arXiv:2607.14085v1 Announce Type: cross
Abstract: Hybrid continuous-discrete-variable quantum processors can represent bosonic degrees of freedom directly in oscillator modes, or qumodes, while using qubits for control, readout, and nonlinear operations. Recently proposed trigonometric continuous-variable (CV) gate sets promote periodic functions of oscillator quadratures to elementary operations, making them natural primitives for compact variables, rotor models, lattice gauge theories, and anharmonic dynamics. Here we experimentally demonstrate and benchmark one-qumode cosine gates, and perform a mode-resolved marginal benchmark of two-qumode cosine-gate implementations, on the QSCOUT trapped-ion quantum platform. Our implementation uses collective motional modes of three- and four-ion $^{171}{\rm Yb}^{+}$ chains and realizes finite-step trigonometric-gate circuits through hybrid qubit-qumode operations and conditional phase-space displacements. In contrast to previous theoretical and compilation work, we focus on the gate-level characterization of the trigonometric primitives. We measure Fock-space transition probabilities, study their dependence on gate parameters and Trotter step number, and compare with simulations incorporating thermal initialization and motional dephasing. We also derive ideal gate matrix elements and phase-space diagnostics, connecting the measurements to the non-Gaussian structure generated by these gates. These results establish trigonometric CV gates as reusable building blocks for bosonic Hamiltonian simulations and hybrid quantum algorithms requiring intrinsically non-polynomial operations.
Science Journals
arXiv:2607.14235v1 Announce Type: cross
Abstract: We present a series of unprecedently long 2D3V PIC simulations of unmagnetized relativistic $e^{-}e^{+}$-pair shocks performed in a front-comoving frame. By implementing a moving-wall boundary condition in the downstream together with continuous injection at the upstream boundary, we maintain a fixed simulation domain size, opening the way to perform substantially longer simulations. Our longest runs extend beyond $100000\,\omega_p^{-1}$, exceeding the duration of the previously published simulations by a factor of several. Across a diverse set of simulations -- varying upstream/downstream lengths, transverse sizes, and particle-per-cell counts -- we find strong evidence that the shock approaches an asymptotic, time-independent state. In the downstream region, the steady state depends only on the upstream temperature at the injection boundary and does not depend on a particular numerical realization. The upstream precursor evolves slower and retains a dependence on the simulation's upstream length, that may be of minor observational consequence, since radiation from astrophysical shocks predominantly originates from the downstream region. We also find that Fermi-type acceleration is limited in energy and a true power-law tail never forms. Another important finding is that the downstream magnetic field has a soliton-like structure, where individual magnetic domains evolve independently, each comprising a compact, highly magnetized core embedded within an extended, weakly magnetized region. The magnetic-field distribution around the centers of these spots has approximately Lorentzian profile.
arXiv:2607.14460v1 Announce Type: cross
Abstract: We study the sample covariance error of centered Gaussians. A remarkable breakthrough [66] established the correct error scaling order and explicitly revealed the critical role of both the effective rank and the true covariance spectrum.
In this work, we move beyond scaling characterizations and determine the precise limiting value of the error's spectral norm. To do so, we develop a generic framework based on Random Duality Theory (RDT). Within this framework, we first determine closed-form, explicit RDT-based upper bounds. We then establish complementary lower bounds by introducing a novel bilinear-quadratic RDT lower-bounding mechanism. By combining this mechanism with a two-replica systems bounding strategy, we show that our lower and upper bounds match in large-dimensional contexts. Our theoretical results are supplemented with numerical evaluations and simulations, demonstrating an excellent agreement already for problem sizes on the order of thousands.
arXiv:2604.18155v2 Announce Type: replace
Abstract: Scaling the photon-detection area of superconducting nanowire single-photon detectors (SNSPDs) has traditionally been achieved by nanowire meandering. However, material inhomogeneities and fabrication-induced defects, such as line-edge roughness, increase with nanowire length, leading to reduced internal photon-detection efficiency and elevated dark-count rates. This trade-off becomes increasingly pronounced as nanowires are scaled to sub-100 nm widths and sub-5 nm thicknesses required for mid- to far-infrared sensitivity. Here, we demonstrate an antenna-coupled SNSPD architecture that enhances the effective photon-detection area without increasing nanowire length. A crossed bowtie antenna integrated with an 80 nm-wide, 3 nm-thick WSi nanowire yields 15.7$\times$ increase in effective detection area at 7.4 $\mu$m compared to a bare nanowire of identical geometric footprint, while maintaining the same internal detection efficiency and dark-count rate. Antenna coupling provides a scalable approach to increasing photon-detection area while reducing the noise-equivalent power, offering performance benefits for applications in astronomy, biological imaging, and molecular spectroscopy.
arXiv:2604.19982v2 Announce Type: replace
Abstract: Spatial join is a fundamental operation in spatial databases. With the rapid growth of 3D data in applications such as LiDAR-based object detection and 3D digital pathology, there is an increasing need to support spatial join over 3D datasets. However, existing techniques are largely designed for 2D data, leaving 3D spatial join underexplored and computationally expensive. We present 3DPipe, a pipelined GPU framework for scalable spatial join over polyhedral objects. 3DPipe exploits GPU parallelism across both filtering and refinement stages, incorporates a multi-level pruning strategy for efficient candidate reduction, and employs chunked streaming to handle datasets exceeding GPU memory. Its pipelined execution overlaps CPU data preparation, host-device data transfer, and GPU computation to improve throughput. Experiments show that 3DPipe achieves up to 9.0$\times$ speedup over the state-of-the-art GPU solution, TDBase, while maintaining excellent scalability. 3DPipe is open-sourced at https://github.com/lyuheng/3dpipe.
arXiv:2502.15500v3 Announce Type: replace
Abstract: We report on a detailed exploration of the properties of conversion (definitional equality) in dependent type theory, with the goal of certifying decision procedures for it. While in that context the property of normalisation has attracted the most light, we instead emphasize the importance of injectivity properties, showing that they alone are both crucial and sufficient to certify most desirable properties of conversion checkers. We also explore the certification of a fully untyped conversion checker, with respect to a typed specification, and show that the story is mostly unchanged, although the exact injectivity properties needed are subtly different.
arXiv:2604.20032v2 Announce Type: replace
Abstract: More than half of the Top 500 supercomputers employ GPUs as accelerators. On GPU-accelerated platforms, developers face a key diagnostic gap: profilers show source lines where stalls occur, but not why they occur. Furthermore, the same kernel may have different stalls and underlying causes on different GPUs. This paper presents LEO, a root-cause analyzer for NVIDIA, AMD, and Intel GPUs that performs backward slicing from stalled instructions, considering dependencies arising from registers as well as vendor-specific synchronization mechanisms. LEO attributes GPU stalls to source instructions with the goal of explaining root causes of these inefficiencies. Across 21 workloads on three GPU platforms, LEO-guided optimizations deliver geometric-mean speedups of 1.73$\times$--1.82$\times$. Our case studies show that (1) the same kernel may require different optimizations for different GPU architectures, and (2) LEO's structured diagnostics improve code optimization with large language models relative to code-only and raw-stall-count baselines.
arXiv:2607.14922v1 Announce Type: cross
Abstract: We demonstrate the use of high-resolution spot-profile analysis low-energy electron diffraction to determine the mean grain size of plasma-enhanced chemical vapor deposition grown few-layer graphene on sapphire (Al$_2$O$_3$). The diffraction patterns exhibit broadened graphene spots, pronounced diffuse scattering, and azimuthally extended features, indicating finite crystallite size and rotational disorder. By analyzing the finite-size broadening of the specular (00) spot with an Airy-type diffraction profile, we determine a mean grain diameter of 3.7$\,$nm for the as-grown graphene layer. Post-growth annealing under ultrahigh-vacuum conditions increases the mean grain size to about 5.7$\,$nm and 6.8$\,$nm, respectively. These results establish SPA-LEED as a sensitive reciprocal-space method for quantifying the structural coherence of directly grown graphene on insulating substrates.
arXiv:2604.22280v3 Announce Type: replace
Abstract: Multimodal Large Language Models (MLLMs) have emerged as a promising foundation for universal multimodal embeddings. Recent studies have shown that reasoning-driven generative multimodal embeddings can outperform discriminative embeddings on several embedding tasks. However, Chain-of-Thought (CoT) reasoning tends to generate redundant thinking steps and introduce semantic ambiguity in the summarized answers in broader retrieval scenarios. To address this limitation, we propose Rewrite-driven Multimodal Embedding (RIME), a unified framework that jointly optimizes generation and embedding through a retrieval-friendly rewrite. Meanwhile, we present the Cross-Mode Alignment (CMA) to bridge the generative and discriminative embedding spaces, enabling flexible mutual retrieval to trade off efficiency and accuracy. Based on this, we also introduce Refine Reinforcement Learning (Refine-RL) that treats discriminative embeddings as stable semantic anchors to guide the rewrite optimization. Extensive experiments on MMEB-V2, MRMR and UVRB demonstrate that RIME substantially outperforms prior generative embedding models while significantly reducing the length of thinking. Code is available at https://github.com/PeppaWu/RIME.
arXiv:2607.15079v1 Announce Type: new
Abstract: Understanding the brain increasingly depends on integrating evidence across scales, modalities, and disciplines. Addressing a single research question therefore requires a coordinated sequence of operations, from surveying prior work to executing analyses and interpreting results in light of domain knowledge. AI agents promise to accelerate this process, but current agents lack domain expertise in brain science, may fabricate claims, drift during multi-step reasoning, and offer few defined points for expert intervention. These failures are especially costly in brain science, where conclusions feed into downstream scientific claims and depend on laboratory-specific expertise and careful human judgment. We present \textbf{BrainPilot} a \textbf{fully open-source} multi-agent system that accelerates brain science research with traceable logs and agent-verified results. A principal investigator (PI) agent coordinates specialist agents grounded in curated domain knowledge: a unified brain science knowledge base containing 7{,}233 indexed items and a skill library of 72 reusable methodology units across seven research domains. Every major step is recorded in the Graph of Trace, an auditable record that links subgoals, tool use, evidence, and claims and allows researchers to follow and inspect the workflow. An Auditor agent further integrates fabrication checking into the workflow. For evaluation, we run three brain science tasks from Agents' Last Exam, introduce our own benchmark, \textbf{BrainPilotBench-v0}, and present additional end-to-end case studies. Across these evaluations, BrainPilot with an open-source backbone model attains performance comparable to state-of-the-art agent framework with less costs.
arXiv:2604.26258v3 Announce Type: replace
Abstract: LLM workflows, which coordinate structured calls to individual LLMs/agents to achieve a particular goal, offer a promising path towards building powerful AI systems that can tackle diverse tasks. However, existing approaches for building such workflows generally rely on human-crafted pipelines and prompts, which presents a substantial bottleneck in real world deployment. How can we automatically induce LLM-based agents and workflows in a data-driven way? This paper describes a simple data-driven approach for automatically inducing agents and LLM workflows. We formulate workflow induction as a bilevel optimization problem: an outer loop which optimizes a high-level sketch of the workflow (in particular how the LLM calls should be structured), and an inner loop which optimizes each individual LLM call one-by one. Both loops are optimized with ``textual gradients'' where for the inner loop we optimize each component in a modular way through ``backpropagating'' textual gradients layer-by-layer. We find that LLM workflows discovered through our \textsc{FlowBot} (work\textbf{flow} induction through \textbf{b}ilevel \textbf{o}ptimization and \textbf{t}extual gradients) approach performs competitively against strong baselines that make use of human-crafted or generated workflows.
arXiv:2604.26979v2 Announce Type: replace
Abstract: In-memory computing (IMC) is a paradigm that enables neural network inference by computing analog matrix-vector multiplications (MVM) directly in memory crossbar arrays, with the potential for energy efficiency gains over conventional von Neumann architectures. In this work we present a simulation framework for N-ary crossbar architectures that retrieves MVM results with minimal implementation assumptions. The XOR and MNIST classification tasks were successfully inferred using a simulated crossbar array of (4x4) 4-states magnetic tunnel junctions (MTJ). MNIST accuracy reached 93.56% (vs. 97.56% software baseline). PCA dimensionality reduction was shown to drastically lower the number of required operations and improve the software baseline, for only a modest reduction in crossbar inference accuracy. We identified weight quantization as the primary error source, and studied its impact alongside systematic non-idealities and random noise. We find that cell-specific random noise is less detrimental than systematic errors due to averaging across the array. Finally, we demonstrate an optimal number of states per cell that balances quantization error against resistance state resolution to minimize total MVM error.
arXiv:2604.27031v2 Announce Type: replace
Abstract: In a continual learning setting, we require a model to be plastic enough to learn a new task and stable enough to not disturb previously learned capabilities. We argue that this dilemma has an architectural root. A finite network has limited representational and plastic resources, yet the required capacity depends on properties of the future task stream that are unknown: how many tasks will be encountered, and how much they overlap in feature space. Regularization-based methods preserve past knowledge within fixed-capacity architectures and therefore implicitly rely on an oracle architecture sized for this unknown future. When tasks are only weakly related, fixed architectures progressively run out of plastic resources; when tasks are few or strongly overlapping, models are often over-provisioned. Inspired by neurogenesis in biology, we propose NORACL to address the stability-plasticity dilemma by tackling the oracle architecture problem through neuronal growth. Starting from a compact network, NORACL grows only when needed by monitoring two complementary signals for representational and plasticity saturation. We evaluate NORACL against oracle-sized static baselines across varying task counts and geometries. Across all settings, NORACL achieves final average accuracies that are better than or on par with oracle-provisioned static baselines while using fewer parameters. Additionally, NORACL yields architectures with interpretable growth, i.e. dissimilar tasks predominantly expand feature-extraction layers, whereas tasks which rely on common features shift growth toward later feature-combination layers. Our analysis further explains why fixed-capacity networks lose plasticity as tasks accumulate, whereas NORACL creates fresh capacity for new tasks through growth. Together, these results show that adaptive neurogenesis pushes the stability-plasticity Pareto frontier of continual learning.
arXiv:2604.27088v2 Announce Type: replace
Abstract: Fluid equations are nonlinear, dissipative, and non-Hamiltonian, which makes their relation to Schr\"odinger evolution and quantum algorithms nontrivial. We derive an exact Eulerian Cole-Hopf-type reformulation of isothermal compressible Navier-Stokes (NS) flow in Schr\"odinger-type amplitude variables. To our knowledge, this gives the first exact Cole-Hopf-type Schr\"odinger-variable reformulation of compressible NS flow. In two dimensions, a Helmholtz decomposition separates the velocity into compressive and vortical potentials, whose logarithmic transforms yield two scalar imaginary-time Schr\"odinger-type equations with nonlinear self-consistent potentials. We show that the mixed density-compressive amplitude $\Psi_\alpha=\rho^\alpha\Theta^{1-2\alpha}$, where $\rho$ is the density, $\Theta$ is the compressive amplitude, and $\alpha\neq 0,\,1/2$, satisfies a nonlinear Schr\"odinger-type equation with a vector-potential-coupled Laplacian. The transformed system is exactly equivalent to compressible NS and is nonlocal only through Helmholtz and Poisson projections. In three dimensions, the density-carrying equation retains the same vector-potential-coupled structure, while the solenoidal sector admits a compressible analogue of Ohkitani's incompressible NS Cole-Hopf formulation. Unlike unitary hydrodynamic Schr\"odinger-flow representations, the present equations are imaginary-time heat or drift-diffusion equations with self-consistent potentials, but they remain an exact change of variables for compressible NS. A two-dimensional Kelvin-Helmholtz unstable shear-layer calculation verifies the transformed equations against a direct compressible NS simulation. The formulation exposes operator structures that may be useful for reduced flow descriptions, quantum algorithms for operator evolution, and quantum partial differential equation solvers.
arXiv:2604.27787v2 Announce Type: replace
Abstract: We study when a sound arithmetic theory $\mathcal S\supseteq S^1_2$ with polynomial-time decidable axioms efficiently proves the bounded consistency statements $Con_{\mathcal S+\phi}(n)$ for a true sentence $\phi$. Equivalently, we ask when $\mathcal S$, viewed as a proof system, simulates $\mathcal S+\phi$. The paper gives two unconditional constraints on possible characterizations. First, for finitely axiomatized sequential $\mathcal S$, if $EA\vdash Con_{\mathcal S}\rightarrow Con_{\mathcal S+\phi}$, then $\mathcal S$ interprets $\mathcal S+\phi$, implying $\mathcal S\vdash^{n^{O(1)}}Con_{\mathcal S}(p(n))\rightarrow Con_{\mathcal S+\phi}(n)$ for some polynomial $p$, and hence $\mathcal S\vdash^{n^{O(1)}}Con_{\mathcal S+\phi}(n)$. Second, if $\mathcal S$ fails to simulate $\mathcal S+\phi$ for some true $\phi$, then for all sufficiently large $k$ it also fails to simulate $S^1_2+\phi_{BB}(k)$, where $\phi_{BB}(k)$ asserts the exact value of the $k$-state Busy Beaver function. Thus any hard true extension yields a canonical Busy Beaver witness to nonsimulation.
The paper's central conjectural proposal is: for sound, finitely axiomatized sequential $\mathcal S$, if $EA\not\vdash Con_{\mathcal S}\rightarrow Con_{\mathcal S+\phi}$, then for every constant $c>0$, $\mathcal S\not\vdash^{n^c}Con_{\mathcal S+\phi}(n)$. Under this proposal, hardness follows when $\phi$ is $Con_{\mathcal S}$ or a Kolmogorov-randomness axiom. The latter yields further conjectural consequences and extensions.
arXiv:2605.03723v2 Announce Type: replace
Abstract: The rise of large language models (LLMs) has created an urgent need to distinguish between human-written and LLM-generated text to ensure authenticity and societal trust. Existing detectors typically provide a binary classification for an entire passage; however, this is insufficient for human--LLM co-authored text, where the objective is to localize specific segments authored by humans or LLMs. To bridge this gap, we propose algorithms to segment text into human- and LLM-authored pieces. Our key observation is that such a segmentation task is conceptually similar to classical change point detection in time-series analysis. Leveraging this analogy, we adapt change point detection to LLM-generated text detection, develop a weighted algorithm and a generalized algorithm to accommodate heterogeneous detection score variability, and establish the minimax optimality of our procedure. Empirically, we demonstrate the strong performance of our approach against a wide range of existing baselines. The python implementation of our proposal is available at https://github.com/Mamba413/DetectLLMSegmentation.
arXiv:2605.05000v2 Announce Type: replace
Abstract: LLM agents have been increasingly adopted for solving security tasks. However, existing evaluations usually require source code access, while commercial off-the-shelf (COTS) binaries dominate deployed software and require reasoning from stripped, optimized machine code. This discrepancy raises an important question: can modern LLM agents reason about vulnerabilities in critical COTS binaries? Motivated by this question, we build SLYP, a REACT-style pipeline for end-to-end vulnerability discovery and validation of COTS binaries. SLYP combines extensible MCP servers for binary exploration and dynamic debugging, and validates candidate vulnerabilities by synthesizing debugger-verified proof-of-concept (PoC) crashes. We evaluate SLYP and production coding agents, including Claude Code and Codex, on COTS Windows binaries centered on a 20-object COM benchmark. SLYP uncovers all 64 vulnerable entry functions while default production agents miss up to 15; SLYP also surfaces more true vulnerabilities than the state-of-the-art static analyzer, which discovers at most 35 with a large number of false positives. For validation, SLYP generates debugger-verified PoCs for 67.5% of cases, while default production agents generate none. Further ablations show that tool sets and model choice materially affect COTS binary reasoning. Our additional evaluation also demonstrates the generalizability of SLYP on Windows kernel targets. To date, SLYP has uncovered 39 zero-day vulnerabilities, 31 in COM/RPC services and 8 in kernel drivers, all disclosed to the Microsoft Security Response Center (MSRC), with 23 assigned CVEs and $203,000 bounty awards.
arXiv:2607.14360v1 Announce Type: cross
Abstract: Food webs have been extensively studied from both ecological and mathematical aspects. However, most of the models studied in this area do not capture the effects of infectious diseases simultaneously. Recently, the idea of including an infectious disease in a food web model has been investigated. We study and simulate a small food chain consisting of only prey, predators, and apex predators governed by the generalized Lotka-Volterra equations, and we implement the Susceptible-Infected-Recovered (SIR) model on only one of the species at a time in the food chain. To study the effects of an infectious disease on the food chain, we introduce a new parameter that increases the predation rate by a factor of $w$ and decreases the hunting rate by a factor of $1/w$ for infected species. When the infectious disease is present in predators, we observe that predators do not become extinct under any set of parameters; however, an oscillation in their population size occurs under some circumstances, which we do not observe in ordinary SIR or the generalized Lotka-Volterra equations alone. When an infectious disease is present in apex predators, oscillations in the population size do not happen; but if the set of parameters is in a specific range the apex predators may become extinct. Furthermore, the chance of survival of the community, known as community persistence, increases for the predators and decreases for the apex predators.
arXiv:2607.15103v1 Announce Type: new
Abstract: The DFT-D3 dispersion correction is routinely added to machine learning force fields (MLFFs) trained on dispersion-deficient functionals such as PBE. Its environment-dependent pair coefficients, however, break the atom-centered separability that fast summation methods require, forcing practitioners either to truncate D3 or to accept a substantial slowdown. We introduce FourierD3, a method that uses a functional low-rank decomposition to restore this separability and enable particle-mesh evaluation in $O(N\log N)$ time without a real-space cutoff on the dispersion sum.
arXiv:2607.14714v1 Announce Type: cross
Abstract: We investigate the shape dynamics and migration of weakly deflated active vesicles driven by processes acting either directly in the membrane or transmitted by the cytoskeleton. For a force-free vesicle, local membrane incompressibility suppresses rigid-body translation, so that migration arises from time-dependent shape deformations. Assuming small excess area enables a systematic analysis of the coupled deformation and migration dynamics in free space, i.e. in the absence of substrate adhesion or confinement. Depending on the strength and frequency of the activity, the vesicle exhibits several dynamical regimes, including synchronized oscillations, quasiperiodic shape changes, transitions between non-propelling and propelling states, and intermittent motion.
arXiv:2605.07742v2 Announce Type: replace
Abstract: Inter-manufacturer plug-and-play communication in agricultural machinery is currently based on the ISO 11783 standard series, which specifies a 250 kbit/s CAN bus communication layer. To support higher-bandwidth use cases, the ISO~23870 series is being developed for next-generation Ethernet-based agricultural machine-to-machine communication. Modern Ethernet/IP-based architectures often make use of a middleware for discovery, data exchange, quality of service configuration, and security. This paper evaluates the Data Distribution Service (DDS) as a candidate middleware for secure, plug-and-play agricultural machinery networking. DDS-based proof-of-concept communication design is presented for a representative Task Controller (TC) and implement scenario, including implement-description topics and separate best-effort and reliable topics for runtime process data. Design was implemented in C++ using the FastDDS library and benchmarked on embedded hardware representative of agricultural machinery. Runtime throughput was evaluated for one-to-one and one-to-two TC-implement scenarios under four DDS security configurations. The results show that DDS security mechanisms substantially reduce maximum throughput on embedded hardware. In the tested best-effort scenarios, signing and encryption reduced mean throughput by approximately 70-84% compared with the unsecured configuration. Encrypted one-to-one best-effort case achieved approximately 4980 received process data updates per second on both the TC and implement, corresponding to about 50 process data updates per second per simulated section for 100 rate-controllable sections. These results indicate that DDS is a technically plausible middleware candidate for secure Ethernet-based agricultural machinery interoperability, while further work is required to evaluate latency, scalability, vendor interoperability, and lower-power devices.
arXiv:2605.09028v3 Announce Type: replace
Abstract: Machine learning-based Android malware detectors often fail in real-world deployment due to domain shift, where models trained on one data source perform poorly on applications from another. This paper presents a comprehensive study on the generalizability and interpretability of permission-based detectors under cross-domain conditions. Using two complementary datasets (PerMalDroid and NATICUSdroid) and five ensemble classifiers, we first establish an intra-domain baseline, where models achieve over 92% accuracy, and then quantify a severe asymmetric performance drop. While models trained on PerMalDroid generalize well to NATICUSdroid (86% accuracy), the reverse direction sees a drastic drop to 73% accuracy. Explainable AI analysis reveals bimodal feature distributions and shows that feature importance is highly unstable, with key permissions losing or gaining influence across domains. The predictive feature sets for different domains are fundamentally mismatched, as models rely on different, dataset-specific permissions. Most importantly, an ablation study demonstrates that for most models, training on a noisy feature set leads to poor generalization, confirming that domain-specific artifacts are a greater obstacle than missing features. To mitigate this, we validate a hybrid training strategy based on the intersection of common features and successfully recover cross-domain performance, achieving 88% accuracy on PerMalDroid and maintaining 97% on NATICUSdroid. These findings highlight the importance of explainable, cross-domain-robust malware detection systems and provide a practical pathway toward improving real-world deployment of permission-based Android malware detectors.
arXiv:2605.09835v3 Announce Type: replace
Abstract: We report a comprehensive measurement of the environmental $\gamma$-ray flux in Hall C of the Gran Sasso National Laboratory. A spatial mapping of the radiation was carried out using a high-purity germanium detector mounted on a movable cart and deployed at eight locations within the hall. The detector response function and full-energy-peak efficiencies were determined through Geant4 simulations validated with calibrated $\gamma$-ray sources, with particular attention devoted to the efficiency modeling and associated systematic uncertainties. In the energy range of 57-2800 keV, the average $\gamma$-ray flux is measured to be $(\mathrm{0.46} \pm \mathrm{0.06}_{stat} \pm \mathrm{0.03}_{syst})$ $\mathrm{cm}^{-2}$ $\mathrm{s}^{-1}$. The radon level was monitored for about a month using a radon detector mounted on the same cart, and a clear correlation is observed between the environmental $\gamma$-ray rate and the ambient radon concentration, consistent with the short-lived daughters of $^{222}\mathrm{Rn}$. This result represents the first high-precision and efficiency-corrected mapping of the $\gamma$-ray flux in Hall C, substantially improving its radiological characterization and providing key input for future rare-event experiments operating in this hall.
arXiv:2605.10669v2 Announce Type: replace
Abstract: The one-centre Coulomb-Sturmian convergent close-coupling method is applied
to proton collisions with the boron atom and singly charged carbon ion.
Here we report an update to our target-structure implementation, in which
configuration state functions are constructed using the method of
coefficients of fractional parentage. To assess the quality of the
structure models for the two targets, we present the excitation energies,
oscillator strengths, and dipole polarisabilities obtained from the present
configuration interaction calculations. Cross sections for total and state-selective target
excitation and electron loss are calculated from 10 keV to 1 MeV. For both
systems, the total excitation cross section is found to be dominated by
excitation of the $2s$ subshell. This emphasises the importance of a
multi-electron description of the target in such scattering calculations.
Comparisons with previous theoretical and experimental data are presented and
discussed. In particular, we find that the present calculation for the
electron-loss cross section in $p$ + C$^{+}$ collisions is in good agreement
with the available measurements across the entire overlapping incident-energy
range.
arXiv:2605.10821v2 Announce Type: replace
Abstract: Diffusion-based vision-language-action (VLA) models have emerged as strong priors for robotic manipulation, yet adapting them to real-world distributions remains challenging. In particular, on-robot reinforcement learning (RL) is expensive and time-consuming, so effective adaptation depends on efficient policy improvement within a limited budget of real-world interactions. Noise-space RL lowers the cost by keeping the pretrained VLA fixed as a denoising generator while updating only a lightweight actor that predicts the noise. However, its performance is still limited due to inefficient autonomous exploration. Human corrective interventions can reduce this exploration burden, but they are naturally provided in action space, whereas noise-space finetuning requires supervision over noise variables. To address these challenges, we propose UniSteer, a Unified Noise Steering framework that combines human corrective guidance with noise-space RL through approximate action-to-noise inversion. Given a human corrective action, UniSteer inverts the frozen flow-matching decoder to recover a noise target, which provides supervised guidance for the same noise actor that is simultaneously optimized via reinforcement learning. Real-world experiments on diverse manipulation tasks show that UniSteer adapts more efficiently than strong noise-space RL and action-space human-in-the-loop baselines, improving the success rate from 20% to 90% in 66 minutes on average across four real-world adaptation tasks.