This paper intends to realize negative refraction with absorption suppressed by the electromagneticly induced transparency(EIT) in a dense four-level atomic system. Without the two equal transition frequencies responding to the probe field, the atomic system displays a negative refraction with the simultaneously negative permittivity and negative permeability(Left-handedness). The response of the probe field is amplified and propagates transparency in some frequency extents. Therefore, our aim for searching the low-loss negative refraction can be achieved in the scheme, given the main applied limitation of the negative refractive materials is the large amount of dissipation and absorption. However, an excessive signal field intensity would increase the absorption near the resonance in our scheme.

The deployment of Large Language Model (LLM)-generated SQL queries in Artificial Intelligence of Things (AIoT) systems introduces critical security risks, as prompt injection attacks can manipulate LLMs into producing unauthorized queries that expose sensitive data or execute destructive operations. Existing NL2SQL research focuses on query accuracy, while MCP server implementations provide only SQL-level protections without fine-grained role-based access control. This paper proposes SecureMCP, a policy-enforced LLM data access framework integrating Role-Based Access Control (RBAC) with an MCP server to establish multi-layer defense for LLM-generated SQL execution. The framework incorporates five defense modules -- check_policy for table-and-column-level RBAC, explain_gate for cost-explosive query blocking, SQL Interceptor for dangerous pattern detection, Risk Level Filter for SQL risk classification, and DB Isolation for cross-database restriction -- operating in a sequential fail-closed pipeline mapped to six prompt injection types grounded in the OWASP Top 10 for LLM Applications. We evaluate SecureMCP on the IoT-SQL dataset (11 tables, 173 columns, 239,398 records) using Qwen3-8B. Experiment A demonstrates that defense modules preserve execution accuracy, with EX-in-ALLOW remaining within 65.1%-76.4% across four RBAC roles, matching the unprotected baseline of 63.8%. Experiment B shows that SecureMCP achieves 82.3% Policy Compliance on 2,400 adversarial queries, with genuine defense failures limited to 3.4%. The defense-in-depth analysis reveals check_policy accounts for 78.7% of blocks, while secondary modules contribute an additional 17.5 percentage-point improvement. The Injection Incorporation Rate of 72.5% confirms high LLM susceptibility, establishing the necessity of external policy enforcement.

Recent autonomous research systems -- AI-Scientist, PaperOrchestra, AutoSOTA, DeepResearch, InternAgent, ResearchAgent and others -- show LLM agents can ideate, run experiments and write papers, but each fixes a particular control-flow shape (linear pipeline, state machine, single-agent loop, or fixed-recipe skill pack) at the framework level. We argue this rigidity has five roots: (1) workflows are dynamic and discipline-specific (lab work, surveys, simulations, theory all loop differently); (2) ideation is bounded by LLM context and cross-domain ideation needs knowledge a single context cannot hold; (3) summary-only views miss the paper body, yet full-text access is uneven, so the cumulative corpus must do the work; (4) a paper's open-source repository is often the only complete specification of its experimental scheme, but the paper-to-code link is neglected; (5) no tool persists cross-run knowledge retrievably into a finite LLM context. We present PARNESS, an open-source framework built on four design moves. (i) A thin DAG kernel with a four-field Agent contract decouples scheduling from domain semantics, so any discipline's loop is expressible as user-editable YAML. (ii) A full-text PDF-parsing and literature-library subsystem indexes paper bodies, figures and tables as typed objects, with graceful abstract-only fall-back. (iii) A knowledge-graph index over papers, ideas, experiments and code repositories, with scenario-typed retrieval (similar / contradictory / cross-domain / counter-intuitive), surfaces a focused slice into each LLM call. (iv) A small extension surface lets any modern coding agent (Claude Code, Cursor, Copilot, OpenCode) add or replace any module. To our knowledge PARNESS is the first open-source system combining declarative pipelines, full-PDF and code-repository indexing, and cross-run knowledge. Source: https://github.com/gtrhythm/PARNESS

Droughts and flash droughts (rapidly developing droughts; FDs) remain impactful events that are known to desiccate landscape and destroy crops. In particular, droughts in Africa are often more impactful than in other locations, such as the United States or Europe, due to many regions in Africa heavily depending on local agriculture for sustenance. In recent years, large machine learning (ML) models, such as GraphCast and AIFS, have emerged as effective tools for global weather prediction. However, sparse data observations and few ML studies in Africa have left it unclear if these ML models retain their skill when focused on Africa. As such, this project seeks to examine the predictability of drought and FD in Africa using a CrossFormer model based on the Community Research Earth Digital Intelligence Twin (CREDIT) framework developed by NSF NCAR. Our CrossFormer model, termed DroughtFormer, incorporates variables from the ERA5 and GLDAS2 reanalyses and the IMERG and MODIS satellite observations, and employs dry air mass and moisture conservation, to predict soil moisture, vegetation health, and other drought-related surface variables. While DroughtFormer displayed lower accuracy in predicting precipitation and FD indices, it showed significant skill in predicting the remaining variables, delivering stable and skillful forecasts out to 90-day lead times (either beating out or having comparable skill to climatology). In particular, DroughtFormer skillfully represented climate anomalies for key variables, such as soil moisture (though it struggled with the magnitude of the anomalies). Thus, DroughtFormer showed significant promise in representing and predicting agricultural level drought in a region that is heavily impacted by drought events.

Biological systems operate under simultaneous energetic and informational constraints, yet direct evidence that such constraints shape real metabolic networks is limited. The Network-Weighted Action Principle predicts that networks under these constraints should organize toward high modularity. We tested this prediction in marine microbiome metabolic networks reconstructed from Tara Oceans metagenomes using two complementary approaches. Composite metrics of protein-deployment efficiency and functional-repertoire complexity (n=10) failed under causal-inference diagnostics, with apparent structure dominated by shared-component bias. In contrast, network modularity (n=7) was high (Q ~ 0.987), but this value was shown to arise from sparsity alone. The biologically meaningful signal is the excess over null models: modularity exceeded configuration-model, label-permutation, and bipartite-incidence nulls by Delta Q ~ 0.15-0.40 (p < 0.001), with the largest effect under the bipartite-incidence control. Fine-grained communities recovered by the network partition are not arbitrary: 25% recur across samples, and the most consistent modules map to known functional units, including enzyme subunits, biosynthetic sequences, and transporter complexes. Together, these results show that modularity excess - rather than absolute modularity - is the appropriate signature of biological organization, and that such excess is consistent with cost-minimization principles operating at the scale of natural metabolic networks.

The Model Context Protocol (MCP) is the standard interface between large language model (LLM) agents and external tools. At organizational scale, however, it exposes two structural problems. First, every API integration is shipped as a dedicated server process with its own deployment, dependency tree, and credential handling; recent empirical work shows the overwhelming majority of these servers are thin wrappers around REST APIs. Second, the per-tool registration model causes context window consumption to grow linearly with catalog size, forcing real deployments to expose only a small fraction of the APIs an organization actually uses. We present DADL (Dunkel API Description Language), a YAML format describing a REST API's endpoints, authentication, pagination, response shaping, and access classification in a single declarative file. A DADL file is interpreted by an execution layer at runtime; no per-API server process is deployed and no integration code is generated, though the runtime is itself a server. Because all tools share that runtime, credentials and authorization are managed centrally, and the catalog reaches the LLM through a fixed-size Code Mode interface independent of size. The result is an Enterprise Tool Library: a versioned, auditable collection of API integrations any team can extend, share, and consume through one authentication and authorization boundary. The DADL v0.1 specification is released under CC BY-SA 4.0, and a public registry contains 1,833 tool definitions across 20 services. On this catalog, Code Mode reduces the context cost of tool advertisement from approximately 142,000 tokens to approximately 1,000, a 142x reduction; the per-call cost of search and execute invocations is additional and depends on the task.

A variational framework for the quantization of gravitational fields is developed based on an extension of the stationary action principle. Within this framework, the Wheeler-DeWitt equation for the gravitational wave functional is recovered without assuming operator promotion of the canonical momentum, thus avoiding the ambiguity of operator ordering in canonical quantization. The derivation is based on three main ingredients. First, motivated by information-theoretic considerations, the classical stationary action principle is generalized by incorporating a correction term constructed from the relative entropy associated with field fluctuations. Second, an ensemble formulation on superspace is enhanced to incorporate this entropy correction. Third, the formalism is further refined to provide a unified treatment of quantization and constraints, thereby addressing the long-standing ambiguity concerning the ordering of quantization and constraint reduction. The framework is then applied to gravitational fields coupled to a massless scalar field. Using an emergent time parameter defined via the rate equation of the gravitational fields, a Schrodinger equation for the scalar-field wave functional is recovered, supplemented by an additional quantum correction term suppressed at order $G\hbar^2$. Finally, we comment on possible connections between the notion of relative entropy employed here and holographic dualities in quantum gravity.

Let R be a commutative ring with identity and let J be an ideal of R. In this paper, we introduce and investigate the notion of the i-extended ideal-based cozero-divisor graph of R. This graph, denoted by $\overline{Γ''}_{Ji}(R)$, is a simple graph of R whose vertex set is ${x \in R \ J : xR + J \not= R}$. Two distinct vertices $x$ and $y$ are adjacent if and only if $x^m \not \in y^nR+J$ and $y^n \not \in x^mR+J$ for some positive integers m and n with $n\leq i$ and $m\leq i$.

Asymmetric damping is widely used in passive vehicle suspensions, with rebound damping often recommended to exceed compression damping by a factor of two to three. Despite its prevalence, this guideline remains largely empirical and lacks a systematic derivation based on vehicle dynamics and excitation conditions. This paper presents a scenario-driven optimization framework that provides a principled explanation for the effectiveness of asymmetric damping. A minimal quarter-car model is employed to isolate the key mechanisms governing the trade-off between ride comfort, road holding, and transient response, using standardized ISO~8608 road excitations. Rebound and compression damping ratios are treated as independent design variables, and optimal configurations are identified via a stochastic Cross-Entropy algorithm applied to a non-convex, simulation-based objective function. Performance is assessed through ISO~2631 weighted RMS acceleration, tire--ground contact force variability, and settling time. The results show that symmetric damping is often sufficient under moderate excitation, whereas asymmetric damping becomes necessary under severe conditions, with commonly cited rebound-to-compression ratios emerging as scenario-dependent near-optimal solutions rather than universal constants.

Adaptive mesh refinement (AMR) is indispensable for efficient finite element analyses. However, its performance depends not only on the refinement itself but also on strategy to mark elements for refinement and the way it is tuned. This work compares classical marking methods (maximum, Dörfler bulk-chasing, quantile) with non-classical, statistically based approaches (z-score, Isolation Forest), all driven by the residual-based Kelly error estimator and tested on steady solid and fluid mechanics problems. The study finds quantile and z-score markings to be the most robust, Dörfler effective for large bulk parameters, and maximum marking sensitive to irregular fields. Isolation Forest can rival top classical methods with a generous contamination level but may fail under aggressive settings. These results offer practical guidance for selecting marking strategies that balance refinement aggressiveness and computational cost in adaptive FEM workflows.

In the bottleneck multiple knapsack problem, we are given a set of items and a set of knapsacks, where each item has a profit and a weight, and each knapsack has a capacity. Our goal is to assign items to knapsacks so as to maximize the minimum profit received by any knapsack subject to the capacity constraint. When all knapsacks have identical capacity, we give a $(\frac{2}{3} - \varepsilon)$-approximation algorithm for any constant $\varepsilon > 0$. This result almost matches the $(\frac{2}{3} + \varepsilon)$ inapproximability bound for the bottleneck multiple subset sum problem (Caprara et al., 2000). When the knapsacks can have arbitrary capacities, we propose a $(\frac{1}{2} - \varepsilon)$-approximation algorithm for any constant $\varepsilon > 0$. We also prove a hardness bound of $(\frac{1}{2} + \varepsilon)$ for any constant $\varepsilon > 0$.

We review a new natural inflationary mechanism operated by repulsive-like primordial black holes (PBHs). In particular, working within the ``Swiss - Cheese" cosmological framework, we find that a Universe filled with PBHs, whose spacetime metric presents a repulsive-like behaviour, is characterised by an early quasi-de-Sitter cosmic expansion phase. Notably, for light PBHs with $m < 5 \times 10^8 \mathrm{g}$, evaporating before Big Bang Nucleosynthesis (BBN), one is met with an exponential inflationary phase with graceful exit and reheating proceeding through PBH evaporation. Furthermore, one finds as well that PBHs with $m \sim 10^{12}\mathrm{g}$ and abundances $0.107 < Ω^\mathrm{eq}_\mathrm{PBH} < 0.5$ near matter-radiation equality can act as an early dark energy component, easing in this way naturally the Hubble tension.

Orbital angular momentum (OAM) has been regarded as a potential dimension for optical communication and related fields. Despite several studies, the transmission of OAM beams through time-varying scattering media remains a challenge. In this paper, we report a method for OAM transmission through time-varying scattering media using dual orthogonal polarization channels, in which one channel carries a perfect vortex beam (PVB) carrying data, and the other serves as a reference plane wave. By calculating the second-order cross-correlation of speckle patterns generated by the PVB and the plane wave, the original data can be decoded. It is also shown that multiplexed PVBs carrying specific topological charges can produce interference patterns in the acquired cross-correlation images, thereby greatly enhancing transmission capacity. Experimental results show that the proposed method can transmit at least 6 bits per beam with 100% accuracy. Moreover, the proposed method is highly robust to variations in the receiving end's position in both radial and axial directions. The proposed method enables the practical deployment of OAM transmission in realistic optical communication and related applications.

Societal complexity may be at a historical peak. Distinct from entropy, complexity tends to rise as systems move away from order, crest at an intermediate state, and decline as entropy continues increasing. The use of a thermodynamic analogy and the timing of major technological milestones, from fire to artificial intelligence, shows that the acceleration and recent compression of transformative events fit the derivative of a logistic growth curve. This pattern suggests that the rapid rise in structural and technological novelty may soon begin slowing. Notably, the trajectory parallels the bell-shaped rate of global population growth, consistent with the view that demographic expansion fuels innovation. If complexity growth is indeed cresting, societies face the challenge of managing heightened fragility while adapting to diminishing returns in transformative change. This perspective explores whether the rapid acceleration of technological innovation observed in recent centuries may reflect a civilizational system approaching the region of maximal complexity often associated with the edge of chaos.

We introduce the \emph{Private Contiguous-Block Retrieval (PCBR)} problem, where a user retrieves a block of $D$ messages with contiguous indices from $K$ replicated messages stored across $N$ non-colluding servers, while hiding the identity of the requested block from each server. This problem is motivated by storage and streaming systems where files are split into ordered segments. Unlike multi-message Private Information Retrieval (MPIR), where any $D$-subset may be requested, PCBR restricts the demand family to contiguous blocks. This relaxation raises a natural question: Can this structure be exploited to improve retrieval efficiency? We answer this question for balanced $\{0,1\}$-linear schemes. We establish an upper bound on the achievable retrieval rate for all problem parameters, derive a lower bound on the subpacketization level required by any scheme achieving the rate upper bound, and construct a rate-optimal scheme whose subpacketization level matches the lower bound for a broad range of problem parameters. Although the optimal PCBR rate coincides with the best-known MPIR rate converse bound, existing MPIR schemes can be suboptimal for PCBR and can require a much larger subpacketization level. In contrast, our scheme exploits the contiguous-block structure to achieve the optimal rate with reduced subpacketization.

Pairwise comparisons from multiple judges are central to large language model evaluation and preference modeling, yet standard ranking pipelines often pool judgments into a single score vector, treating systematic judge disagreement as noise. We propose Heterogeneous Judge-Aware (HJA) ranking, a structured multi-judge ranking framework that separates consensus ranking, judge-specific sensitivity to consensus, and residual preference disagreement. HJA thereby treats ranking, judge sensitivity, and structured disagreement as separate inferential targets. We establish conditions under which this decomposition is identifiable and develop an anchored alternating algorithm that preserves the identifying geometry. For confidence quantification, we study a fixed-panel repeated-comparison regime in which the judge panel may remain fixed or modest while information grows through repeated judgments. This yields uncertainty statements for consensus and judge-specific ranking contrasts, sensitivity parameters, pairwise probabilities, and summaries of residual disagreement.Experiments on synthetic and real multi-judge comparison data show that HJA improves recovery, robustness, uncertainty calibration, and near-tie performance relative to pooled and sensitivity-only baselines. The fitted model also provides diagnostics for judge disagreement and model-affinity patterns, giving a statistically grounded framework for ranking under heterogeneous comparative judgments.

We investigate the dynamical and spectral consequences of capacitance-network-mediated interactions in superconducting transmon arrays beyond effective nearest-neighbor descriptions. While weak coupling regimes are well captured by an effective nearest-neighbor interacting models, we show that increasing capacitive connectivity induces a pronounced departure from this approximation in dynamical observables. Using Out-of-Time-Ordered Correlators (OTOCs), we demonstrate that such network-mediated couplings significantly accelerate operator scrambling, leading to rapid saturation compared to the nearest-neighbor limit. This dynamical crossover is accompanied by a shift in spectral statistics away from Poissonian behavior toward level repulsion, with the ratio parameter remaining intermediate between Poisson and Gaussian Orthogonal Ensemble (GOE) limits. This indicates the emergence of partial ergodicity rather than fully developed quantum chaos. As this behavior arises within experimentally realistic regimes of current superconducting transmon devices, identifying when network-mediated couplings qualitatively alter information dynamics is directly relevant for scalable superconducting architectures.

This paper develops efficient GMM estimation when the moment conditions are misspecified. We observe that the influence function of the standard GMM estimator under misspecification depends on both the original moment conditions and their Jacobian, motivating a new class of estimators based on augmented moment conditions with recentering. The standard GMM estimator is a special case within this class, and generally suboptimal. By optimally weighting the augmented system, we obtain a misspecification-efficient (ME) estimator with the smallest asymptotic variance for the same GMM pseudo-true value. In linear models, the asymptotic variance of ME estimator reduces to the textbook efficient-GMM variance formula $(G'W^{*}G)^{-1}$, where $W^{*}$ is the inverse of the variance of residualized moments after projection on the Jacobian $G$. We consider a feasible double-recentered bootstrap estimator, which can be considered as a misspecification-robust and efficient version of Hall and Horowitz (1996) recentered bootstrap GMM estimator, and also consider a split-sample ME estimator. Finally, we establish uniform local asymptotic minimax bounds over a class of weighting matrices. We illustrate the proposed methods in simulation and empirical examples.

Multilinear varieties, defined as the sets of rational points of varieties cut out by multilinear functions, were first introduced and studied by Gowers and Milićević[Proc. Edinb. Math. Soc., 2021] for finite $\mathbb{K}$. In this paper, we investigate multilinear varieties over infinite fields from a geometric perspective. We establish two fundamental results: a codimension formula for the Zariski closure of a multilinear variety, and the existence of a high-dimensional irreducible subvariety passing through any given $\mathbb{K}$-rational point. These results serve as a geometric foundation for analyzing various ranks of tensors and homogeneous polynomials, including partition rank, analytic rank, geometric rank, (collective) strength and (collective) Birch rank. As applications, we resolve the Adiprasito-Kazhdan-Ziegler conjecture [arXiv:2102.03659, 2021] on the stability of partition rank for perfect infinite fields. We thereby settle the stability conjecture for collective strength [Selecta Math., 2024], as well as the conjecture on the linear equivalence between strength and Birch rank [arXiv:2410.00248, 2024] for such fields. Moreover, our results immediately yield a strengthening of the theorems of Bik-Draisma-Snowden [arXiv:2401.02067, 2024] and Lampert-Snowden [arXiv:2406.18498, 2024], for multilinear varieties over infinite fields.

Despite widespread adoption of smartwatches worldwide, open-benchmarks for wrist-based gesture recognition remain surprisingly limited. In this work, we introduce the first open-access multi-modal benchmark, OpenWatch, for wrist-based gesture recognition using synchronized inertial and physiological sensing on a commercial smartwatch. It contains over 10 hours of Inertial Measurement Unit (IMU) and Photoplethysmography (PPG) data across 50 participants and a vocabulary of 59 labelled gesture sequences. Furthermore, we present a subject-independent evaluation protocol including traditional and deep learning methods for time-series classification. On top of this, we develop two novel methodologies for hand-gesture recognition: (i) MixToken, a task-specific mixture-of-experts that fuses per-channel IMU filterbank features with cross-channel statistical tokens through learned logit mixing, and (ii) NormWear-Lora, a low-rank adaptation module for smartwatch foundation models. Our benchmarking results reveal that PPG signals carries a substantial predictive benefit (+12.5% F1-score) for foundational smartwatch models. In addition, we show that task-specific architectures (i.e. MixToken) substantially outperforms finetuned smartwatch foundation models in terms of accuracy (F1-score=90% vs 66%) and memory efficiency (223k vs 136M parameters). Finally, we also provide clear empirical guidance on the trade-offs between specialized architecture design, modality fusion, data augmentations, and foundation-model adaptation for resource-constrained wearable sensing.

Given graphs $H$ and $F$, the generalized Turán number $\mathrm{ex}(n,H,F)$ is the maximum number of copies of $H$ among all $n$-vertex $F$-free graphs. The friendship graph $F_k$ consists of $k$ triangles sharing a common vertex. In this paper, we determine the value of $\mathrm{ex}(n,K_3,(t+1)F_k)$, where $K_3$ is a triangle, $t\geq 1$ is an integer, and $(t+1)F_k$ denotes a union of $(t+1)$ pairwise vertex-disjoint copies of $F_k$. Moreover, we characterize the extremal structure. Our result can be viewed as a generalization of the result of Zhu, Chen, Gerbner, Győri, and Hama Karim, as well as of the remaining case left open by Wang, Ni, Liu, and Kang. In contrast to the extremal graphs of $F_k$, the extremal graphs of $(t+1)F_k$ undergo a fundamental change. This structure is also different from those of previous similar problems.

For a finite group $G$, $N_\infty$ operads encode collections of norm maps, and by work of Blumberg--Hill and Rubin their homotopy category is equivalent to the poset of $G$--transfer systems on the subgroup lattice of $G$. In \cite{ABB+25} the authors defined the \emph{width} $w(G)$ as the minimal size of a generating set for the complete $G$--transfer system and identified it with the number of conjugacy classes of proper meet irreducible subgroups of $G$, and the \emph{complexity} $c(G)$ as the maximum, over all transfer systems $T$, of the size of a minimal generating set for $T$. We compute $w(G)$ for the semidihedral groups $\SD_{2^n}$ ($n\ge 4$) and the affine Frobenius groups $\AGL(1,p^n)\cong \mathbb{F}_{p^n}\rtimes \mathbb{F}_{p^n}^\times$, extending existing calculations and highlighting how subgroup lattice structure governs equivariant multiplicative complexity. We also compute $c(D_{p^n})$ for dihedral groups of order $2p^n$ with $p$ an odd prime, establishing $c(D_{p^n})=\lfloor 3n/2\rfloor+1$, and derive the lower bound $c(\SD_{2^n})\ge\lfloor 5(n-1)/2\rfloor$.

Verification presents a major bottleneck in Integrated Circuit (IC) development, consuming nearly 70% of total effort. While the Universal Verification Methodology (UVM) improves reuse through structured verification environments, constructing subsystem-level UVM testbenches and generating high-quality stimuli still require extensive manual coding, repeated EDA tool runs, and deep protocol and micro-architectural expertise. We present UVMarvel, an automated verification framework that leverages Large Language Models (LLMs) to build UVM testbenches for subsystem-level RTL. UVMarvel introduces an Intermediate Representation (IR) and a Bus Protocol Library to translate heterogeneous specifications into protocol-correct subsystem-level UVM testbenches, and employs a Signal Tracker and a Verilog Patching Library to guide LLM-based stimuli refinement. UVMarvel is the first framework capable of automatically constructing subsystem-level UVM testbenches across mainstream bus protocols, and it achieves an average code coverage of 95.65%, while reducing verification time from several human working days to a 4.5-hour automated execution.

Recent IXPE observations of Her X-1 reveal correlations between flux, polarization degree, and polarization angle across its 35-day superorbital cycle. These measurements have been interpreted as strong evidence that the 35-day period is driven by nearly free precession of the neutron star. We show that this interpretation carries far-reaching implications for the dynamics of the crustal superfluid. In particular, maintaining precession over the $\sim 50$-year observational baseline of Her X-1 would require that superfluid vortices remain unpinned for centuries and experience extremely weak mutual friction while traversing the heavy-ion lattice of the inner crust -- conditions that challenge conventional wisdom and standard models of glitch dynamics. Under the condition of weak pinning, nearly free precession of the crust may be sustained by a balance between the internal and external torques.

We present the GASKAP-HI pilot absorption survey of neutral hydrogen (HI) in the Magellanic system. This survey provides 3219 sightlines across the Large (LMC) and Small Magellanic Clouds (SMC) and the Magellanic Bridge (MB) towards 1.4-GHz continuum sources, representing a 15-fold increase on pre--GASKAP-HI sampling of the Magellanic System. We find 344 candidate detections of cold gas at Magellanic velocities (vLSRK >= 90 km s-1), with signal-to-noise ratio > 3 detection rates of 44% (LMC; 192 of 438), 73% (SMC; 85 of 117) and 4% (MB; 35 of 793). We examine the candidate detections within the MB, Gaussian decompose these and examine the cold gas across the MB. Here we find that the majority of cold gas detections are found closer to the SMC. We also find potential evidence of the recent formation of cold gas on the outskirts of a shell within the MB. We find a mean cold gas fraction of fCNM = 0.12 +- 0.08 for the MB, which is very similar to the SMC and lower than the LMC value of 0.14. Overall, we reveal cold gas distributed extensively across the Magellanic system, including within the MB, and surmise that the cold gas in the MB is either pulled from the SMC as part of the formation of the MB, or formed in the turbulence of those same interactions.

Wormholes are a hypothetical object that connects disparate points in spacetime. It is a theoretically well-motivated black hole alternative and offers a potential observationally testable arena for probing strong-field gravity with horizon-scale images. We perform general relativistic magnetohydrodynamic (GRMHD) simulations and general relativistic radiative transfer (GRRT) calculations of accretion flows onto a Kerr-like wormhole. Adopting a Kerr black-bounce metric with a fixed throat parameter $\ell = 2.5\,\rm M$, we explore the effects of spin using both two- and three-dimensional simulations. The accretion flow is initialized as a magnetized geometrically thick torus near one mouth of the wormhole, while the opposite mouth is initially gas-free. We find that the spin parameter influences the dynamical properties on both sides of the wormhole through the frame-dragging effects. Based on the GRMHD results, we compute ray-traced images at $230\,\mathrm{GHz}$ using \texttt{RAPTOR}, and analyze the horizon-scale image structure through higher-order photon trajectories. Our GRRT calculations show that emissions originating from the immediate vicinity of the throat can dominate, in contrast to the case of a Kerr black hole. It provides the variable component of the signal and imprints a clear quasi-periodic modulation in the light curves. These properties would be useful to either confirm or rule out such exotic compact objects through horizon-scale observations.

CDFCI is a shared-memory parallel numerical program for computing low-lying eigenpairs of large-scale, non-relativistic fermionic Hamiltonians. The software is designed to handle a broad class of many-body quantum models, including both ab initio electronic structure Hamiltonians and lattice-based Hamiltonians arising in condensed matter physics. CDFCI combines an efficient coordinate-descent-based selected configuration interaction algorithm with dedicated parallelization strategies, achieving high performance on modern multi-core architectures. Benchmark results on representative quantum chemistry and condensed matter test cases demonstrate that CDFCI attains state-of-the-art accuracy with competitive performance compared to established selected configuration interaction (such as CIPSI or SHCI) and DMRG implementations. The software is open-source, extensively documented, and provides a Python interface for seamless integration with PySCF and other many-body simulation workflows.

Magnetically driven mass accretion in protoplanetary disks plays a crucial role in understanding disk evolution and planet formation. However, the $α$ prescription lacks a direct connection to physical processes, and no systematic scaling law yet exists for the accretion rate as a function of disk quantities. While local shearing-box simulations offer a powerful approach to analyzing accretion structure at low computational cost, they suffer from a problem: the toroidal magnetic field generated by Keplerian shear accumulates within the computational domain, disrupting a geometry consistent with global wind-driven accretion. In this study, we introduce the super-box-scale diffusion (SBD) scheme into non-ideal MHD shearing-box simulations. The SBD scheme continuously damps the horizontally averaged horizontal magnetic field components, thereby mitigating this problem and maintaining the field-line symmetry required for global wind-driven accretion for more than 500 orbital periods. Comparison with self-similar solutions supports the SBD method, with the vertical structure and plasma-beta dependence of the accretion rate agreeing to within 23--28\%. We then conduct a parameter survey of 46 cases using a magnetic diffusivity table constructed from ionization equilibrium calculations, covering disk radius, surface density, magnetic field strength, and dust-to-gas ratio. We find that the surface field-line pitch and mass accretion rate follow power-law scalings with the midplane plasma beta, an effective ambipolar Elsasser number, and the normalized thickness of the magnetically active layer. These relations reproduce the numerical results to within a factor of 2--3 across the explored parameter space and, in most cases, to within a factor of 2. They provide a framework for predicting the mass accretion rate from local disk physical quantities without invoking an $α$ parameter.

A search for the charmless purely baryonic decay $\mathinner{\mathitΛ^0_b\!\to \mathitΛ p \overline{p}}$ is performed using proton-proton collision data recorded by the LHCb experiment at a centre-of-mass energy of $\sqrt{s}=13\,\text{TeV}$ and corresponding to an integrated luminosity of $6.0\,\text{fb}^{-1}$. The signal decay is observed with a significance of 5.1 standard deviations. Its branching fraction is measured for the first time, relative to that of the topologically similar decay $\mathinner{\mathitΛ^0_b\!\to \mathitΛ K^+ K^-}$. Contributions from intermediate charmonium resonances decaying to the $p \overline{p}$ and $K^+ K^-$ final states are explicitly excluded with a requirement on the invariant mass of the companion hadron system, $m(h\bar{h}) < 2.85\,\text{GeV}$, where $h$ stands for a proton or a charged kaon. The relative branching fraction is found to be $$ \frac{B(\mathinner{\mathitΛ^0_b\!\to \mathitΛ p \overline{p}})}{B(\mathinner{\mathitΛ^0_b\!\to \mathitΛ K^+ K^-})} = (5.1 \pm 1.3_{\text{(stat)}} \pm 0.3_{\text{(syst)}}) \times 10^{-2} \,. $$

This paper extends the Single Packet Header Binary Image (SPHBI) intrusion detection methodology from IoT to Modbus TCP, evaluating five approaches spanning a gradient of protocol depth on the CIC Modbus 2023 dataset (11.4 million packets, eight detectable attack types). TCP/IP headers alone achieve only 51.8% binary accuracy, confirming that header-level heterogeneity exploited in IoT traffic is absent in uniform SCADA environments. Adding eight bytes of application-layer information improves binary accuracy to 98.1% with just 63 parameters, directly relevant to per-packet classification on resource-constrained OT edge devices. The best-performing approach achieves 94.4% +/- 2.2pp multiclass accuracy across nine classes (95% CI [92.9%, 95.9%], 10 seeds) with 56,873 parameters, roughly 430 times fewer than comparable ResNet50-based approaches. Per-class recall analysis shows seven of eight detectable attack types identified with recall above 94%, while replay attacks remain structurally undetectable by any single-packet method.