4D facial expression synthesizing is a critical problem in the fields of computer vision and graphics. Current methods lack flexibility and smoothness when simulating the inter-frame motion of expression sequences. In this paper, we propose a frequency-controlled 4D facial expression synthesizing method, FC-4DFS. Specifically, we introduce a frequency-controlled LSTM network to generate 4D facial expression sequences frame by frame from a given neutral landmark with a given length. Meanwhile, we propose a temporal coherence loss to enhance the perception of temporal sequence motion and improve the accuracy of relative displacements. Furthermore, we designed a Multi-level Identity-Aware Displacement Network based on a cross-attention mechanism to reconstruct the 4D facial expression sequences from landmark sequences. Finally, our FC-4DFS achieves flexible and SOTA generation results of 4D facial expression sequences with different lengths on CoMA and Florence4D datasets. The code will be available on GitHub.

Adaptive ansatz construction has emerged as a powerful technique for reducing circuit depth and improving optimization efficiency in variational quantum eigensolvers. However, existing adaptive methods, including ADAPT-VQE, rely solely on first-order gradients and therefore ignore the underlying geometry of the quantum state space, limiting both convergence behavior and operator-selection efficiency. We introduce Geo-ADAPT-VQE, a geometry-aware adaptive VQE algorithm that selects operators from a pool using the natural gradient rule. The geometric operator-selection rule enables the ansatz to grow along directions aligned with the underlying quantum-state geometry, thereby improving convergence and reducing the algorithm's susceptibility to shallow local minima and saddle-point regions. We further provide an asymptotic convergence result. We present numerical simulations involving five molecules, which demonstrate that Geo-ADAPT-VQE achieves faster and more stable convergence compared to existing methods, while producing significantly shorter ansatz. In particular, Geo-ADAPT achieves up to 100-fold reduction in energy error compared to existing methods.

A banded matrix is a real square matrix where nonzero entries appear around the main diagonal. In this article, we consider linear complementarity properties of (variants) of banded matrices. Focusing on triangular matrices and the newly defined bidiagonal southwest matrices, we describe several results characterizing the Q-property in terms of the sign patterns and determinant of the given matrix. As a byproduct, we describe all Q-matrices of size 2 by 2. Extending these results to Euclidean Jordan algebras, we consider matrix-based linear transformations and study the Q-property. In particular, we show that a rank-one linear transformation of the form a\otimes b has the Q-property if and only if either a>0,b>0, or a<0, b<0.

Galaxy clusters are the largest gravitationally bound systems, and they continue their growth through mergers in a hierarchical ΛCDM Universe. Therefore, we can describe the merger stage of a cluster as the dynamical state of clusters. Previous studies have investigated this phenomenon, but several limitations remain, including reliance on dichotomous classifications, constraints on the number of indicators used, absence of reliability, and incompatibility of methods between observation and simulation studies. To overcome this, we developed an enhanced and observation-applicable cluster dynamical state classification method using the Bayesian classifier with the class-conditional Gaussian mixture distribution model using the N-cluster Run simulation data. The Bayesian classifier was designed for two merger stages (merger and relaxed) as well as three merger stages (recent merger, ancient merger, and relaxed) to provide a more detailed interpretation of the merger processes. In the results, using a larger number of indicators yields better results, with their order of importance being: magnitude difference, center offset, sparsity, Kuiper V statistic, and mirror asymmetry. Additionally, our analyses show that a projected classifier (built on the 6D space, but evaluated on lower dimensional projections) consistently produces better outcomes than non-projected classifiers (i.e., classifiers built directly on the corresponding low dimensional spaces), which means limited observation data can be used to classify with enhanced performance. Furthermore, the new classification method outperforms our previous research. This new method can suggest a way of overcoming previous limitations and provides new insights by providing the reliability of dynamical state classification results.

We study the problem of selecting optimal two-block partitions to accelerate the mixing of finite Markov chains under group-averaging transformations. The main objectives considered are the Kullback-Leibler (KL) divergence and the Frobenius distance to stationarity. We establish explicit connections between these objectives and the induced projection chain. In the case of the KL divergence, this reduction yields explicit decay rates in terms of the log-Sobolev constant. For the Frobenius distance, we identify a Cheeger-type functional that characterises optimal cuts. This formulation recasts two-block selection as a structured combinatorial optimisation problem admitting difference-of-submodular decompositions. We further propose several algorithmic approximations, including majorisation-minimisation and coordinate descent schemes, as computationally feasible alternatives to exhaustive combinatorial search. Our numerical experiments reveal that optimal cuts under the two objectives can substantially reduce total variation distance to stationarity and demonstrate the practical effectiveness of the proposed approximation algorithms.

A graph of order $n$ is said to be $k$-\emph{factor-critical} $(0\le k<n)$ if the removal of any $k$ vertices results in a graph with a perfect matching. A $k$-factor-critical graph $G$ is \emph{minimal} if $G-e$ is not $k$-factor-critical for any edge $e$ in $G$. In 1998, Favaron and Shi posed the conjecture that every minimal $k$-factor-critical graph is of minimum degree $k+1$. A natural extension of this notion arises from $\{1,2\}$-factors. A spanning subgraph of $G$ is called a $\{1,2\}$-factor if each of its components is a regular graph of degree one or two. A graph is $k$-\emph{$\{1,2\}$-factor critical} if the removal of any $k$ vertices results in a graph with a $\{1,2\}$-factor. A recent conjecture in the area states that every minimal $k$-$\{1,2\}$-factor critical graph $G$ satisfies $k+1\le δ(G)\le k+2$. In this paper, we prove that the conjecture holds for $k$-planar graphs, that is, graphs in which the deletion of any set of $k$ vertices yields a planar graph. In particular, this resolves the conjecture for planar graphs.

Let $α(G)$ denote the cardinality of a maximum independent set. An independent set $I$ of $G$ is critical if $\left|I\right|-\left|N(I)\right|\ge\left|J\right|-\left|N(J)\right|$ for every independent set $J$ of $G$. Let $\text{core}(G)$ and $\text{corona}(G)$ be the intersection/union of all maximum independent sets of $G$. Let $\text{ker}(G)$ and $\text{diadem}(G)$ be the intersection/union of all critical independent sets of $G$. In this paper we prove that \[ \left|\text{corona}(G)\right|+\left|\text{core}(G)\right|\le2α(G)+k, \] \noindent where $k$ is the number of vertex-distinct odd cycles in $G$, thus confirming a recent conjecture in the area. Moreover, we prove that \[ \left|\text{nucleus}(G)\right|+\left|\text{diadem}(G)\right|\le2α(G), \] \noindent thereby confirming another conjecture (Levit--Mandrescu 2014). As an application of these facts, we obtain a chain of inequalities \[ \left|\text{nucleus}(G)\right|+\left|\text{diadem}(G)\right|\le2α(G)\le\left|\text{corona}(G)\right|+\left|\text{core}(G)\right|\le2α(G)+k. \] \noindent The paper concludes with a collection of related open problems.

A graph is almost bipartite if it contains exactly one odd cycle, and it is Konig-Egervary if the sum of the independence number and the matching number equals the order of the graph. We introduce the class of Bipartite-Almost Bipartite graphs (BAB-graphs), defined through a controlled union of a bipartite graph and several almost bipartite non-Konig-Egervary graphs. This family unifies and generalizes the previously studied classes of almost bipartite non-Konig-Egervary and R-disjoint graphs. While an almost bipartite non-Konig-Egervary graph contains a single odd cycle, an R-disjoint graph has exactly k pairwise disjoint odd cycles. A BAB-graph may contain many odd cycles that are not necessarily disjoint. We describe the structure of BAB-graphs by means of the Gallai-Edmonds decomposition and obtain explicit expressions for nucleus(G), diadem(G), and ker(G), which allow us to extend several known results for the previous classes. Moreover, we show that the determinant of the adjacency matrix of a BAB-graph can be factorized in terms of the determinants of the adjacency matrices of its component graphs. As a consequence, we confirm the conjecture stating the validity of this factorization for R-disjoint graphs. Finally, we derive combinatorial consequences of these results and establish new bounds for |corona(G)| + |ker(G)|.

We propose a variable ADAF disk model for X-ray binary systems. In this model, the accretion flow consists of an outer thin disk and an inner optically thick advection-dominated accretion flow (ADAF) torus. The size of the turbulent ADAF is variable. A complete cycle of ADAF contraction, transition to a thin disk, and subsequent re-expansion corresponds to the rapid rise, peak, and decay phases observed in the X-ray outbursts of black hole binaries. This cycle also tracks the canonical evolution through the low-hard, high-soft, and back to the low-hard state in the hardness-intensity diagram. The model unifies the presence of near-ISCO Fe emission lines with the truncated disk paradigm, as observed in the black hole system GX 339-4. It explains the 35-day period in the neutron star system Her X-1 more effectively through variable ADAF sizes than through a precessing disk. This variable ADAF framework may be extended to explain similar phenomena in active galactic nuclei.

Ferromagnetic domain walls -transitional regions between magnetic domains- are an essential ingredient for racetrack memory, a device concept that promises to deliver faster and more compact memory storage compared to other non-volatile memory devices. Motivated by recent experiments that have found inertial effects in spin dynamics, we explore its consequences on domain wall motion. We find that the inertial dynamics of the individual magnetic moments induce massive dynamics of the domain wall. We investigate these massive dynamics driven by a magnetic field, spin-transfer torque, and spin-orbit torque. We show that, in the absence of Gilbert damping, the domain wall dynamics become chaotic, resembling that of an electron in a two-dimensional crystal. For finite damping, field-like driving of the inertial domain wall significantly increases its velocity compared to conventional massless dynamics, potentially enabling faster racetrack operations. Additionally, in the limit of low driving, we observe that the domain wall width contracts due to the spin inertia of the ferromagnet.

Situational awareness (SA) is essential for effective team performance in time-critical clinical environments, yet its dynamic and distributed nature remains difficult to characterize. In this preliminary study, we apply Transition Network Analysis (TNA) to model visual attention in multiperson VR-based cardiac arrest simulations. Using eye-tracking data from 40 clinicians assigned to four standardized roles (Airway, CPR, Defib, TeamLead), we construct gaze transition networks between clinically meaningful areas of interest (AOIs) and extract metrics such as entropy and self-loop rate to quantify attentional structure and flow. Our findings reveal that individual and team's visual attention is dynamically and adaptively redistributed across roles and scenario phases, with those in CPR roles narrowing their focus to execution-critical tasks and those in the TeamLead role concentrating on global monitoring as clinical demands evolve. TNA thus provides a powerful lens for mapping functional differentiation of team cognition and may support the development of phase-sensitive analytics and targeted instructional interventions in acute care training.

Jets are common eruptive phenomena in the solar atmosphere which may occur repeatedly. Many studies of their fine dynamics have been conducted. However, the fine dynamics of persistent interactions among various features that drive recurrent coronal jets have not been studied in detail. In this paper, we use observations from the Solar Orbiter to report persistent interactions between rising loops/mini-filaments and a fan-spine-like structure, which produced more than 22 ejections. Many loops and mini-filaments under the fan-spine-like structure rose with speeds of $8 - 58\,\mathrm{km~s^{-1}}$ and an average of $27\,\mathrm{km~s^{-1}}$. These rising loops and mini-filaments interacted with the fan-spine-like structure successively, producing ejections with speeds ranging from 25 to 186\,\rm km\,s$^{-1}$ and an average at $80\,\rm km~s^{-1}$. We observed the fine dynamics of the drivers of these recurrent jets in detail, including partial eruption of mini-filaments, formation of a new mini-filament by contraction of remaining threads from the partially-erupted mini-filament, and interaction between rising loops (or mini-filaments) and the fan-spine-like structure. Brightenings appeared near the footpoint of these rising structures, followed by the formation of current sheets. Some arcades at the outflow region contracted with speeds of around $10\,\rm km~s^{-1}$, and the outflow region moved at around $8\,\rm km~s^{-1}$ toward the opposite direction. Bright blobs were observed in the current sheets, and they propagated at speeds averaging at $21\,\rm km\,s^{-1}$ and had an average width of 296\,km. We emphasize the vital roles of persistent rising loops and/or mini-filaments in producing recurrent jets by interacting with the fan-like structure, and show their detailed dynamics with unprecedentedly-high-resolution observations.

We investigate the low-energy dynamics of small-amplitude surface oscillations of spherical superfluid droplets in vacuum. Starting from the effective field theory of superfluid phonons, we derive an effective action governing the surface oscillations under a fixed particle-number constraint. The normal-mode eigenfrequencies $ω_{\ell}$ for each angular momentum quantum number $\ell$ are determined and shown to depend on a dimensionless parameter measuring the ratio of surface tension to bulk compressibility energy. We identify a critical value of this parameters at which the breathing mode ($\ell = 0$) becomes mechanically unstable, and show that all multipole surface modes with $\ell \geq 2$ enter the low-energy regime when the surface tension is sufficiently small. Within this regime, we further quantize the surface oscillations, whose quanta correspond to ripplons, allowing the construction of general multi-ripplon states obeying angular-momentum selection rules. We also apply our formalism to a concrete example: a weakly interacting two-component Bose mixture realizing a self-bound superfluid droplet. The resulting description is universal in the sense that it applies to surface dynamics of generic nonrelativistic superfluids with a free interface, independent of microscopic details.

We study the quantum dynamics of the Schwarzschild interior in the Ashtekar-Barbero formulation, focusing on the fate of the classical singularity and the annihilation-to-nothing scenario. Using minisuperspace Wheeler-DeWitt quantization, we first analyze the standard Schrödinger representation and show that the annihilation-to-nothing behavior appears only for a specific choice of factor ordering and is not generic. We then introduce a generalized uncertainty principle (GUP), which induces minimal-length effects through a deformation of the canonical algebra. Solving the modified Wheeler-DeWitt equation and constructing Gaussian wave packets localized at the horizon, we find that the annihilation-to-nothing behavior is suppressed once the GUP corrections are included. Our results indicate that minimal-length effects qualitatively alter the quantum interior dynamics and challenge the robustness of this scenario as a mechanism for singularity resolution.

We resolve a conjecture of Hanotel and Loubenets concerning CHSH inequality in bipartite qutrits. It states that nonseparable pure states of two qutrits do not violate the CHSH inequality when each party is restricted to spin-1 observables. We prove a stronger result that \emph{all} bipartite states on $\mathbb{C}^3 \otimes \mathbb{C}^3$ satisfy the CHSH inequality under spin-1 measurements, regardless of whether the state is pure or mixed.

We demonstrate a highly efficient GPU implementation of the Wannier interpolation of electron-phonon matrix elements in the EPW code. Building on a systematic analysis of the computational complexity of the algorithm for electron-phonon interpolation, we designed a GPU porting strategy that integrates naturally into the current EPW implementation, and is seamlessly portable to NVIDIA, AMD, and Intel GPUs. We demonstrate this development via extensive benchmarks on conventional semiconductors such as silicon and monolayer MoS$_2$, as well as a large-scale application to topological stanene nanoribbons of width as large as 20nm, which was intractable with previous implementations. Compared to the single MPI parallelization scheme of EPW v5.9, the resulting hybrid MPI-GPU-OpenMP scheme achieves up to 29-fold speedup on leadership-class supercomputers equipped with NVIDIA and Intel accelerators, namely Vista at the Texas Advanced Computing Center, Perlmutter at the National Energy Research Scientific Computing Center, and Aurora at the Argonne Leadership Computing Facility. This framework also achieves nearly ideal scalability up to thousands of GPU nodes on the Aurora supercomputer. With this development, EPW is ready to support electron-phonon physics calculations on exascale platforms.

In this paper, we present a data-driven output feedback controller for nonlinear systems that achieves practical output regulation, using noise-free input/output measurement data. The proposed controller is based on (i) an inverse model of the system identified via kernel interpolation, which maps a desired output and the current state to the corresponding desired control input; and (ii) a data-driven reference selection framework that actively chooses a suitable desired output from the dataset which has been used for the identification. We establish a verifiable sufficient condition on the dataset under which the proposed controller guarantees practical output regulation. Numerical simulations demonstrate the effectiveness of the proposed controller, with additional evaluations in the presence of output measurement noise to assess its robustness empirically.

We present a systematic methodology for designing slow-light photonic integrated circuits with a frozen mode based on a special kind of exceptional point of degeneracy (EPD) of order three named stationary inflection points (SIPs). This is realized through three-way coupled waveguides with lateral gratings operating at telecommunication wavelengths. We provide two designs and analyze sensitivity to geometric perturbations. We have fabricated a periodic waveguide with integrated taper loads and demonstrate reasonable agreement with full-wave simulations. These findings confirm the feasibility of integrating SIP-based delay functionalities in standard silicon photonic platforms.

Conventional stochastic control methods have several limitations. They focus on optimizing the average performance and, in some cases, performance variability; however, their problem settings still require an explicit specification of the probability distributions that determine the system's stochastic behavior. Distributionally robust control (DRC) methods have recently been developed to address these challenges. However, many DRC approaches involve handling infinitely many inequalities. For instance, DRC problems based on the Wasserstein distance are commonly obtained by solving semi-infinite programming (SIP) problems. Our proposed method eliminates the need for SIP when solving discrete-time, discounted, distributionally robust optimal control problems. By introducing a penalty term based on a specific distributional distance, we establish upper bounds, and under appropriate conditions, demonstrate the equivalence between distributionally robust optimization problems and mean-variance minimization problems. This reformulation reduces the original DRC problem to a discounted mean-variance cost optimization problem. In linear-quadratic regulator settings, the corresponding control laws are obtained by solving the Riccati equation. Numerical experiments demonstrate that the theoretical maximum value of the discounted cumulative cost for the proposed method is lower than that for the conventional method.

Atom probe tomography (APT) fills a crucial need in the characterization workflow of materials by its ability to inform the 3D chemical microstructure at the nanoscale. As with any characterization techniques, APT has strengths and limitations that inform the interpretation of the data. Therefore, a challenge for the materials characterization community, and the APT community in particular, is the need to establish repeatable and reproducible workflows around the APT data acquisition, reconstruction, analysis, and sharing, in order to inform interpretation. Data interpretation also requires the continued development of our understanding of the physical processes responsible for field evaporation. We review recent developments in the experimental analysis of field evaporation and in the modeling of field evaporation leading to new understanding of common artifacts observed in reconstructed data. We then discuss current challenges with data analysis, translation of results, and data interpretation in the absence of community-agreed standards, and therefore, the crucial need for standardization at every stage of APT research, from data collection all the way to data reporting. This perspective is a summary of the invited presentations and discussions that took place during a workshop (August 4-5, 2024, Alexandria, Virginia, USA).

Embedded cryptography stands or falls on entropy quality, yet small devices have few trustworthy sources and little tolerance for heavyweight protocols. We build a Quantum Entropy as a Service (QEaaS) system that moves QRNG-derived entropy from a Quantis device to ESP32-class clients over post-quantum-secured channels. On the server side, the design exposes two paths: direct quantum entropy through a custom OpenSSL provider and mixed entropy through the Linux system pool. On the client side, we extend libcoap's Zephyr support, integrate wolfSSL-based DTLS 1.3 into the CoAP stack, and add a BLAKE2s entropy pool that preserves the standard Zephyr extraction interface while introducing an injection API for server-provided entropy. Benchmarks on ESP32 hardware, targeting 100 iterations per configuration, show that ML-KEM-512 completes a DTLS 1.3 handshake in 313 ms on average without certificate verification, 35% faster than ECDHE P-256. Pairing ML-KEM-512 with ML-DSA-44 lowers the mean to 225 ms. Certificate verification adds roughly 194 ms for ECDSA but only 17 ms for ML-DSA-44, so the fully post-quantum configuration remains 63% faster than classical ECDHE P-256 with ECDSA even under full verification. Local BLAKE2s pool operations stay below 0.1 ms combined. On this platform, post-quantum key exchange and authentication are not only feasible; they are faster than the classical baseline.

Sensors based on optical microcavities enhance light-matter interactions within an ultraconfined volume, enabling high-sensitivity detection across a wide range of sensing applications. In these systems, environmental perturbations modify the intrinsic resonance properties of the cavity, typically manifested as frequency shifts, linewidth broadening, or mode splitting. However, the minimum resolvable change in these spectral properties fundamentally limits the overall sensor sensitivity. Here, we propose a new avalanche sensing scheme enabled by Kerr nonlinearity. Instead of relying on the detection of frequency shifts, our approach exploits abrupt state transitions in a Kerr frequency comb to amplify weak perturbations. We provide a theoretical analysis of the underlying mechanism of this scheme and validate the concept through both coupled-mode theory (CMT) modeling and full-wave electromagnetic simulations.

We present a general theoretical framework to capture light-matter interactions beyond the electric-dipole approximation (EDA), applicable to extended nano- and microscale materials interacting with spatially structured electric fields without truncation at finite multipolar order. The approach is based on the Power-Zienau-Woolley (PZW) Hamiltonian for light-matter interactions and a representation of the material's Hamiltonian in a basis of maximally localized Wannier functions (MLWFs), obtainable from first-principles calculations. We utilize this approach to clarify the limitations of the ubiquitous dipole approximation. We consider electric fields with both uniform and non-uniform intensities and a range of ratios of system size to the wavelength of light. Through this analysis, we identify the conditions under which the EDA breaks down, leading to significant errors in the light-induced dynamics. Contrary to conventional belief, we find that the EDA is remarkably robust for uniformly illuminated 1-D or 2-D materials when light propagates perpendicular to the material. For 3-D materials or non-perpendicular illumination of lower-dimensional materials, conventional wisdom holds and the EDA begins to break down when the wavelength becomes comparable to the system size. Furthermore, the EDA fails when the material is illuminated partially or non-uniformly. For slowly varying field intensities this failure can be corrected by finite-order multipolar corrections. However, for fields that vary substantially, correcting via multipolar terms becomes computationally impractical. In contrast, our approach captures beyond-dipole light-matter interactions at the computational cost of a standard dipole calculation. This efficiency enables accurate first-principles simulations of spatially structured light-matter dynamics in nanoscale devices, quantum materials, and interfaces.

Interactive visualization is a common tool for exploring large open-data repositories, where users quickly explore datasets across diverse domains. When it comes to large-scale spatial data, many existing tools rely on server-side rendering to produce small images that can be viewed at the client-side. However, most users prefer client-side rendering that allows quick styling of the data for better visualization experience. This paper presents HiFIVE, a data-management framework for scalable, high-fidelity client-side geospatial visualization. We formalize the visualization-aware tile reduction problem, which captures the trade-off between tile-size and visualization distortion, and prove its NP-hardness. HiFIVE introduces a two-stage solution combining triage and sparsification to selectively prune records, attributes, and values based on information-theoretic and spatial criteria. Experiments demonstrate substantial tile-size reductions while preserving visual fidelity and interactive performance at terabyte scale.

Nearly thirty years ago, Bern, Dixon and Kosower computed all helicity amplitudes for the annihilation of an electron-positron pair into four QCD partons through an electroweak vector boson. More recently, the leading-color two-loop amplitudes for the same process were obtained. When such amplitudes are expressed in the massless spinor-helicity formalism, they effectively correspond to the decay of a transversely polarized vector boson. However, for several reasons, it is highly desirable to extend these calculations to the case where the polarization of the vector boson is longitudinal. Due to the complexity of such computations, repeating them to obtain the result for the ''missing'' polarization of the electroweak boson is a significant undertaking even at one loop. Besides, when attempting new higher-loop computations, it is beneficial to identify the minimal set of quantities (e.g. form factors) that must be determined to obtain the full amount of physically-relevant information. In this paper, we show that amplitudes involving vector-boson decays to massless leptons-although they appear to project onto the transverse polarization-still encode the full information about all polarization states of the vector boson, including the longitudinal one. This follows from the little-group (spin) covariance of the amplitude, which allows us-largely through simple replacement rules-to rewrite the helicity amplitudes as a matrix with open SU(2) spin indices in the massive spinor-helicity (or spin-spinor) formalism. Therefore, knowledge of an amplitude for any polarization component suffices to reconstruct the full covariant matrix.

Autonomous AI agents powered by large language models (LLMs) are increasingly deployed in real-world applications, where reliable and robust behavior is critical. However, existing agent evaluation frameworks either rely heavily on manual efforts, operate within simulated environments, or lack focus on testing complex, multimodal, real-world agents. We introduce SpecOps, a novel, fully automated testing framework designed to evaluate GUI-based AI agents in real-world environments. SpecOps decomposes the testing process into four specialized phases - test case generation, environment setup, test execution, and validation - each handled by a distinct LLM-based specialist agent. This structured architecture addresses key challenges including end-to-end task coherence, robust error handling, and adaptability across diverse agent platforms including CLI tools, web apps, and browser extensions. In comprehensive evaluations across five diverse real-world agents, SpecOps outperforms baselines including general-purpose agentic systems such as AutoGPT and LLM-crafted automation scripts in planning accuracy, execution success, and bug detection effectiveness. SpecOps identifies 164 true bugs in the real-world agents with an F1 score of 0.89. With a cost of under 0.73 USD and a runtime of under eight minutes per test, it demonstrates its practical viability and superiority in automated, real-world agent testing.

Network Coding (NC) is a compelling solution for increasing network efficiency. However, it discards corrupted packets and cannot achieve optimal performance in noisy communications. Since most of the information in corrupted packets is error-free, discarding them is not the best strategy. Several packet recovery techniques such as PRAC and S-PRAC were proposed to exploit corrupted packets. Yet, they are slow and only practical when the packet size is small and communication channels are not very noisy. We propose a packet recovery scheme called Fly-PRAC to address these issues. Fly-PRAC exploits algebraic relations between a group of coded packets to estimate their corrupted parts and recovers them. Unlike previous schemes, Fly-PRAC can recover coded packets at the intermediate node without decoding them. We have compared Fly-PRAC against S-PRAC. Results show when the bit error rate (ε) is 10^-4, Fly-PRAC outperforms S-PRAC by two folds for a payload of 900B. In two-hop communication with ε = 10^-4 and a payload size of 500B, by enabling the recovery in the intermediate node, Fly-PRAC reduces transmissions by 16%. In a Sparse Network Coding (SNC) scenario, with two non-zero elements in the coefficient vectors and a payload of 800B, there is a reduction by 31% on average for decoding delay.

Context: Open-source ecosystems rely on sustained package maintenance. When maintenance slows or stops, Technical Lag (TL), the gap between installed and latest dependency versions accumulates, creating security and sustainability risks. However, some existing TL metrics, such as Version Lag, struggle to distinguish between actively maintained and abandoned packages, leading to a systematic underestimation of risk. Objective: We investigate the relationship between Version Lag and software abandonment by (i) identifying which repository-level signals reliably distinguish sustained maintenance from long-term decline, (ii) quantifying how Version Lag magnitude and persistence differ across maintenance states, and (iii) evaluating how maintenance-aware metrics change the identification of high-risk dependencies. Method: We introduce Maintenance-Aware Lag and Technical Abandonment (MALTA), a scoring framework comprising three metrics: Development Activity Score (DAS), Maintainer Responsiveness Score (MRS), and Repository Metadata Viability Score (RMVS). We evaluate MALTA on a dataset of 11,047 Debian packages linked to upstream GitHub repositories, encompassing 1.7 million commits and 4.2 million pull requests. Results: MALTA achieves AUC = 0.783 for classifying active versus declining maintenance. Most significantly, 62.2% of packages classified as "Low Risk" by Version Lag alone are reclassified as "High Risk" when MALTA signals are incorporated. These discordant packages average 2019 days since their last commit, with 9.8% having archived repositories. Conclusions: Version Lag metrics systematically miss abandoned packages, a blind spot affecting the majority of dependencies in distribution ecosystems. MALTA identifies a substantial discordant population invisible to Version Lag by distinguishing resolvable lag from terminal lag caused by upstream abandonment.

Public power-system datasets often lack electromagnetic transient (EMT) waveforms, inverter control dynamics, and diverse disturbance coverage, which limits their usefulness for training surrogate models and studying cyber-physical behavior in inverter-based microgrids. This paper presents a high-fidelity digital twin dataset generated from a MATLAB/Simulink EMT model of a low-voltage AC microgrid with ten inverter-based distributed generators. The dataset records synchronized three-phase PCC voltages and currents, per-DG active power, reactive power, and frequency, together with embedded scenario labels, producing 38 aligned channels sampled at $Δt = 2~μ$s over $T = 1$~s ($N = 500{,}001$ samples) per scenario. Eleven operating and disturbance scenarios are included: normal operation, load step, voltage sag (temporary three-phase fault), load ramp, frequency ramp, DG trip, tie-line trip, reactive power step, single-line-to-ground faults, measurement noise injection, and communication delay. To ensure numerical stability without altering sequence length, invalid samples (NaN, Inf, and extreme outliers) are repaired using linear interpolation. Each scenario is further validated using system-level evidence from mean frequency, PCC voltage magnitude, total active power, voltage unbalance, and zero-sequence current to confirm physical observability and correct timing. The resulting dataset provides a consistent, labeled EMT benchmark for surrogate modeling, disturbance classification, robustness testing under noise and delay, and cyber-physical resilience analysis in inverter-dominated microgrids. The dataset and processing scripts will be released upon acceptance

Solid-state semiconductor lasers underpin technologies from telecommunications and data storage to sensing, medical diagnostics, and emerging quantum communication. Polaritons-hybrid exciton-photon states have further extended this reach, enabling room-temperature quantum effects such as low-threshold lasing and single-photon nonlinearities. Organic semiconductors are ideal for polaritonics due to their large exciton binding energy, strong optical nonlinearities, and straightforward processing, making them attractive for both classical and quantum photonics. While solution-processed organic films have been widely explored, their optical cavities have almost always been fabricated using vacuum deposition, limiting the realization of truly scalable and low-cost devices. Here, we report the first organic laser microcavities fabricated entirely by solution processing, which operate in the strong coupling regimeThe resulting platform can be driven reliably to high excitation densities, where we observe a reversible, interaction-driven redistribution of the polariton condensate, revealing a distinct polariton lasing behaviour in organic microcavities. Together, the fabrication approach and the observed lasing dynamics establish a route toward scalable polaritonic and quantum photonic technologies and provide new opportunities for studying nonlinear polariton physics in organic systems.