We present Chimera, a flexible and scalable Microcontroller Unit (MCU) designed to accelerate real-time inference of rapidly evolving transformer-based models at the ultra-low-power edge (hundred of mW). The chip, implemented in 22 nm FDX technology, integrates a transformer accelerator tightly coupled within a compute cluster featuring nine general-purpose RV32IMA cores. Scalability extends to the memory hierarchy through a novel L2 memory island subsystem, which enables data sharing across multiple clusters while delivering 563 Gb/s aggregate bandwidth. The L2 subsystem enforces quality-of-service guarantees for latency-critical traffic, achieving up to 16x latency reduction. Chimera achieves peak energy and area efficiencies of 3.1 TOPS/W and 281 GOPS/mm2, demonstrating 1.37x higher energy efficiency and up to 100x higher area efficiency compared to State of the Art (SoA) SoCs. Compared to SoA standalone accelerators, Chimera achieves comparable energy efficiency and up to 1.8x higher area efficiency.

Nonlinear optical (NLO) responses in two-dimensional quantum-confined systems are typically described within bulk-based frameworks as macroscopic spatial averages. In finite-thickness van der Waals multilayers directly relevant to nanoscale devices, this picture substantially breaks down. Here, we establish a general symmetry-based framework for classifying second-order NLO responses in multilayers. We reveal a layer-resolved organization into skin, weak-skin, and hidden effects governed by local symmetry and stacking order. First-principles calculations for both nonmagnetic and spin-polarized systems confirm our predictions, demonstrating that stacking alone suffices to dramatically reshape both the spatial pattern and magnitude of the NLO response, a phenomenon not explainable within standard bulk theory. Our results establish stacking geometry as an effective knob for engineering surface-selective NLO responses in layered materials.

We analyze HI 21-cm observations of the Local Group dwarf galaxy Wolf-Lundmark-Melotte (WLM) from the Local Group L-Band Survey to search for evidence of ram pressure stripping. While previous MeerKAT-16 observations of WLM showed evidence for off-galaxy atomic gas emission with a geometry suggestive of ram pressure stripping, our observations find no evidence for this stripped gas. We demonstrate that our observations would be sensitive to the claimed detections and suggest that an uncorrected observational flaw with the MeerKAT data led to the apparent off-galaxy emission. The lack of off-galaxy emission obviates the need for uncharacteristically high values of the density of the intergalactic medium in this region.

Resistive wall mode is one of the leading causes for tokamak disruptions above the no-wall $β_N$ limit. This paper presents nonlinear three-dimensional resistive MHD simulations of an RWM-induced disruption in a CFETR baseline steady-state equilibrium using the NIMROD code. Linear calculations confirm the dominant presence of the $n=1$ RWM instability, whose growth rate is strongly sensitive to the wall response and becomes weakly dependent on plasma resistivity in the high-$S$ limit, along with a global external-kink-like structure. In the nonlinear phase, the RWM drives rapid flux surface stochastization and a thermal quench, followed by a current quench that is intensified by the post quench increase of Spitzer resistivity. The transient current spike before the current quench is shown to be the outcome of the conservation of poloidal flux and a rapid reduction of internal inductance. During the late current quench stage, closed flux surfaces partially reform from the core region to the edge, relaxing toward the force-free state. Toroidal mode coupling, parallel heat transport, plasma resistivity, and wall conductivity strongly modulate the disruption onset and the quench dynamics. Within the MHD model, these results provide a complete view on the RWM-driven disruption process in advanced tokamak configurations.

Information sharing among competing suppliers can improve decision-making under uncertainty, yet strategic concerns regarding rival exploitation often deter voluntary disclosure. We study information-sharing mechanisms in a Cournot oligopoly with uncertain demand, where a platform aggregates suppliers' signals through privacy-preserving channels and may also possess an exogenous external signal. The central challenge is to balance strategic safety with informational utility: privacy noise reduces the exposure of individual signals, but also lowers the value of the shared information pool. We first characterize a baseline setting in which access to aggregated information is contingent on participation. In a two-firm market without an external signal, firms refuse to share regardless of the privacy level. In an \(n\)-firm market, sharing may arise even without privacy safeguards because non-participating firms lose access to the aggregated signal. Building on this baseline, we show that privacy protection alone is insufficient to incentivize disclosure; it must be combined with a sufficiently informative external signal. We further show that firms with more accurate private signals require stronger privacy protection. Overall, our results characterize the sharing-feasible region and highlight the complementarity between privacy design and the external information environment.

Algorithm registers are public-facing databases that display basic information about algorithms employed in public administration. While several such registers exist across Europe and globally, their capacity to deliver meaningful transparency remains contested. In Germany, the landscape is notably fragmented: no federal-level register exists, yet at least five state- and federal-level initiatives publish information about AI systems with varying scopes and objectives. A recent conceptual proposal by Alina Lorenz (2025), outlines technical and governance requirements for a national AI transparency register in Germany. We repurpose this proposal as an audit instrument, extracting structured checklists from the transparency goals and subgoals it formulates. The resulting checklists, translated from German into English, is made publicly available to support practitioners auditing existing registers or designing new ones. We apply this framework to conduct an external audit of the two main existing German transparency initiatives, MaKI and Lernende Systeme, evaluating the extent to which they fulfill the proposed goals. Our audit reveals that several adaptations are likely needed for these registers to serve as an useful transparency instrument. We further propose a visualization of register transparency levels and derive concrete action items for improving existing German platforms.

Oblivious Transfer (OT) is a fundamental cryptographic primitive enabling privacy-preserving computation and constitutes a core building block for secure multi-party computation while supporting a wide range of security-sensitive applications: private information retrieval, zero-knowledge proofs, and password-authenticated key exchange, to cite a few. While recent advances in OT extension have significantly reduced amortised costs, their reliance on batches of random base OTs and substantial pre-computation phases limits their practicality in scenarios where the number of transfers is modest or where communication latency and client-side computation are critical constraints. In such settings, efficient base OT protocols remain both relevant and necessary. In this work, we introduce $I$-$(OT)^2$, a novel base 1-out-of-2 OT protocol grounded in the quadratic residuosity problem, specifically designed to minimise receiver-side computation and interaction. Our construction is particularly appealing on client--server architectures in which the receiver operates on low-power hardware, such as Internet of Things (IoT) devices. Through a lightweight offline pre-computation phase, $I$-$(OT)^2$ shifts the on-transfer computational burden almost entirely to the Sender, while reducing online communication to only six messages and four digests exchanged. We provide a detailed description of the protocol, accompanied by a formal proof of its security. Moreover, to demonstrate the viability of $I$-$(OT)^2$, we also present an open-source proof-of-concept implementation (in C language) evaluated on real IoT hardware. Results are staggering: for 128-bit security using a 3072-bit RSA modulus, the receiver incurs an average online cost per OT as low as 2.80 μs on desktop platforms and 39.90 μs on IoT devices, more than 10$\times$ faster than the well known SimplestOT.

Paired-comparison data are naturally represented by tournaments, where transitivity corresponds to the existence of a global ranking consistent with all pairwise outcomes. Accordingly, the classical Kendall-Smith coefficient of consistency measures deviations from transitivity in a tournament by counting the number of circular triads (directed $3$-cycles). In this paper, we characterize the fluctuations of the number of circular triads in inhomogeneous random tournaments and develop an inferential framework for the consistency coefficient. Specifically, we consider the $W$-random tournament model, where the comparison probabilities are determined by a tournamenton $W$, the analogue of a graphon in the tournament setting. We show that, for a $W$-random tournament on $n$ vertices, the number of circular triads exhibits three different fluctuation regimes, determined by suitable notions of regularity and uniformity of $W$. We further develop a novel tournamenton multiplier bootstrap that consistently approximates the limiting distribution of the circular-triad count in the relevant asymptotic regime. Combining this with procedures for testing regularity and uniformity, we design an algorithm for constructing confidence intervals for the consistency coefficient that is asymptotically valid for all tournamentons. We also obtain structural characterizations of tournamentons for which the limiting distribution of the number of circular triads exhibits specific degeneracies. These results can also be viewed through the lens of tournament quasirandomness and may be of independent interest.

Ferroelectric materials have the unique ability to maintain an electric polarization which can be reversed under an external applied electric field. This property makes them valuable for applications such as non-volatile random-access memories, transducers, actuators and electro optic modulators. Recently, emergent unconventional ferroelectricity has been demonstrated in moiré superlattices of bilayer graphene and hexagonal boron nitride (hBN) hosting non centrosymmetric stacking order. Whether this phenomenon is also present in noncentrosymmetric single layer graphene (SLG)-hBN moiré superlattices is still under debate. Here we demonstrate a ferroelectric response in an SLG-hBN moiré superlattice. Through Hall measurements, we pinpoint the origin of the hysteretic behavior to abnormal charge screening due to the moiré superlattice band and estimate the spontaneous polarization magnitude in the moiré superlattice structure. Temperature dependent measurements confirm that the hysteretic behavior persists from 2K up to room temperature, opening opportunities for high-mobility, ultrathin non-volatile devices

We present a study of the leading-order asymptotics for VIX option prices in Bergomi models in the short-maturity and small volatility-of-volatility regimes. Both out-of-the-money (OTM) and at-the-money (ATM) asymptotics are considered for one-factor, two-factor Bergomi and $N$-factor models. The leading-order asymptotics are obtained in closed-form, which are translated into predictions for the small-maturity asymptotics of the VIX implied volatility. Numerical illustrations are provided to illustrate the efficiency of the closed-form asymptotic formulas.

The Neural Spectral Element Method (NSEM) evaluates each network only at fixed Legendre-Gauss-Lobatto quadrature nodes and replaces all derivative calls with precomputed spectral differentiation matrices. The resulting deterministic loss enables limited-memory BFGS (L-BFGS) to reach residuals of 10^-9 to 10^-10. A Kosloff-Tal-Ezer coordinate map resolves electrochemical boundary layers, while a mesh-free neural mortar framework couples multi-element domains. On the four-example Poisson-Nernst-Planck (PNP) benchmark of Huang and co-workers, NSEM attains 10^-4 to 10^-7 relative pointwise error with two orders of magnitude fewer collocation points than the adaptive-resampling PINN baseline. Both a tanh multilayer perceptron (MLP) and a basis-aligned Legendre Kolmogorov-Arnold Network (KAN) backbone attain spectral accuracy within the same NSEM infrastructure, with the KAN requiring roughly half the Adam steps to enter the L-BFGS basin of attraction on the 1D PNP benchmark.

Dataset search and reuse are strongly constrained by the quality of metadata such as natural language descriptions, which are often sparse or inconsistent. Although large language models (LLMs) can generate such descriptions automatically, little empirical guidance exists on what makes a good dataset description and what dataset context LLMs actually need. We study these questions through a literature-grounded framework of dataset description quality and a large-scale ablation study using 252 datasets (1,336 CSV files) from the European data portal data.europa.eu. We generate descriptions with LLMs in a baseline scenario and two ablation scenarios: (1) using only dataset titles, (2) titles and schema, and (3) titles, schema and representative data, and evaluate them with an LLM-as-a- judge framework and a semantic descriptive attribute analysis grounded in our quality dimensions. Our results reveal a consis- tent schema penalty: table-schemas alone often degrade narrative quality, while representative data partially restores grounding without improving overall human-facing quality. We further show that different LLMs exhibit stable descriptive personas. These findings provide practical guidance for LLM-supported data publishing workflows.

General matrix multiply (GEMM) dominates both execution time and energy consumption of modern machine learning (ML) workloads, placing increasing pressure on hardware efficiency. While quantization mitigates computational and data movement costs, accuracy-sensitive tasks such as training still require higher-precision floating-point formats. Existing floating-point GEMM accelerators face trade-offs between operating frequency, arithmetic utilization, and buffering overhead. This work presents O-POPE, a scalable outer-product engine that achieves concurrently high utilization, low overhead, and a fast operating frequency by repurposing floating-point unit (FPU) pipeline registers as buffers. This solution leverages the data-reuse advantages of output-stationary outer-product execution and enables 1 GHz (0.72 V) operation in 12 nm FINFET technology with less than 2% buffer area for a 2048-MACs configuration. Our evaluation shows that O-POPE achieves up to 99.97% FPU utilization and improves performance (1.33x), performance density by 9%, and energy efficiency by 8%, compared to state-of-the-art floating-point GEMM accelerators.

The aim of this paper is to investigate the influence of different magnetic field configurations and spatial resolutions on vortex structures. We analyzed a set of six three-dimensional realistic simulations of the solar atmosphere under three different magnetic field configurations: a small-scale dynamo and two initially vertical implanted magnetic fields of 50 G and 200 G. Three different spatial resolutions have been employed: 20x20x14, 10x10x7 and 5x5x3.5 km^3. We applied two vortex detection methods based on the velocity gradient tensor to all of the models: swirling strength and the SWIRL code. We performed a comparison of vortex locations obtained with both methods, and a statistical analyses of the vortex generation mechanisms, the area covered by vortices, their number and characteristic sizes, and temperature profiles as a function of height. We have confirmed that different magnetic field configurations and spatial resolutions impact the area coverage, number, and sizes of vortices. Likewise, the detection methods impact the statistics obtained. Swirling strength detects vortices with any orientation but a height-dependent threshold is needed. SWIRL only detects vertically-oriented vortices but shows a better agreement with the rotating horizontal velocity field. Simulations with a vertical magnetic field of 50 G support the formation of chromospheric vortices without a photospheric counterpart, while most of the vortices in the 200 G model directly connect the photosphere with the chromosphere. Small-scale dynamo simulations are characterized by a large number of horizontal vortices, with vertical vortices being nearly absent at chromospheric layers. Temperature profiles of vortices confirm that they are hotter than their surroundings, regardless the simulation setup.

Speech denoising is an often necessary step not only for human listening, but also for downstream processing by systems lacking robustness to noisy, real-world acoustic conditions. Unfortunately, denoising is a problem where conventional in-domain supervised training is not trivial, as the training targets cannot be annotated by humans: producing a clean version of a naturally-noisy speech recording is itself the task to solve. Supervised training is typically performed through the artificial addition of noise to clean speech recordings, which can only be sourced from controlled domains, a significant limitation due to the poor out-of-domain generalization of neural networks. An alternative is noisy target training (NyTT), which simply replaces the clean speech with in-domain noisy recordings, with the hope that learning to remove the artificial noise will extend to the natural. Though having shown promising results, NyTT's training objective is not minimized by clean speech estimates. We show that by estimating the artificial noise in addition to the naturally-noisy speech, the undesirable optimum can actually be exploited: the residual noise in the speech estimate can be canceled by the noise estimate via simple subtraction. Crucially, the optimum is fully compatible with conventional artificial mixtures, enabling joint training using both types of data with consistent optimization targets, opening the door to improved domain adaptability. The effectiveness of our approach is demonstrated through WHAM! and CHiME-3-based benchmarks.

Given a connected graph $G$ and a terminal set $R \subseteq V(G)$, the Steiner tree problem (ST) asks for a tree that spans all of $R$ with at most $r$ vertices from $V(G)\backslash R$, for some integer $r\geq 0$. It is known from (Garey et al.,1977 ) that ST is NP-complete. A Steiner tree in which all terminal vertices are constrained to be leaves is called a terminal Steiner tree. Our study addresses the existence of a terminal Steiner tree, its complexity across various graph classes, black-box applications of the ST, and a fixed-parameter tractable (FPT) algorithm with respect to the number of terminals.

Necklace polynomials arise in various fields of mathematics, including combinatorics, Lie theory, and Galois theory over finite fields. Their arithmetic properties have been extensively studied in recent years. In this article, we introduce a new class of rational necklace functions that unifies two well-studied families of polynomials: necklace polynomials and Fekete polynomials. We describe several ways in which cyclotomic polynomials appear as factors of these rational necklace functions. Our results unify and generalize various earlier work on necklace polynomials and on Fekete polynomials. In particular, we describe a surprising phenomenon in which certain Galois groups play a hidden role in the appearance of new cyclotomic factors that are not covered by these previous works.

The complexity of biomolecular simulations has substantially increased the demand for High-Performance Computing (HPC) infrastructures, particularly in molecular dynamics and coarse-grained modeling. This work presents a systematic performance and scalability analysis of the LAMMPS simulator for coarse-grained biomolecular simulations, using the antimicrobial peptide Tritrpticin (PDB ID: 1D6X) as the experimental workload. Pure MPI and hybrid MPI+OpenMP executions were evaluated in HPC environments comprising up to 8 compute nodes and 1024 simultaneous cores. Metrics of execution time, speedup, parallel efficiency, statistical variability, and internal time decomposition were investigated. Results showed that pure MPI executions deliver excellent performance in single-node environments but suffer scalability degradation in multi-node executions due to communication overhead and inter-process synchronization. Hybrid MPI+OpenMP configurations proved more efficient at large scale, reducing communication costs and better exploiting the NUMA memory hierarchy. The computational breakdown revealed that communication and electrostatic interaction routines accounted for the largest fraction of execution time at the largest pure-MPI scales. These results reinforce that performance of biomolecular HPC applications depends directly on the balance among parallelization granularity, spatial decomposition, and distributed communication costs. Hybrid MPI+OpenMP strategies represent a more sustainable alternative for coarse-grained biomolecular simulations on modern many-core architectures.

The imprint of different formation channels of binary black holes (BBHs) is encoded in the distribution of time delays between BBH mergers and the formation of their progenitor stars, along with their source properties such as component mass, mass-ratio, spin, and more. This makes it possible for the presence of a potential correlation between the delay-time distribution and compact-object source properties. We report the first measurement of this inevitable signature from the fourth gravitational wave (GW) catalog (GWTC-4) of LIGO-Virgo-KAGRA and identified three sub-populations that show distinct merger rate behavior as a consequence of this. We find that the delay-time distribution of the sources above a mass of $45$ M$_\odot$ is significantly different from the ones below and exhibits strong dependence on the mass-ratio and spin, indicating that GW sources close to equal masses and close to zero effective spin are more delayed in comparison to the values otherwise. Our analysis identifies the presence of at least three source property dependent sub-population of merger rates with the merger rate at redshift $z=0$ varying from $\sim 0.6- 12$ Gpc$^{-3}$ yr$^{-1}$ for the three different sub-populations and hence rule out a Universal merger rate for all the BBHs detected using GW.

Selective-area embedded regrowth of III-V semiconductors by molecular beam epitaxy enables the seamless integration of metals and dielectrics into crystalline material for novel design of optoelectronic devices. However, traditional masks like $SiO_2$ and $Si_{3}N_{4}$ limit the design of high-contrast photonics in the infrared due to their high extinction coefficients at technologically relevant wavelengths. Consequently, there is a need to explore alternative mask materials to expand the selective area molecular beam epitaxy capabilities beyond those traditionally used. This study evaluates the deposition selectivity of the alternative materials $Al_{2}O_{3}$, $TiO_2$, and $HfO_2$, films with preferable spectral responses but higher surface reactivity. It was found that $Al_{2}O_{3}$ exhibits promising selective growth characteristics within typical GaAs growth temperatures, $HfO_2$ demonstrated a high non-selectivity dominated by Ga adsorption on the mask at temperatures up to 650 $^\circ$C, and $TiO_2$ proved reactive during deposition. To achieve selective growth of highly non-selective and even reactive mask materials, a surface modification technique was employed to improve the selective growth characteristics of any given film. Selective growth of $Si_{3}N_{4}$ and $TiO_2$ films was achieved with the application of a thin $SiO_2$ capping layer utilizing growth conditions typical of the GaAs/$SiO_2$ system. The relationship between the thickness of $SiO_2$ caps and growth selectivity was examined, revealing that sub-1 nm capping layers can significantly influence the mask surface chemistry, indicating that by depositing a thin layer of $SiO_2$, $SiO_2$-like selectivity for any mask material can be realized without degrading its optical response.

Let $\mathcal{R}$ be an expansion of the real field such that every subset of $\mathbb{R}$ definable in $\mathcal{R}$ either has interior or is a finite union of discrete sets. Answering a question by Chris Miller, we show that for every $n\in \mathbb{N}$ and every definable subset $A\subseteq \mathbb{R}^{n+1}$ there is $N\in \mathbb{N}$ such that for all $x\in \mathbb{R}^n$ either $A_x$ has interior or is the union of $N$ discrete sets.

Reduced-order models (ROMs) are widely employed to describe complex system dynamics when simulations with full-order models (FOMs) are computationally prohibitive. This study presents POD-AS-PRS, a novel model-reduction framework based on the active subspaces (AS) technique, which performs dimensionality reduction in both the state and parameter spaces, enabling efficient and high-fidelity approximations of quantities of interest (QoI). The approach employs proper orthogonal decomposition (POD) to extract low-dimensional coefficients from CFD snapshots, which are inputs to a residual neural network (ResNet) with linear layers to learn their nonlinear mapping to QoI. Reverse-mode automatic differentiation (AD) is utilized to compute gradients with respect to the coefficients, enabling AS analysis to identify influential modes by shifting the analysis to the POD coefficient space, thereby achieving a dual-stage dimensionality reduction driven by QoI sensitivity rather than modal energy. A surrogate model is subsequently constructed using a polynomial response surface (PRS) based on AS-derived active variables, retaining only the highly influential POD coefficients to ensure accurate and efficient QoI reconstruction. The framework is validated on periodic and chaotic bluff-body flows, demonstrating high accuracy with few influential parameters, while AD-based gradients achieve a two-order-of-magnitude speed-up over finite-difference approximations. Sensitivity analysis further reveals that the influential coefficients are not necessarily proportional to modal energy, highlighting the critical flow structures. Consequently, POD-AS-PRS identifies a low-dimensional manifold of sensitivity-dominant parameters that govern the QoI, elucidating the essential flow structures and their coupling with control parameters, thereby enabling efficient and accurate QoI reconstruction.

As Artificial Intelligence (AI) agents enter their next stage of being deployed ubiquitously throughout the Internet, their discoverability will become a central challenge. The information AI agents need to discover one another, how they will locate it, how to facilitate authentication, integrity, and authorization, how to connect across different platforms, and how to scale across organizational boundaries form a set of unanswered challenges that deployment success will prompt. These are challenges for which one of the Internet's most venerable, solid, and ubiquitous infrastructures is ideally suited: The Domain Name System (DNS). Such a rich, already ubiquitous, and programmatically flexible foundation is an ideal option for discovery of AI agents. In this work, we propose an illustration and rationale for the basic semantics that discovery for AI agents will require. We argue that three key evaluation criteria will become paramount: navigational completeness (the extent to which the necessary metadata, with elements like trust, is included in a discovery solution), lookup complexity, and transaction performance (e.g., latency, speed, or recency). Using data about 119,757 real-world service endpoints and multiple agent tooling ecosystems, we empirically evaluate the first of these considerations to illustrate the appropriateness of using DNS for AI agent discovery. Our results show the size and amount of data necessary are well within the range of a single DNS UDP transaction, whose latency can be on the order of milliseconds. Our evaluations illustrate a promising path toward enabling AI agent discoverability at the Internet's scale, and thereby accelerating secure, stable, and resilient AI agent deployments.

Since Green (2005), the Fourier-analytic transference principle has dominated the landscape of combinatorial theorems relative to sparse arithmetic sets. We demonstrate a different approach using arithmetic regularity. This is more versatile and has the potential to succeed when no obvious `dense model' is forthcoming. Moreover, we contend that, just as the traditional circle method disassembles an arithmetic problem into real and $p$-adic parts which can be solved individually, the arithmetic regularity method generalises this to yield an additional `combinatorial' factor. This framework leads directly to a correct lower bound on the number of configurations in a dense set. We illustrate this using a system comprising a linear equation together with a higher-degree equation.

In a recent paper the $γ$-Boost scheduling policy was shown to minimize the tail of the response time distribution in a light-tailed M/G/1-queue. This policy schedules jobs using a boosted arrival time, defined as the arrival time of a job minus its boost, where the boost of a job depends on its exact job size. The $γ$-Boost policy can also be used when only partial job size information is available, such as the type of an incoming job. In such case the boost $b_i$ of a job depends solely on its type $i$ and $γ$-Boost was shown to optimize the tail among all boost policies, where a boost policy is fully determined by the $b_i$ values. In the partial information setting $γ$-Boost relies on two types of information: job types and arrival times. This paper focuses on the problem of minimizing the tail in a light-tailed M/G/1-queue in the partial job size information setting when the scheduler only makes use of the job types and {\it does not exploit arrival times}. Prior work showed that in case of $2$ job types the so-called Nudge-$M$ policy minimizes the tail in a large class of scheduling policies. In this paper we introduce the $γ$-CounterBoost policy in the partial information setting with $d \geq 2$ job types and prove that it minimizes the tail in an even broader class of scheduling policies called Contextual CounterBoost policies. The $γ$-CounterBoost policy reduces to the Nudge-$M$ policy in case of $d=2$ job types.

In this paper, we construct families of unimodal maps with wild Cantor attractors and study, via symbolic dynamics, the interplay between recurrence properties of the critical orbit. Using kneading and co-kneading techniques, we provide a symbolic characterization of persistent recurrence in terms of cutting and co-cutting times, and relate it to other recurrence conditions, including the Collet--Eckmann condition. As an application, we obtain uncountable families of non-renormalizable unimodal maps of sufficiently high critical order admitting wild attractors. We also analyze the structure of postcritical dynamics in recurrent regimes, constructing examples with embedded odometers and with minimal homeomorphic dynamics on the critical $ω$-limit set, and showing that a minimal homeomorphism together with regular recurrence does not imply conjugacy to an adding machine. Finally, we prove that longbranched combinatorics is incompatible with persistent recurrence, thereby clarifying the boundary between different recurrence regimes.

We study a population-wide tournament in which agents, who care both about their absolute and relative wealth, experiment by searching over correlated objects. We explore the role of the agents' beliefs about the environment; namely, the stochastic processes corresponding to their experimentation. We find that although optimism leads to higher output, it also produces greater inequality. We connect these observations with empirical evidence suggesting a positive relationship between inequality and entrepreneurship.

Understanding how speech foundation models relate to human cortical activity is a key challenge for computational neuroscience. Here, we investigate how internal representations from Whisper predict intracranial ECoG responses during naturalistic speech perception. We introduce a time-resolved neural encoder that combines speech embeddings with a recurrent temporal model and soft attention, allowing us to examine layer-wise brain alignment. Intermediate Whisper layers provide the strongest correspondence with neural activity, supporting a hierarchical match between model representations and cortical speech processing. Comparisons with baselines show that high-resolution ECoG responses benefit from temporally structured modelling beyond linear mappings from the same speech representations. In addition, attention maps reveal temporally local alignment between speech embeddings and neural responses, while a phonemic interpretability analysis identifies anatomically coherent phoneme-category organization among encoding-informative electrodes. Together, these results suggest that speech foundation models offer a useful framework for studying time-resolved cortical speech representations.

We study sign uncertainty principles for trigonometric polynomials of prescribed degree $N$ with respect to a symmetric Borel measure $μ$ on the unit circle $\mathbb{R}/\mathbb{Z}$. For each such measure, we determine the smallest radius of the last sign change for trigonometric polynomials with non-positive $μ$-integral. We further extend these results to polar measures on higher-dimensional spheres $\mathbb{S}^d$, showing that the extremal problem reduces to the one-dimensional case via the polar part of the measure, and we establish a polynomial analogue on $[0,1]$ using orthogonal polynomials on the real line.

We demonstrate room temperature electric field control of the anomalous Hall effect in epitaxial Ni80Fe20 (Py) BaTiO3 (BTO) La0.7Sr0.3MnO3 (LSMO) thin film heterostructures grown on MgO and LaAlO3 substrates. Substrate induced strain states generate distinct magnetic anisotropies, enabling voltage driven tuning between anomalous and topological Hall contributions. Robust ferroelectric polarization in BTO, confirmed by piezoresponse force microscopy, couples strongly to interfacial orbital reconstruction and carrier redistribution. As a result, Hall resistivity exhibits giant low voltage tunability, with up to nearly 93 percent modulation at operating voltages of only 0.5 tand 2 V. Density functional theory calculations further reveal polarization controlled Rashba spin splitting, establishing a direct link between ferroelectric order and emergent quantum transport. These findings establish Py/BTO/LSMO heterostructures as promising candidates for low-power multifunctional spintronic devices, where substrate engineering enables control over emergent quantum transport phenomena.