By combining multi-band data from Gaia DR3, MWISP CO, and LAMOST DR11 LSR/MSR, we investigate the co-evolution of stars and their parent molecular cloud in a snake-like stellar structure, named Snake III. Based on 5-D phase-space selection, we identified 5683 member stars (median age 7.6 Myr) across approximately $300 \times 500 \times 175$ pc$^3$ volume, along with 12 embedded open clusters. Then we use BEEP distances combined with $^{12}$CO velocities to clearly identify the molecular clouds associated with the stellar complex in spatial and kinematics. The molecular cloud density increases with Galactic longitude, with older open clusters forming in cavities near higher-density regions (except ASCC 125), while young field stars currently form preferentially in present-day high-density environments, indicating that cloud density regulates the star-formation sequence. $^{12}$CO excitation temperature, centroid velocity, velocity dispersion and H$α$ emission reveal that early feedback first compresses cloud edges to trigger new stars, then sweeps and disperses the parent clouds. The extremely young cluster (ASCC 125, 4.4 Myr) lies near the densest region yet is surrounded by a shell with bidirectional density-velocity perturbations, consistent with a delayed-triggering scenario under the combined influence of UBC 178 stellar-wind feedback and a suspected supernova blast. Our results naturally demonstrate that snake-like stellar structures are filamentary relics of hierarchical star formation within giant molecular clouds. They provide direct observational evidence that cloud density and early feedback jointly modulate the progression of star formation, offering a clear and young laboratory for studying star-cloud co-evolution.

In chiral crystals, two types of phonon angular momenta have been introduced. One is crystal angular momentum (CAM) arising from the rotational or screw-rotational symmetry and the other is mechanical angular momentum (MAM) associated with the circular motion of atomic displacements about equilibrium positions. Recently, the electron--phonon coupling that respects the screw-rotational symmetry is derived, whereby the CAM between electrons and phonons is interconverted. Here, we show that, in addition to CAM, MAM can also be converted to the electronic degrees of freedom by deriving a second-order perturbative Hamiltonian proportional to phonon MAM. This finding highlights that the electronic motion is directly affected by phonon MAM, and consequently, that phonon degrees of freedom can play a crucial role in phenomena related to electronic orbital and spin polarizations.

Citizens' assemblies are a form of democratic innovation in which a randomly selected panel of constituents deliberates on questions of public interest. We study a novel goal for the selection of panel members: maximizing the entropy of the distribution over possible panels. We design algorithms that sample from maximum-entropy distributions, potentially subject to constraints on the individual selection probabilities. We investigate the properties of these algorithms theoretically, including in terms of their resistance to manipulation and transparency. We benchmark our algorithms on a large set of real assembly lotteries in terms of their intersectional diversity and the probability of satisfying unseen representation constraints, and we obtain favorable results on both measures. We deploy one of our algorithms on a website for citizens' assembly practitioners.

T Corona Borealis (T CrB) is a symbiotic recurrent nova with an $\simeq 80$ yr recurrence interval, the eruptions of which occur on top of a $\simeq 15$ yr long high-brightness state. We show that the high-brightness state is best explained as the response of a high-viscosity ($α=3$) accretion disk to a unique event in which the mass transfer rate from the donor star increases by a factor $\simeq 100$, from $\dot{M}\mathrm{(quies)}= 2 \times 10^{-9} M_\odot$ yr$^{-1}$ up to $\dot{M}\mathrm{(out)}= 1.9 \times 10^{-7} M_\odot$ yr$^{-1}$; it can not be a thermal-viscous disk instability outburst neither a steady nuclear burning event. The constraint that the matter accreted onto the white dwarf in between eruptions equals the envelope mass $M_{ig}$ needed to trigger nova eruptions at the observed recurrence interval requires a white dwarf mass of $M_1= 1.29 M_\odot$, a donor star mass of $M_2= 0.7 M_\odot$, and an inclination of $i= 57.3^o$. As the high-brightness state responds for 95% of $M_{ig}$, the nova eruptions of T CrB are induced by accretion events. Without the 15 yr long enhanced mass transfer events, its nova recurrence interval would be significantly longer, $\simeq 5500$ yr. T CrB exhibits a conspicuous decrease in brightness during the 1-2 yr prior to the nova event. We argue that this pre-eruption dip occurs during the convection phase that precedes the nova eruption and is best explained by the slow, accelerated expansion of the accreted envelope (and inner disk radius) at an average velocity of $v_\mathrm{exp}= 0.02$ km s$^{-1}$ over a 2 yr timescale, likely as a consequence of excess heat being increasingly deposited at the accreted layer by thermonuclear reactions before the nova eruption stage.

Topological phases support edge states that can be robust to material deformations and other perturbations. While well-studied in quantum systems, topological phases have also been observed in stochastic and biochemical systems, yet it remains unclear which of their properties remain similar or different from those in quantum systems. In this paper, we derive analytical expressions for the spectral properties of simple quantum and stochastic models on the same lattice to rigorously characterize these complex systems. Intriguingly, we find that non-reciprocity moves states away from the steady-state in stochastic systems while clustering states at zero-energy in quantum systems. In contrast, making the system more topological does the opposite: it clusters more states around the steady-state in stochastic systems but moves states away from the zero-energy state in quantum systems. These results provide control parameters for selection and modulation of different purposes while quantifying the size of gap which protects the longest-lived states. Lastly, we discover a mode unique to stochastic systems that we dub the topologically emerging state, which persists across different models and dimensions, including in the presence of non-equilibrium currents.

We present magnetohydrodynamic simulations of laser driven plasma outflows propagating along an externally applied poloidal magnetic field, designed to mimic coronal open-field plasma jets. Using the FLASH code with non-ideal terms (resistivity, Biermann battery, and Nernst advection) included, we model a CH target driven by a 3$ω$ (351 nm) beam delivering 5 kJ over 10 ns and a uniform background field $\text{B}_0$ = 0 to 50 T. Under these conditions, the expanding plume develops a central low-density diamagnetic cavity bounded by a high-magnetic-pressure shell. Magnetic flux is advected from the plume center to its edge, and azimuthal diamagnetic currents form that decrease fields inside the cavity and amplify fields outside, producing a radial magnetic-pressure gradient that exerts an inward $\text{J}\times \text{B}$ force and radially confines the flow. We show that the collimation strengthens with increasing applied magnetic field, as stronger fields reduce the plasma $β$ and correspondingly enhance the confining $\text{J}\times \text{B}$ force.

In this paper, we study the nonlinear dispersive waves including the rarefaction and dispersive shock waves in the discrete modified KdV equation through the numerical simulations of the dispersive Riemann problems. In particular, we propose distinct quasi-continuum models to approximate both the spatial profiles and distinct edge features of these two specific dispersive wave structures. Whitham analysis is performed to construct a closed system of partial differential equations which describe the slowly-varying dynamics of all the relevant parameters associated with the periodic traveling waves of the proposed quasi-continuum models. We then perform reduction on such modulation system to obtain a system of two simple-wave ordinary differential equations which lead to the DSW-fitting method that shall provide useful theoretical insights on different edge characteristics of the dispersive shock waves. Furthermore, we compute analytically the self-similar solutions corresponding to the dispersionless systems of the quasi-continuum models, which can be utilized to approximate the numerically observed rarefaction waves of the discrete mKdV equation. A systematic numerical comparison of these theoretical findings with their associated numerical counterparts finally demonstrate the good performance of the proposed quasi-continuum models in approximating both nonlinear dispersive wave patterns.

Dynamic languages (such as Python and JavaScript) offer flexibility and simplified type handling for programming, but this can also lead to an increase in type-related errors and additional overhead for compile-time type inference. As a result, type inference for dynamic languages has become a popular research area. Existing approaches typically achieve type inference through static analysis, machine learning, or large language models (LLMs). However, current work only focuses on the direct dependencies of variables related to type inference as the context, resulting in incomplete contextual information and thus affecting the accuracy of type inference. To address this issue, this paper proposes a method called TypePro, which leverages LLMs for type inference in dynamic languages. TypePro supplements contextual information by conducting inter-procedural code slicing. Then, TypePro proposes a set of candidate complex types based on the structural information of data types implied in the slices, thereby addressing the lack of domain knowledge of LLMs. We conducted experiments on the ManyTypes4Py and ManyTypes4TypeScript datasets, achieving Top-1 exact match (EM) rates of 88.9% and 86.6%, respectively. Notably, TypePro improves the Top-1 Exact Match by 7.1 and 10.3 percentage points over the second-best approach, showing the effectiveness and robustness of TypePro.

Recent claims have suggested the absence of CP violation in theories with a $θ$-vacuum structure, particularly in quantum chromodynamics. We highlight several key points, from a perspective that is not widely discussed in the literature, which clarify why such conclusions are incorrect. In particular, an open boundary in a finite-volume theory must be accompanied by boundary degrees of freedom, the edge modes, in order to preserve large gauge invariance and faithfully capture the topological features of the theory. In the infinite-volume limit, these edge states become non-dynamical, leaving the standard $\uptheta$-vacuum structure intact, irrespective of whether this limit is taken before or after summing over topological sectors. Consequently, the $\uptheta$-vacuum structure does give rise to observable CP violation once the theory is consistently quantised.

Unstructured documents dominate enterprise and web data, but their lack of explicit organization hinders precise information retrieval. Current mainstream retrieval methods, especially embedding-based vector search, rely on coarse-grained semantic similarity, incurring high computational cost and frequent LLM calls for post-processing. To address this critical issue, we propose AnnoRetrieve, a novel retrieval paradigm that shifts from embeddings to structured annotations, enabling precise, annotation-driven semantic retrieval. Our system replaces expensive vector comparisons with lightweight structured queries over automatically induced schemas, dramatically reducing LLM usage and overall cost. The system integrates two synergistic core innovations: SchemaBoot, which automatically generates document annotation schemas via multi-granularity pattern discovery and constraint-based optimization, laying a foundation for annotation-driven retrieval and eliminating manual schema design, and Structured Semantic Retrieval (SSR), the core retrieval engine, which unifies semantic understanding with structured query execution; by leveraging the annotated structure instead of vector embeddings, SSR achieves precise semantic matching, seamlessly completing attribute-value extraction, table generation, and progressive SQL-based reasoning without relying on LLM interventions. This annotation-driven paradigm overcomes the limitations of traditional vector-based methods with coarse-grained matching and heavy LLM dependency and graph-based methods with high computational overhead. Experiments on three real-world datasets confirm that AnnoRetrieve significantly lowers LLM call frequency and retrieval cost while maintaining high accuracy. AnnoRetrieve establishes a new paradigm for cost-effective, precise, and scalable document analysis through intelligent structuring.

Safe multi-agent coordination in uncertain environments can benefit from learning constraints from other agents. Implicitly communicating safety constraints through actions is a promising approach, allowing agents to coordinate and maintain safety without expensive communication channels. This paper introduces an online method to infer constraints from observing the safety-filtered actions of other agents. We approach the problem by using safety filters to ensure forward safety and exploit their structure to work backwards and infer constraints. We provide sufficient conditions under which we can infer these constraints and prove that our inference method converges. This constraint inference procedure is coupled with a decentralized planning method that ensures safety when the constraint activation distance is sufficiently large. We then empirically validate our method with Monte Carlo simulations and hardware experiments with quadruped robots.

Generative recommendation (GR) has recently emerged as a promising paradigm for industrial recommendations. GR leverages Semantic IDs (SIDs) to reduce the encoding-decoding space and employs the Next Token Prediction (NTP) framework to explore scaling laws. However, existing GR methods suffer from two critical issues: (1) a \textbf{seesaw phenomenon} in multi-business scenarios arises due to NTP's inability to capture complex cross-business behavioral patterns; and (2) a unified SID space causes \textbf{representation confusion} by failing to distinguish distinct semantic information across businesses. To address these issues, we propose Multi-Business Generative Recommendation (MBGR), the first GR framework tailored for multi-business scenarios. Our framework comprises three key components. First, we design a Business-aware semantic ID (BID) module that preserves semantic integrity via domain-aware tokenization. Then, we introduce a Multi-Business Prediction (MBP) structure to provide business-specific prediction capabilities. Furthermore, we develop a Label Dynamic Routing (LDR) module that transforms sparse multi-business labels into dense labels to further enhance the multi-business generation capability. Extensive offline and online experiments on Meituan's food delivery platform validate MBGR's effectiveness, and we have successfully deployed it in production.

EX Draconis (EX Dra) is a long period dwarf nova showing ~2 mag outburst which lasts for ~7 d and recur on a timescale of (20-30) d. Its deep eclipses allows one to trace the changes in surface brightness and radius of its accretion disk along the outburst cycle and to perform critical tests of the predictions of the thermal-viscous disk instability (DI) and the mass transfer outburst (MTO) models proposed to explain dwarf nova outbursts. The results of four critical tests are in clear contradiction with DI while in good agreement with MTO expectations. Furthermore, the observed variations in brightness and outer disk radius throughout EX Dra outbursts are well described by the response of a high-viscosity (alpha = 3-4) accretion disk to events in which the mass transfer rate increases by factors of ~30 for ~7 d, in line with MTO expectations. We further argue that the old expectation of accretion disk theory, alpha <= 1, seems unjustified and contradicts the values derived from dwarf nova outburst decline timescales if they are driven by MTO.

The widespread use of unmanned aerial vehicles (UAVs) in low-altitude airspace has raised significant safety and security concerns, motivating the development of reliable non-cooperative UAV surveillance technologies. Integrated sensing and communication (ISAC), enabled by multiple-input multiple-output (MIMO) architectures and orthogonal frequency-division multiplexing (OFDM) waveforms, has emerged as a promising paradigm for leveraging cellular infrastructure to support large-scale sensing without additional hardware deployment. This paper presents the first comprehensive survey dedicated to MIMO OFDM-enabled ISAC for low-altitude non-cooperative UAV surveillance, where the targeted UAVs do not intentionally assist the monitoring system through dedicated signaling or prior coordinate sharing. We first analyze the unique propagation characteristics of low-altitude UAV sensing, including severe clutter, rapid channel variations, and mixed near/far-field effects, and discuss corresponding waveform design principles. We then systematically review existing MIMO OFDM-enabled UAV surveillance techniques along four key dimensions: ISAC system modeling and network optimization, UAV detection and tracking algorithms under single and networked base station (BS) architectures, UAV identification techniques based on micro-Doppler and learning-based approaches, and experimental validations and practical field trials. Subsequently, we summarize open challenges such as sensing under severe clutter and multipath, data scarcity for identification, cooperative multi-BS fusion, and real-world deployment constraints. Finally, we outline promising future research directions toward 5G-Advanced (5G-A) and 6G-enabled low-altitude surveillance systems.

Most AI-based educational tools today adopt a one-on-one tutoring paradigm, pairing a single LLM with a single learner. Yet decades of learning science research suggest that multi-party interaction -- through peer modeling, co-construction, and exposure to diverse perspectives -- can produce learning benefits that dyadic tutoring alone cannot. In this paper, we investigate whether multi-agent LLM configurations can enhance learning outcomes beyond what a single LLM tutor provides. We present two controlled experiments spanning distinct learning contexts. In a convergent problem-solving study ($N=315$), participants tackle SAT-level math problems in a 2$\times$2 design that varies the presence of an LLM tutor and LLM peers, each making different kinds of errors (conceptual vs.\ arithmetic); participants who interacted with both a tutor and peers achieved the highest unassisted test accuracy. In a divergent composition study ($N=247$), participants write argumentative and creative essays with either no AI assistance, a single LLM (Claude or ChatGPT), or both Claude and ChatGPT together; while both LLM conditions improved essay quality, only the two-agent condition avoided the idea-level homogeneity that single-model assistance was found to produce. Together, these studies offer one of the first controlled investigations of multi-agent LLM learning environments, probing whether the move from one-on-one AI tutoring toward richer agent configurations can unlock the collaborative and observational benefits long documented in human social learning research.

Stackelberg prediction games (SPGs) model strategic data manipulation in adversarial learning via a leader--follower interaction between a learner and a self-interested data provider, leading to challenging bilevel optimization problems. Focusing on the least-squares setting (SPG-LS), recent work shows that the bilevel program admits an equivalent spherically constrained least-squares (SCLS) reformulation, which avoids costly conic programming and enables scalable algorithms. In this paper, we develop a simple and efficient alternating direction method of multiplier (ADMM) based solver for the SCLS problem. By introducing a consensus splitting that separates the quadratic objective from the spherical constraint, we obtain an augmented Lagrangian formulation with closed-form updates: the primal quadratic step reduces to solving a fixed shifted linear system, the constraint step is a projection onto the unit sphere, and the dual step is a lightweight scaled ascent. The resulting method has low per-iteration complexity and allows pre-factorization of the constant system matrix for substantial speedups. Experiments demonstrate that the proposed ADMM approach achieves competitive solution quality with significantly improved computational efficiency compared with existing global solvers for SCLS, particularly in sparse and high-dimensional regimes.

In urban transport systems, time-varying demand and network conditions cause the importance of infrastructure elements to evolve, requiring the identification of period-specific critical links to support systemlevel risk and resilience analysis. However, static or time-averaged network analyses struggle to capture the temporal variation of infrastructure importance at the city scale. To address this gap, this study proposes a time-dependent critical link identification framework for large-scale urban transport networks. The problem is formulated as a Quadratic Unconstrained Binary Optimisation (QUBO) model and solved using quantum annealing on D-Wave hardware. Empirical analysis using real-world traffic data reveals a strong temporal concentration of critical links. Rather than persistently influencing system performance, critical links emerge mainly within a small number of key time windows, during which even limited disruptions can lead to substantial network delay amplification. These findings demonstrate the value of time-dependent analysis for risk screening, stress testing, and resilience-oriented transport management.

We develop a logic of secrecy on simplicial models for multi-agent systems. Standard simplicial models provide a geometric semantics for knowledge by representing global states as facets of a chromatic simplicial complex and agents' local states as coloured vertices. However, secrecy cannot in general be captured as a genuinely new modality by relying on the ordinary simplicial knowledge structure alone. This motivates the introduction of an additional secrecy layer. To this end, we define \emph{simplicial secrecy models}, which enrich standard simplicial epistemic models with agent-relative secrecy neighborhood functions attached to local states. On this basis, we introduce a primitive secrecy operator $S_aφ$. Semantically, $S_aφ$ holds when agent $a$ knows $φ$ in the ordinary simplicial sense and, moreover, the truth set of $φ$ belongs to one of the designated secrecy neighborhoods associated with $a$'s current local state. The clause for secrecy thus combines an ordinary knowledge requirement with an additional local-state-based neighborhood requirement, while the frame condition ensures that designated secrecy events remain non-trivial from the perspective of every other agent. We formulate a system $\mathsf{SSL}$ for the resulting language and show that it is sound with respect to the class of simplicial secrecy models. For the genuinely multi-agent case $|A|\ge 2$, we prove completeness by first constructing an auxiliary-colour canonical model and then representing it inside the original class of pure $A$-chromatic simplicial secrecy models. The resulting framework yields a primitive, local-state-based, and geometrically grounded account of secrecy on simplicial models, together with a sound axiomatization and, in the genuinely multi-agent case, a complete one.

We construct infinite families of chirally cosmetic surgeries on chiral hyperbolic knots and purely cosmetic surgeries on hyperbolic manifolds with multiple cusps, disproving conjectures that these phenomena do not appear, including Problem 1.12(d) in the K3 problem list. We also give some hints regarding why chirally cosmetic surgeries appear to be more common than purely cosmetic surgeries on $1$-cusped manifolds.

Recently, quantum computing has gained attention in urban studies as a tool for complex transport planning problems, but its role remains unclear. This paper reviews quantum computing research in urban transport planning and highlights major limits in scalability, robustness, constraint handling, and engineering feasibility.Stable and reproducible advantages of quantum optimisation in real urban systems have yet to be shown. By comparing quantum methods with established classical optimisation methods, it is found that decomposition methods, metaheuristics, and reinforcement learning already provide transparent, scalable, and policy-interpretable solutions for medium and large-sized urban transport networks. In contrast, the contribution of quantum methods largely lies in the exploratory analysis of limited, discrete combinatorial subproblems rather than full system-level optimisation. It is argued in this paper for a shift from technology-driven application narrative towards problem-driven method selection. From an urban transport planning perspective, we have identified the specific problem types where the exploratory use of quantum computing may be relevant, including critical link and node vulnerability identification, combinatorial screening of congestion and failure scenarios, disaster-related condition analysis, constrained path option selection, and small-scale facility location and investment option assessment. It is concluded that hybrid frameworks represent a more realistic pathway for integrating quantum computing into urban transport research, in which classical methods ensure systemlevel consistency and policy interpretability while quantum methods support local combinatorial exploration. Until stable engineering advantages are demonstrated, public agencies and researchers should prioritise method validation, scenario suitability, and cross-disciplinary collaboration.

The SZZ algorithm is the dominant technique for identifying bug-inducing commits and underpins many software engineering tasks, such as defect prediction and vulnerability analysis. Despite numerous variants, including recent LLM-based approaches, performance remains limited on developer-annotated datasets (e.g., recall of 0.552 on the Linux kernel). A key limitation is the reliance on git blame, which traces line-level changes within the same file, failing in common scenarios such as ghost and cross-file cases-making nearly one-quarter of bug-inducing commits inherently untraceable. Moreover, current approaches follow fixed pipelines that restrict iterative reasoning and exploration, unlike developers who investigate bugs through an interactive, multi-tool process. To address these challenges, we propose AgentSZZ, an agent-based framework that leverages LLM-driven agents to explore repositories and identify bug-inducing commits. Unlike prior methods, AgentSZZ integrates task-specific tools, domain knowledge, and a ReAct-style loop to enable adaptive and causal tracing of bugs. A structured compression module further improves efficiency by reducing redundant context while preserving key evidence. Extensive experiments on three widely used datasets show that AgentSZZ consistently outperforms state-of-the-art SZZ algorithms across all settings, achieving F1-score gains of up to 27.2% over prior LLM-based approaches. The improvements are especially pronounced in challenging scenarios such as cross-file and ghost commits, with recall gains of up to 300% and 60%, respectively. Ablation studies show that task-specific tools and domain knowledge are critical, while compression tool outputs reduce token consumption by over 30% with negligible impact. The replication package is available.

This paper provides a statistical analysis of three common methods of regression for Poisson data in the presence of Poisson background, namely the joint fit with two parametric models for the source and the background, the use of a non-parametric model for the background known as the wstat method, and the regression with a fixed background. The non-parametric background method, which is a popular method for spectral data, is found to be significantly biased, especially in the low-count and background-dominated regimes. Similar conclusions apply to the fixed-background regression. The joint-fit method, on the other hand, simultaneously affords reliable hypothesis testing by means of the usual Cash statistic and unbiased reconstruction of source parameters. We also investigate the effect of non-parametric regression on the number of effective degrees of freedom by means of the Efron degree of freedom function. We find that the wstat method adds a significantly larger number of degrees of freedom, compared to the number of free parameters in the source model. The other two methods have a number of degrees of freedom consistent with the number of adjustable parameters, at least for the simple models investigated in this paper.

Despite the long history of the theory of Bose-Einstein condensation, there exist till nowadays some slippery points that are often misunderstood and result in confusion. The report touches some of these points, explaining the following: Global gauge symmetry breaking is the necessary and sufficient condition for the existence of Bose-Einstein condensate. There is no any ``grand canonical catastrophe". The stability of the ideal Bose gas depends on the spatial dimensionality and the shape of a trap. Symmetry-broken averages cannot be neglected. The so-called ``Popov approximation", ascribed to Popov, suggesting to neglect anomalous averages, is neither an approximation nor has anything to do with Popov. There are no thermodynamically anomalous fluctuations in stable equilibrium systems. Representative statistical ensembles are equivalent.

Transport network vulnerability analysis plays a crucial role in safeguarding urban resilience. Traditional vulnerability identification approaches have provided valuable insights, yet they face two major limitations. First, the number of disruption scenarios increases combinatorially with the number of disrupted links considered simultaneously, making classical approaches computationally prohibitive. Second, most studies approximate the impacts of multiple simultaneous link failures through linear aggregation, which fails to capture the nonlinear interaction effects observed in real networks. To address these gaps, we reformulate the bi-level Mixed-Integer Nonlinear Programming (MINLP) model into a quantum-compatible Quadratic Unconstrained Binary Optimisation (QUBO) structure, enabling parallel exploration of complex disruption scenarios while incorporating nonlinear interaction effects. We develop a hybrid optimisation framework that integrates the quantum optimisation algorithm with the Frank-Wolfe method to validate the model's effectiveness on the small-scale network. Then, we further verify the framework through the D-Wave hardware across benchmark networks of different scales, including Sioux Falls, Anaheim, Chicago Sketch, and Berlin Full, to examine scalability and feasibility. The results show that this framework achieves strong solvability and stability. In particular, optimisation for large and larger networks is completed within minutes (Approximately 2.8 minutes for the 914-link, 9.8 minutes for the 2950-link, and 31.2 minutes for the 6018-link on D-Wave), demonstrating a computational efficiency improvement by one to two orders of magnitude compared with classical metaheuristic algorithms. These findings highlight the feasibility and potential of applying quantum computing to network vulnerability identification and open a new avenue for resilience-oriented planning.

Nitrogen-vacancy (NV) centers in nanodiamonds are excellent nanoscale sensors for measuring parameters such as temperature, magnetic field, and viscosity in complex fluidic environments, including living cells. However, the rapid motion of nanodiamonds in such dynamic systems imposes a significant challenge for continuous, real-time tracking and sensing measurements. Here, we present a fast single particle tracking (SPT) method featuring a tetrahedral detection geometry for time-efficient parallel fluorescence collection using four avalanche photodiodes (4-APDs), which eliminates the temporal latency of traditional sequential scanning. We demonstrate an improvement of about an order of magnitude in the temporal resolution and the upper limit of measurable diffusion coefficient compared to previously reported nanodiamond tracking methods based on single APD. The SPT is integrated with multi-parameter quantum sensing based on optically detected magnetic resonance (ODMR) of NV centers. The sensitivities of ODMR-based temperature and 3D rotation sensing are evaluated at different diffusion coefficients, which shows no significant degradation within our measurement range. We apply the system for thermorheology measurements in glycerol/water mixtures under thermal ramps. Additionally, we perform simultaneous translation and rotation tracking in live cells, revealing correlated translational and rotational dynamics. This approach advances multi-parameter nanoscale sensing for soft matter and biological applications, paving the way for real-time nanoscale sensing in highly dynamic fluidic environments.

Layered conductive metal-organic frameworks (LCMOFs) show great promise in energy and electronics due to their high electrical conductivity and tunable pore structures. They are considered ideal "phonon-glass, electron-crystal" materials. However, their intrinsic thermal transport properties, particularly the thermal conductivity in the single-crystalline state, have never been explored before. The applicability of the Wiedemann-Franz law to such complex porous materials is a key scientific question to describe their thermoelectric relationship. We investigated single crystals of three LCMOFs (Cu3HHTP2, Co9HHTP4, Nd3HHTP2) using the microfabricated suspended device. Results showed ultralow thermal conductivities (0.075-0.194 W m-1 K-1) along the π-π stacking direction. Crucially, Nd3HHTP2 exhibited a high electrical conductivity of 398 S cm-1, yet its thermal conductivity (0.148 W m-1 K-1) was comparable to the other two LCMOFs with significantly lower electrical conductivities. Structural characterization revealed that the incommensurate modulation, and in-plane correlated disorder within the Nd3HHTP2 structure are the potential causes of strong phonon scattering and the observed ultralow thermal conductivity.

Semantic operators have increasingly become integrated within data systems to enable processing data using Large Language Models (LLMs). Despite significant recent effort in improving these operators, their accuracy is limited due to a critical flaw in their implementation: lack of holistic data understanding. In existing systems, semantic operators often process each data record independently using an LLM, without considering data context, only leveraging LLM's dataset-agnostic interpretation of the user-provided task. However, natural language is imprecise, so a task can only be accurately performed if it is correctly interpreted in the context of the dataset. For example, for classification and scoring tasks, which are typical semantic map tasks, the standard method of processing each record row by row yields inaccurate results in a wide range of datasets. We propose HoldUp, a new method for semantic data processing with holistic data understanding. HoldUp processes records jointly, leveraging cross-record relationships to correctly interpret the task within the data context. Enabling holistic data understanding, however, is challenging due to what we call LLM data understanding paradox: while large representative data subsets are necessary to provide context, feeding long inputs to LLMs causes quality degradation due to well-known long-context issues. To resolve this paradox, we develop a novel clustering algorithm to identify the latent structure within the dataset through judicious use of LLMs, inspired by bagging. Using this approach as a primitive, we develop novel clustering-based classification and scoring methods to perform these two tasks with high accuracy. Experiments across 15 real-world datasets show that HoldUp consistently outperforms existing solutions, providing up to 33% higher accuracy for classification and 30% higher accuracy for scoring and clustering tasks.

We provide a corrected proof of a theorem of A. Bellis on strong stable sets in the unit tangent bundle of certain hyperbolic surfaces. The theorem states that, for vectors whose geodesic rays encounter arbitrarily short closed geodesics, the strong stable set in the dynamical sense does not coincide with the associated horocyclic orbit. The proof is based on Bellis' idea of constructing geodesic rays that wind around infinitely many closed geodesics.

Automated Program Repair (APR) struggles with complex logic errors and silent failures. Current LLM-based APR methods are mostly static, relying on source code and basic test outputs, which fail to accurately capture complex runtime behaviors and dynamic data dependencies. While incorporating runtime evidence like execution traces exposes concrete state transitions, a single LLM interpreting this in isolation often overfits to specific hypotheses, producing patches that satisfy tests by coincidence rather than correct logic. Therefore, runtime evidence should act as objective constraints rather than mere additional input. We propose TraceRepair, a multi-agent framework that leverages runtime facts as shared constraints for patch validation. A probe agent captures execution snapshots of critical variables to form an objective repair basis. Meanwhile, a committee of specialized agents cross-verifies candidate patches to expose inconsistencies and iteratively refine them. Evaluated on the Defects4J benchmark, TraceRepair correctly fixes 392 defects, substantially outperforming existing LLM-based approaches. Extensive experiments demonstrate improved efficiency and strong generalization on a newly constructed dataset of recent bugs, confirming that performance gains arise from dynamic reasoning rather than memorization.

In 2007, Ando and Egawa proved a theorem which provides a lower bound on the number of contractible edges preserving $4$-connectedness in $4$-connected graphs. In this paper, we refine their bounds, especially for the $4$-connected plane triangulations. In particular, we show that if $G$ is a $4$-connected plane triangulation of order at least $7$, then $G$ contains at least $|V_{\ge 5}|+2$ contractible edges preserving $4$-connectedness, where $V_{\ge 5}$ is the set of vertices of degree at least $5$. We also determine the extremal graphs.