Users often feel regret after using social media, making regret a more ecologically valid target than screen time for understanding when phone use becomes problematic. Existing self-monitoring tools cannot anticipate regret before it occurs, and prior physiological work on social media use has been confined to the lab with research-grade sensors and curated content, leaving the question of in-the-wild prediction open. We deployed a 7-day in-the-wild experience sampling study with 21 participants, combining passive smartphone logging, a low-cost consumer smartwatch (Bangle.js 2, \$80), session-level surveys (1,445 sessions), and exit interviews to investigate when and why social media sessions become regretful, and whether regret can be anticipated before a session begins. Three findings stand out: (i) the gap between intended and actual use predicts regret far more strongly than session duration, with duration's apparent effect collapsing once intention is modeled; (ii) regret is amplified when sessions displace a valued alternative, particularly at night and following productivity-app use; and (iii) pre-session contextual features generalize across participants while physiological signals add person-specific lift, pointing toward a two-layer architecture for just-in-time adaptive interventions. Interview themes of scrolling-as-avoidance and time blindness contextualize these patterns and surface design opportunities beyond timer-based interventions.

The purpose of this paper is to investigate the self-orthogonal embedding problem for linear codes over Z4. We propose several tight bounds on the length of the shortest self-orthogonal embedding over Z4, and determine the exact shortest self-orthogonal embedding length under specific conditions. As an example satisfying these conditions, we establish the exact length of the shortest self-orthogonal embedding for the quaternary Preparata codes. Furthermore, to establish these results, we completely classify the exact length of the shortest doubly even self-orthogonal embedding for binary linear codes in every possible case. Finally, when the shortest self-orthogonal embedding length of a given free code over Z4 is equal to the shortest doubly even self-orthogonal embedding length of its residue code, we present an algorithm to construct all possible shortest self-orthogonal embeddings. With our algorithm, we found twelve linear codes over Z4 whose minimum Lee distances are higher than those of the Z4-linear codes in Aydins database.

Nuclear bound-state $β^-$ decay ($β_{\text b}$ decay) is a novel weak-interaction process that becomes possible when atoms are highly ionized, such as in stellar environments or heavy-ion storage rings. In this work we present a systematic theoretical calculations for the $β_{\text b}$-decay half-lives of interesting candidates for the first time, where both allowed Gamow-Teller transitions and first-forbidden transitions are taken into account by the microscopic projected shell model, and the lepton phase space is calculated by the Takahashi-Yokoi model. We analyzed the structure informations for hundreds of nuclei near the $β$-stability line, and select 16 interesting candidates belonging to two categories, i.e., nuclei with negative $Q$ values and positive $Q$ values in neutral atoms respectively. Among these candidates, we recommend $^{243}\mathrm{Am}^{95+}$, $^{194}\mathrm{Os}^{76+}$, $^{227}\mathrm{Ac}^{89+}$, $^{228}\mathrm{Ra}^{88+}$, $^{241}\mathrm{Pu}^{94+}$, $^{247}\mathrm{Cm}^{96+}$ and $^{250}\mathrm{Cm}^{96+}$ as promising ones for future studies of storage-ring experiments because their $β_{\text{b}}$-decay half-lives are predicted to be much shorter than the half-lives in neutral atoms. These findings provide essential nuclear inputs for astrophysical models and identify specific candidates where experimental verification would be most valuable.

Observations indicate that supermassive black holes (SMBHs) in high-redshift galaxies formed on timescales far shorter than classical growth models allow. One hypothesis suggests intermediate-mass black hole (IMBH) seeds as an efficient growth channel. Using N-body simulations, we demonstrate that in dense stellar-mass black hole (BH) clusters ($\ge 5\times10^9 M_{\odot}/{\rm pc}^3$), runaway gravitational-wave binary BH (BBH) mergers can produce a $\sim 10^3 M_\odot$ IMBH within 10 Myr from the formation of the BH subsystem. This scenario is simple and avoids large uncertainties regarding stellar mergers and evolution in the IMBH formation via very massive stars channel. We find that the runaway GW-merger mechanism relies on hard BBH formation through a chain of exchanged soft BBHs with accumulated hardening, which is far more efficient than three-body scattering. We analyze how IMBH formation depends on cluster density, total mass, initial mass function, and stellar halo potential. We find that due to cluster expansion, the systems forming IMBHs have densities consistent with present-day nuclear star clusters, such as those in the Milky Way and M33. Furthermore, we show that IMBH spin remains low due to repeated mergers, and we estimate the rate of GW190521 and GW231123-like events within the first 100 Myr to be $2.27-247.52$ and $3.23-63.63 $ per Gyr per cluster.

Talagrand's correlation inequality provides a quantitative strengthening of the Harris--Kleitman inequality for increasing Boolean functions. Motivated by a Fourier-analytic conjecture of Friedgut, Kahn, Kalai, and Keller, we prove that $$ \mathrm{Cov}(f,g)\ge 2\sum_{S\neq\emptyset}|S|\hat f(S)^2\hat g(S)^2 $$ holds for all increasing Boolean functions $f,g:\{0,1\}^n\to\{0,1\}$. The proof combines the reverse Bonami--Beckner inequality with Young's convolution inequality. We also establish a sharp pointwise inequality: for every $n\ge1$, every $0\leρ\le1$, and every $f,g:\{0,1\}^n\to[0,1]$, the optimal constant $c_{ρ,n}$ for which $$ \left\langle f,T_ρg \right\rangle\ge c_{ρ,n}\|f*g\|_2^2 $$ holds for all such $f,g$ is $1$ for $0\leρ\le1/2$, $(2(1-ρ))^n$ for $1/2<ρ<1$, and $0$ for $ρ=1$. Integrating this pointwise inequality yields, for $n\ge1$, the slightly improved bound $$ \mathrm{Cov}(f,g)\ge 4\cdot\frac{n+1}{2n}\sum_{S\neq\emptyset}|S|\hat f(S)^2\hat g(S)^2. $$

We present a microscopic analysis of the nuclear matrix element (NME) of the two-neutrino double-$β$ ($2νββ$) decay for open-shell heavy deformed nuclei, taking into account the fact that nuclear level density increases rapidly with excitation energy as well as the contribution of two-body current (2BC). Taking $^{76}$Ge $\rightarrow$ $^{76}$Se decay as an example, we found that due to the rapid increase of the level density of the intermediate nucleus $^{76}$As with excitation energy $E_n$, the single-$β$ Gamow-Teller (GT) matrix elements become highly fragmented with very small magnitude, and exhibit seemingly random sign patterns at high $E_n$ region. This leads to an effective cancellation at high $E_n$ region in calculating the $2νββ$ NME which then turns out to converge at $E_n \lesssim 5$ MeV, indicating that the contribution of high-lying states of the intermediate nucleus to $2νββ$ NME is negligible. Besides, the 2BC in the transition operator is found to contribute $\sim 10\%$ quenching to the $2νββ$-decay NME of $^{76}$Ge.

Classical zero-localization theorems give deterministic certificates that all zeros of a polynomial lie in a prescribed disk, annulus, or related region. When the coefficients are random, each such deterministic certificate becomes a random variable on coefficient space. This paper develops a finite-degree certificate method for random polynomial localization and extends the author's earlier joint work with Mir \cite{SheikhMir2024}. The main result concerns Gaussian polynomials with random leading coefficient: the Cauchy ratios are marginally standard Cauchy, yet they are dependent through their common denominator. We derive the exact dependence-aware certificate integral and prove that its inverse confidence radius has order \(\sqrt{\log n}\), while a fictitious independent-Cauchy model has order \(n\). We also obtain monic coefficient-law certificates, sub-Weibull confidence radii, annular certificates via reversal, Rouché--Chernoff certificates, and an optimized scaled Cauchy envelope. A reproducible Monte Carlo study for monic Gaussian polynomials compares the classical Cauchy radius, the optimized Cauchy envelope, the annular certificate, and the Rouché radius. Across \(5000\) samples for each of \(n=20,50,100\), the Rouché certificate gives the sharpest outer radii, the optimized Cauchy envelope substantially improves the classical Cauchy radius, and all tested certificates satisfy their deterministic containment inequalities numerically.

Vector databases have been designed and optimized for cloud environments; however, emerging scientific AI workloads (e.g., molecular search, meteorological trajectory detection, and literature-driven hypothesis generation) demand efficient, scalable execution on HPC systems. We present a large-scale evaluation of three state-of-the-art vector databases -- Qdrant, Milvus, and Weaviate -- on two production supercomputers, scaling to 256 distributed workers across 64 compute nodes. We evaluate representative workload patterns -- mixed read/write and write-then-read -- using popular benchmarks, multimodal embeddings, and a novel real-world scientific dataset. Our results reveal that workload characteristics can limit latency reduction, additional cores can reduce query throughput by up to 30.67%, and scaling from 16 to 256 workers (16x) only yields a 5.46x improvement. This scaling paradox exposes the fundamental mismatch between cloud-oriented designs and HPC systems, highlighting the need for new, HPC-aware vector database designs.

Multiparameter quantum estimation has emerged as a central task in quantum metrology. In this work, we investigate multiparameter quantum estimation in a hybrid exciton--optomechanical (EOM) system. The system consists of a semiconductor quantum well embedded inside a driven optomechanical microcavity, where the excitonic, optical, and mechanical modes interact coherently through exciton--photon and radiation-pressure couplings. Using the Gaussian-state formalism, we derive the covariance matrix of the steady-state quantum fluctuations and employ both the symmetric logarithmic derivative (SLD) and right logarithmic derivative (RLD) approaches to evaluate the quantum Fisher information matrix associated with the simultaneous estimation of the exciton--photon coupling strength $g$ and the excitonic decay rate $k_x$. We analyze the corresponding quantum Cramér--Rao bounds and determine the most informative precision limit governing the attainable estimation accuracy. The influence of several experimentally relevant parameters, including temperature, driving power, optomechanical coupling strength, and dissipation rates, is investigated in detail. Our results show that strong hybrid interactions and low-temperature regimes significantly enhance the estimation precision, whereas thermal fluctuations and dissipation processes deteriorate the metrological performance. Furthermore, we compare the ultimate quantum limits with experimentally feasible Gaussian measurement strategies based on homodyne and heterodyne detection. We show that heterodyne detection provides better estimation performance than homodyne schemes and can approach the optimal quantum precision limit in suitable parameter regimes.

This work presents NeuDW-CIM, a highly efficient neuromorphic Compute-in-Memory (CIM) macro for Spiking Neural Networks (SNNs) implemented in 65 nm CMOS. The design introduces a custom twin 9T bit-cell for ternary in-puts/weights and a reconfigurable non-linear In-Memory ADC (IMA). The macro supports two specialized modes: 1) Nonlinear Dendrite (NLD) mode, which utilizes reconfigurable IMA to emulate biological dendritic functions, achieving measured accuracies of 97.2% on N-MNIST and 95.5% on DVS Gesture; and 2) Top-K Winner (KWN) mode, featuring an early-stopping mechanism that reduces IMA conversion latency by 30% and digital LIF latency by 10x. Benefiting from the sparse update in KWN mode, NeuDW-CIM achieves a measured energy efficiency (EE) of 0.8 pJ/SOP (1.6x improvement).

Large Language Models (LLMs) show great promise in RTL code generation and optimization. However, real-world RTL designs are typically long, entangled, and poorly modularized, posing a major challenge due to context-length limitations and lack of structure. To overcome these obstacles, we propose a scalable LLM-based RTL optimization framework guided by graph similarity. Our method introduces three collaborative agents: (1) a Partition Agent that decomposes RTL designs into semantically meaningful AST subtrees, guided by AST graph similarity to reusable design templates; (2) an Optimization Agent that generates RTL submodule code based on partitioned subtrees using multi-modal Retrieval-Augmented Generation (RAG) with both AST and RTL guidance; and (3) a Reconstruction Agent that reassembles optimized submodules based on logic-aware ordering and Graph-RAG prompting, ensuring global functional equivalence. Together, these components enable robust, structure-aware optimization of long-context RTL designs, bridging the gap between toy examples and industrial-scale hardware codebases.

Birefringence in test mass substrates is an emerging limitation for current and future laser-interferometric gravitational-wave detectors, particularly as detectors move toward higher circulating power, cryogenic operation, and crystalline optical materials. Spatially varying birefringence alters both the polarization state and spatial mode content of the intracavity field, reducing interference contrast and coupling into length and alignment control signals. Accurate modeling of these effects is complicated by the fact that most frequency-domain simulation tools employ scalar modal propagation and lack native support for polarization and two-dimensional substrate maps. In this work, we present a practical and general method for simulating inhomogeneous birefringence without modifying existing simulation frameworks. The approach represents the two polarization components as independent scalar fields and introduces their coupling through an equivalent triple-Mach-Zehnder construction that reproduces the Jones matrix of a birefringent medium. We demonstrate the method using realistic birefringence maps of the KAGRA sapphire input test masses. The technique is compatible with any frequency-domain interferometer model and enables efficient birefringence studies for next-generation gravitational-wave detectors.

The Doppler backscattering (DBS) diagnostic measures flows and electron density fluctuations. Recent work indicates that DBS can also infer the magnetic pitch angle (Yeoh et al., NF 2026). We present the preliminary design of a DBS for the Pegasus-III Experiment. This DBS will serve two objectives. First, it will advance diagnostic science, by supporting understanding of the DBS instrumentation functions, using DBS to constrain the magnetic equilibrium, and data-driven inference of plasma properties from DBS signals. Secondly, it will support Pegasus-IIIs research directions, such as solenoid-free plasma initiation and O-X-B mode conversion for heating and current drive, since density fluctuations affect mode conversion efficiency and pitch angle measurements can be used to locate the mode conversion window. This ex-vacuum DBS system uses a single channel, tuneable Ka-band source, a corrugated horn antenna, and a homodyne I/Q receiver with baseband digitization. For greater flexibility, which is especially important for pitch angle measurements, the quasioptical elements include a rotatable spinner for O- and X-mode selection and a mirror with 2D steering. Using the \textit{Scotty} beam-tracing code, for a range of poloidal launch angles $8^\circ$ to $18^\circ$ and a corresponding toroidal launch angle between $0^\circ$ to $3^\circ$ for maximal backscattered DBS power, we find that the DBS system is capable of measuring ion-scale density fluctuations $1\leq k_{\perp,c} \leq8 \text{ cm}^{-1}$ at a range of normalized radial coordinates from the outer core ($ρ\sim 0.65$) to just beyond the last-closed flux surface (LCFS), where $ρ=0$ corresponds to the magnetic axis and $ρ=1$ the LCFS. The system is also designed with additional toroidal steering capability, $-4^\circ$ to $8^\circ$, to resolve the toroidal response needed for magnetic pitch angle measurements.

We introduce a new family of weighted Gaussian $L^2$-Poincaré-type inequalities with explicit sharp constants, optimizers, and corresponding sharp $L^2$-gradient stability estimates. This family substantially extends the classical Gaussian Poincaré inequality. Owing to the singular nature of the weights involved, standard approaches to classical Gaussian Poincaré inequalities do not apply. To overcome this difficulty, we develop a new method based on generalized Laguerre polynomial expansions, spherical harmonic decompositions, and a Kelvin-type transform. As an application, we completely characterize the stability of the $L^2$-Caffarelli--Kohn--Nirenberg (CKN) inequalities by establishing sharp stability estimates, together with the stability of the stability inequality results, throughout the entire parameter range. Previous results were available only in a few special cases. We further establish weighted $L^p$-Poincaré inequalities for all $p>1$, and derive stability estimates for the $L^p$-CKN inequalities for $p\geq 2$ throughout the full parameter regime in which sharp constants and optimizers are known. In contrast, earlier $L^p$ results were restricted to highly limited parameter ranges.

We introduce a quantum deformation $\mathbf{U}(\mathfrak{gl}_n[t])$ of the universal enveloping algebra of the current algebra $\mathfrak{gl}_n[t]$, realized as a parabolic subalgebra of quantum affine $\mathfrak{gl}_n$. Unlike the Yangian -- the standard quantization of the current algebra -- our algebra admits a canonical basis. We give a BLM-type realization of $\mathbf{U}(\mathfrak{gl}_n[t])$ via certain subalgebras of affine quantum Schur algebras, and then construct canonical bases for the modified quantum current algebra $\dot{\mathbf{U}}(\mathfrak{gl}_n[t])$ and for its finite dimensional irreducible graded modules. Moreover, we prove a rigidity theorem: every finite dimensional polynomial irreducible module for quantum affine $\mathfrak{gl}_n$ remains irreducible when restricted to ${\bf U}_{\mathbf v}(\frak{gl}_n[t])$ (the specialization of ${\bf U}(\frak{gl}_n[t])$ at a non-root-of-unity complex number ${\mathbf v}$); conversely, every finite dimensional polynomial irreducible ${\bf U}_{\mathbf v}(\frak{gl}_n[t])$-module extends uniquely to a polynomial irreducible module for quantum affine $\mathfrak{gl}_n$. Consequently, the finite dimensional polynomial irreducible modules of ${\bf U}_{\mathbf v}(\frak{gl}_n[t])$ are in bijection with those of the Yangian $Y(\mathfrak{gl}_n)$. This provides the first example of a quantum current algebra with a well-developed canonical basis theory, providing new combinatorial approaches to the representation theory of current algebras.

Using the form factors of the transtions $B\to T$ with $T$ refering to a tensor meson, such as $a_2(1320), f_2(1270),K^*_2(1430), D_2^*(2460)$ and $D^*_{2s}(2573)$, within the covariant light-front quark model (CLFQM), we provide a detailed investigation of the corresponding semi-leptonic decays $B\to T\ellν_\ell$ with $\ell=e,ν,τ$. All the branching ratios of these decays are larger than $10^{-5}$, in which the maximum value can reach up to $10^{-3}$, indicating promising prospects for experimental observation. Furthermore, we also calculate the longitudinal polarization fractions $f_L$ and forward-backward asymmetries $A_{FB}$ for these considered decays. All the decays $B\to T \ellν_{\ell}$ are dominated by the longitudinal polarization, where the polarization fractions can reach up to $\sim70\%$ for the decays $B\to T \ell^{\prime}ν_{\ell^\prime}$ with $\ell^\prime=e, μ$, those of the decays $B\to T τν_τ$ are a little smaller. The $A_{FB}$ values of the decays $B\to T \ell^{\prime}ν_{\ell^\prime}$ and $B\to T τν_τ$ have opposite signs.

We report the first identification of molecular clumps directly associated with the re-brightening regions of the large-scale X-ray jets of SS 433, based on $^{12}$CO ($J$ = 1--0) observations with the Nobeyama 45-m Radio Telescope. Multiple clumps are detected toward the eastern and western jet heads, showing clear spatial correlation with the X-ray emission. The X-ray emission peaks immediately downstream of the molecular clumps, while the hardness ratio is enhanced at their surfaces, indicating that the observed structures cannot be explained by absorption effects. These results provide direct evidence for shock--cloud interactions between the jets and the surrounding interstellar medium. We suggest that turbulence generated at the jet--cloud interface amplifies magnetic fields, producing the observed non-thermal X-ray emission. Our findings highlight the importance of jet--ISM interactions in shaping the X-ray properties of microquasar jets.

Cross-sectional stock prediction is closer to a ranking problem than to ordinary return-magnitude regression, since portfolio decisions depend on the relative ordering of assets within each trading date. Existing temporal, graph-based, and market-conditioned attention models have improved stock representation learning, yet the final prediction head is often treated as a minor implementation detail. This paper argues that, under information-coefficient-oriented evaluation, score formation is a critical bottleneck: an over-flexible head can fit unstable return magnitude, whereas an overly linear head may underuse cross-feature interactions. We therefore develop RankGLU, a residual bottleneck gated linear unit for cross-sectional stock ranking. RankGLU keeps a direct linear scoring path and adds a bounded multiplicative branch, thereby preserving a stable ordering route while allowing controlled nonlinear interactions. The method is evaluated on CSI300 and CSI800 under a unified protocol with cross-sectional score normalization and an IC-augmented objective. Multi-seed experiments show that, on CSI300, RankGLU achieves the strongest mean IC among the internally controlled variants, improving from 0.0654+/-0.0052 for the original backbone and 0.0697+/-0.0030 for the ranking-aware backbone to 0.0727+/-0.0037, a gain that is consistent across all five seeds. Its best-seed result also exceeds the corresponding baselines. Ablation results further indicate that removing the GLU prediction head causes the clearest degradation among the tested component changes. Additional relation-path calibrations can produce high single-seed peaks, but their multi-seed behavior is less stable. The evidence suggests that ranking-aware stock models benefit most reliably from bounded residual score formation rather than from indiscriminate architectural expansion.

Theoretical quantum dynamics calculations scale deeply with system size, rendering classical calculations intractable for complex systems. While quantum computing offers a natural solution, its application to nuclear quantum dynamics remains scarce. Here, we present a quantum algorithm to study photodissociation dynamics on quantum computers, benchmarked on the NOCl molecule. The wavefunction is propagated via a split-operator method, utilizing the Quantum Fourier Transform and unitary transformation matrix to switch representations. To impose outgoing boundary conditions on a truncated grid, we use a non-unitary absorbing potential propagator, implemented through a dilation scheme. The photodissociation cross section is calculated from the auto-correlation function, which is extracted using the Hadamard test. Our quantum computing results agree well with benchmarks under ideal conditions, and we further demonstrate that the algorithm is robust to noise and statistical sampling errors, indicating the promising application of noisy devices to quantum dynamics studies.

The matrix of Lévy's areas of $d$-dimensional Brownian motion is a fundamental object in stochastic analysis. In this article, we study the singular values of this $d \times d$ skew-symmetric random matrix. First, we derive an explicit formula for the density of the singular values and, en passant, present a new short proof of the characteristic function of Lévy's area when $d \ge 3$. This also allows us to extend the well-known formula for the density of Lévy's area to $d \ge 3$. Next, we use these results to characterise the singular spectrum as a determinantal point process with its kernel in explicit form. Finally, we study the asymptotics as $d \to \infty$: the empirical measure of singular values converges to an absolute Cauchy distribution, the largest singular values are of order $d$ with Gaussian fluctuations, the smallest singular values are of order $1/d$, and the local bulk spacings are of order $1/d$, with sine-kernel statistics after rescaling.

Immersive analytics uses augmented reality (AR) to integrate data analysis and authoring within physical environments. However, extensive text entry required for immersive analytics authoring remains a fundamental challenge in AR, as popular natural user interfaces often hinder expressive input. This paper presents the Body-Supported Keyboard (BSK), an ergonomic system that allows the mobile use of a Bluetooth keyboard in AR. We conducted a controlled study with 20 participants to compare the BSK with a standing desk during text transcription and a mobile AR scenario. The results showed slightly higher error rates but comparable task completion times. Participants reported comfort improvements during mobile use and positive usability ratings (mean SUS = 74.5). The BSK allows users to move freely and maintain stable postures while authoring in AR. In general, the findings show evidence of the potential for body-supported input to enhance expressive and ergonomic workflows in immersive analytics and emphasize the importance of comfort and mobility in the design of AR authoring tools.

In this paper, we introduce and study a family of diffusion processes associated with the Pitman-Yor process and the two-parameter Poisson-Dirichlet distribution. The diffusion coefficients indexed by a non-negative parameter $γ$ are smaller than the corresponding one-parameter models in terms of quadratic forms or bilinear forms when $γ$ is positive. The well known Petrov's diffusion corresponds to $γ=0$ among the unlabelled diffusions. If $γ$ is the same as the stable parameter $α$ in the Pitman-Yor process, we obtain both labelled and unlabelled reversible diffusion processes with the Pitman-Yor process and the two-parameter Poisson-Dirichlet distribution as the corresponding reversible measures. We construct these processes analytically through Dirichlet forms. In comparison with existing models in the literature, our models possess two fundamental new features. Firstly, our labelled model is characterized by an explicit generator, which is the first among all models studied so far. This makes it possible to establish the crucial integration-by-parts formula. Secondly, a novel foundational structure of the two-parameter distributions is the existence of a diversity index (a multiple of local time) for the positive stable parameter. By slowing down the diffusion in our model, the essential role of the diversity index is revealed in the evolution of the population. Additionally, we also obtain properties including ergodicity, path behaviour, and finite dimensional approximations.

We prove strong ill-posedness of the 2d incompressible Euler Equation for velocity field in the critical Besov Spaces $B^{1}_{\infty, q}$ for $1<q<\infty$.

Measuring causal effects in networked two-sided marketplaces is challenging due to treatment interference between market participants on different sides. When treatment is applied to one side (e.g., job seekers), their interactions with the other side (e.g., job posters) introduce spillover effects that violate the Stable Unit Treatment Value Assumption (SUTVA) and bias causal estimates. While cluster-based randomization mitigates this problem, prior approaches struggle with a fundamental trade-off: reducing spillover requires isolated clusters that will reduce the number of qualifying clusters, which decreases statistical power. This paper introduces EgoCluster V3, an iterative clustering algorithm that reduces spillover by 3x compared to prior versions while preserving node coverage and doubling test power. We further introduce MultiEgoCluster, which extends V3 through a two-stage procedure that first groups highly connected egos into multi-ego clusters before applying the iterative clustering algorithm. This achieves an additional ~56% spillover reduction and ~38% increase in sample size. Both methods are deployed in production at LinkedIn and have systematically enabled high-impact two-sided marketplace experiments. Since residual bias cannot be fully eliminated through clustering alone, we derive a theoretical bias correction method for average treatment effect (ATE) estimation based on graph structure and propose an approach to generalize results to the general population.

Chemical substitution offers an effective route to tune magnetic ordering and magnetocaloric performance in rare-earth oxides for cryogenic refrigeration. Here we investigate the structural evo lution, magnetic properties, and magnetocaloric effect of polycrystalline zircon-type Gd1-xErxVO4 (x=0, 0.1, 0.25, 0.5, and 0.75). Powder X-ray diffraction confirms that all samples crystallize in the tetragonal zircon structure without detectable impurity phases. Substitution of Gd3+ by the smaller Er3+ ion produces a systematic lattice contraction and modifies the magnetic behavior of the rare-earth sublattice. In particular, the magnetic ordering temperature is suppressed from 3.65(2) K in GdVO4 to 2.76(2) K in Gd0.9Er0.1VO4 , accompanied by a weakening of the spin-flop-like field-induced anomaly observed in the parent compound. A low Er concentration correspondingly improves the low-temperature magnetocaloric performance, with Gd0.9Er0.1VO4 exhibiting a max imum magnetic entropy change of 45.1 J kg-1 K-1 for mu_0 Delta H=7T. These results demonstrate that weak Er substitution effectively tunes the competition among exchange interactions, dipolar coupling, and magnetic anisotropy, optimizing the balance between magnetic ordering and available spin entropy in zircon-type rare-earth vanadates, which is crucial for developing efficient cryogenic refrigeration materials.

The local electronic states of muon (Mu) as an isotope of hydrogen (H) in high-quality single-crystalline ceria (CeO$_2$) are investigated using muon spin rotation/relaxation ($μ\text{SR}$) and first-principles density functional theory (DFT) calculations. Upon positive muon implantation, both paramagnetic (Mu$^0$) and diamagnetic (Mu$^*$) states are observed below $\approx$10 K. Magnetic field dependence of the Mu$^0$ signals combined with DFT results provides evidence for the formation of a polaron state, consisting of Mu bonded to a ligand oxygen and a $4f$ electron localized on a nearby Ce site. The crystal-orientation dependence of the Mu$^0$ signal suggests strong anisotropy of the $4f$ electron due to the spin-orbit coupling with lifted degeneracy. Furthermore, the Mu$^0$ state exhibits transition to the Mu$^*$ state that corresponds to another Mu$^0$ state (exhibiting a diamagnetic response due to fast spin/charge fluctuations) at temperatures above $\approx$10 K, before disappearing above $\approx$30 K. These findings suggest rapid diffusive motion of the $4f$ electrons and/or Mu (as well as H) at higher temperatures.

Conversational AI has enabled users to generate and interpret visualizations through natural language, significantly lowering the technical barrier to entry. The increased accessibility brings visualization novices into data visualization, but also exposes them to misinformation and misinterpretations. We are motivated to examine what issues can arise in interactions with current conversational AI, whether visualization novices can recognize such issues, and how they respond to them. To examine these questions, we conducted a user study on ChatGPT with 20 visualization novices, collecting their conversation logs, semi-structured interview transcripts, and Likert-scale questionnaire responses. Through thematic analysis, we developed a codebook that covers AI execution compliance, issues of AI-generated visualizations, patterns of AI responses, and prompting patterns of users. We summarized four themes, including the quality of outcomes, recurring errors from ChatGPT, misuse by users, factors that affect user trust, confidence, and verification behavior, and human-AI collaboration dynamics. To demonstrate the generalizability of our codebook and findings, we replayed the initial user prompts on Gemini and Claude and compared the outcomes, which revealed distinct failure modes for each model. Based on the results of all analyses, we derive a set of design recommendations for future AI-assisted visualization systems. We conclude with discussions on literacy gaps, diverse human-AI collaboration dynamics, and implications for agentic visualization.

Guiduli asked in 1996 the following problem concerning the maximum spectral radius of a graph under hereditary density constraints. If an $n$-vertex graph $G$ satisfies $e(H)\le c|V(H)|^2$ for every subgraph $H$ of $G$, must one have $λ(G)\le 2cn$? More generally, what remains true when the exponent $2$ is replaced by a constant less than $2$? We study the natural power-law version of this question for all $1<p\le2$. For $1<p\le 2$, define \[ d_p(G)=\max_{\varnothing\ne S\subseteq V(G)}\frac{e(G[S])}{|S|^p}. \] We determine the sharp asymptotic upper bound for $λ(G)$ in terms of $d_p(G)$ and $n$. More precisely, every $n$-vertex graph $G$ with at least one edge satisfies \[ λ(G)\le \begin{cases} \left(\left(\max_{t\in\mathbb N_{\ge1}}\dfrac{t}{(t+1)^p}\right)^{-1}+o(1)\right)d_p(G)\sqrt n,&1<p<3/2,\\[0.4em] \left(\dfrac{3\sqrt3}{4}+o(1)\right)d_p(G)\sqrt{n\log n},&p=3/2,\\[0.4em] (\mathfrak C_p+o(1))d_p(G)n^{p-1},&3/2<p<2, \end{cases} \] and each constant here is best possible. Here $\mathfrak C_p$ is characterized by an exact variational problem over finite kernels. We apply a sparse graphon operator estimate to convert hereditary $p$-density bounds into sharp spectral bounds, and this estimate also explains the transition at the critical exponent $p=3/2$. For the endpoint $p=2$, Wilf's theorem gives the exact finite-$n$ bound $λ(G)\le 2d_2(G)n$, with equality for $K_n$. Thus Guiduli's power-law problem is resolved in its sharp asymptotic form for every $1<p\leq2$, including exact leading constants.

Immersive video differs from conventional flat 2D video in that it is experienced as 180-degree stereoscopic video on a head-mounted display, thereby eliciting bodily and spatial subjective experience. Previous studies have shown that viewing and interpersonal distance affect Presence; however, it remains insufficiently understood how Presence differences are related to psychological closeness to content. In the present study, we examined whether differences in Presence could increase viewers' psychological closeness to performers within the content. This psychological closeness was operationally defined as fan intensity. Specifically, a live performance by a Japanese idol group was recorded as 180-degree immersive video, and a high-Presence condition (1.2 m) and a low-Presence condition (7.6 m) were established by manipulating filming distance. Twenty-four participants with different levels of prior involvement, comprising Avid fans and Casual fans, experienced both conditions in a counterbalanced within-participants design. Fan intensity was measured before and after the experience as perceived psychological overlap between the self and the performers. The results showed that, compared with the low-Presence condition, the high-Presence condition significantly increased all Presence-related measures except the Slater-Usoh-Steed questionnaire, with the largest condition differences observed for Possible Actions, Social Presence, and Observability. Moreover, a mixed analysis of variance on changes in fan intensity revealed a significant main effect of Presence condition, indicating that the high-Presence video produced a greater increase in fan intensity than the low-Presence video. These findings suggest that filming distance in immersive video is not merely a factor that determines angle of view or composition, but a design variable that can enhance Presence and deepen fan intensity.

Multipliers are operators on a Hilbert space that have been implicitly used in applications throughout the years. Multipliers are defined via three sequences and are mainly used in signal processing and psychoacoustics among many others. They modify a signal in three steps: analysis, term-wise scalar multiplication, and synthesis. While the study of multipliers stemmed from applications, its theory has earned an equally important place in the literature in the last few decades. One of the main questions in the theory of multipliers is the invertibility of multipliers. It is known that multipliers are not always invertible, and it has been shown that under mild conditions, the inverse of an invertible multiplier can be expressed as another multiplier of a very specific structure. In this work, we extend such result to the setting of possibly noninvertible multipliers. We solved the challenges of determining an appropriate generalized inverse to replace the inverse, and determining a proper duality relationship of the sequences of the resulting multiplier. We showed that these are nontrivial tasks that required the intricacies of the theory of frames for subspaces.