Quantum fluctuations can give rise to a singular quantum critical point (QCP) in the ground state, whose influence extends to finite temperatures, forming a quantum critical regime (QCR). Recently, it has been shown that in the quantum Ising model, the symmetry-breaking, longitudinal field can induce a quantum supercritical regime (QSR) emanating from the QCP, which hosts a universally enhanced quantum supercritical magnetocaloric effect (MCE). In this paper, we show that the QSR also emerges in the spin-1/2 XXZ model, in both the form of Ising and Berezinskii-Kosterlitz-Thouless (BKT) supercriticality. Using ground-state and finite-temperature tensor-network methods, we investigate quantum supercritical phenomena near a BKT QCP. We reveal a quantum supercritical crossover scaling $T \propto h^{2/3}$ and a Grüneisen ratio scaling $Γ_h \propto T^{-3/2}$ for the BKT QCP, which differ from the corresponding Ising supercritical scalings. Nevertheless, we find that the scaling function $ϕ_Γ(x)$ of the singular Grüneisen ratio for both BKT and Ising cases can be approximately described by the same expression $ϕ_Γ(x) \approx x/(1+x^2)$. Our work extends the study of quantum supercritical phenomena from the Ising to the XXZ Heisenberg model, thereby revealing the presence of BKT quantum supercriticality and broadening the scope of quantum supercritical physics.

Randomized controlled trials (RCTs) yield internally valid causal effect estimates, but generalizing these results to target populations with different characteristics requires an untestable selection ignorability assumption: conditional on observed covariates, trial participation must be independent of potential outcomes. This assumption fails when unobserved effect modifiers are distributed differently between trial and target populations. We develop a sensitivity analysis framework for trial generalization grounded in omitted variable bias (OVB). Our key theoretical contribution is an exact decomposition showing that external-validity bias equals moderation strength $\times$ moderator imbalance: (i) how strongly an unobserved variable shifts the treatment effect, times (ii) how differently that variable is distributed across populations after covariate adjustment. We introduce scale-free sensitivity parameters based on partial $R^2$ values, enabling closed-form bounds and benchmarking against observed covariates -- practitioners can assess whether conclusions would change if an unobserved moderator were "as strong as" a particular observed variable. Simulations demonstrate that our bounds achieve nominal coverage and remain conservative under model misspecification, while comparisons with alternative sensitivity frameworks highlight the interpretive advantages of the OVB decomposition.

Cardiovascular-Kidney-Metabolic (CKM) syndrome represents a growing public health crisis, yet the subclinical heterogeneity of its component systems remains underexplored. Early detection of physiological deviation is critical for preventing irreversible organ damage and mortality. Here, we characterize the prevalence and interplay of CKM impairment in a US cohort (N=841) by integrating continuous wearable data with clinical biomarkers. We assessed cardiovascular, kidney via clinical biomarkers, namely Chol/HDL, eGFR, as well as metabolic health risk through Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). We show that while metabolic and cardiovascular disruptions are significantly associated (r=0.26, p<0.001), early-stage kidney impairment manifests independently. Utilizing a normalized deviance score, we identified significant health impairments in 29.0% of the cohort. Cardiovascular deviation was the most prevalent singular phenotype (13.3%), followed by metabolic (9.1%) and renal (6.25%) deviations, with dual metabolic-cardiovascular impairment occurring in only 2.2% of participants. These findings suggest that high system-specific deviance may serve as an indicator for accelerated physiological aging within the respective organ system. Furthermore, feature ablation analysis revealed that step count, Active Zone Minutes, and resting heart rate are the most potent wearable-derived predictors of cardiovascular and metabolic decline. These findings underscore the necessity of a multi-system subtyping approach, demonstrating that wearable-derived phenotypes can facilitate the early, targeted interventions required to manage the complex landscape of CKM syndrome.

We report the results of identical pre- and post-event surveys given to attendees of a talk, two-sided conversation, and Q&A centered around the book If Anyone Builds It, Everyone Dies at Harvard University in March 2026, covering perceived probability of AI-caused extinction or severe disempowerment resulting from unimpeded AI development, confidence in those estimates, and global priority. Among the 89 matched participants, the post-event median estimate of the probability of existential risk from advanced AI was 70%, and 96% agreed that mitigating AI existential risk should be a global priority. Although these self-selected respondents' pre-event views were already high (50% and 93%, respectively) relative to results of similar surveys that were previously administered to experts and the general public, the event produced increases on all measures when considering the respondents in aggregate. The magnitudes of increases in risk probability were negatively correlated with prior familiarity with the topic: among attendees with little prior familiarity, 60% shifted upward and none shifted downward, whereas among self-described experts, no respondents shifted upward and 20% shifted downward. Self-reported confidence also increased significantly, and confidence shifts were positively correlated with probability shifts. These findings indicate that a structured public engagement event can meaningfully shift risk perceptions, particularly among newcomers to the topic.

People traditionally divine the future by interpreting natural phenomena as oracular signals, especially in societies adhering to traditional beliefs like China. With the advent of Generative AI (GenAI), people gain access to new ways of probing digital oracles for predicting the future. To understand how people use and interpret GenAI for divination in China, we interviewed 22 participants who habitually use GenAI platforms for fortune-telling, complemented by a three-week digital ethnography with 1,842 community posts. Qualitative analysis showed that people who seek psychological comfort are particularly receptive to GenAI-based decision-making. Users valued GenAI's accessibility, convenience, and efficiency while perceiving its lack of spiritual mystique. We observed community dynamics forming around GenAI tools, where users reinforce interpretations by sharing and discussing with each other, repeating queries until responses align with expectations. Our work uncovers how AI technologies change the way people and communities engage in traditional cultural practices while yearning for the same goals.

Let $G$ be a finite simple graph. An independent set $I$ of $G$ is critical if $\left|I\right|-\left|N(I)\right|\ge\left|J\right|-\left|N(J)\right|$ for every independent set $J$ of $G$. A critical independent set is maximum if it has maximum cardinality. The $core$ and the $nucleus$ of $G$ are defined as the intersection of all maximum independent sets and the intersection of all maximum critical independent sets, respectively. In 2019, Jarden, Levit, and Mandrescu posed the problem of characterizing the graphs satisfying $core(G)=nucleus(G)$. In this paper, we provide a complete solution to this problem. Using Larson's independence decomposition, which partitions any graph into a König--Egerváry component $L_G$ an a $2$-bicritical component $L_G^c$, we establish that $core(G)=nucleus(G)$ holds if and only if $core ({L_G^c})=\emptyset$ and no vertex of $corona(G)$ lies in the boundary between $L_G$ and $L_G^c$. We also show that the same boundary condition is equivalent to the identity $diadem(G)=corona(G) \cap L(G)$. Several consequences and related structural properties are also derived.

In contemporary society, social media is deeply integrated into daily life, yet emotional expression often differs between real and online contexts. We studied the Persian community on X to explore this gap, designing a human-centered pipeline to measure alignment between real-world and social media emotions. Recent tweets and images of participants were collected and analyzed using Transformers-based text and image sentiment modules. Friends of participants provided insights into their real-world emotions, which were compared with online expressions using a distance criterion. The study involved N=105 participants, 393 friends, over 8,300 tweets, and 2,000 media images. Results showed only 28% similarity between images and real-world emotions, while tweets aligned about 76% with participants' real-life feelings. Statistical analyses confirmed significant disparities in sentiment proportions across images, tweets, and friends' perceptions, highlighting differences in emotional expression between online and offline environments and demonstrating practical utility of the proposed pipeline for understanding digital self-presentation.

Wave--particle duality is a cornerstone of quantum mechanics, traditionally formulated under definite causal order. We investigate how complementarity is modified when the temporal order of operations is coherently superposed, as in the quantum switch. We show that no universal linear additive complementarity relation exists that simultaneously captures path distinguishability, spatial coherence, and coherence between causal orders. This reveals a fundamental separation between spatial and causal resources, which reside on different subsystems and are therefore not jointly constrained by a single quantum state. While tracing out the order qubit recovers the standard duality relation at the level of the reduced quanton--detector state, coherence between causal orders is not accessible at the level of the reduced description. To capture this contribution, we introduce \emph{causal coherence}, defined as the coherence of the order qubit, which quantifies interference between alternative causal orders and is operationally measurable; we construct explicit processes in which spatial duality is saturated while causal coherence is maximal. We further show that complementarity admits a state-dependent entropic formulation based on incompatible measurements on the causal degree of freedom; unlike generic state-dependent relations, this formulation arises from a universal uncertainty principle and provides a canonical operationally meaningful description. These results establish that complementarity is fundamentally shaped by causal structure and cannot, in general, be fully captured at the level of reduced quantum states alone.

Blockchain protocols often seek to procure computationally challenging work from a decentralized set of participants. While there are simple procurement auctions that result in the minimal cost of acquisition and maximal efficiency, they also lead to concentration in the provider set due to the winner-take-all market structure. We design and analyze single-good procurement auctions that balance social-cost minimization (at the extreme, a winner-take-all auction) with decentralization (at the extreme, a uniform allocation). We first give a dominant-strategy incentive-compatible (DSIC) mechanism explicitly designed to implement non-winner-take-all allocations. Our allocation rule uniquely solves an optimization with respect to a modified social-cost metric that penalizes large, single-player concentrations and is parameterized with a curvature value, $α$, with $α\rightarrow 0$ implementing the uniform allocation and $α\rightarrow \infty$ implementing the winner-take-all allocation. We further quantify the loss in social cost of this mechanism as a function of $α$. We then propose two alternative mechanisms, each addressing a limitation of the DSIC mechanism, namely a lack of Sybil-resistance and a complex payment rule. First, we examine a variation of Tullock contests to achieve a non-winner-take-all Sybil-proof procurement mechanism. Second, we consider a mechanism with the same allocation rule as the DSIC mechanism but with an alternative payment rule in which producers are simply paid proportionally to their bids. This provides a much simpler payment rule which, while not DSIC, still results in the mechanism being ex-post ``safe'' (where there exists a bidding strategy that is guaranteed to result in non-negative utility) for participating bidders. For both non-DSIC mechanisms, we characterize the equilibrium allocations and prove price of anarchy bounds.

The multiplicity distribution of charged particles produced in the central rapidity region ($|η|<2.5$) is calculated within the multi-channel eikonal model using the AGK cutting rules and compared with ATLAS data at $\sqrt{s}=7$ and 13 TeV. The effects of color reconnection and/or string percolation are discussed.

In this paper, we study Riemannian metrics on the three-dimensional lens spaces that are deformations of the standard Riemannian metric along the fibers of the Hopf fibration. In other words, these metrics are axisymmetric. There is a one-parametric family of such metrics. This family tends to an axisymmetric sub-Riemannian metric. We find the cut loci and the cut times using methods from geometric control theory. It turns out that the cut loci and the cut times converge to the cut locus and the cut time for the sub-Riemannian structure, that was already studied. Moreover, we get some lower bounds for the diameter of these Riemannian metrics. These bounds coincide with the exact values of diameters for the lens spaces L(p;1).

In this note, we establish several results concerning the continuity (or weak convergence) of the complex Monge-Ampère operator on compact Hermitian manifolds. At the end of this note, we find a weak solution of the complex Monge-Ampère equation on a compact Hermitian manifold under the assumption of the existence of a smooth subsolution.

The product of resting heart rate and maximum lifespan is approximately constant across adult warm-blooded vertebrates, $N^\star = f_H L \approx 10^9$ cardiac cycles, a regularity documented since Rubner (1908) but lacking a thermodynamic derivation. We derive $N^\star$ from the non-equilibrium second law by treating the adult organism as a metabolic non-equilibrium steady state (NESS) and introducing the closure $\dot{e}_p = σ_0 f$, linking entropy production rate to heart rate via a mass-specific parameter $σ_0 \propto M^0$. Integration yields a finite dissipative budget $Σ= σ_0 N^\star$, identifying $N^\star = Σ/σ_0$ as the correct primitive conserved quantity; lifetime energy per unit mass is a derived consequence valid only under simultaneous constancy of body temperature and $σ_0$. Phylogenetically independent contrasts on 112 endotherm species yield a $\log f_H$--$\log L$ slope of $-0.99 \pm 0.04$ ($p=0.84$ against $-1$); the West--Brown--Enquist null of zero inter-clade variation is rejected ($F=12.7$, $p<0.001$). A factored multiplier $Φ_C = Φ_{\mathrm{duty}} \cdot Φ_{\mathrm{thermal}} \cdot Φ_{\mathrm{mito}} \cdot Φ_{\mathrm{haz}}$, calibrated from independently measured physiology, accounts for longevity deviations across four warm-blooded clades. The integral of physiological frequency defines a biological proper time classifying longevity mechanisms as time dilation (reduce $f$) or budget expansion (reduce $σ_0$). The decisive test is calorimetric measurement of $σ_0 = P/(TfM)$ across three body-mass decades.

Let $\mathfrak{g}$ be a complex simple Lie algebra and $L(λ)$ be a highest weight module of $\mathfrak{g}$ with highest weight $λ-ρ$, where $ρ$ is half the sum of positive roots. A simple $\mathfrak{g}$-module $L_w:=L(-wρ)$ is called integral minimal if the associated variety of its annihilator ideal equals the closure of the minimal special nilpotent orbit. In this paper, we find that the associated variety of any integral minimal module $L_w$ is irreducible and equal to the orbital variety corresponding to the minimal length element in the Kazhdan--Lusztig right cell containing $w$.

This work addresses arbitrary convex vector optimization problems, which constitute a general framework for multi-criteria decision-making in diverse real-world applications. Due to their complexity, such problems are typically tackled using polyhedral approximation. Existing solution concepts rely on additional assumptions, such as boundedness, polyhedrality of the ordering cone, or existence of interior points in the ordering cone, and typically focus on absolute error measures. We introduce a solution concept based on the homogenization of the upper image that employs relative error measures and avoids additional structural assumptions. Although minimality is not explicitly required, a form of approximate minimality is implicitly ensured. The concept is straightforward, requiring only a single precision parameter and, owing to relative errors, remains robust under scaling of the target functions. Homogenization also eliminates the need for the binary distinction between points far from the origin and directions which can lead to numerical difficulties. Furthermore, in practice decision-makers often identify a region where preferred solutions are expected. Our concept supports both a global overview of the upper image and a refined local perspective within such a user-defined region of interest (RoI).We present a decision-making procedure enabling iterative refinement of this region and the associated preferences.

Relativistic jets are among the most fascinating objects in the Universe, and recent high-resolution Very Long Baseline Interferometric (VLBI) observations, including the Global mm-VLBI Array and the Event Horizon Telescope (EHT), are able to resolve their structure close to their launching site. These observations reveal strongly limb-brightened jet structures for Centaurus A (Cen A), M 87 and 3C 84. Thus, the question arises which physical mechanism can generate the limb-brightened structure, and if this structure is common for jets from low-luminosity active galactic nuclei (LLAGN) seen under large viewing angles. Therefore, as a pilot study, we aim to model the EHT observations of Cen A. We performed a 3D two-temperature general-relativistic magnetohydrodynamic (GRMHD) simulation of an accreting supermassive black hole (SMBH) and jet launching to study the plasma dynamics and computed the connected emission via general relativistic radiative transfer (GRRT) calculations considering possible anisotropies in the distribution of the radiating particles. In order to adjust our simulations to the EHT observations of Cen A, we carried out a Bayesian fitting in the Fourier plane. We find that GRMHD simulations of magnetically arrested disks (MADs) combined with anisotropically emitting particle distributions along the direction of the magnetic field, parametrized by a value $η=0.07$, are able to mimic the recent EHT observations of Cen A. In addition, we extracted a black hole mass of $M_\mathrm{BH} = 6\times10^7 M_\odot$ and a viewing angle of $\vartheta=72°$. Our obtained model can reproduce key features of the EHT and Atacama Large Millimeter/submillimeter Array (ALMA) observations in total and polarized emission. Finally, we predict that the black hole shadow in Cen A will be observable at a frequency of $\sim$ 3 THz.

Student simulation can support learning-by-teaching pedagogy where human students (as tutors) teach AI-simulated novice students (as tutees). Recent research often relies on prompt engineering with large language models (LLMs) to simulate novice student behaviour, but it is difficult to keep the AI-simulated student at a stable novice knowledge level. A key reason is that many LLMs are trained to be broadly capable, so even when prompted to "act like a novice," the LLMs can still produce expert-level explanations during the learning-by-teaching interaction process. As a result, the AI-simulated student may drift beyond the intended knowledge level, reducing the credibility of the simulation for studying learning-by-teaching processes. Thus, we propose a knowledge-level simulation approach based on machine unlearning. We investigate this approach using a dataset of multiple-choice questions on Python programming concepts. We apply machine unlearning to transform a knowledgeable LLM into a novice-level AI student (i.e., teachable agent), then evaluate whether the teachable agent can relearn targeted knowledge components through learning-by-teaching dialogue interactions. Finally, we analyse the dialogue logs to characterise how the agent's behaviour changes over time, including its question asking, error patterns, and responsiveness to instruction. The results show that (1) unlearning produces simulated student agents with more novice-like responses than prompt-only baselines, (2) the agents recover a measurable portion of the unlearned knowledge under structured exposure, and (3) dialogue analyses reveal identifiable trajectories of conceptual change and teaching moves that predict learning recovery.

We study conserved charges of the staggered fermion Hamiltonian in 3+1 dimensions. By decomposing staggered fermions into Majorana components and exploiting lattice translation symmetries, we construct a set of conserved non-singlet charges. We analyze their algebra andshow that, although the charges exhibit nontrivial non-commutativity on the lattice, they generate axial SU(2)_L \tines SU(2)_R transformations for low-energy degrees of freedom in the continuum limit. Possible implications for anomalies are discussed.

With the rapid advancement of AI in code generation, cybersecurity detection engineering faces new opportunities to automate traditionally manual processes. Detection authoring - the practice of creating executable logic that identifies malicious activities from security telemetry - is hindered by fragmented code across repositories, duplication, and limited organizational visibility. Current workflows remain heavily manual, constraining both coverage and velocity. In this paper, we introduce AVDA, a framework that leverages the Model Context Protocol (MCP) to automate detection authoring by integrating organizational context - existing detections, telemetry schemas, and style guides - into AI-assisted code generation. We evaluate three authoring strategies - Baseline, Sequential, and Agentic - across a diverse corpus of production detections and state-of-the-art LLMs. Our results show that Agentic workflows achieve a 19% improvement in overall similarity score over Baseline approaches, while Sequential workflows attain 87% of Agentic quality at 40x lower token cost. Generated detections excel at TTP matching (99.4%) and syntax validity (95.9%) but struggle with exclusion parity (8.9%). Expert validation on a 22-detection subset confirms strong Spearman correlation between automated metrics and practitioner judgment ($ρ= 0.64$, $p < 0.002$). By integrating seamlessly into standard developer environments, AVDA provides a practical path toward AI-assisted detection engineering with quantified trade-offs between quality, cost, and latency.

We explore the low scale implications of SUSY models based on the gauge symmetry SU(4)_CxSU(2)_LxSU(2)_R. We include the non-holomorphic terms arises from the perturbations on D-branes, which also break SUSY. These terms significantly change the implications of Yukawa unification, since they are directly included in the threshold contributions to Yukawa couplings. With these contributions, YU can be compatible with low fine-tuning and yet heavy Higgsino-like LSP, but these solutions receive strong negative impact from the dark matter observations. Besides, in contrast to the earlier results, the NH contributions accommodate heavy gluino masses from about 2.2 to 10 TeV compatible with Yukawa unification. These gluinos can be probed up to about 2.5 in the collider experiments of high luminosity, and to about 6 TeV in future experiments of 100 TeV center of mass energy. In contrast to the gluino mass scales, the NH contributions can drive the squarks of the third family to the light masses. We observe that the Yukawa unification solutions can be compatible with the sbottom mass of about 1.5 TeV, and they are more likely to be tested soon. The stop can also be as light as about 1.5 TeV, and it can be nearly degenerate with the LSP neutralino. Even though these solutions are beyond the sensitivity of the current collider analyses, they can be subjected to be tested in future experiments such as those of Future Circular Collider proposal. We project the current analyses for stop-neutralino degenerate solutions to the Future Circular Collider experiments, and realize that the signal processes with semi-leptonic final states can yield about 5 sigma significance with appropriate cuts on the kinematic variables. Such a large significance can be realized when L ~ 97 fb^-1. We also present a slight improvement by performing BDT analyses, which can yield a similar significance with L ~ 54 fb-1.

One of the most popular approaches for solving total variation-regularized optimization problems in the space of measures are Particle Gradient Flows (PGFs). These restrict the problem to linear combinations of Dirac deltas and then perform a Euclidean gradient flow in the weights and positions, significantly reducing the computational cost while still decreasing the energy. In this work, we generalize PGFs to convex optimization problems in arbitrary Banach spaces, which we call Atomic Gradient Flows (AGFs). To this end, the crucial ingredient turns out to be the right notion of particles, chosen here as the extremal points of the unit ball of the regularizer. This choice is motivated by the Krein-Milman theorem, which ensures that minimizers can be approximated by linear combinations of extremal points. We investigate metric gradient flows of the optimization problem when restricted to such sparse representations, for which we define a suitable discretized functional that we show to be to be consistent with the original problem via the means of $Γ$-convergence. We prove that the resulting evolution of the latter is well-defined using a minimizing movement scheme, and we establish conditions ensuring $λ$-convexity and uniqueness of the flow. Then, using Choquet's theorem, we lift the problem into the Wasserstein space on weights and extremal points, and consider Wasserstein gradient flows in this lifted setting. Our main result is that the lifting of the AGF evolution is again a metric gradient flow in the Wasserstein space, verifying the consistency of the approach with respect to a Wasserstein-type dynamic. Finally, we illustrate the applicability of AGFs to several relevant infinite-dimensional problems, including optimization of functions of bounded variation and curves of measures regularized by Optimal Transport-type penalties.

An amplitude analysis of the singly Cabibbo-suppressed decay $D^0 \to K^+ K^- π^0 π^0$ is performed, for the first time, to determine the relative magnitudes and phases of different intermediate processes. The analysis uses $e^+e^-$ collision data collected with the BESIII detector at the center-of-mass energy 3.773~GeV corresponding to an integrated luminosity of 20.3 $\rm fb^{-1}$. The absolute branching fraction of $D^0 \to K^+ K^- π^0 π^0$ is measured to be \BF. The dominant intermediate process is $D^0 \to K^{*}(892)^+K^{*}(892)^-$, with a branching fraction of $(2.79 \pm 0.13_{\rm{stat.}} \pm 0.11_{\rm{syst.}}) \times 10^{-3}$. Amplitude analysis reveals that the $D^0 \to K^{*}(892)^+K^{*}(892)^-$ decay is S-wave dominant. The longitudinal polarization fraction of $D^0 \to K^{*}(892)^+ K^{*}(892)^-$ is measured to be $0.468\pm0.046_{\rm{stat.}}\pm0.011_{\rm{syst.}}$.

Within a neural-network-based holographic framework, we incorporate lattice QCD (LQCD) and Hadron Resonance Gas (HRG) data to train the model and predict the location of the QCD critical endpoint (CEP). The training dataset consists of the entropy density, baryon number susceptibility, and baryon density. The metric warp factor $A(z)$ and the gauge kinetic function $f(z)$ are parameterized by neural networks and determined through the training procedure. The resulting model reproduces the equation of state at vanishing chemical potential in good agreement with both LQCD and HRG data. Extending the analysis to finite chemical potential, we solve the equations of motion and obtain thermodynamic observables consistent with LQCD results at finite density. After incorporating the HRG constraints, the predicted position of the CEP shifts toward larger chemical potentials compared to recent studies. We further employ symbolic regression to derive analytic expressions for $A(z)$ and $f(z)$, providing convenient functional forms for future phenomenological applications. Finally, we perform a data-driven validation using synthetic thermodynamic data generated from an existing analytical holographic model. The neural-network framework reproduces the corresponding CEP location with good accuracy, showing close agreement within numerical uncertainties.

In this paper I develop a breakdown frontier approach to assess the sensitivity of Local Average Treatment Effects (LATE) estimates to violations of monotonicity and independence of the instrument. I parametrize violations of independence using the concept of $c$-dependence from Masten & Poirier (2018) and allow for the share of defiers to be greater than zero but smaller than the share of compliers. I derive identified sets for the LATE and the Average Treatment Effect (ATE) in which the bounds are functions of these two sensitivity parameters. Using these bounds, I derive the breakdown frontier for the LATE, which is the weakest set of assumptions such that a conclusion regarding the LATE holds. I derive consistent sample analogue estimators for the breakdown frontiers and provide a valid bootstrap procedure for inference. Monte Carlo simulations show the desirable finite-sample properties of the estimators and an empirical application shows that the conclusions regarding the effect of family size on unemployment from Angrist & Evans (1998) are highly sensitive to violations of independence and monotonicity.

Quantum Key Distribution (QKD) protocols require Information-Theoretically Secure (ITS) authentication of the classical channel to preserve the unconditional security of the distilled key. Standard ITS schemes are based on one-time keys: once a key is used to authenticate a message, it must be discarded. Since QKD requires mutual authentication, two independent one-time keys are typically consumed per round, imposing a non-trivial overhead on the net secure key rate. In this work, we present the authentication-with-response scheme, a novel ITS authentication scheme based on $\varepsilon$-Almost Strongly Universal$_2$ ($\varepsilon$-ASU$_2$) functions, whose IT security can be established in the Universal Composability (UC) framework. The scheme achieves mutual authentication consuming a single one-time key per QKD round, halving key consumption compared to the state-of-the-art.

More than thirty years ago, Erdős, Faudree, Rousseau, and Schelp posed a fundamental question in extremal graph theory: What is the optimal constant $c_k$ such that $r(C_{2k+1}, G) \le c_k m$ for any graph $G$ with $m$ edges and no isolated vertices? In this paper, we make a significant step towards answering this question by proving that $r(C_{2k+1}, G) \le (2 + o(1)) m + p,$ where $p$ denotes the number of vertices in $G$. Additionally, we extend the work of Goddard and Kleitman and independently Sidorenko, who proved that $r(K_3, G) \le 2m + 1$ for any graph $G$ with $m$ edges and no isolated vertices. We generalize their findings to the clique version, establishing that $r(K_r, G) \le c_r m^{(r-1)/2}$, and to the multicolor setting, showing that $r_{k+1}(K_3; G) \le c_k m^{(k+1)/2}.$

Zykov's theorem shows that $r$-partite Turán graph uniquely has the maximum number of $K_t$ among all $n$-vertex $K_{r+1}$-free graphs for $2\le t\le r$. The clique tensor is a high-order extension of the adjacency matrix of a graph. Yu and Peng \cite{peng1} gave a spectral version of the Zykov's theorem via clique tensor. In this paper, we give some upper bounds on the spectral radius of the clique tensor of a graph, which can be viewed as the localizations of the spectral version of Zykov's theorem.

This paper proposes a distributed event-triggered control method that not only guarantees consensus of multi-agent systems but also satisfies a given LQ performance constraint. Taking the standard distributed control scheme with all-time communication as a baseline, we consider the problem of designing an event-triggered communication rule such that the resulting LQ cost satisfies a performance constraint with respect to the baseline cost while consensus is achieved. The main difficulty is that the performance requirement is global, whereas triggering decisions are made locally and asynchronously by individual agents, which cannot directly evaluate the global performance degradation. To address this issue, we decompose allowable degradation across agents and design a triggering rule that uses only locally available information to satisfy the given LQ performance constraint. For general linear agents on an undirected graph, we derive a sufficient condition that guarantees both consensus and the prescribed performance level. We also develop a tractable offline design method for the triggering parameters. Numerical examples illustrate the effectiveness of the proposed method.

Kuaishou serving hundreds of millions of searches daily, the quality of short-video search is paramount. However, it suffers from a severe Matthew effect on long-tail queries: sparse user behavior data causes models to amplify low-quality content such as clickbait and shallow content. The recent advancements in Large Language Models (LLMs) offer a new paradigm, as their inherent world knowledge provides a powerful mechanism to assess content quality, agnostic to sparse user interactions. To this end, we propose a LLM-driven multimodal reranking framework, which estimates user experience without real user behavior. The approach involves a two-stage training process: the first stage uses multimodal evidence to construct high-quality annotations for supervised fine-tuning, while the second stage incorporates pairwise preference optimization to help the model learn partial orderings among candidates. At inference time, the resulting experience scores are used to promote high-quality but underexposed videos in reranking, and further guide page-level optimization through reinforcement learning. Experiments show that the proposed method achieves consistent improvements over strong baselines in offline metrics including AUC, NDCG@K, and human preference judgement. An online A/B test covering 15\% of traffic further demonstrates gains in both user experience and consumption metrics, confirming the practical value of the approach in long-tail video search scenarios.

Modern software systems heavily rely on third-party dependencies, making software supply chain security a critical concern. We introduce the concept of software supply chain smells as structural indicators that signal potential security risks. We design and evaluate Dirty-Waters, a novel tool for detecting such smells in the supply chains of software packages. Through interviews with practitioners, we show that our proposed smells align with real-world concerns and capture signals considered valuable. A quantitative study of popular packages in the Maven and NPM ecosystems reveals that while smells are prevalent in both, they differ significantly across ecosystems, with traceability and signing issues dominating in Maven and most smells being rare in NPM, due to strong registry-level guarantees. Software supply chain smells support developers and organizations in making informed decisions and improving their software supply chain security posture.