Papers

Coordinated online accounts can amplify messages and manipulate attention, but detecting such behavior is challenging due to confounding factors like account activity and item popularity. We propose a statistical null-model framework that assesses behavioral similarity between accounts by comparing observed coordination indicators against activity- and popularity-conditioned reference distributions. By combining evidence across multiple indicators and partners, our method can identify suspicious coordination links more accurately. Evaluations on nine labeled information-operation datasets involving over 152 thousand accounts show that aggregating p-values multiplicatively across indicators and partners yields improved detection performance. While results vary across information operations, this approach offers a generalizable and statistically principled tool for uncovering coordinated online behavior.

Cross-species portrait animation, which aims to animate a source portrait of species A using a driving video of species B, is particularly challenging due to substantial anatomical differences in facial structures across species. Previous works typically fine-tune human portrait animation models on animal video datasets but often suffer from driving-species leakage. To address this, we propose CrossPortrait, a novel framework for expressive cross-species portrait animation, supporting diverse scenarios including human-to-animal, animal-to-human and cross-species animal-to-animal (e.g. cat-to-dog) animation. Specifically, we decompose the portrait motion into rigid head poses and expression keypoints. To accommodate species-specific facial structures while facilitating cross-species expression alignment, we introduce a species-routed expression encoder that incorporates species-specific LoRAs into a unified expression encoder. Furthermore, we introduce expression-level adversarial domain adaptation (Ex-ADA) to suppress driving-species-specific information in the expression latents, thereby mitigating species leakage and promoting a shared expression space across species. Extensive experiments show that our method enables expressive and identity-preserving portrait animation across diverse species and outperforms other state-of-the-art approaches.

While it is known that quantization of large language models (LLMs) reduces memory usage via lower-bit weights, studies quantifying the resulting impact on energy usage and carbon intensity are scarce. We present the first unified evaluation of weight-only quantization strategies for LLM inference across varying input/output lengths and GPUs, highlighting differences in energy efficiency in addition to accuracy degradation and runtime. We study quantized inference on Llama 2 7B, Phi 3.5 Mini Instruct, OLMo 1B, Qwen 2.5 7B, and Qwen 2.5 14B across GLUE MNLI, MMLU, HumanEval, and GSM8K datasets, evaluating 10 post-training quantization (PTQ) strategies on NVIDIA H100, A6000, A100, and L4 GPUs. We identify a novel trend showing that quantization techniques tend to exhibit peak energy efficiency relative to full-precision baselines when inputs are sufficiently long and outputs are short. Furthermore, we show that quantization strategies become marginally more energy-efficient relative to full-precision models as batch sizes increase, though gains are modest. Notably, fused-kernel implementations such as EETQ int8 and Bitsandbytes int8 offer the highest energy savings, up to 4x compared to FP32 on short text generation, with negligible accuracy loss. Finally, we observe that energy usage closely tracks runtime on our evaluated benchmarks, indicating that, in practice, latency optimization can serve as a reliable proxy for reducing the environmental footprint of LLM services. We conclude with suggested directions for strategically selecting low-carbon quantization strategies based on specific inference requirements.

We propose a unified spatial-density signal to score token importance for both inference phases: bipartite merge for prefill and KV eviction for decode, yielding measurable gains in each stage. To isolate the two phases, we split benchmarks by scoring logic. Prefill probes (MMBench, POPE, ScienceQA) evaluate only the first decoded token; their accuracy reflects prefill logits argmax before any decode KV eviction. Full-pipeline probes (TextVQA, DocVQA, MM-Vet) assess the complete generated sequence and capture both phases. We identify a critical evaluation pitfall: decode pruning claims validated solely on prefill probes measure prefill quality, not decode performance. Experiments show discarding all visual KV during decode barely affects prefill-probe accuracy but degrades TextVQA and MM-Vet drastically; this insensitivity stems from the scoring rule, not compression intensity. Evaluated on seven model variants (InternVL-3.5, Qwen3-VL, LLaVA-1.5) and three full-pipeline benchmarks, our method achieves a three-way Pareto frontier across compute, memory and accuracy. Density-based prefill compression maintains strong accuracy, scales linearly with visual token count, and delivers 100x faster token selection at high resolution. For decode, density top-k KV eviction consistently outperforms strong baselines H2O, SnapKV and StreamingLLM on TextVQA and MM-Vet across three retention ratios. At 0.125 keep ratio on TextVQA, it surpasses H2O by 9.6 points; InternVL-3.5 models (2B to 38B) maintain a 7 to 10 point advantage. Compression scales favorably with model size: every measured 8B to 38B model transition reduces accuracy loss instead of worsening it. We suggest MM-Vet or DocVQA as mandatory full-pipeline validation for any decode-phase claims. Our code is available at https://github.com/Kai-Liu001/TwinPrune

Early warning signals are key tools for anticipating qualitative changes in the dynamics of complex systems. In this work, we investigate whether an information-theoretic measure designed to characterize directional causal dependencies between observables can provide early warning of the approach to bifurcations. The measure considered is the rate of information transfer, which has been successfully applied to characterize directional dependencies in a broad range of fields, from economics to climate science. Three fundamental local bifurcations are considered: saddle-node, pitchfork, and Hopf bifurcations. We first investigate idealized (quasi-)potential stochastic systems for which analytical expressions for the rate of information transfer can be derived. Distinct signatures are found for the three bifurcation types. In particular, the rate of information transfer displays a strong increase and divergence when approaching the saddle-node bifurcation, whereas smoother but systematic changes are found near the pitchfork and Hopf bifurcations. The approach is then tested using low-order climate models exhibiting the same types of bifurcations. The results confirm that changes in the rate of information transfer can precede the corresponding dynamical transitions, suggesting that directional information transfer provides a useful complementary early-warning indicator for detecting the approach to critical transitions in nonlinear stochastic systems.

Training recipes are tuned by hand for one model, cluster and workload, and every new model, accelerator, framework version or dataset means tuning again. Automated tuners search a fixed space of knobs and check only that a job runs, yet a configuration can run to completion while it silently changes training. ATTUNE is a single coding agent that tunes any working recipe for the workload and stack at hand, unattended, within a GPU-hour budget the user sets. It diagnoses each run and changes one lever at a time, guided by a catalog of lessons from past campaigns, and ships a recipe only if it passes a correctness gate: a configuration check against objective-changing settings and known stack defects, and a convergence check of its loss curve against the baseline's. On four held-out models at 64-128 GPUs, ATTUNE raises mean throughput over the strongest public recipe run on our stack by 6.7% on Llama-3.1-405B (best campaign 11.9%) and 33.9% on GLM-4.5 (54.7%), whose public recipe was written for H100. It keeps Mixtral-8x22B's recipe, where nothing it finds clears the noise band. Under a common GPU-hour budget it beats OptiCo, Bayesian optimization and random search on GLM-4.5 and MiniMax-M2; on GLM-4.5 its mean gain is more than four times the best alternative's at less than half the GPU-hours, and on both models its tuning pays for itself within a day of training. The gate rejects no legitimate run and catches 16 of 24 faulty runs, missing only ones that barely move the 20-step loss. Ablations tie its best campaigns to one of its search rules: retrying an out-of-memory layout with recomputation. We release ATTUNE's code and TrainingThroughputBench, a leaderboard of the fastest known recipes for MoE models up to 744B parameters on up to 256 GPUs, each with its throughput, cost per million tokens and a passed correctness check, at https://github.com/amazon-science/attune.

In this paper, we introduce a new class of unbounded Reinhardt domains in $\mathbb C^n$ ($n\geq 2$),which we call horn domains. We study the dimensions of the Bergman spaces,the holomorphic sectional curvature of the Bergman metric, and the holomorphic equivalence problem for such horn Reinhardt domains.

World action models (WAMs) are a category of policy models that ex- plicitly predict both actions and future world states. They have been shown to outperform action-only models across various robotic manipulation tasks. How- ever, representing the future with full-frame video prediction can be computation- ally expensive during both training and inference. This raises a central question: what compact state representation preserves the planning and supervision bene- fits of future prediction? In this paper, we propose BoxWAM, which replaces dense future frames with oriented bounding box trajectories of task-relevant ob- jects. By explicitly imagining object-centric future motion, our method provides a lightweight prediction target that captures the key physical changes induced by robot actions. We show that box trajectory prediction substantially reduces com- putational cost while improving action prediction performance, suggesting that object-centric compact states can serve as an efficient alternative to video-based world action modeling for robotic manipulation. Result videos can be found on https://box-wam.github.io

A tensor product is known for commutative Rota–Baxter algebras through a universal construction, but its underlying algebra need not be the ordinary tensor product $A\otimes B$ and can be substantially larger. We show that, after passing to augmented Rota–Baxter algebras, the ordinary tensor product itself carries a natural Rota–Baxter operator which makes the category monoidal with the usual associator and unit constraints. The resulting structure is intrinsically nonsymmetric: over a field of characteristic zero, this operator and its flip-conjugated counterpart are the only possibilities among linear combinations of five natural terms, while over any field the full category admits no braiding. Its comonoid objects are precisely Rota–Baxter cocycle bialgebras. Moreover, the free Rota–Baxter adjunction yields a bimonad and free cocycle bialgebras on ordinary bialgebras, with compatible Hopf structures for connected filtered inputs, in both the associative and commutative settings.

We study the inverse problem for the fractional conductivity equation with uniformly elliptic conductivities $\gamma\in L^\infty(\mathbb R^n)$. For every $n\geq1$ and $0<s<1$, we prove that the local exterior Dirichlet-to-Neumann map on any nonempty open subset of the exterior of a bounded open domain determines $\gamma$ uniquely almost everywhere in $\mathbb R^n$. No continuity, Sobolev regularity, or prescribed behavior at infinity is required. The proof works directly with the conductivity bilinear form and avoids the fractional Liouville reduction. It combines exterior determination at Lebesgue points, equality of weighted solutions, and a unique continuation principle for the fractional Laplacian in the weighted class \[ L_s=\left\{ g\in L^1_{\mathrm{loc}}(\mathbb R^n): \int_{\mathbb R^n} \frac{|g(x)|}{1+|x|^{n+2s}}\,dx<\infty \right\}. \] Specifically, if $g\in L_s$ and both $g$ and $(-\Delta)^s g$ vanish in a nonempty open set, with the latter equality understood distributionally, then $g=0$ almost everywhere. This result requires neither local boundedness nor Sobolev regularity of $g$. Its proof uses the Caffarelli–Silvestre extension and boundary unique continuation for the associated degenerate elliptic equation.

With recent developments in Artificial Intelligence (AI), the adoption of distributed learning paradigms such as Federated Learning (FL) has emerged. In simple terms, FL enables multiple entities to collaboratively train AI models without sharing local data. The FL paradigm is particularly valuable in privacy-sensitive settings, such as financial services, because it enables institutions to collaboratively improve fraud-detection models while keeping sensitive transaction data local. However, FL schemes are prone to security vulnerabilities, where malicious clients can poison local data or model updates and degrade the global model, and hence the performance of the resulting model might be affected. Therefore, in this work, we propose Enhanced Detective, a dual-stage robust aggregation strategy that combines Median Absolute Deviation (MAD), distance-based update filtering, and FedMedian aggregation. Experiments on an imbalanced credit card dataset evaluate targeted label-flipping and weight-scaling attacks over 50 communication rounds. With 40% malicious clients, Enhanced Detective achieves an AUPRC of 0.796 and a Recall of 0.847.

In this paper, we study planar compressible cavity flows fast-decaying vorticity and establish a Schauder estimate in large scale. As an application, we obtain an a priori asymptotic growth rate estimate of the cavity, that $s^{\frac{1}{2}-\delta}\lesssim h(s)\lesssim s^{\frac{1}{2}+\delta}$. This is by far the most accurate asymptotic estimate for compressible cavity flows and is probably the first paper that takes vorticity into account in the asymptotic analysis.

Evaluating VLA policies on real robots is costly, time-consuming, and difficult to reproduce, making real-to-sim simulation essential for scalable evaluation. While 3D Gaussian Splatting (3DGS) enables rapid real-to-sim scene generation, the reconstruction quality required for reliable policy evaluation remains unquantified. In particular, policies relying on wrist-mounted fisheye cameras (e.g., YUBI handheld gripper data) are heavily exposed to visual artifacts (holes, floaters) in wide-angle views, directly degrading task performance. To address this, we present YUBI-Real2Sim, a unified framework for 3DGS-based scene reconstruction and real-to-sim policy evaluation under dual fisheye wrist views. We develop an integrated pipeline combining 200-degree fisheye camera capture, 3DGS training, 3D foundation models for object modeling, and MuJoCo-based physical simulation, reproducing the observed real-world policy ordering in eight of nine scene-task comparisons. Using this framework, we construct a benchmark across two bimanual robot platforms, three scenes, three tasks, and dual pi0.5-based policies. This systematically measures real-to-sim rank agreement and evaluates policy sensitivity against counterfactual 3DGS artifacts (ceiling holes, floaters, warping) and capture disturbances. We further analyze whether novel-view quality scores can indicate policy-ranking reliability under a scene-held-out evaluation protocol. The results show task-dependent performance and limitations of quality-based reliability prediction, highlighting the need to assess reconstruction quality when using Real2Sim for policy evaluation. These results do not yet establish a generally reliable scene-acceptance rule. Project Page: https://yubi-real2sim.airoa.io.

We introduce a parallel decoder for quantum low-density parity-check (QLDPC) codes based on probabilistic bits (p-bits), tunable stochastic binary units controlled by local fields. The idea is that predicted error configurations, interpreted as explanations of the observed syndrome, are updated stochastically using local Gibbs conditionals, while a progressively strengthened parity penalty drives the search toward syndrome-valid configurations. A coloring of the decoding graph enables parallel block updates, and independent replicas and restarts provide trajectory diversity. The best candidate encountered during the search is returned, mirroring the candidate-selection strategy of recent ensemble decoders. At the stated update budgets, the resulting logical error rates are on par with leading decoders under both code-capacity and circuit-level noise. While the total number of variable resamplings can be substantially larger than the message-update count of BP-based alternatives, the numerical results establish constraint-annealed p-bit dynamics as a competitive stochastic alternative for QLDPC decoding and motivate further study of hardware-native implementations.

We prove that every nonnegative source \(f\in L^1(\Omega)\) in the Monge–Ampère equation \[ \det D^2u=f\quad\text{in }\Omega, \] on a bounded uniformly convex \(C^{1,1}\) domain \(\Omega\subset\mathbb R^n\), \(n\geq2\), is uniquely determined by generalized boundary measurements of Alexandrov solutions. The measurement is the extremal boundary slope, which extends the outward normal derivative and may equal \(+\infty\). For each \(\omega\in\mathbb S^{n-1}\), it suffices to know these slopes for the cylindrical Dirichlet data \[ \frac{t_k^\omega}{2}|P_\omega x|^2,\qquad t_k^\omega\to\infty, \] along any sequence of amplitudes, where \(P_\omega\) projects onto \(\omega^\perp\). The boundary value and extremal slope determine the interior subgradient image up to a Lebesgue null set. An exact change-of-variables formula and a large-amplitude limit then recover the Euclidean X-ray transform of \(f\), yielding uniqueness without a positive lower bound on the source. For nonnegative \(L^\infty\) sources, the extremal slopes are bounded and coincide with the interior normal derivatives, giving a Dirichlet-to-interior-Neumann formulation. The argument also extends to \[ \vartheta(\nabla u)\det D^2u=f, \] where the known weight \(\vartheta\) is locally integrable, Borel measurable, and bounded below by a positive constant. Conditional on existence of the corresponding Alexandrov solutions, the same cylindrical boundary-slope data determine every nonnegative \(L^1\) source.

Let $\varphi\colon A\to B$ be a Jordan homomorphism of associative algebras over a field $\mathbb{F}$ of characteristic different from $2$, with defect ideals $V_{\varphi}$ and $W_{\varphi}$. If $A=\operatorname{Alg}([[A,A],[A,A]])$, then $V_{\varphi}W_{\varphi}=W_{\varphi}V_{\varphi}=0$ and $V_{\varphi}\cap W_{\varphi}\subseteq\operatorname{Ann}(\operatorname{Alg}(\varphi(A)))$. Moreover, the induced map modulo $V_{\varphi}\cap W_{\varphi}$ is standard. The annihilator bound is sharp, even in finite dimension. As an application, when $A$ is unital and $A=K(A)$, every Jordan homomorphism defined on $A$ is splittable and standard. In this setting, the corresponding result of Brešar and Zelmanov holds without the surjectivity hypothesis.

We give new mechanisms for combinatorial auctions in the offline, secretary, and prophet-with-samples models. Specifically, we obtain the following guarantees for social welfare maximization with $m$ items and $n$ XOS buyers: * In the prophet-with-samples version, buyers arrive in adversarial order and draw their values from independent distributions. We give a universally truthful, polynomial-time $(2+\eps)$-approximation mechanism using $O(\log\!\log m)$ samples from each distribution. The approximation factor is tight even for a single item and without the incentive constraints, due to the classic prophet inequality. Compared to the previous state-of-the-art with the same guarantees [Dütting–Kesselheim–Lucier–Reiffenhäuser–Singla (FOCS 2024)], our running time is exponentially faster, and our sample complexity is doubly-exponentially lower, resolving two explicit open problems by Dütting et al. * Using the same techniques, we also obtain improved mechanisms in the secretary and offline versions, where the values are worst-case, and buyers are processed in either random order (secretary) or without any timing restrictions (offline). After a long line of works, the state of the art in both models is an $O((\log\!\log m)^2)$-approximation due to [Assadi–Kesselheim–Singla (SODA 2021)], who explicitly ask whether it can be improved to $O(\log\!\log m)$. We indeed obtain an $O(\log\!\log m)$-approximate universally truthful mechanism for the secretary and offline models.

We prove that for every $(X,\mathcal{B},\mu,T)$, an aperiodic, probability preserving system and $\mathcal{P}$ a finite measurable partition of $X$, there exists an Alpern Multitower whose base is independent of $\mathcal{P}$. This removes the ergodicity assumption in the original proof of the independent Alpern Lemma of \cite{MR3373823}. As a consequence, we remove ergodicity assumption from recent results on limit theorems in deterministic systems \cite{Kosloff_Volny_2022,kosloffVolnyStableCLT,MR4955410}.

Accurate lens surface metrology is essential to determine the manufactured geometry of refractive optical elements and their deviation from the specified design. We demonstrate a transmission metrology method that reconstructs refractive lens surfaces from the displacement of a wavelet-noise-patterned mask. Lens-induced distortion is represented by a dense displacement field. A differentiable ray tracer fits the lens geometry using a calibrated near-telecentric camera model with an effective finite ray origin. We validate the method on commercial lenses. The recovered geometries agree with nominal specifications, and the plano-aspheric reconstruction is compared with chromatic confocal profilometry.

We maximize the weight of edges covered by selected vertices, together with nonnegative individual rewards, under an arbitrary matroid constraint. The continuous coverage objective is quadratic and need not be concave. Our method searches projections onto a small set of directions and solves a concave subproblem for each projection center. A coverage-specific inequality converts the subproblem bounds into a multiplicative guarantee, while matroid rounding restores an integral solution. Let $A$ be the weighted adjacency matrix and $D$ the diagonal matrix of singleton values, restricted to positive-value elements after objective-preserving preprocessing. Set $\varrho=\lambda_{\max}(D^{-1/2}AD^{-1/2})$. For an integer budget $q\ge0$, let $\tau_q$ be the smallest nonnegative threshold for which $A+\tau_qD$ has at most $q$ negative eigenvalues on the span of differences of matroid bases. We obtain the factor $1-\varrho\tau_q/[2(\varrho+\tau_q)]-\varepsilon$ in time $(2+q/\varepsilon)^{O(q+1)}N^{O(1)}$, where $N$ is the binary input length and a zero-over-zero loss is zero. The algorithm returns a rational upper bound on the integral optimum. A separate exact-algebraic construction produces polynomial-bit low-rank rational corrections with positive threshold slack; its proof isolates eigenvalues, keeps eigenspace bases unnormalized, and approximates scalar factors only at the final step. The numerical proposal prototype is not used to justify this complexity statement. Supplied certificates also yield robustness to locally bounded edge additions. These are threshold-dependent coverage guarantees, not a new general spectral-search principle or a universal approximation improvement.

3D Gaussian Splatting (3DGS) allows for high-quality real-time novel-view synthesis. Since its inception in 2023, its explicit and highly configurable scene representation has been extended toward sparse-view generation, dynamic reconstruction, efficiency and compression, streaming, and simultaneous localization and mapping (SLAM). Although Gaussian Splatting can provide real-time rendering, rendering an existing representation is distinct from constructing, updating, or maintaining the scene itself. In this survey, we synthesize current literature, categorize them and provide a connection of current approaches and tools towards an end-to-end pipeline. Explicitly, we categorize GS scene reconstruction into five overlapping families: single-shot generation, dynamic representations, efficiency and compression, streaming and immersive systems, and Gaussian Splatting SLAM. We distinguish render-only throughput from task-specific scene generation, update, tracking, mapping, and optimization costs, and combine controlled sparse-view benchmarking with native evaluations to identify cross-family quality, latency, memory, and update-cost trade-offs.

Selecting vertices covers their incident edges, but a matroid can prevent selecting the combinations that cover the most weight. We study whether pair-selection information improves rounding from a semidefinite relaxation of this problem. A feasible point $(x,Y)$ records individual and paired selection values. Its relaxation objective is $U$, and the matrix $Y-xx^\top$ has rank $d$. We compare the feasible marginal vectors obtained by fixing one element as selected or excluded, then round only the best vector. For $d\ge1$, the resulting basis has value at least $(3/4+1/(4d))U$. The procedure is deterministic and polynomial-time for a supplied exact rational point and an independence oracle. Weighting the covariance spectrum by the objective gives a stronger version of the bound. To show the rank factor is sharp, we construct a rank-$2m$ rational linear matroid and $m$ disjoint four-cycles with integral optimum $3m+1$, relaxation optimum $4m$, and an optimal covariance matrix of rank $m$. Thus the relaxation has limiting reciprocal integrality gap $4/3$, despite its conditional constraints. One valid pair-exclusion inequality makes it exact on this family, as an explicit semidefinite certificate shows. The result concerns rounding and the strength of this relaxation; it does not supply a low-rank optimal point or a fixed universal approximation improvement.

This work presents an algorithm for changing from longitudinal to latitudinal formation of autonomous aircraft squadrons. The maneuvers are defined dynamically by using a predefined set of 3D basic maneuvers. This formation change is necessary when the squadron has to perform tasks demanding both formations, such as landing, lift-off, obstacle avoidance and georeferencing. The time complexity analysis of the transformation algorithm reveals that its efficiency is optimal. Simulations show that the formation change is done without collision, and the proof of correctness ensures its latitudinal formation features.

World models promise to learn how systems evolve and respond to actions, enabling simulation, planning and decision-making. Bringing this promise to biosignals requires more than accurate prediction of physiological recordings. We examine existing biosignal world models through two requirements for intervention use: a genuine action and evidence of its causal effect. We identify three fundamental bottlenecks rooted in physiology: Temporal Latency, the variable delay between an action and its observable effect; System Openness, the influence of drivers beyond the modelled intervention; and Endogenous Dynamics, the system's ongoing evolution without that intervention. Drawing on published evidence, we outline corresponding research directions and propose an Intervention-Effect Validation Checklist to guide evaluation. The aim is to help the biosignal community realise the core promise of world models by learning the latent mechanisms that govern physiological evolution and responses to interventions.

Active learning changes the representations used to acquire subsequent labels. We study this interaction through Collapse-Based Active Neural Learning (CÆNL), which separates acquisition, Dynamic Collapse Regularization (DCR), and its coefficient policy. DCR penalizes a normalized within-class to between-class scatter residual above a frozen reference, with a separate class-separation safeguard. Fixed, scheduled, deterministic-feedback, and learned-feedback policies apply this objective. The analysis establishes scatter invariances, conditional local descent, and coefficient bounds, and shows why clean geometry alone cannot guarantee input robustness. Six-seed classification comparisons are complemented by a 104-run study with separate control selection and ten-seed evaluations on each of two backbones. At 60% annotations on ImageNet-100, entropy acquisition with DCR and MACC-Lite reaches 73.420 +/- 0.512% accuracy on ResNet-50 and 73.086 +/- 0.397% on ResNet-18 (mean +/- seed SD), exceeding entropy alone by 1.742 and 1.834 percentage points. Both gains survive Holm correction under paired-t and exact sign-flip tests, with unchanged feedback settings across backbones. Independently tuned constants attain slightly higher mean accuracy; the paired comparisons establish neither adaptive superiority nor equivalence. Separate six-seed spectral studies yield a small fixed-regularization C4 loss reduction, worse CIFAR-10 diffusion FID, and inconclusive AudioCaps captioning effects. Earlier classification studies find near-zero AutoAttack accuracy at L-infinity radius 8/255 and no added benefit from learned control. The results support the evaluated combined classification treatment and identify the task dependence of geometry-informed regularization.

Let $G$ be a thrackle, where a thrackle is a graph drawing in which each edge meets every other edge exactly once, either at a vertex or at a crossing. It is conjectured that, for all thrackles, the number of edges is less than or equal to the number of vertices. Fulek and Pach created a program to determine whether or not a graph can be a thrackle. Through a reconstruction and improvement of their algorithm, we computationally prove a key dumbbell to be non-thrackleable. Using this exclusion, we improve Xu's 8-face half-edge bound from five to four. Finally, using this stronger restriction with Xu's framework, together with separate arguments for graphs containing triangles and disconnected graphs, we improve the known upper bound toward this conjecture to $m\le1.385(n-1)$ for $n\ge4$.

More than 1.5 billion people have hearing loss. When several people talk at once, hearing aids amplify every voice alike. Auditory attention decoding (AAD) aims to solve this by reading a listener's brain signals to find the attended talker, letting the device amplify only them. A real device will not see the brain alone; smart glasses and earbuds also track eyes, head, and scene. These behavioral signals help, as people often look at who they listen to. However, they show where a listener looks, not whom they hear. A decoder relying heavily on them fails when a listener cannot move their head or listens without looking - exactly when a brain-based device is needed. A multimodal decoder must use these signals without letting them replace the electroencephalogram (EEG). Accuracy alone cannot show whether it does. We present MERIT, a decoder and evaluation method asking: how much accuracy does each input earn? MERIT shuffles each input stream across test examples, measures the accuracy drop, and splits the result across streams using Shapley values. It also blocks two shortcuts: candidate audio is checked before training so it cannot reveal the answer, and the scoring function cannot beat chance if it ignores the brain. On a public dataset with EEG, eye tracking, head motion, and scene video, MERIT improves on the published benchmark, reaching 0.82 four-way accuracy within listeners and 0.74 on unseen listeners (chance is 0.25). The measurement reveals what accuracy hides: adding behavioral streams raises overall accuracy but almost halves the brain's share of it. On two other datasets, the same tests find a label imbalance in a widely used benchmark and flag results that accuracy alone would report as brain decoding.

Simple aggregation of independent responses ("wisdom of crowds") generally improves collective performance through statistical facilitation, but interaction among members can sometimes hinder group outcomes. However, under certain circumstances, coordination studies show that interacting groups can outperform their individual members and nominal groups, and interaction can also be more efficient than independence. We present a reservoir computing model to investigate how interaction can enhance joint performance in terms of accuracy and efficiency. Echo state networks were trained independently to identify Japanese speakers from vowel recordings and tested jointly with and without coupled feedback. Model pairs reproduced the dyadic advantage reported by Bahrami et al. (2010) with interaction via pooled-feedback, but not when independent responses were aggregated together. Model dynamics revealed that interaction was beneficial whereas aggregated outputs were prone to diverging from runaway feedback over time. In terms of efficiency, larger groups of smaller interacting models outperformed smaller groups of larger models while equating for total amount of group computation. Our results offer a formal framework for explaining when and why interaction enhances collective intelligence.

In this paper, we investigate the problem of mitigating non-recurrent congestion in urban transportation networks through predictive and adaptive routing for connected and automated vehicles (CAVs). We propose a collision-aware predictive rerouting framework that integrates microscopic SUMO simulation, DCRNN-based spatiotemporal traffic-state forecasting, collision-window lookahead, and multi-objective route scoring. The routing controller uses predicted congestion, live emission information, route-length penalties, and incident-window constraints to decide when CAVs deviate from their current routes. The completed simulation campaign across three urban network scenarios, five CAV penetration levels, three random seeds, and multiple benchmark rerouting strategies shows that the proposed full framework does not dominate all baselines in aggregate travel time, distance, or emissions. Instead, its principal advantage is operational stability: it maintains comparable network-level efficiency while substantially reducing rerouting frequency relative to active rerouting baselines. These results demonstrate both the promise and the tradeoffs of embedding graph-based prediction and collision awareness into real-time CAV rerouting, and they motivate further work on forecast calibration, uncertainty-aware prediction, and system-level fairness between CAV and non-CAV traffic.

Blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has revolutionized noninvasive imaging of human brain function, but it indirectly senses deoxyhemoglobin through water-proton relaxation and relies on bulky, costly, noisy, enclosed, superconducting-magnet scanners. In contrast, photoacoustic computed tomography (PACT) directly probes hemoglobin absorption using a more compact, lower-cost, quieter, open, magnet-free system. However, functional PACT of the adult human brain has so far been demonstrated only after surgical skull removal, because the skull strongly attenuates optical and acoustic signals. Here, we demonstrate non-invasive transcranial functional PACT (T-fPACT) through the intact adult skull. A densely packed, high-channel-count (3,072-element) hemispherical ultrasonic array provides near-$2\pi$-steradian simultaneous acquisition, enabling high sensitivity and single-shot three-dimensional imaging at 20 Hz that eliminates motion blurring. Participant-specific stabilization further enables measurement of small task-evoked hemodynamic responses over the entire task duration. In healthy adults, T-fPACT localized responses to expected sensorimotor regions, distinguished task-specific motor representations, and yielded consistent localizations across repeated task runs and visits. Comparison with high-field (7 T) fMRI showed spatially corresponding functional regions, while dual-wavelength imaging revealed distinct hemoglobin-weighted responses. These findings overcome the long-standing skull barrier in human brain PACT and establish non-invasive, rapid volumetric photoacoustic imaging of brain function.