We propose and analyse a quantum thermal field-effect transistor (qtFET) composed of left-qubit, middle-qutrit, and right-qubit subsystems. In this architecture, the left qubit is coupled to the middle qutrit, which in turn interacts with the right qubit. Each subsystem interacts independently with its respective baths. The middle subsystem serves as a modulator. We have shown that the qtFET exhibits functionality analogous to that of a conventional electronic field-effect transistor (eFET). The left, right, and middle subsystems of the qtFET correspond to the drain, source, and gate of an eFET in a common gate configuration, respectively. Our results show that the qtFET can precisely modulate thermal currents, highlighting its potential as a fundamental building block for quantum thermal devices and amplifiers in emerging quantum technologies.

Application Programming Interfaces (APIs) are crucial to software development, enabling integration of existing systems with new applications by reusing tried and tested code, saving development time and increasing software safety. In particular, the Java standard library APIs, along with numerous third-party APIs, are extensively utilized in the development of enterprise application software. However, their misuse remains a significant source of bugs and vulnerabilities. Furthermore, due to the limited examples in the official API documentation, developers often rely on online portals and generative AI models to learn unfamiliar APIs, but using such examples may introduce unintentional errors in the software. In this paper, we present AFGNN, a novel Graph Neural Network (GNN)-based framework for efficiently detecting API misuses in Java code. AFGNN uses a novel API Flow Graph (AFG) representation that captures the API execution sequence, data, and control flow information present in the code to model the API usage patterns. AFGNN uses self-supervised pre-training with AFG representation to effectively compute the embeddings for unknown API usage examples and cluster them to identify different usage patterns. Experiments on popular API usage datasets show that AFGNN significantly outperforms state-of-the-art small language models and API misuse detectors.

Context: Stock Android exposes Wi-Fi Direct peer-to-peer APIs, but it does not provide application-transparent communication across multiple Wi-Fi Direct groups. For developers working on non-rooted devices, the main obstacle is architectural: interface-specific transport contexts, relay roles, and forwarding state must be coordinated entirely at application level. Objectives: This paper investigates whether multi-group Wi-Fi Direct communication can be realized as a stock-Android software architecture while preserving forwarding-state consistency and remaining compatible with Android 11 devices without rooting or operating-system modification. Methods: We design SWARNET, a layered artifact composed of a Flutter application layer, a Kotlin native networking layer, interface-bound P2P and legacy-Wi-Fi sockets, relay-state management, and subscription-based forwarding tables. We evaluate the implemented artifact on five stock Samsung Galaxy A10s smartphones across four single-group and multi-group scenarios using archived throughput and packet-loss measurements. Results: The artifact remained operational in all four scenarios. Peak receiver throughput observed from the archived curves was approximately 19.7~Mbit/s in 2d1g, 17.9~Mbit/s in 3d1g, 16.1~Mbit/s in 4d2g, and 16.0~Mbit/s in 5d3g. Packet loss increased with forwarding complexity, reaching about 19--20\% only in the highest-load region of the three-group case. Conclusion: The contribution is an implementable software architecture and a feasibility study showing that stock-Android multi-group Wi-Fi Direct communication can be engineered at application level on non-rooted devices. The results support architectural feasibility in a small static testbed; they do not establish broad resilience, scalability, or deployment readiness.

Meteor clusters are typically defined as groups of meteors that appear close together in both space and time. To date, only a handful of such events have been recorded instrumentally and analysed in detail. In many documented cases, thermal stress has been identified as the most likely cause of meteoroid fragmentation near Earth. This paper documents two further cases and provides a summary of all currently known clusters. The two clusters that were recorded over Hawaii Island in 2023 and 2024 represent two distinct scenarios. The 2024 meteor cluster was characterised by a dominant mass body and, with the fragments arranged along the antisolar direction according to their mass. Such cases enable us to reliably determine the age of the cluster and identify the most likely formation scenario. This cluster was around three days old, and the thermal stress was the most likely mechanism of its formation. The 2023 cluster was not such a case. It does not contain a mass dominant body, nor are its fragments arranged by their mass. Therefore, it was only possible to estimate its age to be no more than four days. Furthermore, other potential formation mechanisms besides thermal stress cannot be ruled out. This fact was observed in all analysed clusters. All clusters known up to date were formed in close proximity to Earth. The volume of a cluster increases with its age. This means that older clusters, formed by the fragmentation far away from Earth may remain undetected, as their fragments are also dispersed too widely to be observed by local experiment. However, global networks can detect such dispersed clusters.

Widespread digital learning has expanded access to education but has resulted in highly sedentary, click-based interaction, contributing to digital fatigue, reduced cognitive flexibility, and health risks associated with prolonged passive screen time. Meanwhile, data literacy has become an essential competency in a data-driven society, yet it is typically taught through passive, disembodied interfaces that offer little physical engagement. We present Kinetiq (Kinetic+IQ), a novel system that integrates fun, full-body micro-movements directly into data and numeracy problem solving. Instead of selecting answers with a mouse, learners interact through natural gestures such as reaching, dodging, heading, elbowing, or knee-raising, thus turning abstract data problem-solving into embodied experiences that integrate thinking with movement. In a preliminary within-subjects study comparing Kinetiq with conventional platforms, participants reported significantly higher affective valence, enjoyment, engagement, and motivation, while maintaining comparable learning gains. We contribute: (1) a task-integrated movement paradigm for data learning, (2) a cross-platform web and mobile app system enabling full-body learning in constrained everyday spaces, and (3) preliminary empirical evidence that embodied micro-movements can enrich the affective experience of data literacy learning.

Corporate bond factor research faces a replication crisis. The crisis stems from two sources that inflate reported factor premia: transaction prices whose measurement error enters both sorting signals and return denominators, creating a correlated errors-in-variables bias, and asymmetric ex-post return filtering that embeds future information into factor construction. Applying our framework to a 'factor zoo' of 108 signals across nine thematic clusters, we show that the majority of previously documented factors do not produce statistically significant bond CAPM alphas after correction. We provide an open source framework via Open Bond Asset Pricing, including error-corrected TRACE data, bias corrected factors, and software for reproducible research.

We calculate the gravitational wave signal from the collapse of a rotating 300 $M_\odot$ star at the upper end of the pair-instability regime. The large-scale asymmetries that develop during the collapse produce a strong signal in the deci-Hz range that has a characteristic shape which is likely amenable to a template-based search. The most ambitious designs for deci-Hz detectors could detect such signals out to distances of 200 Mpc, possibly at a rate of 0.5 per year.

This paper proposes a safe reinforcement learning (RL) framework based on forward-invariance-induced action-space design. The control problem is cast as a Markov decision process, but instead of relying on runtime shielding or penalty-based constraints, safety is embedded directly into the action representation. Specifically, we construct a finite admissible action set in which each discrete action corresponds to a stabilizing feedback law that preserves forward invariance of a prescribed safe state set. Consequently, the RL agent optimizes policies over a safe-by-construction policy class. We validate the framework on a quadcopter hover-regulation problem under disturbance. Simulation results show that the learned policy improves closed-loop performance and switching efficiency, while all evaluated policies remain safety-preserving. The proposed formulation decouples safety assurance from performance optimization and provides a promising foundation for safe learning in nonlinear systems.

LLM inference powers latency-critical production services nowadays. The bursty nature of inference traffic results in over-provisioning, which in turn leads to resource underutilization. While online-offline colocation promises to utilize idle capacity, broad production deployment must overcome two major challenges: (i) large online interference due to slow or frequent preemptions, and (ii) extensive frameworks and drivers modifications, to colocate different models and support preemptions. We present Valve, a production-friendly colocation system that jointly bounds preemption latency and preemption rate. Specifically, Valve enables sub-millisecond compute preemption at most once per online request, and rate-limited sub-layer memory reclamation. These guaranties are provided by a GPU runtime that combines channel-controlled compute isolation, page-fault-free memory reclamation, and dynamic memory reservation. Critically, Valve is practical to deploy, requiring one line of driver modification and 20 lines of framework patch. Deployed on 8,054 GPUs in production, Valve improves cluster utilization by 34.6%, which translates to a 2,170 GPU save. This efficiency gains is achieved with minimal online interference, incurring <5% TTFT increase and <2% TPOT increase across workloads.

Clustering is one of the most fundamental tasks in machine learning, and the k-means clustering algorithm is perhaps one of the most widely used clustering algorithms. However, it suffers from several limitations, such as sensitivity to centroid initialization, difficulty capturing non-linear structure, and poor performance in high-dimensional spaces. Recent work has proposed improved initialization strategies and quantum-assisted distance computation, but the similarity metric itself has largely remained classical. In this study, we propose a quantum-enhanced variant of k-means that replaces the Euclidean distance with a quantum kernel derived from the inner product between feature-mapped quantum states. Using the Iris dataset, we use multiple quantum feature maps, including entangled SU2 and ZZ circuits, to embed classical data into a higher-dimensional Hilbert space where cluster structures become more separable. We will also be testing using another dataset, namely the breast cancer dataset. Similarity between data points is computed through the inner product between two states. Our results show that this approach achieves improved clustering stability and competitive accuracy compared to the classical algorithm, with the SU2 feature map yielding an accuracy of 88.6 % on the Iris dataset and 91.0 % on the breast cancer dataset, despite operating on NISQ-feasible shallow circuits. These findings suggest that quantum kernels provide a richer similarity landscape than traditional distance metrics, offering a promising path toward more robust unsupervised learning in the NISQ era.

Cross-sectional dispersion in firm-level realized skewness is significantly and negatively related to future stock market returns. The predictive power of skewness dispersion is robust to in-sample and out-of-sample estimation and is incremental over a broad set of existing predictors, with only a few alternatives retaining independent explanatory ability. Skewness dispersion also delivers substantial economic gains in portfolio allocation. Its forecasting power is concentrated in months with monetary policy announcements, reflecting an information-based mechanism. The empirical evidence suggests that skewness dispersion captures the gradual incorporation of macro news into prices, which is driven by variation in aggregate risk and valuation adjustments.

Recent advances in deep neural networks have achieved state-of-the-art performance across vision and natural language processing tasks. In practice, however, most models are treated as monolithic black-box functions, limiting maintainability, component-wise optimization, and systematic testing and verification. Despite extensive work on pruning and empirical decomposition, the field still lacks a principled semantic notion of when a neural network can be meaningfully decomposed. We introduce neural decompositionality, a formal notion defined as a semantic-preserving abstraction over neural architectures. Our key insight is that decompositionality should be characterized by the preservation of semantic behavior along the model's decision boundary, which governs classification outcomes. This yields a semantic contract between the original model and its components, enabling a rigorous formulation of decomposition. Building on this foundation, we develop a boundary-aware framework, SAVED (Semantic-Aware Verification-Driven Decomposition), which operationalizes the proposed definition. SAVED combines counterexample mining over low logic-margin inputs, probabilistic coverage, and structure-aware pruning to construct decompositions that preserve decision-boundary semantics. We evaluate our approach on CNNs, language Transformers, and Vision Transformers. Results show clear architectural differences: language Transformers largely preserve boundary semantics under decomposition, whereas vision models frequently violate the decompositionality criterion, indicating intrinsic limits. Overall, our work establishes decompositionality as a formally definable and empirically testable property, providing a foundation for modular reasoning about neural networks.

The purpose in this paper is to study the maximal hypersurfaces with multiple light-cones in Lorentz-Minkowski space by considering the weak solutions to the mean curvature equation with multiple Dirac masses. Such solutions are constructed via an approximation procedure, using regular solutions with smooth sources that converge weakly to the Dirac measures.

We present a numerical scheme for simulating the 2D quantum dynamics of a two-level particle gas with internal degrees of freedom such as spin, pseudo-spin, or a two-band electronic structure. We adopt the Wigner formulation of quantum mechanics consisting of a 4D phase-space representation of the quantum dynamics. The numerical scheme is based on a spectral splitting method applied to the integro-differential Wigner-Weyl formulation of the dynamics. The computational architecture of our method is independent of specific physical implementations, resulting in broad applicability. We illustrate the versatility of our approach by simulating dynamical systems relevant to nanomaterials science, cold atom physics, interacting gases, spintronics, and topological superconductors.

Code generation is important in software engineering, and Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm to improve it through execution-based feedback. However, most RLVR pipelines rely on human-curated tests, making progress bottlenecked by scarce and costly supervision. Existing work tried to use self-generated tests to ground rewards, but the lack of discriminative tests constrains the effect due to the sub-optimal performance of the model on test generation. We aim to improve code generation without ground-truth supervision by co-evolving code and test generation, so that their interactions yield progressively more informative supervision. To this end, we present ZeroCoder, a fully label-free co-evolutionary framework that jointly trains a Coder and a Tester using execution feedback from self-generated code-test interactions. For each problem, ZeroCoder executes sampled solutions against sampled tests to form a passing matrix, identifies a consensus subset of likely-correct solutions and consistent tests via a pluggable selection algorithm, and derives role-specific rewards. To ensure reward quality, ZeroCoder filters low-information instances via rank-based pre-filtering and trains the Tester with a curriculum balancing validity and mutation-driven discriminativeness. We further identify selector drift, the progressive miscalibration of fixed selection rules during co-evolution, and introduce DyB4, a Bayesian selector that uses as few as 10 labeled instances to recalibrate its priors dynamically. Across three models and six benchmarks, ZeroCoder consistently improves code generation and test generation. In the fully label-free setting, it improves code generation by up to 14.5% over the base model on Qwen2.5-Coder-7B-Instruct. With DyB4, the gain reaches 21.6%, while test generation improves by 24.3%, approaching oracle-supervised performance.

Programmable neutral-atom arrays offer a promising route toward scalable quantum computing, where coherent qubit transfer enables non-local connectivity and reduces resource overhead. However, transfer speed and motional heating remain key bottlenecks for fast and deep quantum circuits. Here, we employ a fiber array neutral-atom quantum computing architecture with site-resolved control of trap depths to realize smooth amplitude exchange between static and moving traps, thereby enabling fast and coherent qubit transfer with ultralow motional heating. With a 10 $μ$s in situ transfer between static and moving traps, we obtain a per-cycle heating rate of 0.156(9) $μ$K, sustain over 500 cycles with negligible atom loss, and achieve a quantum state fidelity of 0.99992(5) per cycle. For inter-site transfer between two separated static traps, the operation takes 120 $μ$s with 0.783(17) $μ$K heating per transfer, and remains negligible atom loss for up to 100 repeated cycles with a fidelity of 0.9998(1) per transfer. Furthermore, through experimental studies of parallel transfer, we establish a model that elucidates the relationship between array inhomogeneity and the transfer heating rate. This fast, low-heating coherent transfer capability provides a practical route for improving both speed and fidelity in atom-shuttling based quantum computing.

Operational ocean forecasting systems conventionally employ dynamical ocean models driven by atmospheric forcing derived from numerical weather prediction (NWP) models. Recent advancements in artificial intelligence and machine learning (ML) have led to the development of ML-based atmospheric weather models, which have competitive, if not better, medium range forecast accuracy compared to traditional NWP systems. This study evaluates the impact of ML-based atmospheric forcing on ocean forecast skill through two sets of 10-day forecasts using the UK Met Office GOSI9 configuration of the NEMO dynamical ocean model. Both experiments share identical ocean initial conditions; but differ in atmospheric forcing: one uses ECMWF's ML-based AIFS model, while the other uses the Australian Bureau of Meteorology's physics-based NWP model, ACCESS-G3. Forecasts were initialized on the first day of each month over the period 2023-2024. The quality of the atmospheric forcing was assessed by comparing AIFS and ACCESS-G3 forecast skill against both ECMWF reanalysis v5 (ERA5) and ACCESS-G3 analyses. Results indicate that AIFS consistently outperforms ACCESS-G3, either from the initial forecast time or after the first few days. Oceanic forecast skill was evaluated against both the GOSI9 reanalysis and observations, focusing on key surface variables including sea surface temperature, salinity, sea level, and ocean currents. The ocean forecasts forced with AIFS atmospheric data exhibit comparable or enhanced predictive skill compared to those forced with ACCESS-G3 data. These findings underscore the potential of ML-based atmospheric models to replace traditional NWP forcing in operational ocean forecasting systems, offering improved accuracy and computational efficiency.

Traditional video coding (VVC, HEVC) prioritizes human visual perception, transmitting substantial texture redundancy that severely hinders machine decision-making under constrained bandwidths. In dynamic channels, this redundancy causes severe ``cliff effects'' and prohibitive latency. To address this, we propose a robust multimodal semantic communication framework based on an adaptive Object-Attribute-Relation (O-A-R) hierarchy. Bypassing pixel-level reconstruction entirely, our framework directly fuses visual, textual, and audio streams to construct a decision-oriented topological graph. A bandwidth-adaptive strategy dynamically allocates resources by semantic priority, while a cross-modal mechanism leverages text and audio priors to compensate for severe visual degradation. Experimental results demonstrate that under extreme low bandwidths (1-3 kbps), our method achieves over a 90% bandwidth saving (an approximately 10-fold reduction) compared to state-of-the-art digital schemes, maintaining superior scene-graph accuracy. In deep fading channels (SNR <= 4 dB), it completely eliminates the cliff effect, ensuring graceful degradation by strictly preserving foundational object anchors even when traditional codecs suffer 100% decoding failure. Coupled with an 89\% reduction in end-to-end latency, our framework comprehensively fulfills the real-time survival requirements of embodied agents.

We investigate the quasinormal modes (QNMs) of a thick brane model in $f(T)$ gravity with $f(T) = T + αT^2$. Requiring the energy density to remain positive and the scalar field to be real constrains the parameter $α$ to the range $[-\frac{7}{48},\frac{1}{48}]$. Within this allowed region, we find that the parameter $α$ can induce a brane-splitting structure. The quasinormal frequencies of the system are computed using both the asymptotic iteration method and the Bernstein spectral method. The two approaches show good agreement in the low-overtone regime. For $α<0$, the decay rate of the first QNM decreases as $|α|$ increases, whereas higher overtones exhibit the opposite behavior. To further examine the influence of model parameters on the QNM spectrum, we also perform numerical time-domain evolution of perturbations, whose results are consistent with the frequency-domain analysis. Our results provide a concrete example of quasinormal spectra in thick brane models within $f(T)$ gravity and may offer useful insights for future observational tests of extra dimensions.

Deploying quantum machine learning algorithms on near-term quantum hardware requires circuits that respect device-specific gate sets, connectivity constraints, and noise characteristics. We present a hardware-aware Neural Architecture Search (NAS) approach for designing quantum feature maps that are natively executable on IBM quantum processors without transpilation overhead. Using genetic algorithms to evolve circuit architectures constrained to IBM Torino native gates (ECR, RZ, SX, X), we demonstrate that automated architecture search can discover quantum Support Vector Machine (QSVM) feature maps achieving competitive performance while guaranteeing hardware compatibility. Evaluated on the UCI Breast Cancer Wisconsin dataset, our hardware-aware NAS discovers a 12-gate circuit using exclusively IBM native gates (6 ECR, 3 SX, 3 RZ) that achieves 91.23 % accuracy on 10 qubits-matching unconstrained gate search while requiring zero transpilation. This represents a 27 percentage point improvement over hand-crafted quantum feature maps (64 % accuracy) and approaches the classical RBF SVM baseline (93 %). We show that removing architectural constraints (fixed RZ placement) within hardware-aware search yields 3.5 percentage point gains, and that 100 % native gate usage eliminates decomposition errors that plague universal gate compilations. Our work demonstrates that hardware-aware NAS makes quantum kernel methods practically deployable on current noisy intermediate-scale quantum (NISQ) devices, with circuit architectures ready for immediate execution without modification.

The impact dynamics of viscoelastic droplets on solid surfaces play a critical role in numerous applications, including inkjet printing, spray coating, and microfluidics, where precise control of spreading, retraction, and rebound is essential. This numerical study investigates the coupled influence of fluid viscoelasticity, modeled via the Oldroyd-B constitutive equation, and gravitational-capillary balance on droplet behavior upon impact onto surfaces featuring sharp hybrid wettability. Employing a high-fidelity three-dimensional OpenFOAM-based solver that integrates the volume-of-fluid method, log-conformation formulation for improved numerical stability, and a velocity-dependent dynamic contact angle model, we simulated a 2 cm-diameter droplet impacting at 4 m/s across a range of relaxation times and surface tensions. Results demonstrate that increasing the relaxation time from 0.02 s to 0.12 s enhances elastic energy storage, leading to up to 12.9% larger maximum spreading diameters (from 24.97 mm to 28.09-28.17 mm) and a 16.6% reduction in minimum droplet height across uniform and hybrid surfaces. In contrast, increasing surface tension from 0.05 N/m to 0.15 N/m suppresses maximum spreading by about 1.1% (from 27.21 mm to 26.90 mm) while increasing minimum height by 3.3% (from 2.12 mm to 2.20 mm). On hybrid surfaces with static contact angles of 0° and 160°, the sharp wettability contrast induces pronounced asymmetric spreading and directional fluid migration toward the hydrophilic region, ultimately producing distinctive dustpan- and shoe-like equilibrium morphologies. Variations in surface tension, which simultaneously modulate the Weber and Eötvös numbers, reveal that stronger capillary forces suppress radial expansion while enhancing curvature-driven recoil and redistributing viscoelastic stresses.

We demonstrate that charge fluctuations induced by electron hopping, combined with spin-orbit coupling, lift the sixfold degeneracy of the orbital singlet $^{6}S$ of Mn ions in the topological insulator MnBi$_2$Te$_4$, resulting in single-ion anisotropy. To solve the problem, a multiplet representation is introduced for the creation operators of atomic-state fermions in terms of the operators describing transitions between many-body wavefunctions. Using the operator form of perturbation theory up to the second order, we derive expressions for the populations $n_M$ of Mn ion states with spin projections $M$ of the $^{6}S$ term and determine the single ion anisotropy constants. The calculations reveal that the fluctuation mechanism ensures the possibility of implementing the easy-axis anisotropy observed in MnBi$_2$Te$_4$. Notably, the range of anisotropy constants $D_2$ obtained by varying the model parameters includes the value $D_2 = -0.0095$ meV, required to reproduce the critical field of the spin-flop transition $H_{\text{sf}}$, known from the experiment. The proposed mechanism has a wide range of applicability for describing the anisotropy in compounds where the ground state of a magnetic ion in a weak crystal field is described by an orbital singlet.

We consider the problem of classifying pairs $x,y \in G$ such that $K x K y K = G$ where $G$ is a simple compact connected Lie group and $K$ is a symmetric subgroup. We give a necessary condition on $x,y$ for all simply connected $G$, and a complete classification when $G = \operatorname{SU}(n)$ and any symmetric $K \subseteq G$ except the type AIII case $K \simeq \operatorname{S}(\operatorname{U}(p) \times \operatorname{U}(n-p))$ with $p \neq n/2$. We also present some applications of these results to gate decompositions in quantum computing.

Noise is a major challenge in quantum computing, affecting the reliability of quantum protocols. In this work, we analytically study the impact of various noise processes, such as depolarization, bit flip, and phase flip, on the quantum state teleportation protocol. Each noise process is modeled as a quantum channel and is applied individually to all qubits after the corresponding unitary operations to simulate realistic conditions. We evaluate the fidelity between the ideal and noisy teleported states to quantify the effect of noise. Our analysis shows that the fidelity decreases polynomially, in general, as the noise strength increases for all noise types, highlighting the sensitivity of state teleportation to different noise mechanisms. However, in the low noise regime, the fidelity decreases only linearly, indicating the robustness of the teleportation protocol. These results provide insight into error characterization and can inform strategies for noise mitigation in practical quantum computing applications.

We revisit the longstanding issue of why no well defined probability measure exists corresponding to a classical (Kolmogorov) path integral representation of the Dirac equation in Minkowski space. Two complementary perspectives are compared: (i) Zastawniak's observation that the distributional character of the Dirac propagator (presence of derivatives of the delta distribution) obstructs the construction of a nonnegative transition kernel, and (ii) the indefinite signature of the Minkowski metric which prevents positivity of the action and yields oscillatory integrals. We show how these viewpoints can be unified as different manifestations of a single mathematical obstruction from measure theoretical point of view, and we discuss consequences for stochastic representations of relativistic first-order equations.

The interplay between distinct carrier species in systems with broken Galilean invariance gives rise to a rich landscape of interaction-driven transport phenomena. Here, we develop a comprehensive theory for the electrical conductivity of a non-degenerate two-dimensional mixture of massless Dirac and massive fermions, a system realized in HgTe quantum wells tuned to the charge neutrality point. In this regime, all carriers are thermally activated, enabling a self-consistent, temperature-dependent interplay between the two species. We demonstrate that the conductivity undergoes a distinct crossover as temperature increases: at low temperatures, transport is dominated by massless Dirac carriers, yielding a temperature-independent conductivity reminiscent of graphene's charge neutrality point. As the temperature rises, massive holes become thermally excited, and their mutual Coulomb scattering with Dirac carriers induces a negative, non-Drude correction to the conductivity. We show that this correction is governed by the dominant scattering mechanism: short-range interparticle interactions yield a stronger suppression than long-range Coulomb interactions, and it scales monotonically with temperature. Crucially, the charge neutrality condition ensures that the chemical potential is not externally pinned but is determined self-consistently, making the system's transport response an intrinsic probe of inter-species quantum friction. Our findings establish HgTe quantum wells at charge neutrality as a clean, highly tunable platform for isolating and quantitatively studying interaction-driven transport in the absence of Galilean invariance, offering a direct pathway to explore regimes where interparticle collisions dominate over disorder.

We provide a probabilistic characterization of criticality, subcriticality, and supercriticality for subordinated Schrödinger operators. We also investigate the relationship between the subcriticality of these operators and the uniform boundedness of solutions to the associated wave equation.

During the formation of a thrombus, the architecture of the growing platelet aggregate is heterogeneous, with areas of dense and loosely packed platelets. The surface of activated platelets facilitate biochemical coagulation reactions that ultimately result in the formation of a fibrin network which stabilizes the thrombus. How platelet-plug microstructure and flow jointly govern the onset and development of fibrin is incompletely understood. We developed a novel 2D computational framework that integrates (1) a pre-adhered, discrete platelet aggregate, (2) a reduced coagulation model that generates thrombin, and (3) a fibrin polymerization model. Three platelet-plug configurations were constructed with prescribed interplatelet gaps and simulations were performed with various wall shear rates. We quantified spatiotemporal clotting metrics, including coagulation factor concentrations, fibrin evolution, and gelation onset. Across geometries, gelation initiation accelerated with increasing plug density. For more dense geometries, gelation emerged first near the plug periphery. As the platelet density increased, intraplug transport was increasingly restricted and the thrombin concentrations in between platelets increased. In contrast, the loose plug supported fibrinogen replenishment deeper into the plug core. Despite slower coagulation initiation due to reduced platelet surface area, monomer generation persisted in the interior, causing gelation to begin at the vessel wall. These results suggest a mechanistic tradeoff: rapid sealing of the injured vessel wall by early platelet contraction, i.e. plug densification, may impede the intraplug fibrin formation needed for durable stabilization. The proposed model provides a basis for studies of platelet-coagulation interactions under flow, including therapeutic developments relevant to prevention of cardiovascular disease.

The benefits of learning in one's mother tongue are well documented, yet colonial languages dominate education, marginalizing local languages and limiting access for learners who rely on their mother tongue for understanding. With the rapid growth of educational technology, there is potential to integrate multilingual instruction supporting both colonial and local languages. This study is part of a larger quasi-experiment conducted in Uganda, where learners could choose to learn in English, Leb-Lango (a local language), or in Hybrid mode (a combination of both) in a remote EdTech course. We examined how learners who chose the Hybrid option navigated English and Leb-Lango. While many Hybrid learners did not consistently use both languages, those who did persisted longer in the course. Learners also shared how they managed language complexities. We provide the first empirical evidence of learner agency in bilingual remote EdTech instruction and offer insights for designing inclusive multilingual learning solutions.

What dynamical quantity is actually controlled by higher-order interactions in chaotic oscillator networks remains unclear. In amplitude-active systems, chaos is often interpreted through coherence, yet coherence is not the quantity that governs instability. In this work, we study a minimal globally coupled quartet of nonisochronous Stuart-Landau oscillators with pairwise and symmetric three-body interactions. The pairwise baseline already supports a connected chaotic branch, and higher-order coupling reconstructs rather than creates this irregular dynamics. We show that chaos is organized not by phase coherence but by effective-frequency shear: higher-order coupling regulates amplitude heterogeneity, which nonisochronicity converts into shear, and shear controls how chaos is expressed under higher-order coupling. The Lyapunov response collapses onto a reduced shear-based description, revealing an indirect control pathway. These results establish that higher-order interactions control chaos only indirectly, by regulating an amplitude-shear mechanism rather than acting directly on synchrony.