A Crank-Nicolson finite volume approximation for three-dimensional conservative space-fractional diffusion equation results in large and dense three-level Toeplitz discrete linear systems. Preconditioned Krylov subspace methods with sine transform-based preconditioners are developed to solve these systems, including the preconditioned conjugate gradient (PCG) method for the symmetric case and the preconditioned generalized minimal residual (PGMRES) method for the non-symmetric case. Moreover, we provide detailed analysis of the convergence of these Krylov subspace methods. Specifically, for the symmetric case, we prove the spectra of the preconditioned matrices are uniformly bounded in the open interval (1/2, 3/2), which results in a linear convergence rate of the PCG method. For the non-symmetric case, we demonstrate that the PGMRES method also achieves a linear convergence rate independent of discretization stepsizes from the residual point of view. These results imply that the iteration counts of the PCG and PGMRES methods are uniformly bounded and independent of the matrix sizes. Numerical experiments in both symmetric and non-symmetric cases in two- and three-dimensions are conducted to confirm the optimal performance of the proposed preconditioned Krylov subspace methods.

In this paper, we investigate the production of a fully charmed tetraquark state $T_{4c}$ in $pp$ collisions at forward rapidities through the fragmentation mechanism considering the Color Glass Condensate (CGC) formalism and the solution of the running coupling Balitsky-Kovchegov (BK) equation. The contributions of gluon - and charm - initiated processes are taken into account, and the impact of an intrinsic charm component in the proton's wave function is estimated. Predictions for the transverse momentum distribution of the $T_{4c}$ state are presented assuming different rapidities, distinct quantum numbers of the state and center-of-mass energies. Our results indicate that the higher cross-section is associated with the production of a tensor state $T_{4c}(2^{++})$, which is dominated by the gluon-initiated process. In contrast, the production of the axial-vector state $T_{4c}(1^{+-})$ is dominated by the charm-initiated process and is very sensitive to the presence (or not) of an intrinsic charm.

Context and motivation. Requirements Engineering (RE) quality still lacks empirical evidence on how specific requirement defects affect downstream activities. Problem: However, empirical data on the detailed effects of requirements quality defects is scarce, since it is costly to obtain. Furthermore, with the advent of AI-based development, the requirements quality factors may change: Requirements are no longer only consumed by humans, but increasingly also by AI agents, which might lead to a different efficient and effective requirements style. Principal ideas: We propose to extend the RE research toolbox with Agentic AI simulations, in which software engineering (SE) processes are replicated by standardized agents in qualitative simulations. We argue that their speed and simplicity makes them a valuable addition to RE research, although limitations in replicating human behavior need to be studied and understood. Contribution: This paper contributes a first concept, a research roadmap, a prototype, and a first feasibility study for RE simulations with agentic AI. Study results indicate that even a naïve implementation leads to executable simulations, encouraging technical improvements along with broader application in RE research.

Quantum mechanics and general relativity are the foundational pillars of modern physics, yet experimental tests that combine the two frameworks remain rare. Measuring optical phase shifts of massless photons in a gravitational potential provides a unique quantum platform to probe gravity beyond Newtonian descriptions, but laboratory-based interferometers have not yet reached the sensitivity needed to access this regime. Here, we report the realization of a 50-km table-top Mach-Zehnder fiber interferometer operating at the single-photon level, achieving a phase sensitivity of $4.42\times10^{-6}$ rad root-mean-square (RMS) within the frequency range of 0.01 Hz to 5 Hz. We demonstrate that this sensitivity is sufficient to resolve a phase-shift signal of $(6.18 \pm 0.44)\times10^{-5}$ rad RMS at 0.1 Hz, associated with a modulated gravity-induced signal. Our results establish a milestone for quantum sensing with large-scale optical interferometry, demonstrating the capability to detect gravitational redshifts in a local laboratory, thereby paving the way for testing quantum phenomena within general relativistic frameworks.

Dual-phase liquid xenon time projection chambers are the core detector elements of many experiments that conduct searches for Dark Matter and rare events, as well as in neutrino and high-energy physics. As part of this detector technology, high-voltage electrodes are instrumental for the generation of observable signals and their physical interpretation. Thus, electrode design and manufacturing has to fulfill stringent requirements, and their production is associated with significant engineering challenges. In this work we describe the successful development of electrodes on the 1.5 m-scale, from their design and simulation to subsequent assembly and high-voltage testing in a gaseous argon environment. The produced electrodes were recently installed as an anode and a cathode during an upgrade to the XENONnT experiment.

Large Language Models (LLMs) are increasingly used to automatically generate optimized CUDA kernels, substantially improving developer productivity. However, despite rapid generation, these kernels often contain subtle correctness bugs and lack formal safety guarantees. Runtime testing is inherently unreliable - limited input coverage and reward hacking can mask incorrect behavior - while manual formal verification is reliable but cannot scale to match LLM output rates, creating a critical validation bottleneck. We present ProofWright, an agentic verification framework that bridges this gap by integrating automated formal verification with LLM-based code generation. ProofWright provides end-to-end guarantees of memory safety, thread safety, and semantic correctness for LLM-generated CUDA kernels. On KernelBench L1, ProofWright verifies safety properties for 74% of generated kernels, uncovers subtle correctness errors missed by conventional testing, and establishes semantic equivalence for a class of element-wise kernels. With a modest overhead of 3 minutes per kernel, ProofWright demonstrates that scalable, automated formal verification of LLM-generated GPU code is feasible - offering a path toward trustworthy high-performance code generation without sacrificing developer productivity.

Web3 applications, built on blockchain technology, manage billions of dollars in digital assets through decentralized applications (dApps) and smart contracts. These systems rely on complex, software supply chains that introduce significant security vulnerabilities. This paper examines the software supply chain security challenges unique to the Web3 ecosystem, where traditional Web2 software supply chain problems intersect with the immutable and high-stakes nature of blockchain technology. We analyze the threat landscape and propose mitigation strategies to strengthen the security posture of Web3 systems.

We study the $(w_0, \, w_a)$ parametrization of the dark energy (DE) equation of state, with and without the effective field theory of dark energy (EFTofDE) framework to describe the DE perturbations, parametrized here by the braiding parameter $α_B$ and the running of the Planck mass $α_M$. We combine the EFTofLSS full-shape analysis of the power spectrum and bispectrum of BOSS data with the tomographic angular power spectra $C_\ell^{gg}$, $C_\ell^{κg}$, $C_\ell^{Tg}$ and $C_\ell^{Tκ}$, where $g$, $κ$ and $T$ stand for the DESI luminous red galaxy map, Planck PR4 lensing map and Planck PR4 temperature map, respectively. To analyze these angular power spectra, we go beyond the Limber approximation, allowing us to include large-scale data in $C_\ell^{gg}$. The combination of all these probes with Planck PR4, DESI DR2 BAO and DES Y5 improves the constraint on the 2D posterior distribution of $\{w_0, \, w_a\}$ by $\sim 50 \%$ and increases the preference for evolving dark energy over $Λ$ from $3.8 σ$ to $4.6 σ$. When we remove BAO and supernovae data, we obtain a hint for evolving dark energy at $2.3 σ$. Regarding the EFTofDE parameters, we improve the constraints on $α_B$ and $α_M$ by $\sim 40 \%$ and $50 \%$ respectively, finding results compatible with general relativity at $\sim 2 σ$. We show that these constraints do not depend on the choice of the BAO and supernovae likelihoods.

In multi-fluid description of heavy-ion collisions, the primary scatterings and particle production are described in terms of interaction between fluids, so called friction. These friction terms can be derived from kinetic theory, but they are not unique. We compare different approaches to derive the friction terms, introduce a new ``charge transfer" friction, which allows to move charge to the midrapidity fireball, and implement them in the MUFFIN model. The charge transfer friction is more consistent with the assumption of three fluids clearly separated in momentum space, and allows better comparisons of the experimental data and underlying equation of state. It also leaves room for entropy generation due to dissipation in individual fluids, and we present the first results obtained using viscous multi-fluid dynamics.

Physics-Informed Neural Networks (PINNs) have recently emerged as powerful tools for solving partial differential equations (PDEs), with the Deep Energy Method (DEM) proving especially effective in fracture mechanics due to its energy-based formulation. Despite these advances, existing DEM approaches require dense collocation near cracks, face stability challenges, and typically treat discrete and continuous fracture models separately. To overcome these limitations, we introduce the Extended Deep Energy Method (XDEM), a unified deep learning framework that incorporates both displacement discontinuities and crack-tip asymptotics in the discrete setting, while flexibly coupling displacement and phase fields in the continuous setting. This integration enables accurate fracture predictions using uniformly distributed, relatively sparse collocation points. Validation across benchmark problems including stress intensity factor evaluation, straight and kinked crack growth, and complex crack initiation demonstrates that XDEM consistently outperforms standard DEM in accuracy and efficiency. By bridging discrete and phase-field models within a single framework, XDEM establishes a robust foundation for applying AI to fracture mechanics and opens new avenues for predictive modeling in engineering and materials science.

We demonstrate a multi-beam scanning transmission electron microscopy (STEM) imaging that integrates down-sampling with super-resolution image reconstruction via a compressive sensing framework. A custom condenser aperture with six randomly positioned circular holes is employed to produce a multi-beam STEM probe, with the beam shape and distribution tuned through defocus. While the raw multi-beam images exhibit overlapping patterns, reconstruction using Adam optimization and total variation normalization yields high-fidelity images that closely reproduce the original sample structures, even from substantially down-sampled data. The proposed approach offers a pathway toward significant acceleration of such techniques through multibeam sparse sampling and computational reconstruction potentially useful for the analytical scanning methods in general.

Network centrality is a foundational concept for quantifying the importance of nodes within a network. Many traditional centrality measures--such as degree and betweenness centrality--are purely structural and often overlook the dynamics that unfold across the network. However, the notion of a node's importance is inherently context-dependent and must reflect both the system's dynamics and the specific objectives guiding its operation. Motivated by this perspective, we propose a dynamic, task-aware centrality framework rooted in optimal control theory. By formulating a problem on minimum energy control of average opinion based on Laplacian dynamics and focusing on the variance of terminal state, we introduce a novel centrality measure--termed U-centrality--that quantifies a node's ability to unify the agents' state. We demonstrate that U-centrality interpolates between known measures: it aligns with degree centrality in the short-time horizon and converges to a new centrality over longer time scales which is closely related to current-flow closeness centrality. This work bridges structural and dynamical approaches to centrality, offering a principled, versatile tool for network analysis in dynamic environments.

A promising platform for semi-device-independent quantum information is prepare-and-measure experiments restricted only by a bound on the energy of the communication. Here, we investigate the role of shared entanglement in such scenarios. For classical communication, we derive a general correlation criterion for nonlocal resources and use it to show that entanglement can fail to be a resource in standard tasks. For quantum communication, we consider the basic primitive for energy-constrained communication, namely the probabilistic transmission of a bit, and show that the advantages of entanglement only can be unlocked by non-unitary encoding schemes that purposefully decohere the entangled state. We also find that these advantages can be increased by using entanglement of higher dimension than qubit. We leverage these insights to investigate the impact of entanglement for quantum random number generation, which is a standard application of these systems but whose security so far only has been established against classical side information. In the low-energy regime, our attacks on the protocol indicate that the security remains largely intact, thereby paving the way for strengthened security without more complex setups and with negligible performance reductions.

By removing a fractal from time-rolled Minkowski spacetime, we construct an extendible spacetime without closed timelike curves whose every extension contains closed timelike curves. This settles a question posed by Geroch.

In flat-band superconductors, the electron pairing is strongly enhanced so that the critical temperature scales linearly with the interaction strength. Identifying the governing pairing mechanism in flat-band superconducting systems is therefore a central task, which may be constrained by experimental probes via low-temperature scaling measurements. A key observable underlying the Meissner effect and the resulting divergent dc conductivity is the superfluid weight. While it is well established that the minimal quantum metric provides the dominant contribution to the superfluid weight in conventional superconductors with isolated flat bands, recent studies indicate that the unconventional pairing can generate additional nonlocal quantum geometric terms. This motivates us to derive the low-temperature scaling law of the superfluid weight in two-dimensional flat-band superconductors with sufficiently isolated bands. In particular, we consider the gap function with point or line nodes classified by the Weierstrass preparation theorem. Beyond the superfluid weight, we additionally deliver explicit low-temperature scaling laws of the order parameter, the tunneling conductance, the specific heat, the Sommerfeld coefficient, and the spin-lattice relaxation rate to provide complementary experimental discriminants of the underlying pairing symmetry. The implications of our results are also elucidated by applying them to a selection of superconducting states in $C_{6v}$-symmetric systems.

We investigate the phase structure of the deterministic and disordered versions of the Russian Doll Model (RDM), which is a generalization of Richardson model of superconductivity in a finite system with time-reversal symmetry breaking parameter $θ$. It is one of the simplest examples of the cyclic RG where $\log N$ plays the role of the RG time. The deterministic model is integrable and shares the same Bethe Ansatz (BA) equations with the inhomogeneous twisted XXX spin chain. We analyze the quantum metric, the Berry curvature, and the fractal dimension in the sector with a single Cooper pair. A rich phase structure in the $(θ,γ)$ parameter plane is found, where $γ\log N$ quantifies the hopping term. For the deterministic RDM we clearly identify the extended domain of non-ergodic multifractal phase on the $(θ,γ)$ parameter plane supporting the reentrance transitions between the localized, ergodic, and multifractal phases. We find the pattern of phase transitions in the global charge $Q(θ,γ)$, which arises from the BA equation. In particular, in the multifractal phase in the deterministic model $Q(γ)$ exhibits the analogue of "charge concentration" and fortuity phenomena discussed in the context of black hole microstates at finite $N$. The BA equations in RDM exactly coincide with the equations defining the ground states in the theory on the worldvolume of the vortex strings in $N_F=2N_C$ ${\cal N}=2$ SQCD at a strong coupling point $\frac{1}{g_{YM}^2}=0$ with identification $θ_{RDM}= θ_{4D}-π$. We conjecture that the Hamiltonian of the RDM model describes the mixing in particular 2d-4d BPS sector of the Hilbert space. Our findings provide an example of the BPS multifractality regime for the probe operator in the sector of Hilbert space, and we comment on the possible application to dense QCD with $θ$ term.

We report the emergence of massless Dirac fermions in moiré-reconstructed bands of bilayer graphene (BLG) aligned with hexagonal boron nitride (hBN). Magnetotransport measurements reveal that while the primary BLG band retains a parabolic dispersion with a Berry phase of $2π$, the moiré-induced secondary bands at $n/n_0 = \pm 4$ host chiral massless quasiparticles with a Berry phase $π$ and a Fermi velocity $v_m \approx 3.6 \times 10^5 \mathrm{m s^{-1}}$. This transition from massive to massless carriers arises from topological band reconstruction driven by the hBN moiré potential. Our results demonstrate that moiré engineering in BLG/hBN offers a powerful route to tune band topology and realize coexisting Dirac and massive fermions within a single crystalline platform.

We show that there is a strong dependence of the radio LDF electric field amplitudes at ground level on the position of $X_{\rm max}$ in the atmosphere, even accounting for differences in the EM energy of the showers. Since an $X_{\rm max}$ dependence leads to a primary composition dependence, this implies that information on the mass composition is encoded not only in the LDF shape but also in its amplitude. This $X_{\rm max}$ dependence can be explained in terms of two competing scalings of the measured electric field: One goes with $(1/ρ)^J$, where $ρ$ is the air density at $X_{\rm max}$ and $J$ is a zenith dependent non-linearity factor describing coherence loss. This density scaling tends to decrease the geomagnetic emission of deeper showers. The other scaling goes with $(1/R)$, where $R$ is the distance from $X_{\rm max}$ to the core at ground, and instead increases the measured electric field of deeper showers. At low zenith angles, the $(1/R)$ scaling is stronger and leads to larger measured electric fields as $X_{\rm max}$ increases. The picture at higher zeniths, i.e., lower densities, is more nuanced. In this region, the deflections due to the Lorentz force are much larger and introduce extra time delays between the particle tracks, decreasing the coherence of the emission. This loss of coherence is highly dependent on the strength of the geomagnetic field and can slow down, or even reverse the increase of the radio emission with decreasing air density. This strong, yet historically overlooked LDF amplitude dependence on $X_{\rm max}$/composition could be used to directly infer, even bypassing any $X_{\rm max}$ reconstruction, the cosmic ray primary composition on an event-by-event basis. It could also have some repercussions on other radio reconstruction methods, such as a possible $X_{\rm max}$/composition bias on shower electromagnetic energy reconstruction methods.

Quantum computing has quickly emerged as a revolutionary paradigm that holds the potential for greatly enhanced computational capability and algorithmic efficiency, in a wide range of areas. Among the various hardware platforms, neutral atom quantum processors based on Rydberg interactions are gaining increasing interest because of their scalability, qubit-connection flexibility, and intrinsic appropriateness for solving combinatorial optimization challenges. This paper provides an overview of the present capabilities, standards, and applications of neutral atom quantum computers. We first discuss recent hardware advancements and register mapping optimization techniques that enhance circuit fidelity and performance. We next review their uses as quantum simulators, in both classical and quantum hard problems, such as MIS and QUBO problems, quantum many-body models and molecules in chemistry and pharmacology. Applications for enhancing machine learning are also covered.

We study resource allocation problems in which a central planner allocates resources among strategic agents with private cost functions in order to minimize a social cost, defined as an aggregate of the agents' costs. This setting poses two main challenges: (i) the agents' cost functions may be unknown to them or difficult to specify explicitly, and (ii) agents may misreport their costs strategically. To address these challenges, we propose an algorithm that combines preference-based learning with Vickrey-Clarke-Groves (VCG) payments to incentivize truthful reporting. Our algorithm selects informative preference queries via D-optimal design, estimates cost parameters through maximum likelihood, and computes VCG allocations and payments based on these estimates. In a one-shot setting, we prove that the mechanism is approximately truthful, individually rational, and efficient up to an error of $\tilde{\mathcal O}(K^{-1/2})$ for $K$ preference queries per agent. In an online setting, these guarantees hold asymptotically with sublinear regret at a rate of $\tilde{\mathcal O}(T^{2/3})$ after $T$ rounds. Finally, we validate our approach through a numerical case study on demand response in local electricity markets.

We theoretically investigate the transient spectral function during the photoinduced melting of charge order in a correlated electron system, to unravel the dynamical processes triggered by different initial excitations. We employ a one-dimensional interacting spinless fermion model introducing a pulsed laser light, and perform a comparative study by the Hartree-Fock approximation and by the exact diagonalization method to numerically solve the time-dependent Schrödinger equation. We find characteristic behavior in the transient spectral function, whose features strongly depend on the pump light frequency $ω_p$. When $ω_p$ is resonant with the collective phase mode of frequency $Ω_c\simeq Δ_{\rm CO}/2$, where $Δ_{\rm CO}$ is the charge gap, the transient spectral function exhibits a photoinduced in-gap weight which triggers large responses. With increasing the laser intensity, the development of in-gap weight directly turns into the collapse of the gap. This charge-order destabilization process is in sharp contrast to the case of $ω_p>Δ_{\rm CO}$, where the photoirradiation induces interband electron-hole excitations giving rise to a shrinkage of the gap. The impact of quantum fluctuations and spatial inhomogeneity on the photoinduced dynamics is also discussed.

Active filaments are a workhorse for propulsion and actuation across biology, soft robotics and mechanical metamaterials. However, artificial active rods suffer from limited robustness and adaptivity because they rely on external control, or are tethered to a substrate. Here we bypass these constraints by demonstrating that non-reciprocal interactions lead to large-scale unidirectional dynamics in free-standing slender structures. By coupling the bending modes of a buckled beam anti-symmetrically, we transform the multistable dynamics of elastic snap-through into persistent cycles of shape change. In contrast to the critical point underpinning beam buckling, this transition to self-snapping is mediated by a critical exceptional point, at which bending modes simultaneously become unstable and degenerate. Upon environmental perturbation, our active filaments exploit self-snapping for a range of functionality including crawling, digging and walking. Our work advances critical exceptional physics as a guiding principle for programming instabilities into functional active materials.

A Markov chain $X^i$ on a finite state space $S$ has transition matrix $P$ and initial state $i$. We may run the chains $(X^i: i\in S)$ in parallel, while insisting that any two such chains coalesce whenever they are simultaneously at the same state. There are $|S|$ trajectories which evolve separately, but not necessarily independently, prior to coalescence. What can be said about the number $k(μ)$ of coalescence classes of the process, and what is the set $K(P)$ of such numbers $k(μ)$, as the coupling $μ$ of the chains ranges over couplings that are consistent with $P$? We continue earlier work of the authors ('Non-coupling from the past', $\textit{In and Out of Equilibrium 3}$, Springer, 2021) on these two fundamental questions, which have special importance for the 'coupling from the past' algorithm. We concentrate partly on a family of couplings termed block measures, which may be viewed as couplings of lumpable chains with coalescing lumps. Constructions of such couplings are presented, and also of non-block measure with similar properties.

Spatio temporal data consist of measurement for one or more raster fields such as weather, traffic volume, crime rate, or disease incidents. Advances in modern technology have increased the number of available information for this type of data hence the rise of multidimensional data. In this paper we take advantage of the multidimensional structure of the data but also its temporal and spatial structure. In fact, we will be using the NCAR Climate Data Gateway website which provides data discovery and access services for global and regional climate model data. The daily values of total precipitation (prec), maximum (tmax), and minimum (tmin) temperature are combined to create a multidimensional data called tensor (a multidimensional array). In this paper, we propose a spatio temporal principal component analysis to initialize CP decomposition component. We take full advantage of the spatial and temporal structure of the data in the initialization step for cp component analysis. The performance of our method is tested via comparison with most popular initialization method. We also run a clustering analysis to further show the performance of our analysis.

We prove that recognizing the phase of matter of an unknown quantum state is quantum computationally hard. More specifically, we show that the quantum computational time of any phase recognition algorithm must grow exponentially in the range of correlations $ξ$ of the unknown state. This exponential growth renders the problem practically infeasible for even moderate correlation ranges, and leads to super-polynomial quantum computational time in the system size $n$ whenever $ξ= ω(\log n)$. Our results apply to a substantial portion of all known phases of matter, including symmetry-breaking phases and symmetry-protected topological phases for any discrete on-site symmetry group in any spatial dimension. To establish this hardness, we extend the study of pseudorandom unitaries (PRUs) to quantum systems with symmetries. We prove that symmetric PRUs exist under standard cryptographic conjectures, and can be constructed in extremely low circuit depths. We also establish hardness for systems with translation invariance and purely classical phases of matter. A key technical limitation is that the locality of the parent Hamiltonians of the states we consider is linear in $ξ$; the complexity of phase recognition for Hamiltonians with constant locality remains an important open question.

Single-molecule spectroscopy (SMS) is an exceptionally sensitive technique, but its inherently limited photon budget produces noisy data that can readily lead to subjective analyses, fitting errors, and reduced statistical power, obscuring true subpopulations and their dynamics. Here, we present an unbiased, objective method to cluster two-dimensional single-molecule data and demonstrate it on fluorescence lifetime--intensity correlations. The clustering method is based on Gaussian mixture modeling, with the optimal number of clusters determined through {information criteria (the Akaike and Bayesian information criteria and integrated completed likelihood) and supplemented by cluster quality metrics such as average cluster tightness and the fraction of points outside confidence ellipses, which guide the selection of statistically robust and physically meaningful clusters. The protocol was benchmarked on simulated datasets spanning clean, smeared, and noisy overlap-limited regimes, and applied to experimental data from Alexa Fluor 647 and QD 605. This approach reliably recovers relevant subpopulations even in the presence of noise and overlapping distributions, providing an objective framework for analyzing single-molecule heterogeneity, with limitations arising primarily under severe geometric overlap or extreme state-occupancy imbalance where distinct populations are no longer separable.

Despite decades of research, software bug localization remains challenging due to heterogeneous content and inherent ambiguities in bug reports. Existing methods, such as Information Retrieval (IR)-based approaches, often attempt to match source documents to bug reports, overlooking the context and semantics of the source code. On the other hand, Large Language Models (LLMs) (e.g., Transformer models) show promising results in understanding both texts and code. However, they have not yet been adapted well to localize software bugs using bug reports. They could also be data or resource-intensive. To bridge this gap, we propose, IQLoc, a novel approach that capitalizes on the strengths of both IR and LLMs for bug localization. In particular, we leverage the transformer-based model's understanding of code semantics to reason about its suspiciousness and to reformulate search queries and thus enhance bug localization using Information Retrieval. To evaluate IQLoc, we refine the Bench4BL benchmark dataset and extend it by incorporating ~30% more recent bug reports, resulting in a benchmark containing ~7.5K bug reports. We evaluated IQLoc using three performance metrics and compare it against eight baseline techniques. Experimental results demonstrate its superiority, achieving up to 100.40% and 78.08% in MAP, 61.49% and 64.58% in MRR, and 76.98% and 100.90% in HIT@K for the test bug reports with random and time-wise splits, respectively. Moreover, IQLoc improves MAP by 118.70% for bug reports with stack traces, 111.87% for those that include code elements, and 127.45% for those containing only descriptions in natural language. By integrating program semantic understanding into Information Retrieval, IQLoc mitigates several longstanding challenges of traditional IR-based approaches in bug localization.

We characterize the inverse of an analytic Fredholm operator-valued function A(z) near an isolated singularity within a general Banach space framework. Our approach relies on the sequential factorization of A(z) via Fredholm quotient operators. By analyzing the properties of these quotient operators near an isolated singularity, we fully characterize the Laurent series expansion of the inverse of A(z) in terms of its Taylor coefficients around the singularity. These theoretical results are subsequently applied to characterize the solution of a general autoregressive law of motion in a Banach space.

The classical problem of steady streaming induced by an oscillating object has been studied extensively, but prior work has focused almost exclusively on single-frequency oscillations, which result in symmetric, quadrupole-like flows. Here we demonstrate that dual-frequency oscillations induce asymmetric steady streaming with a non-zero net flux in a direction determined by the polarity of the oscillation \ -- the oscillator serves as a pump. We use numerical simulations and asymptotic analysis at low Reynolds number to examine 2D steady streaming around a cylinder, first focusing on frequency ratio two. The computational experiments show asymmetrical streaming and pumping, i.e., net flux downstream. It is well known from asymptotic analysis that steady streaming is second order in amplitude, and we show pumping occurs at third order. We then extend the analysis to general frequency ratios, where we give necessary conditions for pumping and predict the order in amplitude at which pumping occurs. Finally, we corroborate the theoretical results with computational simulations for different frequency ratios, and we discuss the implications for using dual-mode vibrations to pump fluids in lab-on-a-chip and other applications.

In this work, we investigate the velocity effects on information degradation due to the Unruh effect in accelerated quantum systems (with finite interaction time). We consider a detector moving along a spatial trajectory within a two-dimensional plane. The quantum systems studied were: accelerated single-qubit, quantum interferometric circuit, and which-path distinguishability circuit. Thus, for non-relativistic velocity regime, we obtained analytical expressions such as transition rates, quantum coherence, visibility, distinguishability, and the complementarity relation. On the other hand, for the ultra-relativistic velocity regime, we saw that the Unruh effect is suppressed and therefore the detector does not respond in this case. Our findings revealed that velocity effects imply mitigation of information degradation, this interesting behaviors happen because of the composite effect of both velocity and acceleration. The results obtained show that the addition of the non-relativistic, transverse and constant motion of an accelerated detector can play a protective role in quantumness in systems at high accelerations, although the effects are very small.