We present a multi-wavelength study of the Flat Spectrum Radio Quasar PKS 1222+216, analyzing its long-term variability of radio data obtained in 2013-2020 from the iMOGABA, MOJAVE, and VLBA-BU-BLAZAR programs, along with $γ$-ray data from Fermi-LAT. We found that the radio flux densities at 15, 22, 43, and 86 GHz declined exponentially by 37%-56% over a year following a $γ$-ray flare in November 2014. We estimated jet physical parameters through Gaussian model fitting of VLBA 43 GHz data, identifying 10 jet components. The cooling timescales of the jet emission regions, i.e., newly ejected components C9, C10, and C11, range from 43 to 222 days, with the estimated jet viewing angles of approximately 8 degrees and magnetic field strengths of 77-134 mG in the jet emission regions. Additionally, by determining the magnetic field strength at different frequencies, we found that the magnetic field scales as $B\propto r^{-0.3\pm0.04}$, indicating a non-equipartition condition ($k_\text{r}\gtrsim 1$) or a slow decline in magnetic field strength profile ($m<1$). By analyzing component ejection times, we discovered that the $γ$-ray flare in 2014 coincided with the interaction between the moving component C9 and the stationary feature A1. We estimated that the $γ$-ray emission region is located at $9.2\pm1.0$ pc from the central engine, beyond the BLR and dusty torus, suggesting that the seed photons for inverse Compton scattering originate from the jet itself, external CMB radiation, or a surrounding sheath. Our results favor a scenario where $γ$-ray emission originates further downstream from the central engine through interactions between moving and stationary components. Additionally, our study presents an alternative method for estimating magnetic field strengths in AGNs undergoing long-term synchrotron cooling based on the associated timescale.

Utilizing established risk factors and prognostic models can often improve the construction of a newer risk model that uses novel biomarkers in a smaller, internal study. However, directly borrowing information from an established prognostic model is often unsuitable due to differences in study populations, patient outcomes measured, and other specific features of the internal study design. To better enable the use of established prognostic information when constructing a novel risk model, we propose an estimation approach centered around the idea that the risk rankings rather than the risk scores from an established prognostic model are often more transportable to the internal study context. To leverage external ranking information, our approach introduces the ranking parameters associated with the regression coefficients of an internal risk model and estimates the internal risk model parameters by penalizing a ranking-based discrepancy measure between the ranking parameters and the rankings implied by the established prognostic model. Our method does not require the external prognostic model to have a specific form, but only requires one to compute risk score rankings from an external model. Simulation studies demonstrate that our method leads to competitive predictive performance and performs particularly well when the true internal and external prognostic models have high rank correlation but large discrepancies between their underlying risk scores. We demonstrate the use of our approach through the development of a prognostic model for advanced prostate cancer patients who were treated with an immune checkpoint inhibitor

Spoofing attacks are among the most destructive cyber threats to terrestrial systems, and they become even more dangerous in space, where satellites cannot be easily serviced, and operators depend on accurate telemetry to ensure mission success. When telemetry is compromised, entire spaceborne missions are placed at risk. Prior work on spoofing has largely focused on attacks from Earth, such as injecting falsified uplinks or overpowering downlinks with stronger radios. In contrast, onboard spoofing originating from within the satellite itself remains an underexplored and underanalyzed threat. This vector is particularly concerning given that modern satellites, especially small satellites, rely on modular architectures and globalized supply chains that reduce cost and accelerate development but also introduce hidden risks. This paper presents an end-to-end demonstration of an internal satellite spoofing attack delivered through a compromised vendor-supplied component implemented in NASA's NOS3 simulation environment. Our rogue Core Flight Software application passed integration and generated packets in the correct format and cadence that the COSMOS ground station accepted as legitimate. By undermining both onboard estimators and ground operator views, the attack directly threatens mission integrity and availability, as corrupted telemetry can bias navigation, conceal subsystem failures, and mislead operators into executing harmful maneuvers. These results expose component-level telemetry spoofing as an overlooked supply-chain vector distinct from jamming or external signal injection. We conclude by discussing practical countermeasures-including authenticated telemetry, component attestation, provenance tracking, and lightweight runtime monitoring-and highlight the trade-offs required to secure resource-constrained small satellites.

Code agents powered by large language models can execute shell commands on behalf of users, introducing severe security vulnerabilities. This paper presents a two-phase security analysis of the OpenClaw platform. As an open-source AI agent framework that operates locally, OpenClaw can be integrated with various commercial large language models. Because its native architecture lacks built-in security constraints, it serves as an ideal subject for evaluating baseline agent vulnerabilities. First, we systematically evaluate OpenClaw's native resilience against malicious instructions. By testing 47 adversarial scenarios across six major attack categories derived from the MITRE ATLAS and ATT\&CK frameworks, we have demonstrated that OpenClaw exhibits significant inherent security issues. It primarily relies on the security capabilities of the backend LLM and is highly susceptible to sandbox escape attacks, with an average defense rate of only 17\%. To mitigate these critical security gaps, we propose and implement a novel Human-in-the-Loop (HITL) defense layer. We utilize a dual-mode testing framework to evaluate the system with and without our proposed intervention. Our findings show that the introduced HITL layer significantly hardens the system, successfully intercepting up to 8 severe attacks that completely bypassed OpenClaw's native defenses. By combining native capabilities with our HITL approach, the overall defense rate improves to a range of 19\% to 92\%. Our study not only exposes the intrinsic limitations of current code agents but also demonstrates the effectiveness of human-agent collaborative defense strategies.

In this paper, we implement and evaluate a conditional diffusion model for asset return prediction and portfolio construction on large-scale equity data. Our method models the full distribution of future returns conditioned on firm characteristics (i.e.\ factors), using the resulting conditional moments to construct portfolios. We observe a clear bias--variance tradeoff: models conditioned on too few factors underfit and produce overly diversified portfolios, while models conditioned on too many factors overfit, resulting in unstable and highly concentrated allocations with poor out-of-sample performance. Through an ablation over factor dimensionality, we reveal an intermediate number of factors that achieves the best generalization and outperforms baseline portfolio strategies.

Movable antennas (MAs) have emerged as a promising technology for wireless sensing by reconfiguring antenna positions to exploit additional spatial degrees of freedom (DoFs). This paper investigates a robust movable antenna placement strategy for near-field wireless sensing to minimize the worst-case squared position error bound (SPEB). By temporarily relaxing the minimum inter-element spacing constraint, we first establish the optimality of centro-symmetric antenna position distribution, which simplifies the identification of the worst-case source, locating it at the array broadside on the Rayleigh boundary. Moreover, by leveraging moment-based analysis with the Richter-Tchakaloff theorem, we derive a closed-form optimal solution with three points supported on the center and two edges of the array. Guided by this structural insight, we finally develop an efficient three-point discrete deployment strategy to ensure the minimum inter-element spacing. Simulations demonstrate that the proposed design consistently outperforms conventional fixed antenna arrays and matches the exhaustive search benchmark at negligible computational complexity.

Propagating uncertainties introduced by chemical reaction rate parameters to high-fidelity numerical simulations of complex combustion devices is necessary to ascertain impact on computational predictions. However, the high cost of detailed computations combined with the need to conduct multiple simulations to propagate uncertainty makes such an estimation computationally challenging. In order to reduce the computational cost, a two-step framework for quantifying uncertainty introduced by detailed chemical kinetics model parameters using reduced chemistry models is developed here. First, reduced-manifold states are uniquely reconstructed in full-composition space by following trajectories at an unburnt mixing state and integrating forward to a prescribed progress variable constraint. Second, parametric uncertainty is propagated by sampling perturbed rate coefficients from mechanism covariance matrices and integrating each realization to the target state, yielding uncertainty maps for reduced-space quantities. The method is applied in two configurations: a subsonic multi-tube combustor with interacting jet flames and recirculation, and a three-dimensional reacting high-speed flowpath. Uncertainty-instrumented estimated are reported for a trajectory time (time for the reconstructed unreacted mixture to reach the local target state) and for the time to equilibrium, revealing order-of-magnitude spatial variations driven by mixing, stratification, and residence-time effects. The largest relative variability occurs in low-to-intermediate temperature regimes associated with induction and the onset of heat release, where branching-related chemistry amplifies sensitivity, particularly away from stoichiometric conditions. The method provides a scalable route to spatially resolved, physically interpretable chemistry-UQ for practical reacting-flow simulations.

We study a multi-field model in Loop Quantum Cosmology for a maximally symmetric spacetime governed by the Einstein--Hilbert action minimally coupled to scalar fields. Using a Legendre transformation, we formulate the Hamiltonian dynamics in canonically equivalent geometrodynamical and Yang--Mills--type representations, incorporating nontrivial couplings through a geometric structure on the multi-field configuration space. Implementing the $\barμ$-scheme polymerization, we obtain the loop-quantum-corrected Friedmann equations. By focusing on the two-field model as an example, we analyze the effective dynamics for specific potentials. The \textit{quantum bouncing, transition, and slow-roll inflationary} phases are investigated numerically, and viability of the models is assessed by evaluating the number of e-folds during the inflationary phase for certain given initial conditions. The global behavior of the background evolution is further examined through linear stability and dynamical-systems analyses.

In this paper, we give a complete description of the black hole threshold, locally near the Reissner-Nordström family, in the infinite-dimensional moduli space $\mathfrak M$ of dynamical spherically symmetric solutions to the Einstein-Maxwell-neutral scalar field system. In a neighborhood of the full Reissner-Nordström family in $\mathfrak M$, we prove the following: (i) Any solution that forms a black hole eventually decays to a Reissner-Nordström black hole. (ii) Any solution that fails to collapse into a black hole eventually becomes superextremal along null infinity and exists globally in the domain of dependence of the bifurcate characteristic initial data. (iii) The subset of this neighborhood consisting of black hole solutions admits a $C^1$ foliation by hypersurfaces of constant final charge-to-mass ratio, up to and including extremality. (iv) The mutual boundary between the set of black hole solutions and noncollapsing solutions, i.e., the black hole threshold, is the extremal leaf of the foliation. Black holes which are not on the threshold are asymptotically subextremal. Our quantitative control of near-threshold solutions allows us to prove "universal" scaling laws for the location of the event horizon and its final area and temperature (surface gravity), with critical exponent $\frac 12$. Moreover, we show that the celebrated Aretakis instability is activated for an open and dense set of threshold solutions and that generic near-threshold subextremal black holes experience a transient horizon instability on the timescale of their inverse final temperature.

In [CCHT25], the authors introduced multiple Eisenstein series of arbitrary rank in positive characteristic and the $q$-shuffle algebra $\mathcal{E}$ associated with them. In the present paper, we establish a class of linear independence results for multiple Eisenstein series. We also prove that the $q$-shuffle algebra $\mathcal{R}$ of multiple zeta values embeds into the inverse limit of the spaces of multiple Eisenstein series with respect to the rank $r$, and that $\mathcal{E}$ is isomorphic to the tensor square of $\mathcal{R}$. As an application, we show that $\mathcal{E}$ is an associative algebra, thereby verifying the conjecture proposed in [CCHT25]

We investigate the phenomenology induced by generic PQ-breaking operators within the axion kinetic misalignment framework. We analyze their impact on the relic density of axion dark matter (DM), the PQ quality problem, axion-mediated fifth-force, as well as Big Bang Nucleosynthesis (BBN) and Cosmic Microwave Background (CMB) constraints. A nonzero initial axion velocity gives rise to brief periods of early matter domination and axion kinetic domination, leading to a nonstandard cosmological evolution. We compute the resulting gravitational wave (GW) signal from global cosmic strings and find that, because these nonstandard epochs are extremely short, the signal is highly suppressed and beyond the reach of existing experiments. Finally, we perform a parameter space scan, identify the regions and benchmark point that are consistent with all experimental constraints.

We review the definition and main properties of Kalinin effectivity and describe methods for constructing effective spaces together with several examples. We analyze the Kalinin effectivity of wonderful compactifications and prove that the wonderful compactifications of hyperplane arrangements and of configuration spaces associated to Kalinin effective compact complex manifolds are themselves Kalinin effective. As an application, we show that the Deligne-Mumford space of real rational curves with marked points is effective. Finally, we apply Kalinin effectivity to study Smith-Thom maximality for Hilbert squares.

We study closed subschemes $X$ in ${\mathbb P}^n$ of dimension one, locally defined at any point by at most $n$ equations such that the analytic spread of $I_{\mathfrak{m}}$ is at most $n$, where $I \subseteq \Bbbk[x_0, \ldots, x_n] $ is the defining ideal of $X$ and ${\mathfrak{m}} = (x_0, \ldots, x_n)$. In this situation, we show that, under mild conditions, all the powers of $I_{\mathfrak{m}}$ have positive depth, hence the limit depth of $I_{\mathfrak{m}}$ is $1$ unless $I$ is a complete intersection. Moreover, the regularity of the Rees ring is at most one and the fiber cone is Cohen-Macaulay. This applies to every ideal defining a monomial curve in ${\mathbb P}^3$.

The inverse reconstruction of material properties from optical extinction efficiency (Qext) is constrained by the high-dimensional nature of Mie scattering. We demonstrate that the Qext manifold possesses an intrinsic, physics-governed sparsity universal across dielectric materials. By analyzing the spectral topology of a diverse polymer library, we identify a critical Information Bottleneck at the onset of the Mie transition (r approx 0.1 um), where a peak in spectral entropy signifies a fundamental limit on signal compressibility. While the Fast Fourier Transform (FFT) is conventionally used for spectral analysis, we show it is physically mismatched for this domain; its periodic boundary assumptions induce spectral leakage that forces a massive basis expansion to resolve Mie ripples. Conversely, the Discrete Cosine Transform (DCT) mirrors the non-periodic geometry of extinction profiles, uncovering inherent compressibility by capturing over 90% of signal energy using fewer than 10 harmonic modes. Even at the Mie bottleneck, the DCT maintains a 12-fold compression advantage over the FFT at a 99% energy threshold. Notably, while both bases converge to identical error floors for a fixed energy threshold, the DCT achieves this fidelity with significantly lower hardware overhead. Stress-testing under 10% additive Gaussian noise confirms the Information Bottleneck is spatially and structurally invariant, proving this complexity peak is a fundamental physical constant of the manifold. By mapping this sparsity onto a compressed sensing architecture, we resolve a 2.5-20 um spectral range using between 22 and 170 sensors: enabling a 51%-94% reduction in hardware complexity that breaks the traditional Nyquist sampling limit (350 sensors) for high-fidelity clinical and remote sensing applications.

Symmetry and topology are fundamental concepts deeply intertwined in various fields of physics, especially in the studies of quantum phases of matter. The critical role that Coulomb interactions play in symmetry breaking during topological transitions is a fundamental problem that has not been fully understood. Utilizing gated indium arsenide-gallium antimonide bilayers, we demonstrate that Coulomb interactions play a critical role in symmetry breaking and topological transitions. Whereas the quantum spin Hall insulator (QSHI) dominates the high-density regime, gating the system into the dilute regime enhances interlayer Coulomb interactions and leads to an emergent excitonic topological order (ETO) with spontaneous time-reversal-symmetry breaking. Moreover, applying a magnetic field drives a transition from the QSHI to the ETO accompanied by Coulomb-induced spin-rotation-symmetry breaking, which selects triplet electron-hole pairing in the lowest Landau levels. These results underscore an intricate interplay between symmetry and topology under Coulomb interactions in electron-hole bilayers.

The even-even superheavy nuclei with $104 \leqslant Z \leqslant 126$ and $N\leqslant 258$ have been investigated using a microscopic five-dimensional collective Hamiltonian (5DCH) based on constrained triaxial relativistic Hartree-Bogoliubov calculations with the PC-PK1 density functional. The 5DCH approach effectively captures the characteristic of isospin dependence of nuclear binding energies, two-nucleon separation energies, and $α$-decay energies across isotopic chains and demonstrates consistent accuracy as $Z$ increases, underscoring the model's predictive power. The collective potentials, average quadrupole deformations, and characteristic collective observables: $E(2^+_1)$, $R_{42}$, and $B(E2; 2^+_1\to 0^+_1)$ reveal a shape transition from well-prolate deformation around $N=150$ and $N=210$ to medium-deformed $γ$-soft shape around $N=176$ and $N=246$, and finally to a spherical shape near $N=184$ and $N=258$ for the isotopic chains with $104\leqslant Z\leqslant 118$. Oblate deformations are favored for $Z\geqslant 120$ isotopes around $N=178$. Remarkably, for a substantial range of transitional superheavy nuclei with $N\gtrsim184$ and $N\gtrsim240$, no $0^+$ states bounded by the fission saddles are predicted within their very shallow potential wells due to quantum shape fluctuations (QSFs). Additionally, sharp variations predicted for two-neutron separation energies $S_{2n}$ and $α$-decay energies $Q_α$ at $N=184$ and $258$ in mean-field calculations are significantly reduced and shifted to $N=182$ and $256$ in the 5DCH calculations, which is caused by the rapid evolution of the dynamical correlation energies related to QSFs around the nuclear spherical shells.

Accurate measurement of the pulse duration of ultrashort, ultra-intense laser pulses at focus is essential for strong-field science. Most existing diagnostics, however, cannot allow direct in situ measurement in the focal region because of damage-threshold limits and unavoidable spatial averaging. We present a direct single-shot far-field diagnostic based on a plasma grating. In this method, the pulse duration is encoded in the axial length of an interference-written plasma grating and retrieved from the corresponding Bragg-diffraction signal. Comparison with near-field (pre-focus) autocorrelator measurements and far-field (at-focus) scanning measurements confirms single-shot pulse-duration retrieval in the focal region over 35-130 fs, and the method remains effective at a peak intensity of $\sim 10^{16}{\rm W/cm^2}$. In principle, the measurable range can be extended to 15-300 fs and to higher peak intensities. The method is insensitive to the laser central wavelength and offers a practical approach to far-field diagnostics in high-power laser systems.

Intra-pixel sensitivity variations (IPSVs) in charge-coupled devices (CCDs) and complementary metal-oxide-semiconductor (CMOS) detectors constitute a significant source of astrometric error for undersampled stellar observations. Since laboratory-based IPSV measurements suffer from limited applicability, we propose a computational method to directly infer IPSV from stellar images and validate it with simulated data. By minimizing the flux residuals between theoretical and observed stellar models through least-squares fitting, we can successfully recover the IPSV, which is treated as nearly identical across pixels. Simulations demonstrate that the reconstructed IPSV achieves high accuracy, and the instrumental point spread function (IPSF) restored using this IPSV improves stellar centroiding by nearly 30$\times$, effectively eliminating periodic pixel-phase errors. The method remains robust under different morphologies of IPSV and varying sampling conditions. Additionally, the framework can be extended to an iterative IPSF-IPSV closed-loop scheme that updates both components simultaneously, providing a practical pathway for continuous detector calibration in future space-based astronomical surveys.

With the increasing volumes of Large Language Models (LLMs) and the expanding context lengths, attention computation has become a key performance bottleneck in LLM serving. For fast attention computation, recent practices often parallelize the attention heads on multiple GPUs, and also widely adopt attention sparsification to reduce the computation amount -- which selectively computes a subset of attention pairs under a preset sparsity budget. In this paper, we notice that attention heads of an LLM model often exhibit heterogeneous-yet-stable sparsity elasticities, which motivates us to enforce head-adaptive sparsity budgets to attain better efficiency while preserving high inference quality. Yet, from the system aspect, with heterogeneous sparsity levels, attention computation time on different heads would be inconsistent, yielding cross-GPU resource bubbles under head-parallel deployment. To further minimize such bubbles, we propose a novel attention deployment strategy called Sparsity-aware Head-Parallel Load Balance (S-HPLB). Experiments on long-context benchmark show that, S-HPLB can achieve a $2.88\times$ improvement in average attention computation latency without quality degradation.

We propose a dynamical model for group formation and switching behavior in systems where each group competes for members through attraction functions that are inversely proportional to their current sizes. This attraction is modulated by group-specific bias terms, which can reflect social, economic, or reputational advantages. New entrants choose groups probabilistically based on these weighted attraction scores. We derive the conditions under which the system converges to a stationary equilibrium, where group proportions remain stable over time. The model exhibits rich nonlinear behavior, especially under varying bias strengths and inverse preference intensities. We analyze equilibrium conditions both theoretically and via simulations.

We propose a pulsar candidate cross matching algorithm to sift radio pulsar search candidates from repeated observations of the same sky location such as globular clusters, high energy sources, or supernova remnants. Our method uses both the candidate spin period ($P$) and dispersion measure (DM) value; if two or more candidates from different observations have similar spin periods to within 1\%, and dispersion measure values within 10\%, they are likely to correspond to the same candidate detection. We have demonstrated the effectiveness of our method through the discovery of the pulsar M12B with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). This pulsar has a spin period of 2.76\,ms and a dispersion measure of $42.70 \pm 0.05\,\mathrm{cm}^{-3}~\mathrm{pc}$. This pulsar has a profile with three peaks, being faint, showing scintillation. It is in an approximately 0.53-day orbit. Our discovery indicates that more pulsars might be effectively discovered if the algorithm is applied to the search results from other archival globular cluster observations.

Extreme solar particle events (ESPEs) are caused by rare, enormously strong solar eruptions and can produce globally detectable spikes in tree-ring radiocarbon 14C, known as Miyake events, which serve as precise chronological tie-points and indicators of extreme solar activity. After production, radiocarbon is subjected to the complex carbon cycle, including large-scale atmospheric transport, which is crucially important for fast and strong Miyake events with highly inhomogeneous 14C production. A new 3D dynamical model, SOCOL:14C-Ex, of the radiocarbon atmospheric production and transport is presented here, which can model fast changes in the 14C atmospheric concentrations with high temporal and spatial resolution. Precise response curves of $Δ^{14}$C to a reference ESPE (100xGLE#69) were computed for various event dates. They can be directly applied to analyse Miyake events under different conditions. Seven strong events over the past 14 millennia (AD 993, AD 774, 664 BC, 5260 BC, 5411 BC, 7177 BC, and 12351 BC) were analysed by fitting the reference curves to the available annual D14C data, identifying the most probable values and confidence intervals of their parameters -- strength, event's date and background level. By applying corrections for the geomagnetic and atmospheric (CO2) factors, the strengths of the corresponding ESPEs were assessed. The strongest ESPE is confirmed to be that of 12351 BC, while that of AD 774 remains the strongest event during the Holocene. To conclude, a new tool, based on the radiocarbon atmospheric transport model SOCOL:14C-Ex, is presented to analyse fast changes in the $^{14}$C production.

Understanding the emergence of the thermodynamic arrow of time in microscopic systems is of fundamental importance, particularly given that unitary evolution preserves time-reversal symmetry. While projective measurements introduce temporal irreversibility, identifying this asymmetry from single evolution trajectories in the presence of stochastic fluctuations presents a considerable challenge. Here, we harness machine learning to identify the arrow of time from individual trajectories generated by a programmable ten-qubit quantum processor based on a nitrogen-vacancy center in diamond. We implement quantum circuits that realize unitary evolutions where heat flows from hotter to colder subsystems and their time-reversed counterparts. Projective measurements inserted in these processes induce entropy production, and their outcomes constitute the evolution trajectory. We demonstrate that an unsupervised clustering algorithm autonomously divides the experimental trajectories into two distinct groups without prior knowledge, while a convolutional neural network identifies the temporal direction of these trajectories with approximately 92% accuracy. In addition, we show that a diffusion-based generative model reproduces essential signatures of directional energy flow and entropy production. Our results establish machine learning as a powerful tool for uncovering underlying physical processes from complex experimental data, advancing the interface between quantum thermodynamics and artificial intelligence.

To meet the evolving demands of sixth-generation (6G) wireless channel modeling, such as precise prediction capability, extension capabilities, and system participation capability, multi-modal intelligent channel modeling (MMICM) has been proposed based on Synesthesia of Machines (SoM) which explores the mapping relationship between multi-modal sensing in physical environment and channel characteristics in electromagnetic space. Furthermore, for integrating heterogeneous sensing, reasoning across scales, and generalizing to complex air-space-ground-sea communication environments, two new paradigms of MMICM are explored, including fine-tuned large language models (LLMs) for Channel Modeling (LLM4CM) and Wireless Channel Foundation Model (WiCo). LLM4CM leverages pre-trained LLMs on channel representations for cross-modal alignment and lightweight adaptation, enabling flexible channel modeling for 6G multi-band and multi-scenario communication systems. WiCo, which pre-trained on physically valid channel realizations and their associated environmental and modal observations, embeds electromagnetic equations for physical interpretability and uses parameterized adapters for scalability. This article details the architectures and features of LLM4CM and WiCo, laying a foundation for artificial intelligence (AI)-native 6G wireless communication systems. Then, we conducts a comparative analysis of the two emerging paradigms, focusing on their distinct characteristics, relative advantages, inherent limitations, and performance attributes. Finally, we discuss the future research directions.

Large language models (LLMs) are increasingly deployed as AI agents that operate in short reasoning-action loops, interleaving model computation with external calls. Unlike traditional chat applications, these agentic workloads require inference serving systems to balance low latency, stable token emission, and throughput under multiple request arrivals from different AI agents. Recent deployments highlight a shift toward running small language models (SLMs) locally on consumer-grade GPUs, driven by privacy, compliance, and cost constraints. When heterogeneous requests overlap on a single GPU, long prefills and short decodes contend for resources, creating head-of-line blocking that destabilizes interactive performance. By analyzing agent workloads, we observe that their execution naturally separates into cold prefills, which process long system prompts, resume prefills, which append tool outputs to cached contexts, and short decodes, which are latency-critical. This mix intensifies contention compared to conventional chatbot serving. We present AgentServe, a single-GPU serving system that ensures stable multi-agent execution under such conditions by isolating prefills from decodes, applying dynamic budgeting to resume prefills, and allocating GPU resources through pre-established CUDA Green Context slots with adaptive control. Evaluation results show that AgentServe significantly improves latency stability while sustaining competitive throughput, achieving up to 2.8x TTFT improvement and 2.7x TPOT improvement over state-of-the-art baselines across different settings.

Exotic nuclear structures such as halos are mostly studied using reactions. In Coulomb breakup, the radioactive projectile dissociates through its interaction with a heavy target. Often, a spectroscopic factor for the core-halo structure is inferred from experimental data. In this work, we present a new calculation of the Coulomb breakup of the one-neutron halo nucleus $^{11}$Be performed with a coupled-channel effective particle-rotor model of that nucleus, which accounts for the excitation of the $^{10}$Be core. Changes in the spectroscopic factor have no effect on the cross sections when the asymptotic normalisation coefficient is fixed, hence confirming the insensitivity of Coulomb-breakup cross sections to spectroscopic factors.

In this paper, we first provide an alternative proof of the uniqueness of the ground state solution for NLS with inverse square potential and power nonlinearity $|u|^pu$ for all $0<p<\frac 4{d-2}$ in dimensions $d\ge 3$. While the uniqueness result was previously obtained by Mukherjee-Nam-Nguyen using a functional analytic approach, our method successfully adapts the classical ``shooting method'' to the case with the singular potential, accompanied by a more detailed analysis on the ground state equation. Based upon this result and a comprehensive spectral analysis, we construct the stable/unstable manifolds of the ground state standing wave solutions and classify solutions on the mass-energy level surface of the ground state in dimensions $d=3, 4, 5$.

In this paper, we proposed a generative model that learns to synthesize the 4D facial expression with the neutral landmark. Existing works mainly focus on the generation of sequences guided by expression labels, speech, etc, while they are not robust to the change of different identities. Our LM-4DGAN utilizes neutral landmarks to guide the facial expression generation while adding an identity discriminator and a landmark autoencoder to the basic WGAN for achieving better identity robustness. Furthermore, we add a cross-attention mechanism to the existing displacement decoder which is suitable for the given identity.

This paper develops fundamental mathematical theory for second-order Filippov systems. These are discontinuous ordinary differential equations with solutions defined in the sense of Filippov, and whose first Lie derivatives vary continuously across discontinuity surfaces. Unlike generic Filippov systems, discontinuity surfaces consist only of crossing regions and their boundaries where both adjacent vector fields are tangent to the discontinuity surface. Crossing orbits spiral around invisible-invisible tangency surfaces, and we derive a formula for the attractive or repulsive strength of these surfaces. We prove crossing orbits cannot converge to tangency surfaces in finite time (no Zeno), and that the limiting dynamics consists of Filippov solutions on the tangency surfaces (second-order sliding motion). We derive a vector field that governs this motion, and characterise the stability of equilibria on tangency surfaces. The methodology is applied to a model of a mechanical oscillator with compliant impacts, and a model of ant colony migration. We also relate second-order Filippov systems to second-order sliding mode control, and show that for two-dimensional systems the results reduce to known theory.

We show that a class of optimized e-value combinations, arising from a standard construction of e-processes, remains valid even when the tuning parameter is optimized based on the data. This result holds for independent e-values, and, more generally, for a new class called simultaneous e-variables, whose dependence structure lies between independence and sequential validity. We further propose an improved combination test for such e-values based on elementary symmetric polynomials.