Reconfigurable intelligent surfaces (RISs) have recently attracted interest for non-terrestrial networks (NTNs), especially for improving satellite communication performance. However, RIS-assisted urban NTN designs that jointly support reliable communication and user positioning under blockage, while maintaining low online complexity, remain limited. This paper proposes a blockage-aware and shadowing-aware RIS-assisted framework for joint communication and positioning in an urban low-Earth-orbit (LEO) satellite downlink. A terrestrial RIS is used both to reinforce the blockage-sensitive satellite--user link and to create an additional reflected path that enhances delay-domain positioning observability. We develop a reduced two-dimensional positioning model based on the direct-path delay and the RIS-assisted excess delay, and combine the resulting position error bound (PEB) with the received signal-to-noise ratio (SNR) into a unified utility. A blockage-aware three-mode policy then adapts RIS operation among communication-oriented, balanced, and positioning-oriented modes according to the direct-link condition. To improve robustness, spatially correlated RIS--user shadowing is tracked across coherence blocks using a state-space model and a scalar Kalman filter, and the filtered estimate is used in a robust codebook-based RIS selection strategy with low online complexity. Numerical results show that the proposed framework provides a controllable SNR--PEB tradeoff, improves positioning accuracy while maintaining competitive SNR, stabilizes codeword selection under shadowing uncertainty, and increases joint success probability with RIS size and phase resolution, with diminishing returns at high hardware complexity.

Low-rank matrix recovery can be solved to statistical optimality by convex matrix optimization under the classical assumption of restricted isometry property (RIP). However, for large problems, the convex formulation is commonly replaced by a smooth rank-constrained factored nonconvex problem for which algorithmic theory typically only guarantees convergence to second-order critical points. In this paper, we develop a sharp and statistically optimal theory for second-order critical points of the factored nonconvex matrix LASSO (nuclear-norm--regularized least-squares estimator) under RIP with particular emphasis on the overparametrized regime where the search rank $r$ exceeds the ground-truth rank $r_*$. Our recovery error bounds reveal the precise role of nuclear norm regularization, interpolating between the classical convex rate and known rates for the unregularized nonconvex problem. Complementing this positive result, we give examples showing that, contrary to popular belief, rank overparametrization does not always improve the optimization landscape even under RIP. This negative result raises questions about the fundamental statistical recovery capability of rank-constrained nonconvex approaches in comparison to convex approaches which have worse computational scaling. All of our results generalize to arbitrary convex functions with nuclear-norm regularization under restricted strong convexity and smoothness. In particular, we give sharp conditions under which second-order critical points of the nonconvex problem either (1) approximately recover low-rank approximate minima of the convex problem or (2) exactly recover a low-rank global optimum if one exists.

In this paper, we consider 2D incompressible Euler equations in an unbounded domain with a free surface and a fixed bottom at finite depth. The fluid motion is under the influence of gravity and surface tension. We construct initial data with a flat free surface and small velocity, such that the $L^\infty$ norm of the vorticity gradient has at least a double-exponential growth rate within the lifespan of the corresponding solution. This work generalizes the result of Zlatos to the free-surface setting and Hu--Luo--Yao to the case of an unbounded domain.

This paper proposes an extension to discrete Phase-Type distributions (DPH) by introducing random rewards. These allow for modeling a system in which a visit to a certain state does not emit a deterministic reward. Instead, the rewards follow either a Bernoulli or a geometric distribution. Utilizing this increased flexibility, we further sketch a possible use case for these random rewards by introducing the Inertia-Escalation model (IEM), a process with latent severity levels characterized through two parameters: Inertia ν and escalation η. We also discuss parameter inference for such models. To validate and explore random rewards and the IEM, we conducted extensive simulations and applied the model to two datasets: historical warfare and the Telco customer churn dataset.

We investigate the interplay between shear viscosity and diffusion in a 2D Yukawa liquid subjected to an external magnetic field.

Green software engineering is emerging as a crucial response to information technology's rising energy impact, especially in continuous development. However, there remain challenges in devising automated methods for identifying energy regressions across commits and their associated code change patterns. In particular, little effort has been put into automatically detecting regressions at the commit level by identifying statistically significant changes in energy consumption. In this paper, we introduce EnergyTrackr, an approach designed to detect energy regressions across multiple commits that can then be used to identify code anti-patterns potentially contributing to the increase of software energy consumption over time. We describe our empirical evaluation, including repository mining and source code analysis, made on 3,232 commits from three Java projects, and show the approach's ability to identify significant energy changes. We also highlight recurring anti-patterns such as missing early exits or costly dependency upgrades. We expect EnergyTrackr to assist developers in accurately monitoring energy regressions and improvements within their projects, identifying code anti-patterns, and helping them optimize their source code to reduce software energy consumption.

Many string processing problems can be phrased in the streaming setting, where the input arrives symbol by symbol and we have sublinear working space. The area of streaming algorithms for string processing has flourished since the seminal work of Porat and Porat [FOCS 2009]. Unfortunately, problems with efficient solutions in the classical setting often do not admit efficient solutions in the streaming setting. As a bridge between these two settings, Saks and Seshadhri [SODA 2013] introduced the asymmetric streaming model. Here, one is given read-only access to a (typically short) reference string $R$ of length $m$, while a text $T$ arrives as a stream. We provide a generic technique to reduce fundamental string problems in the asymmetric streaming model to the online read-only model, lifting several existing algorithms and generally improving upon the state of the art. Most notably, we obtain asymmetric streaming algorithms for exact and approximate pattern matching (under both the Hamming and edit distances), and for relative Lempel-Ziv compression. At the heart of our approach lies a novel tool that facilitates efficient computation in the asymmetric streaming model: the suffix random access data structure. In its simplest variant, it maintains constant-time random access to the longest suffix of (the seen prefix of) $T$ that occurs in $R$. We show a bidirectional reduction between suffix random access and function inversion, a central problem in cryptography. On the way to our upper bound, we propose a variant of the string synchronizing sets ([Kempa and Kociumaka; STOC 2019]) with a local sparsity condition that, as we show, admits an efficient streaming construction algorithm. We believe that our framework and techniques will find broad applications in the development of small-space string algorithms.

We develop a wildfire simulation model that evolves the temperature scalar field using an energy balance equation accounting for heat generation, transport, and loss. For these equations, we develop quasi-implicit time integration schemes using direction splitting of the differential operators. We use the Peaceman-Rachford and Strang splitting methods, including the Crank-Nicolson method. Based on these discretizations, we derive variational formulations and explore the Kronecker product structure of the matrices. In the wildfire model, there are some non-linear terms that we treat explicitly. We perform a detailed analysis of how treating these terms affects the stability of the time integration scheme. Namely, we show that a quasi-implicit time integration scheme achieves 10 times higher simulation accuracy. We present two wildfire simulations. The first is a simulation of the 2024 wildfire disaster in the Valparaíso region of Chile. The second one is a simulation of the 2019 wildfire disaster in Las Palmas de Gran Canaria, Spain. We discuss the numerical results and compare them against satellite images and measurement records. We also present a numerical experiment for comparison with the state-of-the-art wildfire simulation model FARSITE. Our sequential code has a linear computational cost of ${\cal O}(N)$. We also present the parallel scalability of the WILDFIRE-IGA-ADS code to illustrate the possibility of running the code on a local workstation.

The young supernova remnant SNR 0540-69.3 in the Large Magellanic Cloud offers a detailed view of an energetic pulsar-wind nebula interacting with the surrounding ejecta. We present infrared observations of the central region of SNR 0540-69.3 obtained with the JWST NIRSpec and MRS integral field units. From the observations we reconstruct the 3D morphology of the strongest emission lines in the inner ejecta ($\lesssim$ 1000 km/s), which reveals the distribution of H I, He I, [Ne II], [Ne III], [S III], [S IV], [Fe II], and [Ni II]. The 3D morphology of most lines is dominated by two highly fragmented lobes of approximately similar size. Based on the assumption that the lobes are symmetric around the pulsar, we infer a pulsar kick velocity of ~300 km/s away from the observer. There are differences in the 3D morphologies of individual emission lines due to a combination of varying physical conditions and abundances. The detection of H I 1.8756 $μ$m in the inner ejecta confirms the classification of the SN as a Type II and shows that hydrogen was mixed down to low velocities of < 400 km/s in the explosion. We compare the results to the Crab nebula and conclude that asymmetries originating in the explosion most likely play a major role in shaping the PWNe.

We show that pseudoconvex Reinhardt domains in dimension two with isomorphic semigroups of holomorphic endomorphisms are biholomorphically or anti-biholomorphically equivalent. Moreover, we show that every Stein manifold that retracts to a properly embedded copy of the punctured complex line, is determined (up to biholomorphic or anti-biholomorphic equivalence) by its semigroup of holomorphic endomorphisms.

Mobile Crowd Computing (MCdC) leverages the idle computational capacity of consumer smartphones to enable distributed task processing at scale; however, widespread real-world adoption remains constrained by the absence of developer-oriented frameworks capable of transparently managing device heterogeneity, fault tolerance, and connectivity volatility. This paper introduces CROWDio, a centralized MCdC platform comprising three tightly integrated subsystems: (i) a declarative SDK that abstracts distributed execution to a single function annotation, eliminating the need for explicit parallelism management; (ii) a tiered checkpointing mechanism that enables fault-tolerant task resumption under the memory and execution constraints inherent to mobile runtimes; and (iii) a pluggable multi-criteria scheduling framework driven by continuous live device telemetry, supporting interchangeable decision strategies without modification to the dispatch core. Empirical evaluation across six heterogeneous Android devices spanning CPU-bound, AI/NLP inference, and data-parallel workloads demonstrates that capability-aware adaptive scheduling reduces total execution time by up to 56.9% relative to naive round-robin dispatch, while the checkpointing subsystem incurs a bounded overhead of only 2-3 s per task regardless of checkpoint frequency. A system-wide Jain's Fairness Index of 0.889 confirms equitable and stable workload distribution across heterogeneous worker devices.

This study experimentally validates a numerical model of electromagnetic propagation through the human head during the pronunciation of different vowels, with the goal of improving our understanding of the underlying physical phenomena. A realistic finite element model was created from magnetic resonance images acquired while pronouncing the vowels /a/, /i/, and /u/. The model was validated against scattering matrix measurements obtained from two subjects whose geometries were modeled. Despite several potential sources of discrepancy, the simulations and measurements showed good qualitative agreement, confirming the validity of the approach. Similar transmission coefficient patterns were observed across subjects for the same vowels. Within the investigated frequency range of (1-6 GHz), the electric field exhibited a Mie scattering pattern. Local minima and maxima in the transmission coefficient, characterizing different articulatory configurations, were correlated with local variations in the electric field amplitude. The transmission coefficient's shape results from an interplay between resonance patterns and antenna placement, while the degree of mouth opening influences the shape of scattering modes. Although technically challenging, this numerical approach proved effective for studying electromagnetic propagation in the human head. The resulting robust numerical model and improved understanding of the underlying physics are expected to facilitate the development of radio-frequency-based silent speech interfaces.

Owing to the extremely low gravity of small near-Earth asteroids (NEAs), it has been assumed that impact-generated rock fragments escape into space and thus do not contribute to the accumulation of regolith. However, centimeter-sized stones returned from the small NEA Bennu by NASA's OSIRIS-REx mission exhibit impact craters up to a few millimeters wide, implying that impact fragments and impact-processed rocks are retained despite the microgravity environment. To understand how, we combined detailed physical analysis of Bennu samples, laboratory experiments of impacts into simulant rocks, and 3D numerical simulations of disruptive impacts into boulders. We find that the majority (85% by mass) of impact fragments eject toward and penetrate the asteroid's weak, porous surface, leading to their retention. In addition, crater depth-to-diameter ratios (d/D) suggest that the Bennu samples (median crater d/D = 0.36 $\pm$ 0.1) are structurally representative of the asteroid's large boulders (median crater d/D = 0.33 $\pm$ 0.08, measured previously). Our analyses indicate that most of Bennu's surface rocks (those with diameters $\lesssim$ 20 m) could be products of in situ collisional disruption. This impact-driven mechanism of regolith production likely occurs on other small NEAs with highly porous surfaces.

Assuming the consistency of $\mathsf{ZFC}$, we construct a model of set theory in which the boldface $\mathbfΣ^1_3$-uniformization property holds, yet the lightface $Σ^1_4$-uniformization property fails, separating these two principles for the first time. We also indicate how to create a universe where $Σ^1_3$-uniformization holds, but $Σ^1_4$-uniformization fails using inner models with large cardinals.

We study whether zero-sum decision rules, maximin and minimax, harm agents' interests in positive-sum strategic environments relative to Nash equilibrium behavior or, more generally, than best response behaviour. Contrary to an influential evolutionary view, we give illustrations where maximin serves an agent's interests better than Nash equilibrium behaviour. We also show that these illustration are not atypical or idiosyncratic because, in our main result, the class of such games where a maximin profile strictly Pareto dominates all Nash equilibria has the same cardinality as the class of games in which a Nash equilibrium strictly Pareto dominates all maximin profiles. Thus, neither behavior is generally superior. We further identify additional mechanisms favoring maximin over Nash equilibrium, including coordination failures under multiple equilibria, where maximin can outperform Nash play in realised-pay-off terms. A systematic analysis of strictly ordinal symmetric 3x3 games shows that these effects arise with non-trivial frequency. Our findings, therefore, suggest that the observed rise in zero-sum thinking in many rich countries, when associated with a maximin decision rule, will not be readily displaced through its generation of inferior pay-offs.

We prove that for solutions of the Euler equation on the sphere, the vorticity gradient can grow at most double-exponentially in time, and we show that this upper bound is sharp by constructing explicit solutions with odd symmetry that exhibit double-exponential growth in the hemisphere. We also extend the results to the case of a rotating sphere. This seems to be the first result on the growth of the vorticity gradient for ideal fluids on a compact manifold with non-trivial geometry.

True random number generators (TRNGs) underpin modern cryptography, yet existing implementations face fundamental trade-offs between speed, scalability, and entropy quality. Here, we demonstrate that stochastic switching in the bistable regime of spin-wave dynamics provides a physical entropy source for high-quality random number generation. Our magnonic random number generator (mRNG), based on a lithography-patterned microstrip on yttrium iron garnet (YIG), exploits thermal fluctuations near the nonlinear bistable regime to generate random bitstreams that pass all 15 NIST SP 800-22 statistical tests at rates with 20 Mb/s. We implement a random-bit multiplier using synchronized mRNG units and demonstrate scalability to 200-nm-wide nanoscale waveguides, establishing spin-wave bistability as a viable physical entropy source for integrated random number generation.

This article discusses the application of stochastic intervention to find the optimal treatment distribution yielding a high value of expected potential outcome under the setting where the number of treatments is allowed to vary with $n$. The primary motivation is to obtain a novel summarization of the effect of various treatments which would guide practitioners towards better decision regarding which intervention to choose.

Neutrino trident scattering is a rare process in the Standard Model characterized by two charged leptons in the final state. In this work, we investigate the possibility of probing the neutrino trident process using the Scattering and Neutrino Detector (SND) at the Large Hadron Collider during its high - luminosity run (HL - LHC). In addition, we present, for the first time, the predictions for the neutrino trident scattering at SHiP beam - dump experiment, where a similar detector is expected to be installed. We demonstrate that these two experiments probe the process in a complementary energy range. Assuming the upgraded detector configuration, we estimate the cross-sections associated with all possibles leptonic final states in coherent and incoherent processes. The corresponding number of neutrino trident scatterings in the SND at HL-LHC and SHiP are presented. Our results indicate that this process can be observed in these forthcoming experiments for some specific combinations of leptons in the final state.

In this paper, we introduce a notion of singularity comparison for plurisubharmonic functions based on the Bedford--Taylor capacity. We establish comparison principles for the complex Monge--Ampère operator on pluripolar sets in the Cegrell classes. As applications, we obtain a characterization of this relation via auxiliary functions in the energy class and prove a corresponding uniqueness result for the Monge--Ampère equation.

For a graph $G$, let $\mathscr{C}_5(G)$ denote the graph whose vertices are the induced $5$-cycles of $G$, where two vertices are adjacent whenever the corresponding cycles share an edge. We investigate the iterative behavior of the pentagon graph operator $\mathscr{C}_5(G)$ , positioning it as the natural continuation of the quadrangle graph operator and the broader induced-cycle graph operator program. We construct explicit pentagon-vanishing, pentagon-periodic, and pentagon-expanding graphs. In particular, the dodecahedron and the icosahedron provide natural periodic examples, while an icosahedral tadpole-hat construction yields expanding families. Our main result proves that every graph is exactly one of three types with respect to $\mathscr{C}_5(G)$: vanishing, periodic, or expanding. The paper suggests a broader theory for the operators $C_k$ generated by induced cycles of fixed length $k$.

We investigate consensus formation and flocking behavior in multi-agent systems subject to two distinct types of delays: a transmission delay accounting for information exchange between agents, and a reaction delay representing the processing time before agents adjust their states. For a simplified linear two-agent system, we provide explicit insight into how these delays affect asymptotic stability. We then derive sufficient conditions for asymptotic consensus and flocking in the general multi-agent setting with a nonlinear, globally positive influence function. These conditions require the delays to be sufficiently small relatively to the initial data and the decay rate of the influence function. The analysis is based on a Lyapunov functional approach combined with a Halanay-type inequality. Our results establish rigorous conditions under which collective behavior emerges in delayed multi-agent systems where both communication and reaction lags are non-negligible, with applications to biological, social, and engineered systems.

Mathematical models are increasingly a part of microbiological research. Here, we share our perspective on how modeling advances the discipline by: (i) enforcing logical consistency, (ii) enabling quantitative prediction, (iii) extracting hidden parameters from data, and (iv) generating intuitive understanding. We map a spectrum of modeling frameworks, from whole-cell simulations to minimal logistic growth equations, and provide interactive examples for some common frameworks. Building on this overview, we outline pragmatic criteria for choosing an appropriate level of description to capture phenomena of interest. Finally, we present a case study in modeling of microbial ecosystems from our own work to illustrate how mechanistic modeling can yield generalizable intuition. This perspective aims to be an introductory roadmap for integrating mathematical modeling into experimental microbiology.

Analyzing correlation between variables is often both the tool and the goal of modern science. A crucial question is whether the correlation between two variables is a direct correlation or only an indirect correlation through a confounder. We review the existing measures of direct correlation and organize them into two families, each corresponding to a systematic construction: (i) removing the direct correlation from the original joint distribution and quantifying the resulting distributional shift, and (ii) intervening on one variable via do-calculus and quantifying how the distribution of the other variable responds. For every Kullback--Leibler-based measure in either family, we propose a Jensen--Shannon-based regularized analogue. Since the square root of the Jensen--Shannon divergence is a bounded metric, the regularized measures take values in $[0,1]$ and are free of the singularity of the Kullback--Leibler divergence. We further analyze the achievable upper bound of each regularized measure under the observed marginal $p(x,z)$, which depends on the alphabet size and is in general strictly below $1$; this sets the correct scale against which observed values should be read. The properties and the differences of the proposed measures are illustrated on a decision-making toy model and on three public real datasets: Titanic survival, UCI Adult (Census Income), and the UC~Berkeley 1973 graduate admissions. Bootstrap $95\%$ confidence intervals are reported for every numerical value.

We highlight the important role of the Fourier transform in deriving inversion formulas for the integral transforms of tomographic imaging. We demonstrate this principle by deriving inversion formulas for the divergent beam transform and the V-line transform, the latter arising in contemporary models of single-scattering optical tomography.

We construct dyadic lacunary counterexamples for two problems of Erdős on pointwise behavior of dilates on the circle. The main device is a dyadic spike block: rare positive spikes create long positive runs in the lacunary averages, while a deterministic lower floor prevents cancellation from the remaining stages. The endpoint construction gives a mean-zero $f\in\bigcap_{1\le q<\infty}L^q(\mathbb T)$ and a sequence $n_j=2^{m_j}$, $n_{j+1}/n_j\ge2$, such that $$ \|f-S_Nf\|_2\ll (\log\log N)^{-1/2}, \qquad \limsup_{N\to\infty} \frac1N\sum_{j\le N}f(n_jx)=+\infty $$ for almost every $x$. Thus Matsuyama's positive theorem at exponent $c>1/2$ cannot be extended to the endpoint $c=1/2$, and Erdős Problem #996 has a negative answer. A second choice of parameters gives, for every $2\le p<\infty$, functions $f\in L^p(\mathbb T)$ with $$ \limsup_{N\to\infty} \frac{\sum_{j\le N}f(n_jx)} {N(\log N)^{1/p-\varepsilon}} =+\infty \qquad(\varepsilon>0) $$ almost everywhere; the case $p=2$ answers Erdős Problem #995. We also include a bounded small-set companion construction.

We present NOEMA3D, a unique high-resolution study of purely molecular gas kinematics at $z \sim 1.1$ to 1.6, providing a dedicated view of cold gas dynamics at the late stages of the peak epoch of cosmic star formation. Using deep ($> 20$ hr on source per target) IRAM-NOEMA CO observations of 10 massive ($10.45 < \log(M^*/M_\odot) < 11.43$)) main-sequence galaxies, complemented by high-resolution JWST imaging, we resolve the molecular gas kinematics and morphology on kiloparsec scales. We find that all galaxies exhibit ordered rotation with moderate intrinsic turbulence (median $σ_0 \sim 32 \pm 10$ km/s, median $V_c/σ_0 \sim 8.6 \pm 2.9$), consistent with dynamically turbulent disks at late cosmic noon. After modeling the axisymmetric rotation with the forward-modeling code DysmalPy, we reveal spatially coherent velocity residuals in all but one more inclined system. The inferred in-plane non circular motions reach amplitudes of $\sim 50$-100 km/s, significantly larger than typically observed in local disk galaxies. Interpreting these non-circular motions as radial flows we find that the velocity residuals spatially coincide with non-axisymmetric structures -- spiral arms and bars -- demonstrating a direct link between galaxy morphology and gas transport at $z \sim 1$-2. In spiral galaxies, the residual velocity patterns are typically dominated by inflows, while barred systems display an apparent inflow-outflow pattern, characteristic of in-plane bar-driven gas motions. We further find that the inferred molecular gas inflow rates are substantial, with a typical net inflow rate of the order of the star formation rate ($\dot M \sim -50 M_\odot$/yr). This implies that spiral arms and bars at cosmic noon are highly efficient at funneling cold gas toward galaxy centers, perhaps driving the buildup of bulges and feeding central star forming regions and supermassive black holes.

Attosecond pulses from X-ray free-electron laser (XFEL) have opened new opportunities for probing ultrafast electronic dynamics on the Angstrom--attosecond spatiotemporal scale. Most attosecond XFEL concepts rely on generating an ultrashort high-current spike through either external laser modulation or accelerator-based beam manipulation. Despite their different implementations, these approaches share the same essential physics, namely that the XFEL amplification is confined to a short effective lasing window within the electron beam. However, existing studies are often scheme-specific and do not yet provide a unified quantitative picture of how fundamental electron-beam properties constrain high-power attosecond performance. In this work, we investigate the general physics and scheme-independent requirements for generating high-power attosecond X-ray pulses from a short current spike. From the perspective of post-saturation superradiant evolution, we show that the effective lasing length of the electron beam governs both the attainable peak power and the pulse duration. We further examine the distinct roles of slice energy spread, slice emittance, energy chirp, undulator tapering, and transverse beam tilt. Our results reveal the trade-off between peak power, pulse shortening, and single-spike probability, and provide facility-independent guidelines for optimizing electron-beam phase-space manipulation toward terawatt-class attosecond XFEL operation.

Treatment of cancer involves heterogeneous, complex care pathways. The relationship between these longitudinal trajectories, baseline mental health, and prognostic outcomes remains poorly understood. We introduce an interpretable time-analysis framework leveraging these temporal dynamics, analyzing care patterns spanning up to 37 years for >8,000 patients. Using Dynamic Time Warping (DTW) and Hierarchical Clustering on sequence data of healthcare encounters, we identified nine distinct, robust trajectory phenotypes. We evaluated their prognostic utility by incorporating them into generalized linear models alongside conventional clinical, demographic, and socioeconomic covariates. The trajectory clusters significantly enhanced mortality prediction and maintained independent predictive significance. Compared to a low-utilization reference group (mortality 31.5%), all eight remaining clusters exhibited substantially higher mortality odds. We uncovered two primary high-risk trajectory patterns: long-term, complex care pathways reflecting chronic disease courses (up to 196 events; mortality OR up to 3.38, 95% CI 2.13-5.37), and shorter but intense trajectories indicating rapid progression (median 78 events; OR 2.32, 95% CI 1.82-2.97). Unexpectedly, the high-utilization complexity clusters were associated with significantly lower baseline anxiety scores, highlighting a divergent relationship between trajectory intensity, mortality risk, and initial psychological burden. These results demonstrate that incorporating temporal healthcare utilization data uncovers robust trajectory phenotypes capturing multidimensional prognostic information. This offers significant explanatory power beyond established static variables for refining risk stratification in precision oncology.

Graph-based code indexing can improve context retrieval for LLM-based code agents by preserving call chains and dependency relationships that keyword search and similarity retrieval often miss. ABCoder is an open-source framework that parses codebases into a function-level code index called UniAST. Its existing parsers combine lightweight AST parsers for syntactic analysis with language servers for semantic resolution, but because LSP-based resolution requires a JSON-RPC call for each symbol lookup, these per-symbol calls become a bottleneck on large TypeScript repositories. We present abcoder-ts-parser, a TypeScript parser built on the TypeScript Compiler API that works directly with the compiler's AST, semantic information, and module resolution logic. We evaluate the parser on three open-source TypeScript projects with up to 1.2 million lines of code and find that it produces reliable indexes significantly more efficiently than the existing architecture. For a live demonstration, watch: https://youtu.be/ryssr7ouvdE