In the realm of collaborative filtering recommendation systems, Graph Neural Networks (GNNs) have demonstrated remarkable performance but face significant challenges in deployment on resource-constrained edge devices due to their high embedding parameter requirements and computational costs. Using common quantization method directly on node embeddings may overlooks their graph based structure, causing error accumulation during message passing and degrading the quality of quantized this http URL address this, we propose Graph based Node-Aware Dynamic Quantization training for collaborative filtering (GNAQ), a novel quantization approach that leverages graph structural information to enhance the balance between efficiency and accuracy of GNNs for Top-K recommendation. GNAQ introduces a node-aware dynamic quantization strategy that adapts quantization scales to individual node embeddings by incorporating graph interaction relationships. Specifically, it initializes quantization intervals based on node-wise feature distributions and dynamically refines them through message passing in GNN layers. This approach mitigates information loss caused by fixed quantization scales and captures hierarchical semantic features in user-item interaction graphs. Additionally, GNAQ employs graph relation-aware gradient estimation to replace traditional straight-through estimators, ensuring more accurate gradient propagation during training. Extensive experiments on four real-world datasets demonstrate that GNAQ outperforms state-of-the-art quantization methods, including BiGeaR and N2UQ, by achieving average improvement in 27.8\% Recall@10 and 17.6\% NDCG@10 under 2-bit quantization. In particular, GNAQ is capable of maintaining the performance of full-precision models while reducing their model sizes by 8 to 12 times; in addition, the training time is twice as fast compared to quantization baseline methods.

High-resolution 3D point clouds are highly effective for detecting subtle structural anomalies in industrial inspection. However, their dense and irregular nature imposes significant challenges, including high computational cost, sensitivity to spatial misalignment, and difficulty in capturing localized structural differences. This paper introduces a registration-based anomaly detection framework that combines multi-prototype alignment with cluster-wise discrepancy analysis to enable precise 3D anomaly localization. Specifically, each test sample is first registered to multiple normal prototypes to enable direct structural comparison. To evaluate anomalies at a local level, clustering is performed over the point cloud, and similarity is computed between features from the test sample and the prototypes within each cluster. Rather than selecting cluster centroids randomly, a keypoint-guided strategy is employed, where geometrically informative points are chosen as centroids. This ensures that clusters are centered on feature-rich regions, enabling more meaningful and stable distance-based comparisons. Extensive experiments on the Real3D-AD benchmark demonstrate that the proposed method achieves state-of-the-art performance in both object-level and point-level anomaly detection, even using only raw features.

We review the equation of state of matter in neutron stars from the solid crust through the liquid nuclear matter interior to the quark regime at higher densities. We focus in detail on the question of how quark matter appears in neutron stars, and how it affects the equation of state. After discussing the crust and liquid nuclear matter in the core we briefly review aspects of microscopic quark physics relevant to neutron stars, and quark models of dense matter based on the Nambu--Jona-Lasinio framework, in which gluonic processes are replaced by effective quark interactions. We turn then to describing equations of state useful for interpretation of both electromagnetic and gravitational observations, reviewing the emerging picture of hadron-quark continuity in which hadronic matter turns relatively smoothly, with at most only a weak first order transition, into quark matter with increasing density. We review construction of unified equations of state that interpolate between the reasonably well understood nuclear matter regime at low densities and the quark matter regime at higher densities. The utility of such interpolations is driven by the present inability to calculate the dense matter equation of state in QCD from first principles. As we review, the parameters of effective quark models -- which have direct relevance to the more general structure of the QCD phase diagram of dense and hot matter -- are constrained by neutron star mass and radii measurements, in particular favoring large repulsive density-density and attractive diquark pairing interactions. We describe the structure of neutron stars constructed from the unified equations of states with crossover. Lastly we present the current equations of state -- called "QHC18" for quark-hadron crossover -- in a parametrized form practical for neutron star modeling.

We present the cross section for $e^{+}e^{-}\to hZ$ with arbitrary sets of electron and $Z$ boson polarizations at the full next-to-leading order in various extended Higgs models, such as the Higgs singlet model (HSM), the inert doublet model (IDM) and the two Higgs doublet model (2HDM). We systematically perform complete one-loop calculations to the helicity amplitudes in the on-shell renormalization scheme, and present the full analytic results as well as numerical evaluations. The deviation $\Delta R^{hZ}$ in the total cross section from its standard model (SM) prediction is comprehensively analyzed, and the differences among these models are discussed in details. We find that new physics effects appearing in the renormalized $hZZ$ vertex almost govern the behavior of $\Delta R^{hZ}$, and it takes a negative value in most cases. The possible size of $\Delta R^{hZ}$ reaches several percent under the theoretical and experimental bounds. We also analyze the deviation $\Delta R^{hZ}_{XY}$ in the total cross section times decay branching ratios of the discovered Higgs boson by utilizing the $\texttt{H-COUP}$ program. It is found that the four types of 2HDMs can be discriminated by analyzing the correlation between $\Delta R^{hZ}_{\tau\tau}$ and $\Delta R^{hZ}_{bb}$ and those between $\Delta R^{hZ}_{\tau\tau}$ and $\Delta R^{hZ}_{cc}$. Furthermore, the HSM and the IDM can be discriminated from the 2HDMs by measuring $\Delta R^{hZ}_{WW}$. These signatures can be tested by precision measurements at future Higgs factories such as the International Linear Collider.

On the basis of the percolation picture from the hadronic phase with hyperons to the quark phase with strangeness, we construct a new equation of state (EOS) with the pressure interpolated as a function of the baryon density. The maximum mass of neutron stars can exceed $2M_{\odot}$ if the following two conditions are satisfied; (i) the crossover from the hadronic matter to the quark matter takes place at around three times the normal nuclear matter density, and (ii) the quark matter is strongly interacting in the crossover region and has stiff equation of state. This is in contrast to the conventional approach assuming the first order phase transition in which the EOS becomes always soft due to the presence of the quark matter at high density. Although the choice of the hadronic EOS does not affect the above conclusion on the maximum mass, the three-body force among nucleons and hyperons plays an essential role for the onset of the hyperon mixing and the cooling of neutron stars.

We present a method to simulate toponium formation events at the LHC using the Green's function of non-relativistic QCD in the Coulomb gauge, which governs the momentum distribution of top quarks in the presence of the QCD potential. This Green's function can be employed to re-weight any matrix elements relevant for $t\bar{t}$ production and decay processes where a colour-singlet top-antitop pair is produced in the $S$-wave at threshold. As an example, we study the formation of $\eta_t$ toponium states in the gluon fusion channel at the LHC, combining the re-weighted matrix elements with parton showering.

We formulate a new two-variable river environmental restoration problem based on jump stochastic differential equations (SDEs) governing the sediment storage and nuisance benthic algae population dynamics in a dam-downstream river. Controlling the dynamics is carried out through impulsive sediment replenishment with discrete and random observation/intervention to avoid sediment depletion and thick algae growth. We consider a cost-efficient management problem of the SDEs to achieve the objectives whose resolution reduces to solving a Hamilton-Jacobi-Bellman (HJB) equation. We also consider a Fokker-Planck (FP) equation governing the probability density function of the controlled dynamics. The HJB equation has a discontinuous solution, while the FP equation has a Dirac's delta along boundaries. We show that the value function, the optimized objective function, is governed by the HJB equation in the simplified case and further that a threshold-type control is optimal. We demonstrate that simple numerical schemes can handle these equations. Finally, we numerically analyze the optimal controls and the resulting probability density functions.

Using the idea of a smooth crossover from the hadronic matter with hyperons to quark matter with strangeness, we show that the maximum mass of neutron stars with quark matter core can be larger than those without quark matter core. This is in contrast to the conventional softening of equation of state due to exotic components at high density. Essential conditions to reach our conclusion are (i) the crossover takes place at relatively low densities, around 3 times the normal nuclear density, and (ii) the quark matter is strongly interacting in the crossover region. By these, the pressure of the system can be greater than that of purely hadronic matter in the crossover region and leads to the maximum mass greater than 2 solar mass. This conclusion is insensitive to the different choice of the hadronic equation of state with hyperons. Several implications of this result to the nuclear incompressibility, the hyperon mixing, and the neutrino cooling are also remarked.

We investigate the computational complexity of the following problem. We are given a graph in which each vertex has an initial and a target color. Each pair of adjacent vertices can swap their current colors. Our goal is to perform the minimum number of swaps so that the current and target colors agree at each vertex. When the colors are chosen from {1,2,...,c}, we call this problem c-Colored Token Swapping since the current color of a vertex can be seen as a colored token placed on the vertex. We show that c-Colored Token Swapping is NP-complete for c = 3 even if input graphs are restricted to connected planar bipartite graphs of maximum degree 3. We then show that 2-Colored Token Swapping can be solved in polynomial time for general graphs and in linear time for trees. Besides, we show that, the problem for complete graphs is fixed-parameter tractable when parameterized by the number of colors, while it is known to be NP-complete when the number of colors is unbounded.

We introduce a methodology and investigate the feasibility of measuring quantum properties of tau lepton pairs in the $H \to \tau^+ \tau^-$ decay at future lepton colliders. In particular, observation of entanglement, steerability and violation of Bell inequalities are examined for the ILC and FCC-ee. We find that detecting quantum correlation crucially relies on precise reconstruction of the tau lepton rest frame and a simple kinematics reconstruction does not suffice due to the finite energy resolution of the colliding beams and detectors. To correct for energy mismeasurements, a log-likelihood method is developed that incorporates the information of impact parameters of tau lepton decays. We demonstrate that an accurate measurement of quantum properties is possible with this method. As a by-product, we show that a novel model-independent test of CP violation can be performed and the CP-phase of $H \tau \tau$ interaction can be constrained with an accuracy comparable to dedicated analyses, i.e., up to $7.9^{\circ}$ and $5.4^{\circ}$ at ILC and FCC-ee, respectively.

We extend the standard top-quark Yukawa coupling with a dimension-6 operator in order to accommodate a CP violating complex phase with manifest gauge invariance. This leads to a new $ttHH$ contact interaction, along with many Goldstone boson couplings. We investigate the impact of the new interactions on a muon collider process $\mu^-\mu^+\to \nu_\mu\bar{\nu}_\mu t\bar{t}H$ compared with the standard dimension-4 top-Yukawa coupling. The unitarity bounds on the coefficient of the new physics operator is obtained from $W^-_LW^+_L$ and $HH$ initiated processes.

Indirect reciprocity is a key mechanism behind the evolution of cooperation. Oishi et al. analytically showed the formation of two exclusive groups under the KANDORI assessment rule in the case of perfect information and no implementation error, regardless of the population size $N$. Here, we numerically show the formation of many exclusive groups under the JUDGING assessment rule in the same case. Introducing degrees of exclusive groups, we numerically examine the stability of the group formation under imperfect information and implementation error.

Although phase diagrams can be leveraged to investigate high transition temperature (high-$T_c$) superconductivity, the issue has not been discussed thoroughly. In this study, we elucidate the phase diagram of the overdoped side of high-$T_c$ cuprates via systematic anisotropic transport measurements for Pb-doped Bi-2212 single crystals. We demonstrate that the characteristic temperatures of the "weak" pseudogap opening and electronic coherence cross each other at a critical doping level, while those of the "strong" pseudogap merges into that of superconducting fluctuations above the critical doping level. Our results indicate the importance of Mottness in high-$T_c$ superconductivity.

This study reports the observation of the large anomalous Nernst effect in polycrystalline ferromagnetic Co$_{2}$MnGa (CMG) slabs prepared by a spark plasma sintering method. By optimizing the sintering conditions, the anomalous Nernst coefficient reaches ~7.5 $\mu$V K$^{-1}$ at room temperature, comparable to the highest value reported in the single-crystalline CMG slabs. Owing to the sizable anomalous Nernst coefficient and reduced thermal conductivity, the dimensionless figure of merit in our optimized CMG slab shows the record-high value of ~8$\times$10$^{-4}$ at room temperature. With the aid of the nano/microstructure characterization and first-principles phonon calculation, this study discusses the dependence of the transport properties on the degree of crystalline ordering and morphology of crystal-domain boundaries in the sintered CMG slabs. The results reveal a potential of polycrystalline topological materials for transverse thermoelectric applications, enabling the construction of large-scale modules.

We study a pseudoscalar dark-matter model arising from a complex singlet extension of the standard model (SM), and show that the dark-matter--nucleon scattering is suppressed when two CP-even scalars are degenerate. In such a degenerate-scalar scenario we explore the model parameter space which satisfies constraints from the direct detection experiments and the relic density of dark matter. In addition, we discuss a possibility to verify such a scenario by using the recoil mass technique at the International Linear Collider. We find that a pair of states separated by 0.2 GeV can be distinguished from the single SM-like Higgs state at 5$\sigma$ with integrated luminosity of 2 ab$^{-1}$.

The T2K collaboration: reports evidence for electron neutrino appearance at the atmospheric mass splitting, |\Delta m_{32}^2|=2.4x10^{-3} eV^2. An excess of electron neutrino interactions over background is observed from a muon neutrino beam with a peak energy of 0.6 GeV at the Super-Kamiokande (SK) detector 295 km from the beam's origin. Signal and background predictions are constrained by data from near detectors located 280 m from the neutrino production target. We observe 11 electron neutrino candidate events at the SK detector when a background of 3.3\pm0.4(syst.) events is expected. The background-only hypothesis is rejected with a p-value of 0.0009 (3.1\sigma), and a fit assuming \nu_{\mu}->\nu_e oscillations with sin^2(2\theta_{23})=1, \delta_{CP}=0 and |\Delta m_{32}^2|=2.4x10^{-3} eV^2 yields sin^2(2\theta_{13})=0.088^{+0.049}_{-0.039}(stat.+syst.).

We investigated the elastic properties of the iron-based superconductor Ba(Fe1-xCox)2As2 with eight Co concentrations. The elastic constant C66 shows large elastic softening associated with the structural phase transition. The C66 was analyzed base on localized and itinerant pictures of Fe-3d electrons, which shows the strong electron-lattice coupling and a possible mass enhancement in this system. The results resemble those of unconventional superconductors, where the properties of the system are governed by the quantum fluctuations associated with the zero-temperature critical point of the long-range order; namely, the quantum critical point (QCP). In this system, the inverse of C66 behaves just like the magnetic susceptibility in the magnetic QCP systems. While the QCPs of these existing superconductors are all ascribed to antiferromagnetism, our systematic studies on the canonical iron-based superconductor Ba(Fe1-xCox)2As2 have revealed that there is a signature of "structural quantum criticality" in this material, which is so far without precedent. The elastic constant anomaly is suggested to concern with the emergence of superconductivity. These results highlight the strong electron-lattice coupling and effect of the band in this system, thus challenging the prevailing scenarios that focus on the role of the iron 3d-orbitals.

Using the Subaru/FOCAS IFU capability, we examine the spatially resolved relationships between gas-phase metallicity, stellar mass, and star-formation rate surface densities (Sigma_* and Sigma_SFR, respectively) in extremely metal-poor galaxies (EMPGs) in the local universe. Our analysis includes 24 EMPGs, comprising 9,177 spaxels, which span a unique parameter space of local metallicity (12+log(O/H) = 6.9 to 7.9) and stellar mass surface density (Sigma_* ~ 10^5 to 10^7 Msun/kpc^2), extending beyond the range of existing large integral-field spectroscopic surveys. Through spatially resolved emission line diagnostics based on the [NII] BPT-diagram, we verify the absence of evolved active galactic nuclei in these EMPGs. Our findings reveal that, while the resolved mass-metallicity relation exhibits significant scatter in the low-mass regime, this scatter is closely correlated with local star-formation surface density. Specifically, metallicity decreases as Sigma_SFR increases for a given Sigma_*. Notably, half of the EMPGs show a distinct metal-poor horizontal branch on the resolved mass-metallicity relation. This feature typically appears at the peak clump with the highest Sigma_* and Sigma_SFR and is surrounded by a relatively metal-enriched ambient region. These findings support a scenario in which metal-poor gas infall fuels episodic star formation in EMPGs, consistent with the kinematic properties observed in these systems. In addition, we identify four EMPGs with exceptionally low central metallicities (12+log(O/H) <~ 7.2), which display only a metal-poor clump without a surrounding metal-rich region. This suggests that such ultra-low metallicity EMPGs, at less than a few percent of the solar metallicity, may serve as valuable analogs for galaxies in the early stages of galaxy evolution.

Unsupervised anomaly localization, which plays a critical role in industrial manufacturing, aims to identify anomalous regions that deviate from normal sample patterns. Most recent methods perform feature matching or reconstruction for the target sample with pre-trained deep neural networks. However, they still struggle to address challenging anomalies because the deep embeddings stored in the memory bank can be less powerful and informative. More specifically, prior methods often overly rely on the finite resources stored in the memory bank, which leads to low robustness to unseen targets. In this paper, we propose a novel subspace-guided feature reconstruction framework to pursue adaptive feature approximation for anomaly localization. It first learns to construct low-dimensional subspaces from the given nominal samples, and then learns to reconstruct the given deep target embedding by linearly combining the subspace basis vectors using the self-expressive model. Our core is that, despite the limited resources in the memory bank, the out-of-bank features can be alternatively ``mimicked'' under the self-expressive mechanism to adaptively model the target. Eventually, the poorly reconstructed feature dimensions indicate anomalies for localization. Moreover, we propose a sampling method that leverages the sparsity of subspaces and allows the feature reconstruction to depend only on a small resource subset, which contributes to less memory overhead. Extensive experiments on three industrial benchmark datasets demonstrate that our approach generally achieves state-of-the-art anomaly localization performance.