The local relaxation algorithm is promising for fast solution of Poisson's equations, which computes the electric field distribution in a stepwise manner via local curl-free updates while strictly enforcing Gauss's law. We propose a novel hierarchical local relaxation (HLR) method for speeding up the convergence of curl-free iterations. The local algorithm reformulates the Poisson's equation into the electric-field form and sweeps each cell to minimize the associate electric energy, avoiding the solution of linear systems. The updates with hierarchical meshes significantly accelerate the slow convergence of low-frequency components of the residual in the local curl-free update process. Convergence analysis is performed to obtain the convergence of the hierarchical relaxation approaches. Numerical results show that the HLR methods have the nice properties in accuracy and efficiency and the hierarchical construction leads to an overall computational complexity of $\mathcal{O}(N\log N)$ with respect to the number of grid points. Particularly, the applications in solving the Poisson--Boltzmann and Poisson--Nernst--Planck equations demonstrate the attractive performance for problems which frequently solve the Poisson's equations.

In this work, we present a capacitively coupled GaAs p +-i-n/substrate photodetector (CC-GaAs PIN/S PD), which also represents a preliminary step toward 3D detection (x, y, time) of high-energy X-ray pulses. Although the final 3D detector will be based on a separate absorption and multiplication avalanche photodiode (SAM APD) design, the present device exhibits characteristics that offer valuable insights into the performance expected once a multiplication layer is incorporated into the final device. In particular, we present a fabrication strategy that employs multi-step annealing in the low-temperature range of 280 330C to achieve Cr/Au ohmic contacts on lightly doped n-GaAs, which is also required for the photodetector architecture. Simultaneously, the same contact preparation process was applied to p + GaAs. Furthermore, the fabricated CC-GaAs PIN/S PD includes an additional contact designed to reduce leakage current by applying the same bias as that of the anode. Measurements performed using an 80 MHz laser demonstrated the photodetector's ability to detect pulses corresponding to 10^6 electrons per pulse.

Diffusion coefficients are key thermophysical properties for modeling mass transport in liquids, but experimental data are scarce, making reliable prediction methods indispensable. In the present work, we introduce a new method for predicting diffusion coefficients of molecular components at infinite dilution in pure liquid solvents by integrating the Stokes-Einstein (SE) equation with machine learning (ML). Unlike previous ML approaches, the resulting hybrid Enhanced Stokes-Einstein (ESE) model provides strictly physically consistent predictions for diffusion coefficients as a function of temperature across a broad range of binary mixtures. Trained and validated using an extensive compilation of literature data for infinite-dilution diffusion coefficients in binary liquid systems, ESE achieves significantly higher prediction accuracies than the previous state-of-the-art model, SEGWE, while requiring only the SMILES strings encoding of the molecular formulae of the components of interest as additional inputs, which are always available. This simplicity makes ESE broadly applicable, e.g., for process design and optimization. The ESE model and its source code are fully disclosed and are directly accessible via an interactive web interface at https://ml-prop.mv.rptu.de/.

We report detection of two compact absorption features surrounding the M87 nucleus and some extended absorption features perpendicular to the M87 jet using the Atacama Large millimeter-submillimeter-Array (ALMA) Band 3 data. One compact absorption feature appears at the position of the M87 AGN and the other $0.5\arcsec$ away from the AGN. These two compact features appear as well-defined negative structures in the integrated intensity map from the velocity range of $-500$ to $+2000~{\rm km~s^{-1}}$. These two features are separated by a distance of $\sim$40~pc assuming the distance of M87. The origin of these features could be dense molecular fragments associated with super massive black holes (SMBHs), possibly associated with a rotating torus filament, or in-falling molecular fragments/objects feeding the nucleus. The extended absorption features perpendicular to the jet appear in all velocity ranges and can be caused by shock compressed regions associated with the jet knots A and C.

The study of materials behavior under extreme conditions is fundamental to science and modern technology. Fast ramp compression is a unique method for exploring materials behavior and phase transformations under extreme conditions. One unexplored feature of this method is the nanoscale structure of the material under dynamic compression. This leaves a gap in understanding the details of phase transformations under fast ramp compression. Here, we made a first step in the exploration by applying the Williamson-Hall (WH) analysis to X-ray diffraction data (XRD) measured in magnesium subjected to fast ramp compression at four pressures. We found that at $P = 309 GPa$ magnesium in bcc-like phase has an average crystalline size $D = (2.2 \pm 0.7) nm$ and microstrain $\varepsilon = (-0.011 \pm 0.007)$. At $P = 409 GPa$, magnesium demonstrates $D = (4.5 \pm 3) nm$ with $\varepsilon = (-0.003 \pm 0.007)$. At $P = 563 GPa$, Fmmm magnesium has crystalline size $D = (2.6 \pm 0.5) nm$ with microstrain $\varepsilon = (-0.004 \pm 0.004)$. At $P = 959 GPa$, we revealed that sh-magnesium exhibits average size of $D > 12 nm$ and relatively high value of microstrain $\varepsilon = (0.011 \pm 0.002)$. In the result, we report the first microstructural evolution insights of magnesium under fast ramp compression.

The interplay between tumor cells and macrophages plays a central regulatory role in cancer progression. In this study, we developed a mathematical model that incorporates tumor cells, M1 type macrophages, M2 type macrophages and an M3 type macrophage population characterized by dual phenotypic features. First, we analyzed the fundamental mathematical properties of the model and derived the conditions under which the system attains a tumor free stable state or a coexistence state of tumor and immune cells. Second, global sensitivity analysis revealed that key parameters governing macrophage polarization and intercellular communication vary dynamically during tumor development. Bifurcation analysis further identified the polarization rate of M1 type macrophages $κ$ and the baseline level of resting macrophages $M_0$ as critical determinants of the system's dynamical behavior. Notably, using approximate Bayesian computation for parameter inference and dynamic simulations, the model successfully recapitulated the evolutionary trajectories of eight tumor samples. The results demonstrate that lower tumor burden is significantly associated with higher M1 type macrophage infiltration and delayed peak time of M3 type macrophage activation. Moreover, survival analysis indicated that both enhanced M1 type macrophage infiltration and delayed peak time of M3 type macrophage activation are correlated with longer survival time. In summary, this study not only provides a theoretical framework for understanding the dynamic mechanisms underlying tumor macrophage interactions but also proposes two potential clinical prognostic markers: the level of M1 type macrophage infiltration and the peak time of M3 type macrophage activation.

In this paper, we study fixed-point sets of $S^{1}$-actions and compatible complex structures on quaternionic manifolds. We obtain an equation involving the first Chern classes of the fixed-point set and of a quaternionically flat manifold with compatible complex structure of closed type. In addition, if the first Chern class of the fixed-point set is not trivial, then the quaternionic manifold does not admit hypercomplex structures containing given compatible complex structure on any open set containing the fixed-point set. Moreover, we determine the connected components of the fixed-point set arising from quaternionic $S^{1}$-actions on the quaternionic projective space. We apply these results to Pontecorvo's example $\mathrm{SO}^{\ast}(2n+2)/\mathrm{SO}^{\ast}(2n) \times \mathrm{SO}^{\ast}(2)$.

Rapid emergence of smart mobility necessitates high-volume bursty data transmission over a single link between a target vehicle and its designated edge computing-enabled Base Station (BS) or Roadside Unit (RSU), which must be completed within a short time period when the vehicle traverses the coverage area. However, in bandwidth-limited scenarios, conventional communication systems face a fundamental throughput ceiling at each single vehicle. This limitation persists even when all time-frequency resources are allocated to a single vehicle, as the underlying channel lacks sufficient spatial diversity to support higher data rates. To break this throughput ceiling, in this paper, we propose a novel reflection-enhanced transmission framework by strategically employing dedicated specular reflecting surfaces along roadways to proactively augment the transmission environments. This setup concentrates dispersed signals from multiple directions toward a target vehicle, analogous to the light-focusing effect of a concave magnifying lens, thereby enhancing the spatial diversity and achievable rank of an individual channel. This allows a BS to allocate more transmission layers to one single user, consequently significantly raising the throughput ceiling for individual vehicles. Moreover, we also introduce dynamic virtualization methods for reflecting panel patch groups, compatible with existing communication systems, to flexibly manage interference with other coexisting users. Furthermore, collaborative rotation among multiple reflecting panels is introduced to enhance signal concentration. Finally, the schematic effectiveness is rigorously validated through 3GPP-compliant system-level simulations, demonstrating significant throughput boosts.

Large-eddy simulations (LES) of a planar turbulent lean hydrogen-air jet flame at Re = 11000 are performed using a tabulated flamelet model based on mixture-averaged diffusion that incorporates detailed transport, including differential and preferential diffusion, wall heat loss, and thermodiffusion. The approach is extended to turbulent combustion in LES using a presumed-shape probability density function formulation that accounts for sub-filter effects. The flame exhibits a highly corrugated front, driven by local variations of mixture fraction induced by strong thermodiffusive transport. These effects significantly alter both the flame structure and morphology. The LES results are systematically compared to a reference direct numerical simulation across varying LES filters through different mesh resolutions to evaluate the predictive capability of the model. The LES accurately reproduces instantaneous flow structures and thermodiffusive effects. Global flame characteristics including flame length, surface area, and consumption speed, are well captured and show limited sensitivity to mesh resolution. The role of thermodiffusion is also examined, showing that its incorporation leads to a more reactive flame and should not be neglected in the formulation. Heat losses are incorporated into the tabulated chemistry framework for completeness but are found to have a negligible impact, consistent with the walls weak influence in the present configuration. Overall, the results demonstrate that the proposed approach provides reliable predictions of the main flame characteristics, with remaining discrepancies primarily associated with unresolved sub-filter effects that deserve further investigation.

We study the boundedness of families of algebraic flat connections with bounded irregularity. As an application, we study the boundedness of families of holonomic $D$-modules with dominated characteristic cycles.

As part of his work on special Lagrangian (sLag) submanifolds with isolated conical singularities, Joyce proved a criterion for the existence of sLag smoothings, along a small variation of complex structure, for the union of two connected, compact, embedded sLags, with the same phase, intersecting transversely. Here we construct infinitely many examples of pairs of non-compact, embedded sLags, of the same phase and with arbitrary dimension, intersecting only at infinity in a non-transverse way, which satisfy Joyce's criterion: along a small variation of complex structure, a sLag smoothing of their union exists on the stable locus where a slope inequality for periods of the holomorphic volume form holds. At least under a natural symmetry assumption, this slope inequality is also necessary for the existence of such smoothing. Our approach uses the Leung-Yau-Zaslow transform and the analysis of deformed Hermitian Yang-Mills connections with Calabi ansatz, due to Jacob and Sheu. In the unstable case, we prove that if a family of Lagrangian smoothings evolving under the natural Calabi-symmetric version of the mean curvature flow (due to Chan and Jacob) admits a limit, then this must be the union of the original sLags. As an application we show that in our examples, in dimension two, the condition for the existence of the sLag smoothing is in fact equivalent to the stability of the corresponding object in the Fukaya-Seidel category, with respect to a known Bridgeland stability condition imported from algebraic geometry, and in the unstable case the limit of the Calabi-symmetric mean curvature flow in our result coincides with the Harder-Narasimhan decomposition, consistently with a general conjecture of Joyce. A similar (although weaker) result also holds in dimension three.

In Kondo insulators the many-body Kondo lattice effect drives the formation of bands containing heavy charge carriers with a hybridization gap, leading to insulating properties. These renormalized bands can host non-trivial topologies driven by strong electron-electron interactions, but probing narrow heavy bands at low temperatures is challenging. We use inelastic neutron scattering (INS) to probe the Kondo lattice CeNiSn, which hosts both semimetallic transport properties and a hybridization gap. The INS response exhibits momentum-dependent magnetic excitations and a spin-gap in the low-temperature Kondo coherent state, which electronic structure calculations corroborate as arising from the renormalized heavy band structure. Dynamical-mean field theory demonstrates that this renormalized band structure corresponds to a topological Kondo insulating state, and hence the INS probes bulk excitations of heavy topological bands. This identification of a Kondo insulator addresses the long-standing mystery of the electronic properties of CeNiSn, and demonstrates the manifestation of a topological many-body coherent state in spectroscopic measurements of strongly correlated narrow band materials.

Hole spins in group IV quantum dots are a highly promising way to develop CMOS compatible spin qubits owing to their inherent spin-orbit coupling, which enables fast, coherent, and electrical spin control. However, spin-orbit coupling not only enables multiple spin-control mechanisms, but also exposes the qubits to charge noise. In this work, we perform a systematic study of the spin control mechanism in a planar silicon hole quantum dot. We use g-matrix formalism to discern contributions from the various spin driving mechanisms and identify regions where spins are less sensitive to charge noise. By mapping out the dependence of the Rabi frequency on the magnetic field orientation, we observe the largest Rabi frequency in the in-plane direction and the smallest Rabi frequency close to the out-of-plane direction. These results enhance the understanding of how different mechanisms contribute to spin driving within an industrially relevant architecture and aid in establishing the operating conditions for the rapid and coherent manipulation of hole qubits.

We demonstrate the generation of $12.1 \pm 0.2$ dB squeezed light from a periodically poled lithium niobate (PPLN) waveguide optical parametric amplifier (OPA). While single-pass OPAs offer squeezed light with THz-order bandwidths, loss from spatial mode mismatch between the squeezed light and the local oscillator (LO) previously capped the squeezing level at $\sim$10 dB [K. Hirota et al., Opt. Express 34, 7958 (2026)]. In this work, we minimize this loss by introducing a machine-learning-optimized spatial light modulator (SLM) in the path of the LO. Specifically, we employed a double-reflection configuration to increase the spatial degrees of freedom, and directly used the measured squeezing level as the optimization's objective function.

We review recent developments in tensor network approaches, focusing on renormalization group methods. Since they are free from the negative sign and complex action problems, there is growing interest in their application to lattice field theories, particularly with a view toward future studies of quantum chromodynamics (QCD) at finite temperature and density. They are also of broad interest in quantum field theory, with recent advances in approaches that allow one to directly investigate universal aspects of critical behavior by making use of theoretical insights from conformal field theory. We highlight several recently explored topics that are expected to play important roles in forthcoming tensor-network studies of QCD.

The deployment of Multipath QUIC (MPQUIC) in Unmanned Aerial Vehicle (UAV)-assisted Space-Air-Ground Integrated Networks (SAGINs) is severely hampered by the out-of-order (OFO) packet delivery problem. Frequent stream handovers, high mobility, and massive multi-access contention in these networks introduce severe transport-layer challenges. Existing solutions typically isolate multipath scheduling from congestion control, which leads to suboptimal performance and transient congestion in highly dynamic environments. To overcome these limitations, this paper proposes the GPR Hierarchical Synergistic Framework, representing the first joint optimization of multipath scheduling and congestion control for multi-access MPQUIC in SAGINs. Our framework introduces the GradNorm Probabilistic Self-Predictive (GPASP) module to forecast latent states and filter task-irrelevant information in high-dimensional, noisy observation spaces. Furthermore, we develop a Proactive Handover-Aware Congestion Control (PHACC) algorithm that leverages neural network-driven decisions to proactively distinguish handover-induced packet losses from actual network congestion. To address decision-making lag caused by neural network inference latency, a Neural-network Preference Estimation (NNPE) algorithm is designed for highly efficient, real-time scheduling. Extensive ns-3 simulations demonstrate that the proposed framework significantly outperforms state-of-the-art baselines, achieving substantial goodput improvements and a marked reduction in OFO degrees.

We investigate critical phenomena in the $O(2)$ models using symmetry-twisted partition functions that can be efficiently computed within the tensor renormalization group framework. We first demonstrate, taking the three-dimensional model as an example, that symmetry-twisted partition functions detect the spontaneous breaking of global continuous symmetry. We then consider the same model in two dimensions, where the Berezinskii--Kosterlitz--Thouless (BKT) transition occurs. Since symmetry-twisted partition functions directly provide the helicity modulus at a finite twist angle, we determine the BKT transition point. These results are presented based on Ref.~\cite{Akiyama:2026dzg}. Finally, in addition to the original paper~\cite{Akiyama:2026dzg}, we apply this approach to the two-dimensional generalized $O(2)$ model and confirm that it successfully identifies the phase transitions between the ferromagnetic and nematic phases, as well as between the nematic and paramagnetic phases.

Hashing retrieval is a pivotal technology for large-scale similarity search, widely applied in retrieval-augmented generation (RAG) for large language models (LLMs), massive image repositories, and bioinformatics sequence matching. However, traditional electronic hashing implementations face severe bottlenecks in power consumption and latency when processing high-dimensional data, while existing photonic neural networks often lack robust mechanisms for direct binary code generation under analog noise. To address these challenges, we propose a hardware-software co-designed photonic spiking hashing framework. We utilize the nonlinear thresholding dynamics of a distributed feedback laser with saturable absorber (DFB-SA) to realize the final binarization of a single-step spiking neural network (SNN). Crucially, a hardware-aware quantization margin loss is introduced to maximize the decision margin, effectively mitigating bit flips caused by optical intensity fluctuations. Validated on MNIST (image) and 20 Newsgroups (text) datasets, our system demonstrates robust binary code generation and high retrieval accuracy comparable to digital baselines. Most significantly, the proposed photonic architecture exhibits superior efficiency with an encoding latency of 2.294 ns/query and an energy consumption of 73.70 pJ/query. This work offers a robust and viable path for ultra-fast, energy-efficient optoelectronic neuromorphic computing in high-throughput information retrieval tasks.

Conventional LLM inference architectures suffer from high energy and latency due to frequent data movement across memory hierarchies. We propose Ouroboros, a wafer-scale SRAM-based Computing-in-Memory (CIM) architecture that executes all operations in situ, eliminating off-chip migration. To maximize its limited first-level capacity, we introduce three innovations: Token-Grained Pipelining: Replaces sequence-level pipelining to mitigate length variations, boosting utilization and reducing activation storage. Distributed Dynamic KV Cache Management: Decouples memory from compute to leverage fragmented SRAM for efficient KV storage. Communication-Aware Mapping: Optimizes core allocation for locality and fault tolerance across the wafer. Experimental results show Ouroboros achieves average gains of $4.1\times$ in throughput and $4.2\times$ in energy efficiency, peaking at $9.1\times$ and $17\times$ for the 13B model. (*Due to the notification of arXiv "The Abstract field cannot be longer than 1,920 characters", the appeared Abstract is shortened. For the full Abstract, please download the Article.)

In this note, we give a non-perturbative construction of a lightlike domain wall separating IIA and IIB string theories in 10D in the framework of discrete light-cone quantization (DLCQ). In this setting, generalizations of the BFSS conjecture relate the 10D flat space limit to matrix string theories (MSTs) for IIA and IIB. The former is equivalent to the large-$N$ limit of 2D Super Yang-Mills theory, while the latter is the large-$N$ limit of 3D ABJM theory with $\pm 1$ Chern-Simons levels. Our construction requires the string coupling to vanish at the location of the wall, and we show that BPS IIA $D0$-branes become non-BPS IIB $D0$-branes as they cross it, as anticipated in \cite{Heckman:2025wqd}.

The coming data releases of Gaia are expected to result in an upheaval of exoplanet science, in particular for long period giant planets ($0.2 {\rm M_{J}} \leq M\leq25 {\rm M_{J}}$). One class of exoplanets which Gaia will help investigate is circumbinary planets. Using the current knowledge of the circumbinary exoplanet population as well as expectations for the Gaia sensitivity, we investigate the impact Gaia will have on our understanding of circumbinary planets. We compare our results to a pre-launch estimate, the main differences arising from a better understanding of the circumbinary planet population, which result in a lower expected yield than previously predicted, though still significant compared to the known population. We make a rough yield estimate, with conservative detection criteria and parameter-space cuts, predicting in the 10s - 100s of detections in Gaia DR4. More importantly, we show how the yield estimate varies strongly with different assumptions on the injected circumbinary population, showing Gaia's sensitivity to the mass and orbital period distribution of circumbinary planets. We find that Gaia circumbinary exoplanet detections will be biased towards planets closer to the instability zone surrounding the binary, due to the larger number of binaries on wider orbits and the limited timespan of Gaia. We also assess the impact Gaia will have on known circumbinary systems, one being that it may resolve the question of reliability of the claimed planets orbiting post-common-envelope binaries, with Gaia DR5 being sensitive to between 3 and 11 out of 32 such planet candidates.

Heterostructures composed of transition metal dichalcogenides (TMDCs) and organic molecules have been extensively explored for optoelectronic devices. To maximize their application potential, it is essential to investigate the electronic band structures, which govern the charge response of the interfaces to external perturbations. Based on $GW$ calculations, we present a study of organic-inorganic heterostructures with anthracene molecules adsorbed on monolayer MoS2. Building on previous investigations of organic molecule self-assembly at surfaces, we systematically analyze anthracene configurations with various molecular orientations and surface coverages. Partially self-consistent $GW_0$ provides qualitatively different level alignments from those in DFT. Whereas the systems with sparse, horizontally adsorbed anthracenes exhibit type-I alignment, densely packed anthracenes in the head-on position lead to type-II alignment, which indicates the strong dependence of quasiparticle corrections on the interfacial configuration. These findings highlight the importance of level-alignment predictions for both interpreting experiments and guiding the design of organic-inorganic heterostructures.

The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many widely used electronic-structure databases are assembled having materials screening as primary goal rather than robust force-field learning, are limited in their scope to a specific class of chemical compounds, and/or employ inconsistent DFT functionals and settings. Here we introduce MAD-1.5, a highly curated dataset designed explicitly for training broadly applicable atomistic models across the periodic table at high levels of theory. MAD-1.5 extends the MAD dataset with targeted enrichment strategies that improve the coverage of chemical space to 102 elements while keeping the total number of configurations compact. All structures are computed with a single, standardized all-electron DFT workflow using the r$^2$SCAN meta-GGA functional and consistent convergence settings, ensuring uniformity across chemically heterogeneous systems. The dataset encompasses molecules, clusters, bulk crystals, surfaces, and low-dimensional structures, and its quality and consistency are further enhanced by outlier removal using uncertainty quantification. We demonstrate the high accuracy that can be achieved with the proposed dataset by training PET-MAD-1.5, a generally applicable r$^2$SCAN interatomic potential that covers 102 elements in the periodic table and achieves exceptional levels of benchmark accuracy and stability in challenging simulation protocols.

In this short note we prove Hilbert--Schmidt stability for graph products of abelian groups and $C^*$-algebras on chordal graphs. In particular, this shows that right-angled Artin groups on chordal graphs are Hilbert--Schmidt stable.

Teenagers are avid users of Discord, a fast growing platform for synchronous communication where they often interact with strangers. Because Discord combines private DMs, semi-private voice channels, and public servers in one place, it creates a hybrid environment that can produce complex and underexplored safety risks for teenagers. Drawing on 16 interviews with teenage Discord users, this study examines their strategies for navigating risky social interactions in the platform. Our findings reveal that when teenagers encounter risks during social interactions, they exercise vigilance by evaluating suspicious interactions before forming friendships, using safety tools, and engaging in controlled risk-taking to safeguard their privacy and security. At the community level, they mitigate risks through selective participation in servers, a practice supported by vigilant governance structures. We discuss how vigilance enables teenagers to act during risky encounters to protect themselves, advancing understanding of teenagers' agency in risk navigation and informing teen-centered designs for safer online environments.

We investigate the infinite-dimensional limit of nonequilibrium surface growth by numerically integrating stochastic growth equations on a fully connected graph. In particular, we study the Edwards-Wilkinson (EW), Kardar-Parisi-Zhang (KPZ), and tensionless KPZ (TKPZ) equations. Using a network discretization adapted to the all-to-all interaction topology, we analyze the global roughness, height-fluctuation statistics, time power spectra, and two-time correlations. For the EW equation, we obtain an exact expression for the roughness that matches the numerical simulations and shows that the interface becomes flat as $N \to \infty$. We also compute analytically the time power spectrum, show that height fluctuations are Gaussian, and derive an explicit expression for the two-time height autocorrelation function, indicating that the aging behavior is trivial. For the KPZ equation, finite-size and strong-coupling effects can cause deviations from EW behavior at moderate system sizes $N$, often accompanied by numerical instabilities; however, these differences disappear as $N$ increases. In the large-$N$ limit, KPZ dynamics converges to EW behavior, as the four observables analyzed exhibit identical scaling properties. Overall, our results indicate that on a fully connected graph the KPZ nonlinearity is irrelevant as $N\to\infty$, leading to EW-like dynamics with asymptotically flat interfaces. These findings are interpreted in the context of the upper critical dimension of the KPZ universality class.

Counter-Rotating Disk (CRD) galaxies have two co-spatial stellar disks rotating in opposite directions, and provide a rare opportunity to study external gas accretion and dynamical assembly processes. In the 16th data release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, only 64 CRDs were visually identified. Using this as a training sample, we developed an automated pre-selection method that reduces the number of galaxies requiring visual inspection by removing systems unlikely to host counter-rotation. Applying this method to MaNGA Data Release 17, we identified 126 confirmed CRDs and an additional 143 candidate galaxies, more than doubling the MaNGA CRD sample. With this extended sample, we analyzed their Baldwin, Phillips, and Terlevich (BPT) emission-line diagrams and compared them with a matched control sample of early-type galaxies (ETG). We found no statistically significant difference in photoionization sources between CRDs and the ETG control sample, indicating emission-line diagnostics cannot solely be used to identify counter-rotating disks, nor do they correspond to a distinct present-day photoionization signature. Our method facilitates efficient discovery of CRDs in large spectroscopic surveys, enabling stronger statistical studies of their formation and evolution.

Accurate channel modeling has become critical for evaluating multiple-input multiple-output (MIMO) performance, especially as 5G standardization matures and efforts toward 6G begin. Recent studies within the 3rd Generation Partnership Project (3GPP) have shown that the tapped delay line (TDL) model, currently used for performance testing, fails to capture the spatial propagation characteristics required for realistic MIMO evaluation. To address this limitation, the reduced clustered delay line (rCDL) model has been introduced as a more accurate alternative with manageable computational complexity, thereby enabling practical implementation in test equipment. This work investigates the rCDL through a comparative analysis with the legacy TDL. First, the angular characteristics of both models are examined. Then, their spatial profiles are compared with real-world measurements from a typical commercial deployment. The results reveal clear deficiencies in the TDL and show that the rCDL better matches measured propagation behavior. As a case study, channel state information (CSI) reporting performance is evaluated in single-user MIMO scenarios. The results show that, with appropriate simulation parameter settings, the rCDL enables clear discrimination between low- and high-resolution CSI reporting schemes, unlike the TDL. These findings confirm the relevance of the rCDL model for MIMO performance evaluation and support its use in current and future standardization efforts.

Let $K$ be a finite unramified extension of $\mathbb{Q}_p$, and $E$ a finite extension of $K$ with ring of integers $\mathcal{O}_E$. We define the overconvergence of multivariable $(φ_q,\mathcal{O}_K^{\times})$-modules over $A_{\mathrm{mv},E}$ and explore some basic properties. We prove the overconvergence at the perfectoid level using the geometry of relative Fargues-Fontaine curve.

The Delay-Tolerant Networking (DTN) community has created solid results during the last three decades. One aspect that still requires focus, however, is the simplification of configuring systems participating in DTN communication. In this workshop paper, an approach is proposed for configuring DTN endpoint reachability. The core idea is to extend the Border Gateway Protocol (BGP), enabling it to advertise the reachability of DTN endpoints specified by Endpoint Identifiers (EIDs) via specific next-hop Convergence Layer Adapters (CLAs). The approach is mainly intended for usage in edge nodes accessible from an IP network, which communicate via a DTN gateway to a larger DTN infrastructure. It may be applied to simplify access to the Solar System Internet from hosts connected to the Internet. The feasibility of the approach has been confirmed using an implementation intended for validation purposes.