We prove that the one-loop contribution from tensor modes to the thermodynamic entropy of near-extremal black holes is universal. Our proof applies to asymptotically flat, Anti-de-Sitter and de-Sitter black holes; it also covers spherical, axial and planar symmetries. We consider black hole configurations with and without matter sectors and explicitly discuss Abelian gauge fields and neutral scalar fields with arbitrary potential. We demonstrate that under certain conditions, the thermodynamics of near-extremal black holes contains a one-loop contribution from the tensor modes that equals $\frac{3}{2}\log (T_{\rm Hawking}/T_q)$. The proof of this theorem also shows explicitly how the Schwarzian modes appear universally in near-extremal geometries in dimensions four, five and six. We apply this theorem to Kerr-de-Sitter black holes as an explicit example.

We present the results of the Exploration of Local VolumE Survey - Field (ELVES-Field), a survey of the dwarf galaxies in the Local Volume (LV; $D<10$ Mpc) over roughly $3,000$ square degrees, focusing on the field dwarf population. Candidates are detected using a semi-automated algorithm tailored for low surface brightness dwarfs. Using tests with injected galaxies, we show the detection is $50\%$ complete to $m_g\sim20$ mag and $M_\star \sim 10^6$ $M_\odot$. Candidates are confirmed to be true nearby dwarfs through distance measurements including redshift, tip of the red giant branch, and surface brightness fluctuations. We identify isolated, field dwarfs using various environmental criteria. Over the survey footprint, we detect and confirm 95 LV dwarfs, 44 of which we consider isolated. Using this sample, we infer the field dwarf mass function and find good agreement at the high-mass end with previous redshift surveys and with the predictions of the IllustrisTNG simulation. This sample of isolated, field dwarfs represents a powerful dataset to investigate aspects of small-scale structure and the effect of environment on dwarf galaxy evolution.

We present a practical system for privacy-aware large language model (LLM) inference that splits a transformer between a trusted local GPU and an untrusted cloud GPU, communicating only intermediate activations over the network. Our system addresses the unique challenges of autoregressive LLM decoding over high-latency wide-area networks (WANs), contributing: (1) an asymmetric layer split where embedding and unembedding layers remain local, ensuring raw tokens never leave the trusted device; (2) the first application of lookahead decoding to split inference over WANs, amortizing network round-trip latency across multiple tokens per iteration; (3) an empirical inversion attack evaluation showing that split depth provides a tunable privacy-performance tradeoff -- an attacker can recover ~59%% of tokens at a 2-layer split but only ~35%% at an 8-layer split, with minimal throughput impact; (4) ablation experiments showing that n-gram speculation accepts 1.2-1.3 tokens per decoding step on average (peak of 7 observed on code), with acceptance rates consistent across model scales; (5) formal verification that lookahead decoding produces token-identical output to sequential decoding under greedy argmax, with zero quality degradation; and (6) scaling validation on Mistral NeMo 12B (40 layers), demonstrating that the system generalizes to larger models with only 4.9 GB local VRAM and matching 7B throughput. Evaluated on Mistral 7B and NeMo 12B over a ~80ms WAN link, our system achieves 8.7-9.3 tok/s (7B) and 7.8-8.7 tok/s (12B) with lookahead decoding, with an RTT decomposition model (validated at <6.2%% cross-validation error) projecting 15-19 tok/s at 20ms RTT.

We investigate the influence of a relatively small perturbation on various generalized inverses functions or quantities derived from a tensor $\mathcal{A}$.When a small tensor perturbation \(\mathcal{E}\) is introduced, it becomes challenging to analyze generalized inverses of the perturbed tensor \( \mathcal{D} =\mathcal{A}+\mathcal{E}\) and to determine how this perturbation affects a generalized inverse of $\mathcal{A}$.Our main goal is to understand the relationship between $\mathcal{D}^\Game$ and \( \mathcal{A}^\Game \), where $(\cdot)^\Game$ denotes a specific generalized inverse or a class of generalized inverses.In particular, classes of tensor inner, outer, and $(\mathcal{B},\mathcal{C})$ inverses are considered.

We study the late-time cosmological dynamics of a spatially flat FLRW universe in the framework of $f(Q)$ gravity, where $Q$ denotes the nonmetricity scalar. The matter sector is modeled as a bulk viscous fluid with a free equation-of-state parameter $γ$, allowing for a generalized description of cosmic matter beyond the standard dust approximation. We derive the background evolution equations and analyze the resulting expansion history. The model parameters are constrained using a combination of observational datasets, including cosmic chronometers (CC), baryon acoustic oscillations from DESI DR2, and Type~Ia supernovae (GRBs and Union3). Using the best-fit parameters, we further employ the statefinder and $\mathrm{Om}(z)$ diagnostics to distinguish the viscous $f(Q)$ scenario from the standard $Λ$CDM model. In addition, we examine the evolution of the deceleration parameter, which exhibits a transition from an early decelerated phase to the current accelerated expansion, and analyze the effective equation of state behavior. Our results show that bulk viscosity within $f(Q)$ gravity provides a viable and observationally consistent description of late-time cosmic acceleration.

We introduce NESSiE, the NEceSsary SafEty benchmark for large language models (LLMs). With minimal test cases of information and access security, NESSiE reveals safety-relevant failures that should not exist, given the low complexity of the tasks. NESSiE is intended as a lightweight, easy-to-use sanity check for language model safety and, as such, is not sufficient for guaranteeing safety in general -- but we argue that passing this test is necessary for any deployment. However, even state-of-the-art LLMs do not reach 100% on NESSiE and thus fail our necessary condition of language model safety, even in the absence of adversarial attacks. Our Safe & Helpful (SH) metric allows for direct comparison of the two requirements, showing models are biased toward being helpful rather than safe. We further find that disabled reasoning for some models, but especially a benign distraction context degrade model performance. Overall, our results underscore the critical risks of deploying such models as autonomous agents in the wild. We make the dataset, package and plotting code publicly available.

We present an analytic approximation model for non-rigid point set registration, grounded in the multivariate Taylor expansion of vector-valued functions. By exploiting the algebraic structure of Taylor expansions, we construct a structured function space spanned by truncated basis terms, allowing smooth deformations to be represented with low complexity and explicit form. To estimate mappings within this space, we develop a quasi-Newton optimization algorithm that progressively lifts the identity map into higher-order analytic forms. This structured framework unifies rigid, affine, and nonlinear deformations under a single closed-form formulation, without relying on kernel functions or high-dimensional parameterizations. The proposed model is embedded into a standard ICP loop -- using (by default) nearest-neighbor correspondences -- resulting in Analytic-ICP, an efficient registration algorithm with quasi-linear time complexity. Experiments on 2D and 3D datasets demonstrate that Analytic-ICP achieves higher accuracy and faster convergence than classical methods such as CPD and TPS-RPM, particularly for small and smooth deformations.

Large Language Models (LLMs) have emerged as powerful re-rankers. Recent research has however showed that simple prompt injections embedded within a candidate document (i.e., jailbreak prompt attacks) can significantly alter an LLM's ranking decisions. While this poses serious security risks to LLM-based ranking pipelines, the extent to which this vulnerability persists across diverse LLM families, architectures, and settings remains largely under-explored. In this paper, we present a comprehensive empirical study of jailbreak prompt attacks against LLM rankers. We focus our evaluation on two complementary tasks: (1) Preference Vulnerability Assessment, measuring intrinsic susceptibility via attack success rate (ASR); and (2) Ranking Vulnerability Assessment, quantifying the operational impact on the ranking's quality (nDCG@10). We systematically examine three prevalent ranking paradigms (pairwise, listwise, setwise) under two injection variants: decision objective hijacking and decision criteria hijacking. Beyond reproducing prior findings, we expand the analysis to cover vulnerability scaling across model families, position sensitivity, backbone architectures, and cross-domain robustness. Our results characterize the boundary conditions of these vulnerabilities, revealing critical insights such as that encoder-decoder architectures exhibit strong inherent resilience to jailbreak attacks. We publicly release our code and additional experimental results at https://github.com/ielab/LLM-Ranker-Attack.

Previously we derived the leading term asymptotic solution of temperature distribution in skin heating by an electromagnetic beam at an arbitrary incident angle. The asymptotic analysis is based on that the penetration depth of the beam into skin is much smaller than the size of beam cross-section. It allows arbitrary incident angle. We expand the temperature in powers of the small depth to lateral scale ratio. The incident angle affects all terms in the expansion while the lateral heat conduction appears only in terms of positive even powers. The previously obtained leading term solution captures only the main effect of incident angle. The main effect of lateral heat conduction is contained in the second order term, which is mathematically negligible in the limit of small depth to lateral scale ratio. At a moderate length scale ratio (e.g., 0.1), however, the contribution from lateral conduction is quite significant and needs to be included in a meaningful approximate solution. In this study, we derive closed form analytical expressions for the first order and the second order terms in the asymptotic expansion. The resulting asymptotic solution is capable of predicting the temperature distribution accurately including the effects of both incident angle and lateral heat conduction even at a moderate length scale ratio.

Designing systems for autonomous transport of groups of living agents has received a lot of attention in recent years due to a wealth of important potential applications. Biomimetic approaches are often sought, and a range of herding algorithms, inspired by how dogs herd sheep, as well as leadership algorithms mimicking leader-follower systems, have been introduced. However, they suffer from a common problem: shepherding algorithms require that agents evade the shepherd, and leading algorithms require that agents follow. This can cause problems in real-world applications where the behavioral responses of the agents to a transporter are likely to be heterogeneous over both long and short timescales. Here, we introduce an algorithm that adaptively switches between leading and herding depending on the response it receives from the agents to mitigate this problem. We show via simulation that this mixed algorithm can transport groups with any follower and evader composition, and we compare its performance with lead-only and herd-only algorithms. We also show that the mixed algorithm can deal with groups where individual agents randomly switch their strategy over time, as long as sufficient time is provided to complete the task relative to the switching rate. Given that our algorithm overcomes issues associated with herd-only and lead-only algorithms and might also, as a side effect, mitigate the issue of habituation to robotic transporters, it takes us one step closer to realizing many of the proposed applications for these types of algorithms.

Quality assurance (QA) during construction often relies on inspection records and laboratory test results that become available days or weeks after work is completed. On large highway and bridge projects, this delay limits early intervention and increases the risk of rework, schedule impacts, and fragmented documentation. This study presents a construction-phase digital twin framework designed to support element-level QA and readiness-based decision making during active construction. The framework links inspection records, material production and placement data, early-age sensing, and predictive strength models to individual construction elements. By integrating these data streams, the system represents the evolving quality state of each element and supports structured release or hold decisions before standard-age test results are available. The approach does not replace established inspection and testing procedures. Instead, it supplements existing workflows by improving traceability and enabling earlier, data-informed quality assessments. Practical considerations related to data integration, contractual constraints, and implementation challenges are also discussed. The proposed framework provides a structured pathway for transitioning construction QA from delayed, document-driven review toward proactive, element-level decision support during construction.

We study the parametric amplifier Hamiltonian within the framework of the Dunkl formalism. We introduce the Dunkl creation and annihilation operators and show that their quadratic combinations generate an $su(1,1)$ Lie algebra. The spectral problem is solved exactly using two algebraic methods: the $su(1,1)$ tilting transformation and the generalized Bogoliubov transformation. The exact energy spectrum and the corresponding eigenfunctions are obtained in terms of the Dunkl number coherent states. Furthermore, we compute the Mandel $Q$ parameter and the second-order correlation function $g^{(2)}(0)$ to analyze the statistical properties of the Dunkl squeezed states. We show that, for the squeezed vacuum, the Mandel parameter remains independent of the Dunkl deformation, whereas the correlation function exhibits an explicit dependence on the Dunkl parameter $μ$, which modifies the photon bunching effects. Finally, we show that our results reduce to the standard parametric amplifier case in the limit of vanishing Dunkl deformation parameter.

We analyze the winning coalitions that arise under Bloc voting when voters preferences are single-peaked. For small numbers of candidates and numbers of winners, we determine conditions under which candidates in winning coalitions are adjacent. We also analyze the results of pairwise contests between winning and losing candidates and assess when the winning coalitions satisfy several proposed extensions of the Condorcet criterion to multiwinner voting methods. Finally, we use Monte Carlo simulations to investigate how frequently these coalitions arise under different assumptions about voter behavior.

We develop an entropy-stable high-order numerical method for the two-dimensional compressible Euler equations on general curvilinear meshes. The proposed approach is based on a nodal discontinuous Galerkin spectral element method (DGSEM) that satisfies the summation-by-parts (SBP) property. At the semidiscrete level, entropy stability is established through the SBP structure and the discrete metric identities associated with curvilinear coordinate mappings. By incorporating entropy-stable numerical fluxes at element interfaces, a global discrete entropy inequality is obtained. To further control nonphysical oscillations near strong discontinuities, the entropy-stable DG formulation is combined with a modified oscillation-eliminating discontinuous Galerkin (OEDG) method, which was originally proposed in [59]. We observe that the zero-order damping coefficient in the original OEDG method naturally serves as an effective shock indicator, which enables localization of the oscillation control mechanism and significantly reduces computational cost. Moreover, while the original OEDG formulation relies on local orthogonal modal bases and is primarily restricted to simplicial meshes, we reformulate the OE procedure using projection operators, allowing for a systematic extension to general curvilinear meshes. The resulting method preserves conservation and entropy stability while effectively suppressing spurious oscillations. A series of challenging numerical experiments is presented to demonstrate the accuracy, robustness, and effectiveness of the proposed entropy-stable OEDG method on both Cartesian and curvilinear meshes.

This paper presents a longitudinal open dataset of Spanish public procurement extracted from the Official State Gazette (BOE) covering the period 2014-2024. The dataset integrates structured information on contracts, contracting authorities, suppliers, amounts, and procedures, enabling large-scale quantitative analysis of public procurement dynamics in Spain. We describe the data extraction and normalization pipeline, provide descriptive statistical analyses of temporal and sectoral trends, and discuss potential applications in transparency research, public policy evaluation, and computational social science. The dataset is released to facilitate reproducible research on public procurement and government contracting.

Motivation: Antifungal resistance has become an increasing global concern in both clinical and environmental health. Detecting known resistance mutations directly from sequencing reads, in special metagenomic samples, remains a major challenge. As fungal pathogens are often neglected compared with bacterial pathogens, most available tools are designed for bacterial taxa, whereas tools targeting fungi typically require assembled genomes. In metagenomic datasets, assembly-based strategies may result in substantial information loss due to genome fragmentation, low-abundance species, or incomplete recovery of resistance loci. Results: Here, we present FUNGAR, an open-source pipeline for the rapid identification of antifungal resistance genes and mutations directly from short-read data. FUNGAR employs translated alignments with DIAMOND and curated data from the FungAMR database to detect amino acid substitutions across all six open reading frames. The pipeline produces structured, reproducible reports linking detected variants to their associated antifungal drugs and can be easily customized for new species or databases.

Large-scale human mobility simulation is critical for many science domains such as urban science, epidemiology, and transportation analysis. Recent works treat large language models (LLMs) as human agents to simulate realistic mobility trajectories by modeling individual-level cognitive processes. However, these approaches generate individual mobility trajectories independently, without any population-level coordination mechanism, and thus fail to capture the emergence of collective behaviors. To address this issue, we design M2LSimu, a mobility measures-guided multi-prompt adjustment framework that leverages mobility measures derived from shared data as guidance to refine individual-level prompts for realistic mobility generation. Our framework applies coarse-grained adjustment strategies guided by mobility measures, progressively enabling fine-grained individual-level adaptation while satisfying multiple population-level mobility objectives under a limited budget. Experiments show that M2LSimu significantly outperforms state-of-the-art LLM-based methods on two public datasets.

Recent experimental investigations have demonstrated that doping a semiconductor is a route to increase the thermal boundary conductance at metal/semiconductor interfaces. In this work, the influence of the electrical properties on heat transfer across metal/doped semiconductor junctions is investigated. Specifically, thermal boundary conductance at the interfaces between p- and n-doped silicon and titanium is measured by employing frequency-domain photothermal radiometry under varying external conditions. The influence of the doping level of the semiconductor, the barrier height, and the space charge area is analyzed. In particular, a 40% increase in the interface thermal conductance with the application of a current at n-doped silicon/titanium interfaces is reported. The enhancement of the thermal boundary conductance is explained by the shrinking of the surface charga area induced by the electric current. This study opens the way to modulating interfacial heat transfer at metal/semiconductor interfaces through fine tuning of electrical effects.

Introduction: While the origin and evolution of proteins remain mysterious, advances in evolutionary genomics and systems biology are facilitating the historical exploration of the structure, function and organization of proteins and proteomes. Molecular chronologies are series of time events describing the history of biological systems and subsystems and the rise of biological innovations. Together with time-varying networks, these chronologies provide a window into the past. Areas covered: Here, we review molecular chronologies and networks built with modern methods of phylogeny reconstruction. We discuss how chronologies of structural domain families uncover the explosive emergence of metabolism, the late rise of translation, the co-evolution of ribosomal proteins and rRNA, and the late development of the ribosomal exit tunnel; events that coincided with a tendency to shorten folding time. Evolving networks described the early emergence of domains and a late big bang of domain combinations. Expert opinion: Two processes, folding and recruitment appear central to the evolutionary progression. The former increases protein persistence. The later fosters diversity. Chronologically, protein evolution mirrors folding by combining supersecondary structures into domains, developing translation machinery to facilitate folding speed and stability, and enhancing structural complexity by establishing long-distance interactions in novel structural and architectural designs.

This paper introduces a real-time method for reverse engineering a vehicle's CAN bus without prior knowledge of the vehicle or its CAN system. By comparing inertial measurement and CAN data during significant vehicle events, the method accurately identified the CAN channels associated with the accelerator pedal, brake pedal, and steering wheel. Utilizing an IMU, CAN module, and event-driven software architecture, the system was validated using prerecorded serialized data from previous studies. This data, collected during multiple vehicle drives, included synchronized IMU and CAN recordings. By using these consistent datasets, the improvements made in this work were tested and validated under the same conditions as in the previous studies, enabling direct comparison to earlier results. Faster processing times were produced and less computational power was needed, as compared to the earlier methods. This work could have potential application to making aftermarket autonomous vehicle kits and for cybersecurity applications. It is a scalable and adaptable solution for autonomous CAN reverse engineering in near real-time.

Episode mining is a fundamental problem in analyzing a sequence of numerous events. For discovering strong relationships between events in a complex event sequence, episode rule mining is proposed. However, both the episode and episode rules have strict requirements for the order of events. Hence, partially-ordered episode rule mining (POERM) is designed to loosen the constraints on the ordering, i.e., events in the antecedents and consequents of the rule can be unordered, and POERM has been applied to real-life event prediction. In this paper, we consider the utility of POERM, intending to discover more valuable rules. We define the utility of POERs and propose an algorithm called UPER to discover high-utility partially-ordered episode rules. In addition, we adopt a data structure named NoList to store the necessary information, analyze the expansion of POERs in detail, and propose several pruning strategies (namely WEUP, REUCSP, and REEUP) to reduce the number of candidate rules. Finally, we conduct experiments on several datasets to demonstrate the effectivene

In pattern mining, sequential rules provide a formal framework to capture the temporal relationships and inferential dependencies between items. However, the discovery process is computationally intensive. To obtain mining results efficiently and flexibly, many methods have been proposed that rely on specific evaluation metrics (i.e., ensuring results meet minimum threshold requirements). A key issue with these methods, however, is that they generate many sequential rules that are irrelevant to users. Such rules not only incur additional computational overhead but also complicate downstream analysis. In this paper, we investigate how to efficiently discover user-centric sequential rules. The original database is first processed to determine whether a target query rule is present. To prune unpromising items and avoid unnecessary expansions, we design tight and generalizable upper bounds. We introduce a novel method for efficiently generating target sequential rules using the proposed techniques and pruning strategies. In addition, we propose the corresponding mining algorithms for two common evaluation metrics: frequency and utility. We also design two rule similarity metrics to help discover the most relevant sequential rules. Extensive experiments demonstrate that our algorithms outperform state-of-the-art approaches in terms of runtime and memory usage, while discovering a concise set of sequential rules under flexible similarity settings. Targeted sequential rule search can handle sequence data with personalized features and achieve pattern discovery. The proposed solution addresses several challenges and can be applied to two common mining tasks.

Recently, the superconducting diode effect (SDE), characterized by unequal critical currents in opposite directions, has been observed experimentally and predicted theoretically in models of bulk superconductors and Josephson junctions (JJs). In this work, we construct a Josephson junction using a recently discovered unconventional coplanar magnet, the $p$-wave magnet (PM), with proximity-induced superconductivity, and demonstrate the emergence of a Josephson diode effect (JDE). The barrier region is formed by another unconventional collinear magnet, namely an altermagnet (AM). We illustrate that apart from time-reversal and inversion symmetries, the mirror operation $M_{yz}$ emerges as the key symmetry constraint. Also, unlike earlier models that realize the JDE using unconventional magnets, this setup does not require Rashba spin-orbit coupling (SOC) or different superconductors across the junction. Moreover, we demonstrate that the realization of the JDE in this framework requires only minimal conditions while maintaining high performance. The effect remains robust across a broad parameter regime, and thus making the system particularly promising for applications in quantum circuits and computing technologies.

A detailed experimental campaign is conducted to investigate the shear layer characteristics of an axisymmetric open cavity exposed to a Mach $6$ freestream. Experiments are performed in a Ludwieg tunnel for varying Reynolds numbers ($23000\leq Re_D \leq 74000$) based on cavity depth ($D$). The effects of geometry are examined through length-to-depth ratios ($[L/D]=[2,4,6]$) and non-dimensional rear-face height differences ($[Δh/D]=[-0.5,-0.25,0,0.25,0.5]$). Shear layer evolution is interpreted using qualitative schlieren and Planar Laser Rayleigh Scattering (PLRS) along with quantitative unsteady pressure measurements. For all $[L/D]$, the shear layer remains laminar at low $Re_D$ and develops Kelvin-Helmholtz (K-H) vortices as $Re_D$ increases. For the longest cavity ($[L/D]=6$), transition to turbulence occurs at the highest $Re_D$ due to a longer K-H growth length. Spectral analysis of pressure signals and PLRS intensity shows a shift in dominant frequency from the first Rossiter mode to higher modes for $[L/D]=6$. Except for $[L/D]=6, [Δh/D]=0$, dominant frequencies agree with Rossiter predictions and remain largely Reynolds-number independent. Variation of $[Δh/D]$ leads to mode switching identified using POD of PLRS snapshots. Negative $[Δh/D]$ favors K-H modes (5th-6th Rossiter), whereas positive values promote a strong flapping mode (1st Rossiter) due to pressure build-up inside the cavity. At $[Δh/D]=0$, both modes may coexist depending on $Re_D$. Azimuthal measurements indicate dominant axisymmetric behavior in flapping cases and weaker correlation for K-H dominated shear layers.

We consider a tagged particle in mean field interaction with a free gas of density N at equilibrium. In dimensions $d\geq4$, we prove the convergence of its trajectory, as N goes to infinity, to the one of a diffusion process associated with the linear Landau equation. The proof of the convergence of the martingale problem relies on two key ingredients: long time stability results of the microscopic dynamics, and controls on the probability of particle recollisions.

Let $ψ$ be a continuous decreasing function defined on all large positive real numbers. We say that a real $m\times n$ matrix $A$ is $ψ$-Dirichlet if for every sufficiently large real number $t$ one can find $\mathbf{p} \in \mathbb{Z}^m$, $\mathbf{q} \in \mathbb{Z}^n\setminus\{\mathbf{0}\}$ satisfying $\|A\mathbf{q}-\mathbf{p}\|^m< ψ(t)$ and $\|\mathbf{q}\|^n<t$. By removing a technical condition from a partial zero-one law proved by Kleinbock-Strömbergsson-Yu, we prove a zero-one law for the Lebesgue measure of the set of $ψ$-Dirichlet matrices provided that $ψ(t)<1/t$ and $tψ(t)$ is increasing. In fact, we prove the zero-one law in a more general situation with the monotonicity assumption on $tψ(t)$ replaced by a weaker condition. Our proof follows the dynamical approach of Kleinbock-Strömbergsson-Yu in reducing the question to a shrinking target problem in the space of lattices. The key new ingredient is a family of carefully chosen subsets of the shrinking targets studied by Kleinbock-Strömbergsson-Yu, together with a short-range mixing estimate for the associated hitting events. Our method also works for the analogous weighted problem where the relevant supremum norms are replaced by certain weighted quasi-norms.

We study a model of auction design where a seller is selling a set of objects to a set of agents who can be assigned no more than one object. Each agent's preference over (object, payment) pair need not be quasilinear. If the domain contains all classical preferences, we show that there is a unique mechanism, the minimum Walrasian equilibrium price (MWEP) mechanism, which is strategy-proof, individually rational, and satisfies equal treatment of equals, no-wastage (every object is allocated to some agent), and no-subsidy (no agent is subsidized). This provides an equity-based characterization of the MEWP mechanism, and complements the efficiency-based characterization of the MWEP mechanism known in the literature.

In this work, we present an unmanned aerial vehicle (UAV) wireless dataset collected as part of the AERPAW Autonomous Aerial Data Mule (AADM) challenge, organized by the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) project. The AADM challenge was the second competition in which an autonomous UAV acted as a data mule, where the UAV downloaded data from multiple base stations (BSs) in a dynamic wireless environment. Participating teams designed flight control and decision-making algorithms for choosing which BSs to communicate with and how to plan flight trajectories to maximize data download within a mission completion time. The competition was conducted in two stages: Stage 1 involved development and experimentation using a digital twin (DT) environment, and in Stage 2, the final test run was conducted on the outdoor testbed. The total score for each team was compiled from both stages. The resulting dataset includes link quality and data download measurements, both in DT and physical environments. Along with the USRP measurements used in the contest, the dataset also includes UAV telemetry, Keysight RF sensors position estimates, link quality measurements from LoRa receivers, and Fortem radar measurements. It supports reproducible research on autonomous UAV networking, multi-cell association and scheduling, air-to-ground propagation modeling, DT-to-real-world transfer learning, and integrated sensing and communication, which serves as a benchmark for future autonomous wireless experimentation.

Elucidating the role of the kagome electronic structure in determining the various quantum ground states is of fundamental importance. In this work, we employ in-plane uniaxial stress as a tuning parameter to probe the electronic structure and its impact on the superconducting and normal-state properties of the kagome superconductor LaRu$_{3}$Si$_{2}$, combining magnetotransport measurements with first-principles calculations. We identify a pronounced anisotropy in both the upper critical field and the normal-state magnetoresistance, indicating strong electronic anisotropy despite the three-dimensional crystal structure. Furthermore, we find that the superconducting transition temperature $T_{\rm c}$ increases under in-plane stress applied within the kagome plane, although the enhancement is modest, reaching approximately 0.3 K at 0.6 GPa. Furthermore, the absolute magnetoresistance exhibits a pronounced increase from about 22${\%}$ at zero stress to 35${\%}$ at 0.6 GPa, indicating a substantial modification of the normal state above $T_{\rm c}$. Previous studies have reported time-reversal-symmetry (TRS) breaking below a temperature scale that coincides with the onset of magnetoresistance. The simultaneous enhancement of both $T_{\rm c}$ and magnetoresistance under stress therefore suggests a positive correlation between superconductivity and normal-state electronic and magnetic properties in LaRu$_{3}$Si$_{2}$. Detailed calculations demonstrate that stress-induced changes in $T_{\rm c}$ arise from the joint evolution of the total density of states and the flat band, whereas the large magnetoresistance enhancement is dominated by the stress-driven downward shift of the Ru $dz^{2}$ kagome flat band.

The coefficients $A_n(α,β,ω)$ in the Maclaurin expansion $(1+ωz)^α(1-z)^{-β}= \sum_{n=0}^{\infty} A_n(α,β,ω)z^n$ are studied, where $ω,z \in \mathbb{C}$ with $|z| < |ω|=1$, and $α,β\in (0,1]$. In 1973 Brannan conjectured that $|A_n(α,β,ω)|\le A_n(α,β,1)$ for each positive odd integer $n$, and showed it is true for $n=3$. This has recently been proven for all odd integers $n\ge5$ by a number of authors in aggregate for the special case $β=1$. In this paper hypergeometric integral representations and Watson-type approximations are utilised, from which the general problem is reduced to numerically evaluating the minima of certain simple, explicit, slowly-varying functions over compact domains. From the positivity of these constants it is shown that the conjecture holds for $α, β\in (0,1]$, $0 \le |\arg(ω)| \le π-ϕ_0$ and $n=5,7,9,\ldots$, where $ϕ_0=0.061$.