one of the tasks for which the use of Large Language Models is widely adopted and highly successful. Given this popularity, there are many benchmarks dedicated to code generation that can help select the best model. However, they primarily focus on measuring solution correctness, leaving other aspects, such as code quality and usability, behind. This paper aims to describe a custom tree-fold evaluation methodology for code generated by Large Language Models that bridges this gap. The methodology includes a dedicated correctness benchmark based on a complex multi-level computer science project, code quality verification, and a survey of developers' opinions on generated code samples gathered through a structured code-review process. The proposed methodology's usage and usefulness are demonstrated by evaluating and comparing three general-purpose Large Language Models: GPT-4.1, DeepSeek-V3-0324, and Claude Opus 4. The results show that reviews gathered from developers can yield many new findings, especially those related to the code being in a production-ready state, that would not be possible to obtain using the standard correctness-focused benchmark approach.

We present several finite-temperature recursive Fermi-operator expansion schemes based on the second-order spectral projection (SP2) method. Our approach builds on a previous observation that the electronic structure problem, as formulated through a recursive SP2 expansion, can be mapped onto the architecture of a deep neural network. Using this perspective, we generalize SP2 to finite electronic temperatures by constructing machine learning models that determine optimized recursive expansion coefficients. The same approach is also applied to the prediction of the electronic entropy for fractional occupation numbers. The coefficients are trained for a specified chemical potential and electronic temperature and are not available in closed analytical form. However, by employing an appropriate affine rescaling strategy to the Hamiltonian matrix, we eliminate the need to retrain the model for different temperatures and chemical potentials. Our approach avoids explicit diagonalization and relies solely on highly optimized matrix-matrix multiplication kernels. Compared to state-of-the-art diagonalization, we achieve an order-of-magnitude speedup in the single-particle finite-temperature density matrix calculation for small and moderately sized matrices on modern GPUs and dense matrix multiply units.

Fluid antenna systems (FAS) exploit antenna position reconfigurability to unlock massive spatial diversity within compact form factors, making them a promising enabler for 6G user terminals (UTs). However, practical port switching incurs latency and signaling overhead, which can be particularly detrimental to hyper-reliable low-latency communications (HRLLC) under finite blocklength operation. This paper investigates FASenabled HRLLC by explicitly capturing the coupled effects of spatial correlation, port switching delay, and finite blocklength coding. We derive exact closed-form expressions for the average block error rate (BLER) and average achievable rate over spatially correlated fading channels. The resulting analysis reveals a fundamental design trade-off: increasing the number of ports improves diversity but linearly reduces the effective blocklength, thereby intensifying finite-blocklength penalties. A key theoretical contribution is a rigorous proof that reliability, achievable rate, and energy efficiency are strictly unimodal in the port dimension, ensuring a unique optimal port configuration. Furthermore, we characterize an explicit switching-delay threshold that separates regimes where FAS yields net gains over fixed-position antenna (FPA) systems. Numerical results validate the analysis and show that substantial HRLLC performance gains are achievable when the switching latency remains below the derived bound.

Collaborative edge computing uses edge nodes in different locations to execute tasks, necessitating dynamic task offloading decisions to maintain low latency and high reliability, especially under unpredictable node failures. Although deep reinforcement learning (DRL) and large language models (LLMs) have shown promise for task offloading, DRL often suffers from poor sample efficiency and local optima, while LLMs are difficult to use directly due to inference overhead and output uncertainty. To address these limitations, we propose \textbf{LeDRL}, a hybrid decision framework that couples a \emph{lightweight LLM} with self-attention-enhanced DRL for real-time task offloading. LeDRL constructs structured, context-aware prompts capturing node status, task semantics, and link dynamics to derive high-level strategy priors. These are selectively processed by a self-attention-based alignment module for context-aware policy optimization. A reflective evaluator further distills semantic feedback from past trajectories to refine subsequent prompts and provide consistent guidance. Extensive experiments show that LeDRL outperforms representative baselines in task success rate, convergence speed, and real-time responsiveness across diverse network scales, achieving over 17\% improvement in success rate. Furthermore, we deploy LeDRL on Jetson-based edge devices using our prototype system \textit{CoEdgeSys}, demonstrating its robustness and feasibility under resource constraints. Our code is available at:https://github.com/GalleyG5/LeDRL.git.

In this work, we present an exact static spherically symmetric vacuum solution of the New General Relativity (NGR) field equations. Unlike the Schwarzschild solution in General Relativity (GR), this solution is characterized by two parameters. Subsequently, using the four classical tests of relativistic gravity (perihelion precession, light bending, Shapiro time delay and gravitational redshift), a more stringent constraint on the value of the second parameter was derived compared to the original work [1].

Approximate nearest neighbor search under universal L_p metrics (ANNS-U-L_p) is an important and challenging research problem, as it requires answering queries under all possible p (0<p <= 2) values simultaneously without building an index for each possible p value. The state-of-the-art solution, called MLSH, is a Locality-Sensitive Hashing (LSH)-based ANNS method with barely acceptable query performance. In contrast, graph-based ANNS methods, which offer significantly improved query efficiency on the ANNS-L_p problem (with a fixed p-value), cannot be naively extended to the ANNS-U-$L_p$ problem. In this paper, we propose U-HNSW, the first graph-based method for ANNS-U-L_p. Our scheme uses HNSW graph indexes built on two base metrics ($L_1$ and $L_2$) to generate promising nearest neighbors candidates, and then verifies these candidates with an early-termination strategy that substantially reduces the number of expensive L_p distance computations. Experimental results show that U-HNSW not only achieves up to 2670 times shorter query times than the original MLSH implementation running on a RAM disk (up to 15 times shorter than the idealized MLSH), but also outperforms the original HNSW on the ANNS-L_p problem (with a fixed p-value), except for a few special p values.

Benthic algae as a primary producer in riverine ecosystems develop biofilms on the riverbed. Their population dynamics involve growth and decay processes, the former owing to the balance between biological proliferation and mortality, while the latter to mechanical abrasion because of the transport of sediment particles. Contrary to the assumptions of previous studies, the decay has experimentally been found to exhibit long-memory behavior, where the population decreases at an algebraic rate. However, the origin and mathematical theory of this phenomenon remain unresolved. The objective of this study is to introduce a novel mathematical model employing spin processes to describe microscopic biofilm dynamics. A spin process is a continuous-time jump process transitioning between states 0 and 1, and the continuum limit of these processes captures the long-memory decay and generates generic growth. The proposed framework leverages heterogeneous spin rates, achieved by appropriately superposing spin processes with distinct rates, to reproduce the long-memory decay. Computational simulations demonstrate the behavior of the model, particularly emphasizing rate-induced tipping phenomena. This mathematical model provides a computationally tractable interpretation of benthic algae dynamics and their long-term prediction, relevant to river-engineering applications.

It is well-known that maximum likelihood estimates converge faster than the classic square root rate if the Fisher information is infinite. This is often the case when the effective region depends on the estimated parameters, or when density has a singularity inside the effective region at a point dependent on the estimated parameters. We present a one-parameter family of bivariate absolutely continuous distributions on the half-space with smooth densities. The effective domain is always the same half-space and does not depend on this parameter. The order of magnitude for the weighted least squares estimate is asymptotically smaller than the classic square root rate. For the Gaussian variance mixture case, the maximum likelihood estimate coincides with this weighted least squares estimate.

The Capital Asset Pricing Model (CAPM) relates a well-diversified stock portfolio to a benchmark portfolio. We insert size effect in CAPM, capturing the observation that small stocks have higher risk and return than large stocks, on average. For some size-based stock portfolios, dividing their returns by the Volatility Index makes them closer to independent and normal. In this article, we combine these ideas to create a new discrete-time model, which includes volatility, relative size, and CAPM. We fit this model using real-world data, prove the long-term stability, and connect this research to Stochastic Portfolio Theory. We fill important gaps in our previous article on CAPM with the size factor.

Courcelle's celebrated theorem states that all MSO-expressible properties can be decided in linear time on graphs of bounded treewidth. Unfortunately, the hidden constant implied by this theorem is a tower of exponentials whose height increases with each quantifier alternation in the formula. More devastatingly, this cannot be improved, under standard assumptions, even if we consider the much more restricted problem of deciding FO-expressible properties on trees. In this paper we revisit this well-studied topic and identify a natural special case where the dependence of Courcelle's theorem can, in fact, be improved. Specifically, we show that all FO-expressible properties can be decided with an elementary dependence on the input formula, if the input graph has bounded pathwidth (rather than treewidth). This is a rare example of treewidth and pathwidth having different complexity behaviors. Our result is also in sharp contrast with MSO logic on graphs of bounded pathwidth, where it is known that the dependence has to be non-elementary, under standard assumptions. Our work builds upon, and generalizes, a corresponding meta-theorem by Gajarský and Hliněný for the more restricted class of graphs of bounded tree-depth.

Stochastic reaction networks with mass-action kinetics provide a useful framework for understanding processes -- biochemical and otherwise -- in homogeneous environments. However, cellular reactions are often compartmentalized, either at the cell level or within cells, and hence non-homogeneous. We investigate a model of compartmentalization in which the rate of fragmentation of a compartment depends on the abundance of some designated species inside that compartment. The particular model of study is part of a general framework for compartmentalized chemistry with dynamic compartments that was proposed in (Duso and Zechner, PNAS, 2020). This paper builds on (Anderson and Howells, Bull. Math. Biol., 2023) where the special case where the compartment dynamics do not depend on their contents was studied mathematically. In particular, we demonstrate that the explosivity characterization from (Anderson and Howells, Bull. Math. Biol., 2023) fails in this setting and provide new sufficient conditions for non-explosivity and positive recurrence, under the assumption that the underlying CRN admits a linear Lyapunov function. These results extend the theoretical foundation for modeling content-mediated compartment dynamics, with implications for systems such as cell division and intracellular transport.

Bayesian online learning provides a coherent framework for sequential inference. However, its theoretical understanding remains limited, particularly in the one-pass setting. Existing theoretical guarantees typically require the mini-batch sample size to diverge, a condition that fails in the one-pass regime. In this paper, we propose a new Bayesian online learning algorithm tailored to the one-pass setting, which incorporates a warm-start phase to ensure stable sequential updates. For this algorithm, we show that the sequentially updated posterior attains the optimal convergence rate. Building on this, we establish an online analogue of the Bernstein-von Mises theorem, which guarantees valid uncertainty quantification without diverging mini-batch sample sizes. Our analysis is based on a novel theoretical framework that differs fundamentally from existing approaches in the online learning literature. Numerical experiments on generalized linear models show that the proposed method matches the performance of the batch estimator while outperforming existing online procedures.

Selectively programming large number of non-volatile synaptic weights without compromising scalability is a key challenge for in-memory computing. Here, we demonstrate remote programming of synaptic weights in series-connected chains of 11 vortex-based magnetic tunnel junctions using broadcast radiofrequency signals applied through a shared strip line. The programming relies on frequency-selective reversal of the vortex-core polarity and therefore does not require individual access lines or selector devices. By reconfiguring the binary states of these chains, we reshape the weighted sums they perform on frequency-multiplexed RF inputs. Using a 22-synapse network composed of two such chains, we remotely reconfigure the same hardware to perform two distinct tasks: handwritten-digit classification and drone RF-signature identification. The digit-optimized configuration reaches 94.91 +/- 0.26% accuracy on handwritten digits but only 13.17 +/- 0.47% on drone RF signatures, whereas the drone-optimized configuration reaches 97.33 +/- 0.62% on drones but only 47.59 +/- 1.5% on digits. Broadcast RF programming thus provides a compact and scalable route to rapidly reconfigurable spintronic neuromorphic hardware.

We study fundamental limitations of the generic Quantum Approximate Optimization Algorithm (QAOA) on constrained problems where valid solutions form a low dimensional manifold inside the Boolean hypercube, and we present a provable route to exponential improvements via constraint embedding. Focusing on permutation constrained objectives, we show that the standard generic QAOA ansatz, with a transverse field mixer and diagonal r local cost, faces an intrinsic feasibility bottleneck: even after angle optimization, circuits whose depth grows at most sublinearly with n cannot raise the total probability mass on the feasible manifold much above the uniform baseline suppressed by the size of the full Hilber space. Against this envelope we introduce a minimal constraint enhanced kernel (CE QAOA) that operates directly inside a product one hot subspace and mixes with a block local XY Hamiltonian. For permutation constrained problems, we prove an angle robust, depth matched exponential enhancement where the ratio between the feasible mass from CE QAOA and generic QAOA grows exponentially in $n^2$ for all depths up to a linear fraction of n, under a mild polynomial growth condition on the interaction hypergraph. Thanks to the problem algorithm co design in the kernel construction, the techniques and guarantees extend beyond permutations to a broad class of NP-Hard constrained optimization problems.

We construct an analytical black hole solution in the Einstein-Maxwell-Yang-Mills theory with a conformally coupled scalar field in four dimensions, which generalizes the charged Martínez-Troncoso-Zanelli (MTZ) black hole in the presence of self-gravitating non-Abelian gauge fields. The internal gauge group is determined by the horizon curvature, becoming $SU(N)$ in the case of positive curvature and $SU(N-1,1)$ when the curvature is negative. Moreover, this solution is employed as a conformal seed to obtain new meronic spacetimes dressed with all (super-)renormalizable contributions of the scalar field, which provides the generalization of the Anabalón-Cisterna (AC) solution when self-gravitating non-Abelian gauge fields are included. Finally, we consider the non-Noetherian extension of the conformal scalar fields, which still yields a second-order conformally invariant scalar equation, even though the action is not. In that case, we show that static black hole solutions can also be charged with Yang-Mills fields.

The RNA world hypothesis suggests a pathway of how life emerged on early earth. It assumes that life started with RNA based systems, capable of storing, transmitting and replicating information, envisioning that monomers and short RNA oligomers interact to form longer strands, eventually becoming catalytically active ribozymes. Key reactions in RNA pools are hybridization, dehybridization, templated ligation, and cleavage. Those reactions depend on many environmental parameters and the wide range of possible configurations among interacting strands. In order to scan such high dimensional parameter spaces, efficient descriptions are needed. Motif rate equations project complex strand reactor dynamics onto sequence motif space. Here we present a Bayesian inference framework to infer their parameters from ligation count data produced by strand reactor simulations. This provides a framework to match the simpler motif rate equations to more complex simulations. Additionally, it is a step towards inferring reaction rate constants directly from experimental data, including rigorous uncertainty estimation. This could be an essential procedure to connect theory and experiment, and deepen our understanding of the essential features necessary for life to emerge.

Regression testing in Continuous Integration (CI) pipelines is increasingly costly due to the growing size and execution frequency of test suites. Test Case Prioritization (TCP) mitigates this problem by reordering tests to expose faults earlier. However, most existing techniques rely primarily on historical execution data and coverage metrics, neglecting the rich structural information contained in code changes. This paper proposes a commit-aware, learning-based TCP method that combines structural properties of version-control diffs, test coverage relations, and historical execution behavior into a unified predictive model. Given a new commit, the method estimates the probability that each test suite will reveal at least one failure and prioritizes test execution accordingly. We evaluate our method on five Defects4J projects using a leave-one-project-out cross-project validation setting. Results show that the commit-aware TCP significantly outperform non-commit-aware-baselines in both classification and prioritization effectiveness. Our findings show that including commit structural semantics substantially enhances regression fault detection and enables robust, generalizable learning-based TCP in CI environments.

The longstanding Nash-Williams conjecture asserts that every $K_3$-divisible graph $G$ with $δ(G)\ge 3n/4$ admits a triangle decomposition. In the random setting, Frankl and Rödl showed that, with high probability, $G(n,p)$ contains a triangle packing covering all but $o(n^2p)$ edges whenever $p\ge n^{-1/2+\varepsilon}$. In this paper, we study near-perfect triangle packings in randomly perturbed graphs. We prove that for every $d>0$ and every $p>2d/(1+2d)$, if $G_d$ is a $dn$-regular graph on $n$ vertices, then with high probability the union $G_d\cup G(n,p)$ contains a triangle packing covering all but $o(n^2)$ edges. Moreover, this bound on $p$ is best possible for $0<d\le 1/2$, thereby determining the threshold in this range. A key ingredient in the proof is a new triangle-weighting lemma for weighted complete graphs.

In this contribution, we introduce a mixed interpolation-regression operator for functions defined on certain planar domains. We focus on an ellipse, an annulus and a polygon. An upper bound for the operator is obtained. Cubature formulas for weight functions defined on these domains are studied. The performance of the interpolation-regression methods is illustrated by numerical examples.

Quantum indistinguishability of non-orthogonal quantum states is a valuable resource in quantum information applications such as cryptography and randomness generation. In this article, we present a sequential state-discrimination scheme that enables multiple parties to share quantum uncertainty, in terms of the max relative entropy, generated by a single party. Our scheme is based upon maximum-confidence measurements and takes advantages of weak measurements to allow a number of parties to perform state discrimination on a single quantum system. We review known sequential state discrimination and show how our scheme would work through a number of examples where ensembles may or may not contain symmetries. Our results will have a role to play in understanding the ultimate limits of sequential information extraction and guide the development of quantum resource sharing in sequential settings.

Semiconductor quantum dots (QDs) offer outstanding quantum-optical properties, making them highly attractive for quantum information technologies. However, combining wide-range electrical tunability, efficient photon extraction, elevated-temperature operation, monolithic silicon integration, and telecom-wavelength compatibility remains a major challenge. Here, we demonstrate electrically contacted circular Bragg grating (eCBG) resonators incorporating InGaAs QDs directly grown on silicon, enabling bright single-photon emission in the telecom O-band. Deterministic electron-beam lithography and a ridge-based vertical p--i--n diode architecture enable precise device integration and electrical control of individual emitters. The QD--eCBGs exhibit a quantum-confined Stark shift of approximately 16 nm (11 meV) at 4 K, representing a record for QDs embedded in nanophotonic structures at telecom wavelengths. This is achieved alongside a photon extraction efficiency of $(21.7 \pm 3.0)\%$ into the first lens, while maintaining excellent radiative properties and high single-photon purity, with $g^{(2)}(0)=0.0078 \pm 0.0012$ below saturation and $g^{(2)}(0)=0.0183 \pm 0.0021$ at saturation under pulsed excitation. Robust antibunching persists up to 77 K, with $g^{(2)}(0)=0.0663 \pm 0.0056$, enabling operation with liquid-nitrogen or compact Stirling cryocoolers. Furthermore, spatially separated QD--eCBGs can be electrically tuned into spectral resonance without degrading photon statistics. These results establish a silicon-compatible, electrically addressable telecom O-band quantum light platform combining wide spectral tunability, high single-photon purity, and elevated-temperature operation, providing a scalable route toward practical photonic quantum networks.

We study the degeneracy of intrinsic and environmental parameters for BH observables in a static spacetime sourced by a nonlinear magnetic monopole immersed in a Hernquist dark-matter halo. We explore four complementary probes; the shadow radius $R_{sh}$, eikonal quasinormal-mode frequencies $Mω_R$, weak gravitational lensing $\hatθ_\infty$, and neutrino-antineutrino annihilation $\dot{Q}/\dot{Q}_{Newt}$, and map their degeneracy contours in the $(g/\mathcal{M},α/\mathcal{M})$ plane at fixed $β/\mathcal{M}$. Different parameter combinations yield signatures nearly indistinguishable from a Schwarzschild black hole single-observable diagnostics cannot uniquely constrain the magnetic charge and halo amplitude. The degeneracy contours are, however, mutually non-parallel: the slopes $dα/dg$ along constant-$R_{sh}$ and constant-$Mω_R$ contours differ by a factor $\sim 5$, so their combination breaks the remaining degeneracy and constrains both parameters simultaneously. We compute the QNMs spectra using a high-order WKB method with Padé resummation. The magnetic charge raises the real oscillation frequency while the halo lowers it; the cancellation is observable-dependent and does not persist across all four channels. An expansion around an asymptotically renormalized Schwarzschild background of mass $\mathcal{M}=M+α$ shows that at fixed $\mathcal{M}$ both sectors reduce $R_{sh}$ at first perturbative order. For weak lensing, $\mathcal{M}$ alone determines the leading deflection, first subleading correction depends on $\mathcal{Q}=g^2+4αβ$, separating total halo mass from halo concentration. For neutrino-pair annihilation, the magnetic charge suppresses the deposition rate by raising the lapse, while the halo enhances it through the reverse mechanism.

We perform a consistency check of DESI DR2 BAO constraints ($D_M/r_d, D_H/r_d)$ by reconstructing the same quantities from DES supernovae (SNe) in bins with the same effective redshift $z_{\textrm{eff}} \in \{ 0.510, 0.706, 0.934 \}$ and a Planck $r_d$ prior. Through mock analysis we show that $D_M(z_{\rm eff})$ and $D_{H}(z_{\rm eff})$ can be locally reconstructed model agnostically from $Λ$CDM and extended models, but only if one employs frequentist methods; purely Bayesian reconstructions from Markov Chain Monte Carlo (MCMC) exhibit bias. We find that the ratio of the three $D_M/r_d$ values at different $z_{\textrm{eff}}$ are consistent with a horizontal, thus confirming that the distance duality relation holds up to calibration. However, the $D_H/r_d$ ratio shows a decreasing trend driven by the $z_{\textrm{eff}} = 0.934$ bin, the significance of which varies from $2.5 σ$ with Bayesian methods down to $1.4 σ$ with frequentist methods. We show that replacing DES with DES-Dovekie SNe reduces the significance to $1.7 σ$ and $1.2 σ$ in Bayesian and frequentist approaches, respectively. We conclude that distances reconstructed from SNe show good agreement with DESI BAO distances across the redshifts studied. We also note that $D_M(z_{\rm eff} = 0.510)/r_d$ reconstructed from SNe favours DESI BAO over transversal BAO against a backdrop of a $3.7 σ$ disagreement.

Purpose: Developing and testing a framework that integrates real-time catheter shape reconstruction, interactive simulations, and mixed reality visualization to enable accurate monitoring of catheter-vessel interactions during endovascular navigation. Methods: A finite element model (FEM) of the venous pathway from the right femoral vein to the inferior vena cava was generated from computed tomography data and implemented into an interactive simulation. Catheter motion was imposed as boundary condition, and catheter-vessel contact was modeled with a Lagrange multiplier formulation to compute vessel deformation. The framework was tested in-vitro using a sensorized catheter with Fiber Bragg Grating and electromagnetic sensors as it was advanced through a silicone replica of the vascular anatomy. Real-time sensor read-outs fed the simulation, and the updated catheter and vessel geometries were streamed to Hololens 2. The performance and accuracy of FEM-computed vessel wall displacement were validated against experimental ground-truth obtained via stereo frames triangulation. Results: The simulated time exceeded the real temporal extent by 12% during initial navigation and by 45% when the catheter reached the most tortuous portion. Hololens 2 rendering remained stable at 35-40 frames per second. The median relative displacement error between FEM-computed and ground-truth vessel wall displacements remained below 1 mm and 2.33 mm for these two phases, respectively. Conclusion: The study demonstrates the feasibility of integrating interactive biomechanical simulation with real-time sensor data to enable continuous monitoring of catheter-vessel interactions, with mixed reality visualization serving as a user interface to support operator decision-making.

Enterprise data exists in many forms, such as tables, text, maps, e-mail, and CAD models, that are access-controlled and hidden behind bespoke interfaces. Current agentic AI systems delegate the entire query workflow to a frontier LLM: a single model interprets the request, selects sources or tools, integrates retrieved evidence, judges completeness, and generates an answer, with few constraints, limited use of schemas, and text as the primary representation throughout. We argue that this is an ineffective abstraction for enterprise data. Reliable agentic AI should instead require structure: a constrained query interface over each source and a table-centric integration layer driven by a query processor. We introduce RUBICON, a system that embodies this vision. RUBICON is based on two observations. First, text-to-SQL fails on real enterprise data and must be dramatically subsetted to achieve reliable results. Second, data integration across disparate corporate datasets is best performed using tables as the core abstraction rather than text-centric LLM pipelines. We evaluate RUBICON on two benchmarks: our enterprise-focused RUBICON-Bench, against agentic baselines, and SemBench, against LOTUS and Palimpzest. On RUBICON-Bench, where queries require coordination across heterogeneous enterprise sources, RUBICON achieves 100% end-to-end accuracy, while all agentic baselines, including single- and multi-agent ReAct systems, produce no correct answers. On SemBench, RUBICON surpasses both LOTUS and Palimpzest: it achieves 14.7% higher accuracy, reduces latency by 62.64%, and lowers token cost by 98.64%, demonstrating that a table-centric architecture better matches enterprise data while yielding significant efficiency gains.

Jet modification in heavy-ion collisions provides microscopic access to the properties of the quark-gluon plasma. However, conventional approaches based on traditional global observables, such as \(R_{AA}\), capture limited information about the complex dynamics of parton-medium interactions during hard scatterings. In this work, we apply sequential machine learning architectures to the jet declustering history tree, achieving improved classification performance compared with static models that learn only from a single stage of the jet evolution. We find that models trained on different medium implementations exhibit meaningful performance modification under cross-domain validation, indicating that machine learning is sensitive to implementation-specific features that traditional observables may not resolve. These results suggest new opportunities for using machine learning as an analysis tool to overcome some of the limitations of traditional jet-modification studies.