Weight enumerators are important tools for deciphering the algebraic structure of the related code spaces and for understanding group actions on these spaces. Our study focuses on symmetrized weight enumerators of pairs of Type II codes over the finite field $\mathbb{F}_{2}$ and the ring $\mathbb{Z}_{4}$. These pairs have been examined as invariants for a specified group. In particular, we concentrate on the scenarios where the space of the invariant ring is of degree 8 and 16. Our findings show that in certain situations, the ring produced by the symmetrized weight enumerators precisely matches with the invariant ring of the designated group. This coincidence points to a profound relationship between the invariant ring's structure and the algebraic characteristics of the weight enumerators.

In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

We report a weak-lensing (WL) mass measurement for the merging cluster Abell 754 and impose constraints on the merger trajectory. The trajectory analysis adopts a two-body model with a point-mass approximation and dynamical friction, refined using numerical simulations of major mergers and characterized by Euler angles. We first conduct WL analysis using the two-dimensional shear pattern from the Subaru HSC in combination with Suprime-Cam images to assist in color selection. The WL mass map shows a distinct double-peak structure located around the western and eastern brightest cluster galaxies as reported in the literature. The two-halo component analysis, which utilizes the 2D shear pattern over the cluster entire region and considers the lensing covariance matrix from uncorrelated large-scale structures, indicates mass values of $M_{200}^W=3.13_{-1.00}^{+1.53}\times10^{14}h_{70}^{-1}M_\odot$ and $M_{200}^E=6.41_{-1.97}^{+2.92}\times10^{14}h_{70}^{-1}M_\odot$. Thus, the eastern mass component associated with the X-ray tadpole-shaped gas is the main cluster. No substantial structural components are detected in the line-of-sight velocities of the member galaxies. Utilizing WL parameters, line-of-sight velocities, and X-ray information on morphology and kinematics, we determine an impact parameter of approximately 0.77 Mpc at an initial separation of 2 Mpc from the main cluster. The merger plane is inclined at about 20 degrees relative to the line-of-sight. Interestingly, this system is an off-axis, near-line-of-sight merger. This characteristic arises because the trajectory within the merger plane is altered during the pericenter passage, causing the apparent motion to transition from predominantly along the line-of-sight before the core passage to mainly within the plane of the sky afterward. This study will assist in conducting numerical simulations to understand the XRISM observations.

We investigate the phase structure of a two-dimensional lattice CP(1) model with a $\theta$ term. In particular, we aim to identify a critical region expected to exist along a $\theta=\pi$ line. To explore the phase structure non-perturbatively and avoid the sign problem, we employ the tensor renormalization group method. We make two improvements compared to previous tensor network studies. The first improvement involves refining the initial tensor. Specifically, we construct it using a quadrature method, which achieves higher accuracy compared to the conventional approach. The second improvement consists of analyzing the phase structure using the information of the conformal field theory, namely the central charge and the scaling dimensions, which can be accessed relatively easily via the tensor renormalization group method. Thanks to these improvements, we identify both the onset of the critical region, $\beta_{\rm c}=0.5952(8)$ and its universality class as the SU(2)${}_{k=1}$ Wess-Zumino-Witten model.

The third ``Mineral Detection of Neutrinos and Dark Matter'' (MD$\nu$DM'25) meeting was held May 20-23, 2025 in Yokohama, Japan, hosted by the Yokohama Institute for Earth Sciences, Japan Agency for Marine-Earth Science and Technology (JAMSTEC). These proceedings compile contributions from the workshop and update the progress of mineral detector research. MD$\nu$DM'25 was the third such meeting, following the first in October of 2022 held at the IFPU in Trieste, Italy and the second in January of 2024 hosted by the Center for Neutrino Physics at Virginia Tech in Arlington, USA. Mineral detectors record and retain damage induced by nuclear recoils in synthetic or natural mineral samples. The damage features can then be read out by a variety of nano- and micro-scale imaging techniques. Applications of mineral detectors on timescales relevant for laboratory experiments include reactor neutrino monitoring and dark matter detection, with the potential to measure the directions as well as the energies of the induced nuclear recoils. For natural mineral detectors which record nuclear recoils over geological timescales, reading out even small mineral samples could be sensitive to rare interactions induced by astrophysical neutrinos, cosmic rays, dark matter and heavy exotic particles. A series of mineral detectors of different ages could measure the time evolution of these fluxes, offering a unique window into the history of our solar system and the Milky Way. Mineral detector research is highly multidisciplinary, incorporating aspects of high energy physics, condensed matter physics, materials science, geoscience, and AI/ML for data analysis. Although realizing the scientific potential of mineral detectors poses many challenges, the MD$\nu$DM community looks forward to the continued development of mineral detector experiments and the possible discoveries that mineral detectors could reveal.

In this work, we investigate the effects of first-order phase transitions on the singlet fermionic dark matter in the scotogenic model. It is known that this dark matter candidate tends to conflict with the relevant constraints such as the neutrino oscillation data and charged lepton flavor violating processes if its thermal production mechanism is assumed. We find that the dark matter production mechanisms are modified by first-order phase transitions at some specific parameter regions, where the phase transitions can be one-step or two-step depending on the parameters. If the phase transition is one-step, a sufficiently low nucleation temperature is required to reproduce the observed relic abundance of dark matter. If the phase transition is two-step, the dark matter should never be thermalized, otherwise the abundance would remain too much and overclose the universe. This is because the nucleation temperature cannot be low as in the one-step case. Therefore we require another way of dark matter production, the freeze-in mechanism for the two-step case. We show that the freeze-in mechanism is modified by the temporary vacuum expectation value of the inert scalar field. In both cases, the first-order phase transitions could produce observable gravitational wave spectra. In particular for the one-step phase transition, the generated gravitational waves with sizable energy density are intrinsically correlated with the dark matter production mechanism, and can be detectable by future space-based interferometers.

Atomic force microscopy (AFM) is a key tool for characterising nanoscale structures, with functionalised tips now offering detailed images of the atomic structure. In parallel, AFM simulations using the particle probe model provide a cost-effective approach for rapid AFM image generation. Using state-of-the-art machine learning models and substantial simulated datasets, properties such as molecular structure, electrostatic potential, and molecular graph can be predicted from AFM images. However, transferring model performance from simulated to experimental AFM images poses challenges due to the subtle variations in real experimental data compared to the seemingly flawless simulations. In this study, we explore style translation to augment simulated images and improve the predictive performance of machine learning models in surface property analysis. We reduce the style gap between simulated and experimental AFM images and demonstrate the method's effectiveness in enhancing structure discovery models through local structural property distribution comparisons. This research presents a novel approach to improving the efficiency of machine learning models in the absence of labelled experimental data.

Large Language Models (LLMs) are rapidly transforming the landscape of digital content creation. However, the prevalent black-box Application Programming Interface (API) access to many LLMs introduces significant challenges in accountability, governance, and security. LLM fingerprinting, which aims to identify the source model by analyzing statistical and stylistic features of generated text, offers a potential solution. Current progress in this area is hindered by a lack of dedicated datasets and the need for efficient, practical methods that are robust against adversarial manipulations. To address these challenges, we introduce FD-Dataset, a comprehensive bilingual fingerprinting benchmark comprising 90,000 text samples from 20 famous proprietary and open-source LLMs. Furthermore, we present FDLLM, a novel fingerprinting method that leverages parameter-efficient Low-Rank Adaptation (LoRA) to fine-tune a foundation model. This approach enables LoRA to extract deep, persistent features that characterize each source LLM. Through our analysis, we find that LoRA adaptation promotes the aggregation of outputs from the same LLM in representation space while enhancing the separation between different LLMs. This mechanism explains why LoRA proves particularly effective for LLM fingerprinting. Extensive empirical evaluations on FD-Dataset demonstrate FDLLM's superiority, achieving a Macro F1 score 22.1% higher than the strongest baseline. FDLLM also exhibits strong generalization to newly released models, achieving an average accuracy of 95% on unseen models. Notably, FDLLM remains consistently robust under various adversarial attacks, including polishing, translation, and synonym substitution. Experimental results show that FDLLM reduces the average attack success rate from 49.2% (LM-D) to 23.9%.

We present a calculation of the renormalization coefficients of the quark bilinear operators and the K-Kbar mixing parameter B_K. The coefficients relating the bare lattice operators to those in the RI/MOM scheme are computed non-perturbatively and then matched perturbatively to the MSbar scheme. The coefficients are calculated on the RBC/UKQCD 2+1 flavor dynamical lattice configurations. Specifically we use a 16^3 x 32 lattice volume, the Iwasaki gauge action at beta=2.13 and domain wall fermions with L_s=16.

We extend the Rome-Southampton regularization independent momentum-subtraction renormalization scheme(RI/MOM) for bilinear operators to one with a nonexceptional, symmetric subtraction point. Two-point Green's functions with the insertion of quark bilinear operators are computed with scalar, pseudoscalar, vector, axial-vector and tensor operators at one-loop order in perturbative QCD. We call this new scheme RI/SMOM, where the S stands for "symmetric". Conversion factors are derived, which connect the RI/SMOM scheme and the MSbar scheme and can be used to convert results obtained in lattice calculations into the MSbar scheme. Such a symmetric subtraction point involves nonexceptional momenta implying a lattice calculation with substantially suppressed contamination from infrared effects. Further, we find that the size of the one-loop corrections for these infrared improved kinematics is substantially decreased in the case of the pseudoscalar and scalar operator, suggesting a much better behaved perturbative series. Therefore it should allow us to reduce the error in the determination of the quark mass appreciably.

Accurately predicting spatio-temporal network traffic is essential for dynamically managing computing resources in modern communication systems and minimizing energy consumption. Although spatio-temporal traffic prediction has received extensive research attention, further improvements in prediction accuracy and computational efficiency remain necessary. In particular, existing decomposition-based methods or hybrid architectures often incur heavy overhead when capturing local and global feature correlations, necessitating novel approaches that optimize accuracy and complexity. In this paper, we propose an efficient spatio-temporal network traffic prediction framework, DP-LET, which consists of a data processing module, a local feature enhancement module, and a Transformer-based prediction module. The data processing module is designed for high-efficiency denoising of network data and spatial decoupling. In contrast, the local feature enhancement module leverages multiple Temporal Convolutional Networks (TCNs) to capture fine-grained local features. Meanwhile, the prediction module utilizes a Transformer encoder to model long-term dependencies and assess feature relevance. A case study on real-world cellular traffic prediction demonstrates the practicality of DP-LET, which maintains low computational complexity while achieving state-of-the-art performance, significantly reducing MSE by 31.8% and MAE by 23.1% compared to baseline models.

In this paper, we present extended gas kinematic maps of the Perseus cluster by combining five new XRISM/Resolve pointings observed in 2025 with four Performance Verification datasets from 2024, totaling 745 ks net exposure. To date, Perseus remains the only cluster that has been extensively mapped out to ~0.7$r_{2500}$ by XRISM/Resolve, while simultaneously offering sufficient spatial resolution to resolve gaseous substructures driven by mergers and AGN feedback. Our observations cover multiple radial directions and a broad dynamical range, enabling us to characterize the intracluster medium kinematics up to the scale of ~500 kpc. In the measurements, we detect high velocity dispersions ($\simeq$300 km/s) in the eastern region of the cluster, corresponding to a nonthermal pressure fraction of $\simeq$7-13%. The velocity field outside the AGN-dominant region can be effectively described by a single, large-scale kinematic driver based on the velocity structure function, which statistically favors an energy injection scale of at least a few hundred kpc. The estimated turbulent dissipation energy is comparable to the gravitational potential energy released by a recent merger, implying a significant role of turbulent cascade in the merger energy conversion. In the bulk velocity field, we observe a dipole-like pattern along the east-west direction with an amplitude of $\simeq\pm$200-300 km/s, indicating rotational motions induced by the recent merger event. This feature constrains the viewing direction to ~30$^\circ$-50$^\circ$ relative to the normal of the merger plane. Our hydrodynamic simulations suggest that Perseus has experienced at least two energetic mergers since redshift z~1, the latest associated with the radio galaxy IC310. This study showcases exciting scientific opportunities for future missions with high-resolution spectroscopic capabilities (e.g., HUBS, LEM, and NewAthena).

Accurately calculating band gaps for given crystal structures is highly desirable. However, conventional first-principles calculations based on density functional theory (DFT) within the local density approximation (LDA) fail to predict band gaps accurately. To address this issue, the quasi-particle self-consistent GW (QSGW) method is often employed as it is one of the most reliable theoretical approaches for predicting band gaps. Despite its accuracy, QSGW requires significant computational resources. To overcome this limitation, we propose combining QSGW with machine learning. In this study, we applied QSGW to 1,516 materials from the Materials Project [this https URL] and used machine learning to predict QSGW band gaps as a function of the partial density of states (PDOS) in LDA. Our results demonstrate that the proposed model significantly outperforms linear regression approaches with linearly-independent descriptor generation [this https URL]. This model is a prototype for predicting material properties based on PDOS.

Controlling Mott insulator states has been a long-standing topic in condensed matter physics. Among various controlling parameters, two-dimensional (2D) confinement in epitaxial heterostructures has been demonstrated to convert the correlated metallic nature of SrVO3 into a Mott insulator by reducing the quantum well thickness. Here, we fabricate double quantum well (DQW) structures of SrVO3 with a magnetic barrier of EuTiO3 to tune the hybridization of wave functions by a magnetic field. A significant positive magnetoconductance is observed for DQWs with barriers thinner than 2 nm, while DQWs with thicker barriers do not show such large positive magnetoconductance, instead behaving as a parallel circuit of two single QWs. The observed positive magnetoconductance is accounted in terms of enhanced hybridization of V 3d orbitals across the EuTiO3 barrier under a magnetic field, where the barrier height is reduced by the Zeeman splitting of Ti 3d bands in forced ferromagnetic ordering of localized 4f electrons on Eu2+sites.

Recent James Webb Space Telescope observations of high-redshift massive galaxy candidates have initiated renewed interest in the important mystery around the formation and evolution of our Universe's largest supermassive black holes (SMBHs). We consider the possibility that some of them were seeded by the direct collapse of primordial density perturbations from inflation into primordial black holes and analyze the consequences of this on current dark matter substructures assuming non-Gaussian primordial curvature perturbation distributions. We derive bounds on the enhanced curvature perturbation amplitude from the number of dwarf spheroidal galaxies in our Galaxy, observations of stellar streams and gravitational lensing. We find this bound region significantly overlaps with that required for SMBH seed formation and enables us to probe Gaussian and non-Gaussian curvature perturbations corresponding to the SMBH seeds in the range ${\cal O}(10^5$\text{--}$10^{12}) M_\odot$.

We present a new scheme which numerically evaluates the real-time path integral for $\phi^4$ real scalar field theory in a lattice version of the closed-time formalism. First step of the scheme is to rewrite the path integral in an explicitly convergent form by applying Cauchy's integral theorem to each scalar field. In the step an integration path for the scalar field is deformed on a complex plane such that the $\phi^4$ term becomes a damping factor in the path integral. Secondly the integrations of the complexified scalar fields are discretized by the Gauss-Hermite quadrature and then the path integral turns out to be a multiple sum. Finally in order to efficiently evaluate the summation we apply information compression technique using the singular value decomposition to the discretized path integral, then a tensor network representation for the path integral is obtained after integrating the discretized fields. As a demonstration, by using the resulting tensor network we numerically evaluate the time-correlator in 1+1 dimensional system. For confirmation, we compare our result with the exact one at small spatial volume. Furthermore, we show the correlator in relatively large volume using a coarse-graining scheme and verify that the result is stable against changes of a truncation order for the coarse-graining scheme.

When Abelian monopoles due to violation of the non-Abelian Bianchi identity J{\mu}(x) condense in the vacuum, color confinement of QCD is realized by the Abelian dual Meissner effect. Moreover VNABI affects also topological features of QCD drastically. Firstly, self-dual instantons can not be a classical solution of QCD. Secondly, the topological charge density is not expressed by a total derivative of the Chern-Simons density K{\mu}(x), but has an additional term L(x)=2Tr(J{\mu}(x)A{\mu}(x)). Thirdly, the axial U(1) anomaly is similarly modified, while keeping the Atiyah-Singer index theorem unchanged. However, unless the integrated additional term \chi=(g^2/16\pi^2)\int d^4xL(x) is not zero, these modified relations are not compatible with gauge invariance. The additional term \chi is evaluated in the framework of Monte-Carlo simulations on SU(2) lattices in details with partial gauge fixings such as the Maximal Center gauge (MCG). The term \chi is largely fluctuating around zero before the gradient flow and tends to vanish after small gradient flow time (\tau). The bosonic definition of the topological charge Q_t and its Abelian counterpart Q_a written by Abelian field strengths are measured also on the lattices. When \chi is zero, Q_a=3Q_t is expected theoretically, but a simple lattice definition of Q_a is fluctuating between -2 and -3 in MCG, when Q_t is stabilized around -1.

We investigate the $N_f=2$ Schwinger model with the massive staggered fermions in the presence of a $2\pi$ periodic $\theta$ term, using the Grassmann tensor renormalization group. Thanks to the Grassmann tensor network formulation, there is no difficulty in dealing with the massive staggered fermions. We study the $\theta$ dependence of the free energy in the thermodynamic limit. Our calculation provides consistent results with the analytical solution in the large mass limit. The results also suggest that the $N_f=2$ Schwinger model on a lattice has a different phase structure from that described by the continuum theory.