Papers

Neuroinflammation and glial scar formation in the central nervous system (CNS) emerge from coordinated signaling between neurons, astrocytes, oligodendrocytes and microglia. Conventional embryonic neuron-astrocyte-oligodendrocyte (NAO) TriCultures lack microglia and cannot model innate immune responses. Here, we introduce postnatal neuron-astrocyte-oligodendrocyte-microglia (NAOM) TetraCultures, in which resident microglia are retained from the original brain tissue and co-develop with other CNS cell populations from the onset of culture. Hippocampal and cortical cultures were maintained for 14 days in vitro and challenged with LPS, poly I or scratch injury. Only NAOM TetraCultures mounted robust, region- and stimulus-specific inflammatory responses. LPS induced marked TNF-\alpha and IL-1\beta secretion and increased IBA1 in hippocampal microglia, whereas poly I elicited IFN-\beta release without increasing IBA1. CD68 regulation differed between hippocampus and cortex, and microglial morphology showed pronounced amoeboid transitions after LPS. Astrocytes in NAOM TetraCultures expanded their territorial coverage after LPS, an effect absent in NAO TriCultures. Following scratch injury, IL-1\beta increased, microglia infiltrated lesion zones and astrocytes formed lesion borders. Thus, NAOM TetraCultures recapitulate key features of CNS immunity, glial scar formation and region-specific glial responses while providing a physiologically relevant microglia-containing model.

Existing-image self-tiling aims to transform a given image into a seamlessly repeatable tile while preserving its original semantics and local details. This task requires weak editing to retain the source but leave periodic seams, whereas strong editing improves tileability at the large risk of content drift.Existing diffusion-based tiling pipelines alleviate boundary discontinuities, yet lack awareness of which regions are geometrically important to preserve. In this work, we propose DepthTile, a depth-guided self-tiling method whose generation module requires no retraining of the diffusion backbone. It converts monocular depth into a spatial preservation prior and integrates it into the timestep-dependent masking mechanism of Tiled Diffusion. DepthTile converts depth into a spatial editability map and fuses it with the differential diffusion mask. This strengthens source preservation in near-field regions indicated by the monocular depth prior while retaining editing flexibility near compatible background boundaries. To access when self-tiling is feasible, we introduce the Self-Tiling Ability Score (STAS), a lightweight input-side predictor based on RGB, gradient, depth, and depth-gradient cues. On a 335-image benchmark, relative to the default Tiled Diffusion pipeline, DepthTile reduces MAE, LPIPS, and Tiling Score by 12.8%, 26.7%, and 8.4%, respectively. STAS reliably enables difficulty-aware analysis and further supports compatibility-aware pairwise image tiling.

Prior avatar embodiment research has studied tracking completeness (how faithfully the body, hands, and face are sensed and reproduced) and personalized appearance in isolation, each requiring specialized lab equipment. How these factors interact on consumer hardware remains an open question, because no existing system unifies body pose, face, hands, and personalized appearance on a standalone headset. We present FlowAvatar, a system enabling real-time personalized look-alike avatars with full-body pose, hand articulation, facial expression, and eye gaze tracking from sparse egocentric inputs on consumer XR devices (Meta Quest Pro). Our pose model achieves competitive spatial accuracy and state-of-the-art temporal consistency on the AMASS benchmark against prior works with comprehensive spatial and temporal losses validated through architecture and loss ablations, while running in real-time. Two user studies (N=12, N=10) provide converging evidence that tracking completeness and personalized appearance serve complementary roles: tracking completeness broadly improves perceived motion quality, while only their combination achieves positive total embodiment.

Continuous autoregressive speech language models combine an autoregressive (AR) backbone with a diffusion or flow-matching head. Preference post-training requires choosing how to construct pairs and which modules to update. Controlled interventions show that a perturbed acoustic patch changes subsequent backbone states, yet larger representation changes do not necessarily reduce speech quality. We construct preference pairs through noise or inference-configuration perturbations and formulate a preference update based on reference-relative flow-matching errors. Updating the backbone with a fixed head yields stronger reward improvements than head-only adaptation in our evaluated setting. Across flow-time and module ablations, the lowest training loss does not consistently produce the best speech. These findings support preference post-training that considers both acoustic sampling and autoregressive conditioning, with training choices validated on generated utterances.

Flow Matching (FM) has recently emerged as a principled and efficient generative modeling framework for reinforcement learning (RL), enabling expressive, multimodal policy parameterizations via deterministic probability transport. Compared to diffusion-based policies that rely on stochastic denoising chains, FM uses sampling based on ordinary differential equations (ODEs), with learned velocity fields, which can substantially reduce inference latency and simplify the incorporation of RL objectives. As research in flow-based RL rapidly accelerates across offline continuous control, online fine-tuning, and foundation model alignment, the literature has become highly fragmented. In this survey, we provide a comprehensive taxonomy of flow-matching approaches in reinforcement learning. We organize the literature along two axes: the target distribution being modeled (e.g., action policies, value critics, transition dynamics) and the mechanism of RL signal integration (e.g., energy-weighted regression, flow-based policy gradients, and group relative policy optimization). Furthermore, we survey emerging frontiers such as discrete and non-Euclidean action spaces, provide a systematic comparative analysis against Gaussian and diffusion baselines, and outline critical open problems. Ultimately, this survey serves as a foundational roadmap for the next generation of generative reinforcement learning and alignment.

We study weighted and Sobolev estimates for two fractional Schrödinger operators on $\mathbb{R}^n$, where $n\geq3$ and $0<s<1$. For the additive operator $L=(-\Delta)^s+V$, with a nonnegative, nontrivial potential $V$ belonging to a suitable reverse Hölder class, $RH_q$, we establish a critical-radius form inequality and decay estimates for the semigroup mass. These yield a heat-kernel bound combining polynomial off-diagonal decay with a fixed positive exponent of decay relative to the fractional critical radius $\rho_s$. A decomposition at this scale gives weighted strong-type estimates for the semigroup maximal operator and weighted Hardy–Littlewood–Sobolev estimates for $L^{-\alpha/2}$, $0<\alpha s<n$, together with the corresponding endpoint weak-type estimates. The admissible weights satisfy explicit growth restrictions determined by the kernel decay, and the fractional integration order is $\alpha s$. We also consider the spectral operator $Q=(-\Delta+V)^s$. Assuming $V\in RH_{q_1}$ with $n/(1+s)\leq q_1<n$ and classical critical radius $\rho_1(x)\geq1$, we prove that $\nabla Q^{-1/2}$ maps $H^{1-s,p}(\mathbb{R}^n)$ into $L^p(\mathbb{R}^n)$ for $1<p\leq p_0$, where $1/p_0=1/q_1-1/n$. By duality, its adjoint maps $L^{p^{'}}(\mathbb{R}^n)$ into $H^{s-1,p^{'}}(\mathbb{R}^n)$ for $p_0^{'}\leq p^{'}<\infty$.

Speech timing is fundamental for speech models to recognize words accurately, but how word-timing information is represented in speech models remains underexplored. We study this question using the framework of representational geometry, which investigates whether and how concepts are represented in low-dimensional subspaces of the representation space. Prior work in cognitive neuroscience has shown that when HuBERT feature sequences are averaged across words of the same duration, they form near-circular trajectories from word onset to word offset in a two-dimensional "temporal subspace." Here we show that this circular structure of word timing is a general property of deep speech representations, by demonstrating its emergence in nine additional speech models covering both supervised and self-supervised. We then move beyond averaged word segments and show that in HuBERT, the circular trajectories are consistent enough at the level of individual words to perform word segmentation. Our approach, Rotary Segmentation (RoSeg), achieves 64.3% boundary F-score on Librispeech by detecting word boundaries when cycles complete in the temporal subspace. Although supervision is needed to identify a good subspace, nevertheless this result demonstrates much stronger implicit word knowledge in HuBERT than has previously been shown.

Feed-forward reconstruction recovers camera poses and dense geometry from image sets whose views are assumed to come from a single scene. In practice, uncurated image collections often contain views from disjoint scenes, and reconstructing these views jointly can corrupt camera poses and geometry. Existing pipelines typically resolve view associations in separate stages before final reconstruction, leaving view graph initialization and reconstruction inference only loosely coupled. We introduce Graph3R, which unifies view graph initialization with feed-forward reconstruction through a shared geometric representation. A lightweight verification head predicts pairwise covisibility from the multi-view features of $\pi^3$ to construct a view graph. This graph classifies the input views into scene groups. For each group, we mask the shared features before decoding camera poses and dense geometry. Reusing the multi-view features allows Graph3R to reconstruct disjoint scenes separately with a single backbone pass. Across three benchmarks, Graph3R improves average maximum recall at 100% precision by upto 8.99 percentage points over the baseline, while improving reconstruction quality on mixed-scene inputs. Regarding the multi-session mapping task, Graph3R improves cross-session localization and achieves competitive pose estimation accuracy, while delivering a runtime speedup of approximately $35.9\times$ over the baseline when reconstructing the query group. By integrating learned view association with geometric inference, our approach takes a step toward end-to-end reconstruction from uncurated image collections.

We study classical local conserved charges obtained after finite-defect reduction of doubled four-dimensional Chern–Simons theory. At a finite simple zero, the inhomogeneous derivative term in the affine gauge transformation induced by the twist function vanishes, so the residue evaluations of the two Gaudin currents transform by the ordinary adjoint action. For each simple ideal, we use the Evans-compatible invariant tensors of the principal Drinfeld–Sokolov hierarchy as external algebraic input, construct local circle integrals, and treat the same-zero, distinct-zero, and mixed sectors separately, proving pairwise Poisson commutativity. Under the explicit assumptions of Stedman's simple-pole/simple-zero boundary Hamiltonian, the periodic trivial-monodromy sector, and boundary weights independent of phase-space variables, the reduced Hamiltonian decomposes term by term into finite-pole, infinity, finite-zero, and residual first-class-constraint contributions, which yields conservation of the constructed charges. We distinguish the finite-defect reduced space, the extended space carrying the residual constraint, and the physical quotient. The result is a conditional existence statement for a classical local Abelian Poisson subalgebra; we make no claim of functional independence, maximality, or Liouville completeness. Extensions to higher-order zeros and nontrivial monodromy sectors are outside the scope of this work.

We show that completely positive maps can be decomposed into sums of submaps which are close in completely bounded trace norm but as far apart as possible in terms of geometric channel fidelity. We then show that this kind of behavior extends to a large class of quantum channel divergences. We construct quantitative norm estimates for quantum channel divergences in terms of the Arveson-Radon-Nikodym theorem.

In astrophysical complex TAIGA there are four Imaging Atmospheric Cherenkov telescopes (IACTs), located at a distance of 300-500 m from each other, due to which extensive air showers (EAS) generated by cosmic and gamma radiation are recorded by a single telescope or by multiple telescopes simultaneously (stereo-mode). The analysis of EAS images from two or more Cherenkov telescopes together is of particular interest, since for this approach the reconstruction of physical parameters of the primary gamma ray is much more accurate than in mono-observations. However, the gamma-ray event reconstruction strongly depends on the quality of the hadron background suppression of the classifier, therefore, the gamma event selection stage is one of the important ones in the further analysis of IACT data. We propose and study several neural network methods for gamma event selection in joint TAIGA-IACT image analysis. Neural network methods we suggested involves a single-stage analysis of IACT images from two telescopes, while the second method considers the formation of autoencoder-derived essential features that contain the EAS physical parameters, including the type of primary particle.

Test-time-training (TTT) layers update fast-weight memory while processing an input sequence. A gradient-based update, however, does not by itself establish how the memory revises an existing association. We study the structure and empirical role of prediction-derived updates in released TTT implementations. For dot-product objectives, we characterize the absence of a prediction-error residual and establish conditions under which inner-loop ascent and descent are equivalent after reparameterization. For the LayerNorm-based squared-error objective used by TTT-Linear and TTT-MLP, we derive the inner gradient and identify a prediction-derived component that cancels at the released initialization in the zero-epsilon limit. We then measure the full normalized prediction's contribution on three trained checkpoints. Removing this contribution while retaining the LayerNorm Jacobian gives a mean gradient norm ratio of 0.25 to 0.32 across the models. On 100,303 tokens from War and Peace, the same intervention with local per-token norm matching raises perplexity by 2.8-2.9% on TTT-Linear-760M and 1.3B, whereas freezing all fast-weight updates raises it by factors of 4.2-4.5. Separate synthetic tests illustrate the dependence of plain delta-rule overwriting on step size. The results distinguish a prediction-derived update from its behavioral value and show why implementation analysis and live interventions are both needed to assess test-time memory.

In this PhD thesis, we take an axiomatic approach to studying multivariate divergences, which are measures of the information content of a tuple of classical or quantum states. These divergences are studied in the context of large-sample and catalytic convertibility of tuples of states. This type of convertibility is characterized by the ordering of an axiomatically defined family of divergences. In this thesis, we identify this family of divergences explicitly for two scenarios, one classical and one quantum. In the classical setting, we show that the relevant divergences are certain generalizations of the bivariate Rényi divergences. Unlike previous studies, we do not require the distributions to have the same support, yielding interesting new divergences. In the quantum setting, we consider pairs of classical-quantum states with pure component states, and find that majorization is characterized by all $\alpha$-$z$ relative entropies for $\alpha<1$. This demonstrates the first operational interpretation of these relative entropies.

Conway's $99$-graph is a strongly regular graph with parameters $(v,k,\lambda,\mu) = (99,14,1,2)$ and whose existence has long been an open problem. We use both a sum-of-squares program and the fact that a strongly regular graph with $\lambda = 1, \mu = 2$ has no diamond or $K_{2,3}$ subgraph to derive a loose upper bound for the number of triangles that such a graph could contain. This bound is less than the number of triangles that the graph's spectrum asserts, implying that it does not exist.

Using DESI photometric catalog data, we systematically analyze the spatial alignment of star-forming galaxies (SFGs) and quiescent galaxies (QGs) in clusters. The results reveal that, in both brightest central galaxy (BCG) alignment (BA) and satellite alignment (SA), the alignment signals of SFGs and QGs exhibit opposite trends in several aspects. For the BA signal, SFGs show stronger alignment in cluster outskirts, whereas QGs exhibit stronger alignment in inner regions. For the SA signal, both SFGs and QGs exhibit nearly isotropic signals in outer regions, while in inner regions they show opposite signals. In addition, the BA signal depends on cluster richness and QG fraction, and is more pronounced in mature clusters. Specifically, QGs show a stronger BA signal overall, which increases with luminosity and axis ratio, whereas SFGs show a weaker BA signal with no significant dependence on these parameters. The BA signal also exhibits a strong dependence on satellite galaxy color, which is independent of morphology. We confirm this by analyzing the BA signal in two populations: red disk and blue elliptical galaxies. However, neither BA nor SA shows a significant dependence on redshift at z < 0.5. In summary, these findings reflect how galaxy transport along cosmic filaments drives cluster evolution: early-stage SFGs fall into clusters along filaments, and during cluster relaxation, their spatial orientations are reshaped by gravitational potentials and tidal forces, weakening BA signal and enhancing SA signal. In contrast, QGs, as early cluster members, exhibit a more pronounced anisotropic distribution and maintain relatively stable spatial orientations.

Electroencephalography (EEG) is a non-invasive, scalable source of quantitative biomarkers for the early diagnosis of brain disorders, and deep representation learning has become a powerful paradigm for extracting information-rich latent features from raw signals. However, neural networks tend to under-represent high-frequency information, a concern that is especially acute for EEG given its oscillatory nature and characteristic $1/f^\chi$ power decay, which risks masking disease-relevant high-frequency signatures. In this study, we systematically investigate the spectral constraints of unsupervised time-domain feature extraction across three autoencoder architectures (LSTMVAE, VAEEG, Transformer) and a state-of-the-art EEG foundation model. Using synthetic experiments and empirical validation, we show that while autoencoders capture macro-scale shifts such as alpha slowing, they remain biased against higher frequencies due to a magnitude-driven masking effect induced by the Mean Squared Error loss under 1/f-decaying spectra. Increasing latent dimensionality mitigates this attenuation but sacrifices latent compactness and generative stability. We argue that, as the field advances toward large-scale foundation-level feature extractors, it is imperative to move beyond simple time-domain reconstruction and to integrate spectral-domain loss terms and gradient-balancing techniques, so that the subtle high-frequency transients critical for diagnosing complex brain disorders are preserved in the latent representation.

We establish a functional-analytic framework that justifies the LaSalle convergence argument outlined in [van der Schaft, 2026] on port-Hamiltonian primal-dual gradient dynamics for finite-horizon optimal control of linear systems. In addition to strict convexity of the running cost as in \cite{vanderSchaft2026PDGJuly}, our analysis requires global Lipschitz continuity of the cost gradient. This ensures that the nonlinear gradient operator is well-defined and Lipschitz on the entire underlying space. We establish global well-posedness of the optimizer dynamics: every initial value determines a unique global mild solution, and the associated solution operators form a contraction semigroup. Similar to [Gernandtand Schaller, 2025], this is proved using tools from maximal monotone operator theory. Assuming existence of an optimal solution, we combine a dissipation inequality with an infinite-dimensional LaSalle invariance principle to prove strong convergence of every trajectory with a relatively compact positive orbit to the unique optimal state, control, and costate. Moreover, we present a sufficient condition for compactness through uniform control regularity and show that convergence holds for all initial states. Finally, we present an example with a strictly but not strongly convex running cost for which every mild solution converges strongly to the unique equilibrium, while no global exponential estimate uniformly over initial values can be obtained.

We have developed an apparatus for the measurement of the acousto-electric effect (AEE) from room temperature to liquid nitrogen temperatures. The AEE is excited by bulk acoustic waves (BAWs) for which a lithium niobate piezoelectric actuator is employed. A very low background noise level is realized through careful electrical and mechanical design of the setup. The validity of the setup is tested by measuring the AEE in heavily doped P-type and N-type silicon. The opposite sign of the AEE signal was seen for both samples, showing dependence of the signal on the type of charge carrier being dragged by acoustic waves. The signal showed quadratic dependence on the driving voltage, which is consistent with the AEE literature. Further, a temperature dependence of the AEE signal was measured, which exhibits a monotonic increase with a rise in temperature. The ability to probe the AEE in bulk samples at low temperatures makes this setup a promising tool to investigate the AEE and related phenomena in different materials at cryogenic temperatures.

Mamba-based super-resolution models are efficient alternatives to Transformer-based restoration backbones, but their low-bit post-training quantization remains challenging because selective scan, Mamba's input-dependent state-space operator, induces different activation ranges across spatial tokens. Thus, a single layer-wise clipping range poorly matches all tokens. We propose Token-Group Quantization, a hardware-friendly activation PTQ framework that statically approximates a hardware-unfriendly post-scan grouping oracle, where tokens with similar preferred clipping ranges are assigned to the same group. To avoid runtime post-scan grouping in static low-bit inference, a lightweight predictor infers the groups from pre-scan features available before the quantized scan path. During calibration, we form pseudo-labels by clustering post-scan activation descriptors that capture similar rounding–clipping behavior. At inference, the frozen predictor maps pre-scan features to groups and selects entries from offline-calibrated group-wise clipping-bound tables, without runtime statistics collection, dynamic clipping updates, or post-scan group assignment. To address low-bit restoration degradation, we further introduce a Frequency-Preserving Refinement (FPR) objective for clipping-bound calibration. FPR aligns Fourier amplitude and phase between full-precision teacher and quantized student outputs to preserve high-frequency structures. Across PTQ protocols, bit-widths, backbones, and tasks, our method consistently outperforms prior Mamba and SR PTQ baselines, transfers to Transformer SR and Vision Mamba classification, composes with rotation-based quantization, and achieves 2.23x speedup on Jetson Orin Nano over FP16. Code is available here.

Current ASR models perform well on general benchmarks but still struggle with acoustically ambiguous words. Visual context can aid recognition in screen-based scenarios, yet existing benchmarks provide limited insight into the benefits of different types of visual evidence. To address this gap, we introduce ScreenASR-Bench , a tiered benchmark with three progressively challenging levels: Explicit Textual Evidence (ETE), based on direct on-screen text; Explicit Visual Evidence (EVE), using visible content rather than on-screen text; and Implicit Semantic Evidence (ISE), requiring scene-level inference. Extensive experiments on ASR models, Large Audio-Language Models (LALMs) and Omni models show that incorporating visual context substantially improves recall over the audio-only setting across all three model families. The gains are largest for ETE and smallest for ISE, indicating that models leverage direct visual evidence more effectively than indirect semantic cues. To facilitate future research on visually grounded speech recognition, we publicly release ScreenASR-Bench.

Electromagnetic information theory (EIT) brings electromagnetic physics into wireless channel analysis and offers a physical view of channel structures hidden by conventional models. This paper studies channel instability near electromag- netic singular states, where small environmental perturbations can cause large variations in the end-to-end channel. Using resolvent-based spectral analysis, we develop an electromag- netic multiple-input multiple-output (MIMO) channel model that decomposes the end-to-end channel into a smooth regular component and singularity-associated components. The analysis shows that the singularity-associated components become more sensitive to environmental perturbations near singular states. The proposed model also reveals a limitation of conventional water filling (WF): power allocation based only on instan- taneous channel gains does not account for such instability. We therefore estimate singularity information from broadband MIMO channel state information (CSI) and develop a singularity- aware transmit covariance optimization method. Simulations demonstrate numerical stability of the estimated pole frequencies and illustrate the environmental sensitivity of pole-associated channel components. The proposed allocation reduces weighted singular-channel exposure by 90.32% while retaining 95% of the nominal WF rate.

A graph is elementary if its edges can be coloured with two colours so that the two edges of every induced path on three vertices receive different colours. Elementary graphs are one of the two basic classes in the Chvátal–Sbihi decomposition of claw-free perfect graphs; they include all line graphs of bipartite multigraphs and all complements of bipartite graphs. We prove that every elementary graph $G$ satisfies $\ch(G)=\chi(G)=\omega(G)$, confirming for this class the conjecture of Gravier and Maffray that claw-free graphs are chromatic-choosable. The proof combines the Maffray–Reed description of elementary graphs as augmentations of line graphs of bipartite multigraphs, Galvin's theorem, an extremal (edge-maximal) choice of counterexample, and a Hall-type lemma describing how list colourings of a complete graph minus a matching can fail.

High-density plasma discharges in hydrogen operating at low pressures are of considerable interest for negative ion production used in many applications. Their weak collisionality requires a kinetic, nonlocal description, and a popular approach is to model such discharges numerically using the particle-in-cell (PIC) method combined with Monte Carlo collisions (MCC). However, the most commonly used explicit momentumconserving variant of this method is highly inefficient because it must resolve both the Debye length and the plasma period. In this context, the implicit energy-conserving PIC/MCC scheme appears to be an attractive alternative, as it can model the plasmas of interest using their actual parameters without resorting to artificial scaling techniques. This work considers simulations of two types of high-density plasma discharges performed using electromagnetic or electrostatic energy-conserving PIC/MCC codes in hydrogen, and discusses the observed negative ion production.

Head-mounted body and bimanual motion capture supports self-avatars, teleoperation, and embodied demonstrations. However, a single egocentric camera is vulnerable to a limited field of view, scale ambiguity, and hand-object occlusion. We present EgoWholeView, a wearable capture system of six synchronized wide-angle RGB cameras with a role-specialized pipeline. The outward views localize the rig in a world frame, while an overlapping downward pair independently predicts SMPL-X body and MANO hand parameters. Both predictors are adapted to egocentric fisheye data. To combine the two, reliability cues measured from the predictions themselves weight the fusion of body and hand joint rotations, while shape is aggregated across views and frames. The pair's baseline fixes metric scale, and triangulation recovers root translation. Relative to monocular prediction, fusion reduces PA-MPJPE by 3.7% for the body and 11.2% for the hands, and temporal refinement lowers angular-acceleration magnitude by 32.2% and 31.0%, respectively. On our six-camera recording, the pipeline produces body and hand states in a shared world frame using benchmark-fitted gates without retuning, and temporal refinement lowers hand acceleration magnitude by 41.08%. Using only RGB cameras and no third-person view, EgoWholeView offers a low-cost, portable route to egocentric interaction data.

Four-level indium-gallium-zinc oxide (IGZO) 2T0C memory needs reliable state formation, sensing references, and a path from cell errors to digital weights. We analyze 107 measured DC transfer curves from 37 isolated IGZO transistors (W/L = 3/0.5 micrometers). Four gate-bias operating points inferred from the first sweep are evaluated on later sweeps of 35 devices. Device-resolved operating points reduce second-sweep across-device current standard deviation by 49.4-52.4%, yet retaining initial thresholds increases misclassified points from 37/140 to 55/140. Leave-one-device-out reference estimation gives 0/140 on the second sweep; freezing second-sweep thresholds yields 6/140 on the third. Sampling eight reference transistors on the third sweep gives a mean held-out error fraction of 0.15% over 2,000 selections, versus 4.33% with frozen thresholds on matched held-out devices. We present conditional confusion matrices, a counterfactual common-gain sensitivity, and an INT4 error-propagation proxy. A simultaneous four-state bank with eight reference cells per state requires at least 32 physical reference cells, 12.5% of a proposed 256-cell data bank before peripheral overhead. A charge-conservation model and a partitioned hierarchical error model specify the pulse, retention, and array measurements needed to test transfer from DC device statistics to pulsed 2T0C operation.

Providing more coherent light with space-time coupling offers unprecedented opportunities across diverse scientific disciplines and areas of technology. However, in many applications, tailored laser beams with partial coherence in space and time are desired. Here, we demonstrate a programmable approach for simultaneously shaping the intensity and coherence distributions of spatiotemporally coupled partially coherent wave packets. By jointly engineering the fractal speckle of a dynamic scatterer and applying tailored modulation of the illumination function, we experimentally generate partially coherent laser beams with arbitrarily designed intensity and nontrivial coherence distributions. The proposed approach is compatible with ultrafast laser pulses, enabling the on-demand generation of partially coherent space-time wave packets. The resulting spatiotemporally partially coherent wave packets exhibit non-separable space-time intensity profiles together with engineered spatiotemporal correlation distributions. This work provides a versatile framework for tailoring partially coherent light in space and time, with varied applications in relativistic laser-matter interaction, passive imaging and optical manipulation.

Kagome superconductors AV3Sb5 (A = K, Rb, Cs) offer an outstanding platform for exploring the interplay among charge-density-wave (CDW) order, superconductivity (SC), and nontrivial topological electronic states. For CsV3Sb5, moderate hydrostatic pressure induces a double-dome superconducting phase diagram while monotonically suppressing the CDW phase, which signals an unconventional coupling between SC and CDW. Nevertheless, the microscopic nature of the pressure-modulated superconducting state and its relationship with CDW order remain poorly understood. Here we employ soft directional point-contact spectroscopy (SDPCS), combined with superconducting resistive measurements, to probe the superconducting order parameter of CsV3Sb5 under hydrostatic compression. Our anisotropic PCAR measurements resolve two distinct s-wave superconducting gaps along the c-axis and a strongly anisotropic gap within the ab plane. Two-band Blonder-Tinkham-Klapwijk (BTK) fits to the c-axis spectra yield a large gap satisfying 2Delta1/kBTc > 3.52 and a small gap with 2Deltas/kBTc < 3.52. The spectral-weight-averaged coupling ratio 2Deltaavg/kBTc is enhanced in the pressure window between P1 and P2, suggesting that pressure-induced modifications of the low-energy electronic structure may contribute to the evolution of superconductivity. For ab-plane junctions, the superconducting gap anisotropy is gradually suppressed above P1, accompanied by the emergence of a pronounced peak-dip-hump structure suggestive of enhanced coupling between superconducting quasiparticles and a bosonic excitation. These results shed new light on the intricate entanglement between CDW and SC in kagome superconductivity.

Whole-body human-object interaction (HOI) generation requires coordinating body and finger motion, object trajectories, and time-varying contacts. Existing methods typically rely on iterative diffusion or test-time optimization, resulting in high inference costs. Direct one-step generation is highly desired in downstream applications due to efficient inference. However, it is challenging because these HOI components differ substantially in structure and scale. We present MFlowHOI, an efficient one-step framework for whole-body HOI generation. A Contact-Aware Multipart RVQVAE organizes body, hand, object, and contact signals into a structured latent space, with a shared decoder preserving their global coordination. An ST-DiT then models temporal and cross-part dependencies in this space and generates a complete interaction in a single MeanFlow evaluation. Predicted object-local contact correspondences further enable geometric refinement and interaction-preserving transfer to photorealistic 3D Gaussian avatars with different body proportions. Experiments demonstrate that MFlowHOI outperforms existing base generators in motion quality and text alignment while substantially reducing inference time.

Deep-search agents are commonly trained by distilling tool-use trajectories from stronger teacher models. However, structurally complex questions may remain relatively easy for teachers familiar with key entities in the underlying entity graph, resulting in lower search demand and weaker supervision. We refer to this teacher-specific variation in question difficulty as teacher-relative difficulty and investigate entity familiarity as an early predictor. We first introduce Evidence-Verified Entity Familiarity (EVF), which estimates familiarity by verifying a teacher-generated factual brief against online evidence. Across multiple teachers, EVF is significantly correlated with teacher-relative difficulty with AUCs of 0.60–0.70. However, its high cost makes it impractical for constructing entity graphs at scale. To address this limitation, we propose Self-Reported Entity Familiarity (SEF), a lightweight gating signal that directly asks a teacher whether it is familiar with an entity before admitting that entity into the graph. Across multiple teachers, trajectories from SEF-gated synthesis exhibit greater observed search demand and yield higher downstream student accuracy than those from ungated synthesis. Because familiarity is teacher specific, we further study cross-teacher reuse. We screen graphs collected for other teachers using Kimi-K3's SEF judgments and use the retained graphs to produce 5,411 additional trajectories. Combined with 2,200 trajectories from newly constructed, SEF-gated graphs, these data improve Qwen3.8-27B's accuracy from 44.10% to 66.41% on a BrowseComp subset, and achieving state-of-the-art performance among models of comparable size on BrowseComp, SEAL-0, HLE-text, and xbench. Overall, SEF provides a low-cost way to synthesize more informative supervision for deep-search distillation.

We present a nonabelian complex self-dual antisymmetric tensor model, with gauged internal symmetry, comprising a complex multiplet of massless self-dual antisymmetric tensor fields and its conjugate (interacting with massless Yang-Mills gauge fields). We build the model at the classical level, via a first order formulation, assigning local fields to a component expansion over the Dirac algebra and a basis of internal matrices, with Lorentz and gauge invariance ensured by tracing, and applying an appropriate bilinear form. The full expansion requires auxiliary place-holder fields, which are not potentials, but become curvatures (field strengths) after elimination via their algebraic equations of motion. The component expansion over the Dirac algebra is closed under multiplication by additional internal matrices, which are not gauge group generators, but mix internal components, and link auxiliary curvatures with potentials. The model is shown to be invariant under such transformations, and thus possesses a type of hidden internal symmetry. We analyze the specific case of gauge group $SU(2)\! \times \! U(1)$, and as higher symmetry candidates, we consider both the Lie group $SU(3)$ and the Lie superalgebra $SU(2/1)$ (with the additional matrices acting via Lie brackets, or graded Lie brackets, respectively).